An Abstract of the Thesis of
Greg Gaston for the Degree of Doctor of Philosophy in Physical Geography
presented on 13 July 1993. Title: A Procedure for Estimating the Carbon Budget of
Terrestrial Ecosystems in the Former Soviet Union Using Geographic Information
System Analysis and Classification of AVHRR Derived Global Vegetation Index.
Abstract approved:
Philip L. Jackson
The purpose of this research was to develop methods for using geographic
information system (GIS) analysis and unsupervised classification of Global
Vegetation Index (GVI) images to improve carbon budget estimates of the former
Soviet Union (FSU). A GIS was used to locate the geographic distribution and
estimate the area of agricultural lands in the FSU. Climatic factors were used to
isolate agricultural lands suitable for no-till management. The effects of various
tillage practices on the carbon cycle were estimated.
Four year average, monthly
maximum value GVI composites were clustered on the basis of timing, magnitude,
and duration of "greenness" as recorded by the normalized difference of vegetation
index (NDVI). The resulting 42 image classes were identified by a variety of
quantitative and qualitative methods including expert assessment by scientists with
extensive field experience in the FSU and analysis of temporal NDVI response
curves. The image classes appear to accurately delineate vegetation/landcover
regions. The percent agricultural land, percent forest cover and percent natural nonforest was estimated for each image class.
Equivalent ecosystems described in the
Bazilevich carbon data base were used to calculate the carbon cycle parameters
forest and natural non-forest ecosystems. Carbon estimates for agricultural lands
were calculated from production data.
Comparisons between areal estimates of land cover from this research and
reported statistics are very good. For example, forested land was estimated at 876
million hectares (MHa) forest statistical data from the FSU estimate "commercial"
forests to be 814 MHa. Total agricultural lands are estimated in research to be 211
MHa, the same as reported by the USDA and very close to the 217 MHa reported
by statistics from the FSU. Results from carbon budget analysis include:
- Complete conversion of all climatically suitable lands to no-till management would
sequester over 3 gigatons (Gt) of carbon in the agricultural soils of the FSU.
- Phytomass and net primary productivity (NPP) estimates for forest and non-forest
ecosystems compare well with carbon budget estimates that used maps to identify
carbon quantifiable regions.
A Procedure for Estimating the Carbon Budget
of Terrestrial Ecosystems in the Former Soviet Union
by Geographic Information System Analysis and
Classification of AVHRR Derived Global Vegetation Index.
by
Greg G. Gaston
A THESIS
submitted to
Oregon State University
in partial fulfillment of
the requirements for the degree of
Doctor of Philosophy
Completed 13 July 1993
Commencement June 1994
APPROVED:
Philip L. Jackson, Geosciences, in charge of major
Chairman, Geosciences Department
Dean of Graduate School
Date thesis is Presented: 13 July 1993
Typed by the Author; Greg Gaston
Acknowledgement
I have had the opportunity of working for and with some of the finest and most
supportive people in the world. I would like to thank my advisor Dr. Phil Jackson
for his constant support and encouragement. Dr. Chuck Rosenfeld for (among other
things) letting me fly backseat on air photo missions. Dr. Keith Muckleston for his
expertise and support concerning the (now former) Soviet Union. Dr. Ted Vinson
for bringing me on as a part of his team, and for his incredible ability and
perseverance in navigating the minefields of paperwork necessary to obtain funding.
His dedication, honesty and hard work have been an inspiration. I'd like to thank
my Russian colleagues, especially, Dr. Tatayana Kolchugina, Dr. Marina Botch, and
Dr. Kira Kobak, for opening the eyes of a cold warrior to the warmth and
friendliness of the Russian people and for their invaluable assistance in completing
this project. I wish to thank my co-authors who edited my drafts, translated from
Russian, and provided invaluable insight into this research effort.
I wish to thank my Daddy, Dr. Leonard Gaston who has been constantly
supportive and encouraging in a multitude of ways. My Mother, Brother and Sister
who also have been unfailing in their encouragement. I'd like to thank my dear
wife who hung tough through the whole graduate school thing. Through it all she
held down a job, kept me from going off the deep end, and gave birth to the two
most beautiful sons a man could ask for.
And finally I thank the Lord Jesus Christ, who started us down this road and
has never turned away and I know that he will always be there.
I also acknowledge the funding provided by cooperative Agreement
(CR820239) between the U.S. Environmental Protection Agency (EPA) -
Environmental Research Laboratory, Corvallis, Oregon and Oregon State University.
Jeffrey J. Lee is the Project Officer for the project entitled "Carbon Cycling in
Terrestrial Ecosystems of the Former Soviet Union." The work presented is a
component of the U.S. EPA Global Climate Research Program, Global Mitigation
and Adaptation program, Robert K. Dixon, Program Leader. The papers presented
in this thesis have not been subjected to the EPA's review and, therefore, do not
necessarily reflect the views of the EPA, and no official endorsement should be
inferred.
TABLE OF CONTENTS
Introduction
Estimating Changes in Carbon from Agricultural Management
Remote Sensing and GIS in Carbon Budget Research
Carbon Budget Estimates for the FSU Based on Classified Image
Beyond Global Warming
Combined References
Potential Effect of No-Till Management On Carbon in the Agricultural
Soils of the Former Soviet Union.
Abstract
Introduction
Current Estimate of Soil Carbon in Agricultural Soils
Purpose
Continued Conventional Tillage
Technical Management Options and Soil Carbon
Defining Climatic Limits
Potential Effect of Conservation Tillage on Soil Carbon
Benefits and Problems Associated with No-Till Management
Summary and Conclusions
References
Identification of Carbon Quantifiable Regions in the Former Soviet Union
Using Unsupervised Classification of AVHRR Global Vegetation Index.
Abstract
Introduction
An Appropriate Remote Sensing System
Data and Methods
Research Background
GVI Data Used to Identify Carbon Quantifiable Regions
Problems With Weekly GVI Composites
Clustering and Classification
Interpreting the Image
Quantitative Tools
Qualitative Tools
Basic Geographic Patterns in the FSU
The Image Classes
Agricultural Land
Discrepancies in Classification
Summary and Conclusions
Acknowledgements
References
TABLE OF CONTENTS (contd.)
The Use of Global Vegetation Index (GVI) to Estimate Phytomass and Net Primary
Productivity in Terrestrial Ecosystems of the Former Soviet Union.
96
Abstract
97
Introduction
99
A Carbon Budget
Satellite Remote Sensing Systems
100
102
Approaches for the Use of AVHRR Data
106
Image-Map of the FSU
Linking Phytomass and NPP Data to Image Classes
108
111
Estimates of Area
Evaluating Phytomass and NPP Estimates
113
Phytomass and NPP
115
Summary and Conclusions
Acknowledgement
117
118
References
127
Appendix
114
132
LIST OF FIGURES
Figure
Page
Agricultural Soils of FSU Suitable
for No-Till Management
41
2.
Soil Organic Carbon Versus Depth
42
3.
Classified Image of the FSU
83
1.
4. Temporal NDVI Response Curves for Class 11 and 16
84
5.
Vegetation Map (Sochava,1960): Amur Region
85
6.
Classified Image: Amur Region (Inset: Elevation)
86
7.
Classified Image: South Central Asia (Inset: Elevation)
87
8.
Comparison of Soil/Vegetation Map (Ryabchikov,1988)
and Classified Image in Yamal Region
88
Classified Image: Kamchatka (Inset: Elevation)
89
9.
10. Classified Image: Kamchatka (Inset: Vegetation Map)
90
11. Comparison of the Signatures of Sample Image Classes
122
12. Area of Biomes Identified by Image Classes and Thematic Maps
123
13. Comparison of Phytomass Densities
124
14. Comparison of NPP Densities
125
15. Image Based NPP Estimates Compared to Fung et al. (1987)
126
LIST OF TABLES
Table
Page
1. Carbon Content Of Agricultural Soils of the FSU
43
2. Carbon Content of Agricultural Soils Climatically Suitable
for No-Till Management
44
3. Soil Carbon Content Under No-Till Management
45
4. Percent Distribution of Soil Carbon
46
5. Maps Used as an Aid in Image Interpretation
76
6. A Brief Description of Image Classes
77
7. Arable Lands Located in Image Classes
79
8. Descriptions of Figures (Figure Captions)
80
9. Landcover, Phytomass and NPP Densities for Image Classes
119
10. Total Phytomass and NPP for Image Classes and Landcover Type 121
A Procedure for Estimating the Carbon Budget of Terrestrial Ecosystems in
the Former Soviet Union by Geographic Information System Analysis and
Classification of AVHRR Derived Global Vegetation Index.
Introduction
Natural Processes in the oceans and in terrestrial ecosystems, together with
human activities, have caused a measurable increase in the atmospheric
concentration of CO2. In 1988 the atmosphere contained 748 gigatons (Gt) of
carbon, the largest amount during the last 160,000 years (Post et al., 1990). On
average, the CO2 concentrations in the atmosphere are increasing by 3.0 Gt/yr
(Keeling, 1983; Tans et al., 1990)
The long-term ecological consequences of change in the chemical composition
of the atmosphere are not fully understood; however, a warmer global climate is
possible (PPIGW, 1992). If CO. concentrations were to double, the earth's
temperatures could rise between 1 and 5° C (Schneider, 1990). Global warming
may further disrupt the equilibrium of the natural carbon cycle by accelerating the
rates of plant respiration (Keeling et al., 1989) and decay of organic matter
(Dixon and Turner, 1991).
It may be possible to manage terrestrial ecosystems to help offset increased
amounts of atmospheric CO. (PPIGW, 1992). Before any global strategy for
environmental management aimed at carbon mitigation can be formulated, accurate
assessments of the current carbon budget at a national level must be made.
The Former Soviet Union (FSU) was the largest country in the world,
occupying one-sixth of the land surface of the earth. An understanding of the
2
pools and fluxes of biogenic carbon of the FSU is essential to the development of
a global strategy aimed at mitigating the potential negative impacts of climate
change.
The three papers presented in this dissertation are focused on the use of a
geographic information system (GIS) and satellite remote sensing data to improve
carbon budget estimates
for the FSU. The first paper concentrates on the changes
in the carbon cycle brought about by conversion of land from natural ecosystems
to agricultural production. GIS techniques were used to identify and measure
agricultural lands climatically suited to no-till management. The effect of
changing agricultural management on the carbon budget of agricultural soils was
estimated.
The second paper presents the methodology developed for using remote
sensing information to accurately identify homogeneous regions with similar
vegetation and land cover. These regions formed the basis for new estimates of
carbon storage and flux in the terrestrial ecosystems of the FSU. The linkage of
carbon data from vegetation data bases and calculated values for agricultural lands
to image classes are presented in the third paper. A comparison of these carbon
budget estimates with previous carbon budget budget estimates for the FSU is also
presented
in the third paper.
Estimating Changes in Carbon from Agricultural Management
A significant part of the increase in atmospheric carbon dioxide results from
land use change. The reservoir of carbon stored in soils and vegetation has been
estimated at 2000 Gt (Post et al., 1990). The amount of carbon stored in soil is
greater than the combined carbon storage in vegetation and the atmosphere.
Conversion of lands to agricultural production results in a sharp decrease in
carbon stored in soil (Haas et al., 1957; Hobbs and Brown, 1965; Mann, 1986).
Houghton et al. (1983) state that a significant percentage of previously stored soil
carbon is released to the atmosphere upon conversion to agriculture. Further, they
note that conversion of land to agricultural production worldwide is estimated to
have released 180 Gt of carbon into the atmosphere.
The first paper in this dissertation volume presents the results of the use of
geographic information system (GIS) analysis to estimate the spatial distribution of
arable lands in the FSU. Climatic factors (i.e growing degree days and moisture
balance) were used to predict lands climatically suitable to no-till management.
No-till management has been suggested as a way to increase the amount of
atmospheric carbon sequestered in agricultural soils.
Using the carbon distribution through a soil profile and equations predicting
the change in soil carbon storage from no-till management an estimate was made
for the changes in carbon emission/sequestration in agricultural soils resulting from
changes in management. The results of this research indicate, that while
additional atmospheric carbon could be sequestered in agricultural soils of the
FSU by a conversion to no-till management, the potential change in carbon
equilibrium may not be of major significance for global carbon cycle management.
This research supported the methodology for manipulating, with the GIS, spatial
data from mapped sources which were then linked to carbon data from field
measurements.
Remote Sensing and GIS in Carbon Budget Research
The carbon cycle consists of a combination of pools and fluxes.
The pools are
carbon stored in soil and vegetation, including living vegetation (i.e. phytomass) and
plant detritus (i.e. mortmass)
.
The effluxes are carbon emissions resulting from
plant respiration and decomposition of organic matter. Influxes of carbon from the
atmosphere into terrestrial ecosystems are represented by formation of new organic
matter in soil and vegetation. The annual accumulation of carbon in vegetation
after subtracting the carbon returned to the atmosphere through respiration is known
as Net Primary Productivity (NPP).
Carbon cycle parameters have been quantified by soil scientists, agricultural and
forest scientists, ecologists and botanists for several decades. Carbon cycle
parameters may be expressed in terms of carbon content (for pools) or rate (for
influxes or effluxes). One approach for the estimation of a carbon budget is to
extrapolate a limited number of actual field measurements to characterize the carbon
cycle parameters for a specific ecoregion. An ecoregion has been defined as a
region similar in soil, vegetation, and land cover characteristics (Omernik, 1987).
This approach to carbon budget estimates assumes homogenous carbon quantifiable
ecoregions
regions can be identified based on the distribution of soil, and/or
vegetation and land cover.
If the soil,vegetation and land cover characteristics which comprise an ecosystem
are accurately portrayed on the thematic maps which are used to isolate ecoregions,
then the carbon budget for an ecoregion can be established simply by multiplying
the area of the ecoregion (in hectares) by the contents content(s) and flux(es). The
carbon contents and fluxes for all the ecoregions may be summed to arrive at the
carbon budget for a larger region, or nation.
Kolchugina and Vinson (1993) (and Vinson and Kolchugina, 1993) used this
approach to create a framework to assess pools and fluxes of biogenic carbon in the
FSU. Initially, maps of soil/vegetation complexes (Ryabchikov, 1988), wetlands
(Isachenko, 1988), and arable lands (Cherdantsev,1961) were used to isolate
ecoregions, and data bases which contain natural carbon cycle parameters were
compiled. The areal coverage of the ecoregions was integrated with the carbon
content and flux data bases to establish the carbon budget within the ecoregion.
There were, however, concerns about the information content of the maps used to
identify the carbon quantifiable ecoregions. Each map, at best "...is a snapshot of
the situation seen through the particular filter of a given surveyor, in a given
discipline at a certain moment in time"(Burrough, 1987). Cartographic data are
generally the result of observations taken over a very short time, thus maps are
static sources of information. There are problems with scale changes between maps,
significant differences in the level of detail between maps, and changes in
classification schemes between maps and across political boundaries. The placement
of boundary lines between
areas
that grade into each other over long distances is an
arbitrary decision of the individual cartographer. Additionally, the size and
inaccessibility of much of the land area of the FSU necessitates cartographic
generalization, introducing interpolation errors.
There is wide agreement that satellite data can be used to overcome the
limitations of thematic maps, providing timely, consistent and reliable information
for the entire globe on the areal extent of, and conditions in, terrestrial ecosystems
(Norwin and Greegor, 1983, Badwhar, 1986, Janssen et al., 1990, Justice et al.1985,
Hall et al., 1991).
However, satellite sensor systems record only reflected radiation from the
surface.
Surface conditions represent a complex mixture of vegetation, exposed soil
or rock, water and shadows. The species composition, structure, density and levels
of photosynthetic activity of both the dominant vegetation and understory vegetation
are all a significant part of the signal recorded by satellite remote sensing systems
(Spanner et el., 1990; Knipling, 1970). Other conditions such as the "brightness" or
color of the exposed soil (rock) and the amount and density of shadows present are
all integrated into a satellite observation of terrestrial ecosystems (Huete et al.,
1985). Not only is the data recorded by a satellite sensor a result of complex
surface interactions but many important ecosystem processes cannot be directly
measured.
Soil processes, litter accumulation, and mortmass accumulation for
example are not part of the signal received by satellite remote sensing systems.
In order to successfully use remote sensing information to assist in carbon
budget estimates it was necessary to:
1. Identify the most appropriate sensor system and data set.
2. Develop a methodology that makes remote sensing information useful in carbon
budget estimates.
The sensor system selected was the Advanced Very High Resolution Radiometer
(AVHRR). The AVHRR is a part of NOAA's meteorological satellite program
which is designed to provide daily coverage of atmospheric conditions over the
entire globe. The orbit and resolution of this sensor system while designed for
meteorological data collection, has been successfully used to monitor conditions in
terrestrial ecosystems (Tucker et al., 1983, Hayes, 1985, Townshend et al., 1987,
and Fung et al.,1987). Specific characteristics of the spectral and spatial resolution
of this sensor system and the reasons for selecting this sensor and data set are
presented in the second paper in this volume. The data chosen for this research
effort were monthly average Global Vegetation Index (GVI) composites from a four
year period (1985-1988).
Two basic approaches can be taken to the use of remote
sensing
data from
satellite sensors to estimate carbon budget dynamics: 1. Direct quantitative
calculation of ecosystem characteristics using remote sensing data as a primary
indicator, or 2. Using the unique signal created by various ecosystem characteristics
to identify homogenous vegetation/land cover regions. These regions should be an
accurate representation of surface conditions which can be linked with plot level
field studies to extrapolate carbon budget estimates.
While various methods of direct assessment show some success (for example
Tucker et al., 1981 and 1983. Goward et al. 1985 and 1987). There are enough
questions that are not well answered about the relationships of terrestrial ecosystem
processes and the signals recorded by satellite remote sensing systems (Badwhar et
al., 1986 and Runyon et al., 1991 for example) that the second approach was taken
for this research. The second paper also describes the methodology of using
unsupervised classification of monthly GVI composites to create an image that
delineates regions of similar vegetation and landcover in the FSU. The selection
process of the most appropriate image and the methods used to identify each image
class (the conversion from image class to "informational" class Swain and Davis,
1978) are an important part of the research results presented in this paper.
Carbon Budget Estimates for the FSU Based on the Classified Image
Each of the image classes have been described in terms of vegetation cover,
agricultural land use and geographic location. Appendix one of this volume details
the identification of each of the 42 image classes that characterize the vegetation
and land cover of the FSU. We have assumed that each of the image classes
represent homogenous vegetation and land cover conditions. Further, we have
assumed that these homogenous areas can be described in terms of carbon content
by linking them with a carbon data base. The carbon data base of N.I. Bazilevich
(1986) was used to assign carbon values to the forest and natural non-forest
ecosystems contained in each of the image classes. The Bazilevich data base is
comprised of carbon accumulation data for 1500 vegetation complexes in the FSU.
This data base is a comprehensive source of carbon information for all the
vegetation formations in the FSU. The descriptions of the image classes and
descriptions of vegetation communities in the Bazilevich data base were often
slightly different. Expert assessment considering the geographic extent and regional
characteristics in vegetation communities was necessary to assign the most
appropriate carbon values to each image class.
Accurate assignment of carbon values to each image class using of the data
provided by the Bazilevich data base required the estimation of the individual
components that make up the vegetation and land cover of each image class. For
example, image class 35 is characterized as "forest meadow steppe, with significant
agricultural land use". The forested portions have a very different carbon storage
and flux from the meadow steppe vegetation, both of which are very different from
the carbon cycle found in agricultural land.
The percent forest cover for forests in the FSU averages 65% (Vorobyov, 1985).
We have assumed that even the most dense of forests identified in an image class
will contain non-forested patches that will make up at least 20% of the area
identified as forest. When available, published forest cover was used to assign the
percent forest cover to image classes identified as forest. For example, forest steppe,
represented by image classes 32 and 35 average 15% forest cover according to
Vorobyov (1985). In the absence of published data an expert assessment of forest
density was made for each image class. This assessment considered the geographic
location of image classes and the density of "greenness" as recorded by NDVI. The
forest cover calculated for the image classes was 876 million hectares (MHa). This
compares very well with forest statistical data (Alimov, 1989) which estimates
"commercial forests" at 814 MHa.
The non-forest areas in each image class were either considered agricultural
lands or natural non-forest vegetation. If the natural non-forest vegetation types
found in each image class were not specifically described in the Bazilevich data
10
base, carbon values were assigned that reflect the natural non-forest vegetation most
likely to be found in each geographic location. For example, image class 11,
Sparse Northern Taiga Larch was assigned a percent forest cover of 50%. As there
is no arable land in this image class the non-forest vegetation was assigned
phytomass and NPP values from "typical tundra" which most likely represent the
character of the non forested vegetation in this region. The influence of agricultural
land use on image classes was estimated using the methods outlined in the first
paper in this dissertation volume. Again, good overall correlation was seen between
total agricultural lands reported in agricultural statistics (USDA, 1990; and
Sokolovskiy et al. 1989) and the total area of agricultural lands calculated in this
research.
and Carbon values for agricultural lands were calculated using production
data and "harvest index" equations from Sharp et. al. (1976).
The results presented in this final paper confirm the validity of the methods
used to create the image classes. Comparisons of area calculated from image
classes an available statistical data show very good correspondence. While it is not
possible to compare carbon cycle estimates to "reality", a good correlation with
other carbon budget estimates for the FSU was found. The other estimates of
carbon budget of the FSU were made using very different sources of information to
identify carbon quantifiable regions.
Beyond Global Warming
Most of the scientific literature in the last decade warns that global warming
will result from increased levels of atmospheric carbon dioxide. Warming of global
climate is projected to bring about an number of disasters including drastic sea level
rise that will flood coastal populations and the virtual destruction of agriculture in
the most productive parts of the world. In many cases it appears that some sort of
social agenda or scientific fashion are driving the conclusions published by some
scientists, for example, Houghton and Woodwell (1989) call for "radical" social and
economic changes to "halt any further [climate] change".
The scientific community is not, however, monolithic in this view. Schlesinger
and Jiang (1991) suggest that much more study is needed before calling for massive
social and economic change to possibly avert an uncertain potential climate change.
Reifsnyder (1989) holds Aspect all models that call for CO2 induced warming over
the entire globe. Recent data from Kahl et al. (1993) suggest in fact that rather
than warming in the arctic regions, there has been a cooling trend over the past 40
years in the atmosphere over the arctic regions.
Regardless of the effect (if any) on the global climate from increased levels of
atmospheric CO, the research presented in this volume is useful and valid in a
number of ways. At this time, no other research has used remote sensing data to
identify landcover regions across the entire FSU.
This research further validates the process of land cover mapping using coarse
resolution remote sensing data. There are a number of opportunities to expand on
these initial results. As higher resolution remote sensing data becomes available (or
12
cost effective) the image classes developed using GVI in this research could be used
as a basis for further classification of regions and regional characteristics.
Another
significant possibilitiy, is the integration of these results into ecological process
models that can be used to predict the effect of management activities on natural
and managed ecosystems.
The image/map produced by this research provides a baseline of vegetation and
landcover conditions in the FSU that can be used to detect and assess future change
in terrestrial conditions. Dramatic social and economic changes brought about by
the fall of the Soviet empire make it very important to understand and inventory
current ecosystem conditions. The results of this research help provide a solid base
of geographic information which is essential for intelligent decisions regarding
resource development.
I
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Sovetskaya Ecyclopedia Press,
POTENTIAL EFFECT OF NO-TILL MANAGEMENT
ON CARBON IN THE AGRICULTURAL SOILS
OF THE FORMER SOVIET UNION
by
Greg G. Gaston
Graduate Research Assistant
Tatyana Kolchugina
Visiting Research Associate
and
Ted S. Vinson
Professor
Oregon State University
Corvallis, OR 97331
Submitted to
Agriculture, Ecosystems & Environment
September 21, 1992
Potential Effect of No-Till Management on Carbon
in the Agricultural Soils of the Former Soviet Union
Abstract
Agricultural soils act as both a source and a sink for atmospheric carbon.
Since
the onset of cultivation, the 211.5 million hectares of agricultural soils in the former
Soviet Union (FSU) have lost 10.2 gigatons of carbon.
No-till management
represents a promising option to increase the amount of carbon sequestered in the
agricultural soil of the FSU. No-till management reduces erosion and sequesters
additional carbon in the soil by lowering the soil temperature and raising soil
moisture.
To determine the carbon sequested under no-till management, a data base
containing precultivation estimates of soil carbon for the seven major classes of soil
found in the agricultural areas of the FSU was used to establish an equilibrium
carbon content for each soil. Other published data provided a method to quantify
the change in soil carbon brought about by converting to no-till management.
Soils
suitable for no-till management were analyzed and estimates of changes in carbon
storage were made.
No-till management is not suitable in areas where crop production is limited by
cold, wet soils. Based on the results of a geographic information system analysis
using maps of climatic factors and soil characteristics, 181 million hectares in the
FSU were identified as climatically suitable for no-till management (almost 86% of
all agricultural land). Complete conversion of all climatically suitable land to notill management would sequester 3.3 gigatons of carbon. This represents a 10%
25
increase in carbon in the agricultural soils of the FSU. This estimated accumulation
of carbon is associated with a new soil carbon equilibrium condition.
Accumulation
of carbon in the soil produced by a conversion from conventional to no-till
management is expected to take at least 10 years. The carbon accumulation
produced by conversion to no-till management is not a continuing process; once a
new no-till equilibrium condition has been reached, additional quantities of
atmospheric carbon will not be sequestered in agricultural soils through continued
no-till management.
Key Words: Agricultural Soils, No-Till Management, Carbon, Carbon Sequestration,
Former Soviet Union
Introduction
The reservoir of carbon stored in soils and vegetation has been estimated at
2000 gigatons (Post et al., 1990). The amount of carbon stored in soil is greater
than the combined carbon storage in vegetation and the atmosphere. Conversion of
lands to agricultural production results in a sharp decrease in carbon stored in soil
(Haas et al., 1957; Hobbs and Brown, 1965; Mann, 1986). Houghton et al. (1983)
state that a significant percentage of previously stored soil carbon is released to the
atmosphere upon conversion to agriculture. Further, they note that conversion of
land to agricultural production worldwide is estimated to have released 180 gigatons
of carbon into the atmosphere.
In 1988 the cultivated area of the former Soviet Union (FSU) was 211.5 million
hectares (USDA, 1990). This huge area is almost twice the cultivated area of the
United States (120 min ha.) and over four times the area of Canadian agricultural
lands (46 min ha.). Clearly, agroecosystems in the FSU play a significant role in
the global carbon cycle (Schneider 1989).
A number of management strategies have great potential for conserving carbon
in agricultural soils. By reducing soil erosion and in some cases stabilizing the
amounts of carbon input into the soil, management practices such as ridge tillage,
contour cropping, stubble mulching, and shelter belts all serve to reduce the loss of
carbon from agricultural areas (IPCC, 1992; Barnwell, 1992). It has been estimated
that 50% of the carbon transported from agricultural soils by erosion eventually ends
up in the atmosphere (Kern and Johnson, 1991).
However, when considering addtional storage of atmospheric carbon in
agricultural soils, fewer management options are available. Growing cover crops for
green manure, irrigation of dry lands, addition of carbon from outside sources
(municipal sewage sludge, for example), and conservation tillage have all been
suggested as options for sequestering atmospheric carbon in agricultural soils. Kern
and Johnson (1991) indicate that of the possible conservation tillage options "...
only no-till significantly results in increased [carbon] sequestration."
Purpose
The purpose of the work reported herein is to assess the potential effect of no-
till management on carbon in the agricultural soils of the FSU. The scope of work
includes:
(1) an assessment of the carbon contents currently found in the
agricultural soils of the FSU, (2) a consideration of climatological factors that would
limit the use of no-till management, (3) a spatial analysis of the effect of limiting
climate factors on agriculturally productive areas, and (4) an estimate of changes in
soil carbon brought about by the introduction of no-till management to suitable
agricultural areas. The findings of this research have implications for policy
development. Given the policy development potential, a discussion of the problems
associated with implementing no-till management in the FSU is presented.
Current Estimates of Soil Carbon in Agricultural Soils
To investigate the potential effects of various management strategies on carbon
sequestration in agricultural soils, current carbon contents in the soils must be
established.
Current carbon contents can be established knowing the area of
agricultural lands in the FSU, identifying the soil types within the agricultural lands,
and assessing the effect of long-term cultivation on the soil carbon in the
agricultural lands.
Agricultural lands of the FSU were located using a map of arable lands
produced by Cherdantsev (1961) (Figure 1).
The location and extent of agricultural
lands described and mapped in other available sources (Symons, 1972; Medvedev,
1987; Lydolph, 1970) appeared to correlate well with the first three classes of the
arable lands map (class 1, < 60% arable; class 2, 30-60% arable; class 3, >30%
arable). Using an average percentage for each class (i.e. 80%, 45%, and 15%) the
total area of agricultural lands in the FSU was calculated to be 210 million hectares.
This compares very favorably with the 211.5 million hectares reported in USDA
statistics (USDA, 1990). The areal extent of various soil types within the limits of
arable land were established using a soil/vegetation association map produced by
Moscow State University (Ryabchikov, 1988).
The arable lands and soil/vegetation maps were converted to a computer
compatible format by manual digitization. A spatial analysis of the maps was
performed with a geographic information system (GIS) and the area of agricultural
land and corresponding soil types were identified. The results of this effort are
presented in Table 1.
Kobak (1988) compiled a data base of soil carbon parameters from 70 Soviet
and international sources (e.g., Kononova, 1963; Schlesinger, 1977; Bohn, 1978;
Post et al., 1982; Kobak and Kondrashova, 1986). Average soil carbon cycle
parameters were presented for over 40 soil types in the FSU. The soil types in
Kobak's data base were related to the soil/vegetation complexes defined on the
soil/vegetation association map. Based on Kobak's data, carbon contents were
assigned to each
soil type. The carbon contents were assumed to be representative
of an initial or precultivation condition. These data are presented in Table 1
(column 3). The initial or precultivation carbon pools are the product of the area
for a given soil type and the soil carbon content. The results are presented in Table
1 (column 4).
Conversion of soils to agricultural production releases carbon to the atmosphere.
The rate of carbon release is relatively high after the initial cultivation of virgin
soil, but the rate decreases with time (Houghton et al., 1983; Haas et al., 1957).
The greatest rates of change in carbon storage occur in the first 20 years of
cultivation. Mann (1986) used published data from 625 paired soil samples to
develop regression equations that allow one to predict current carbon contents of
cultivated soil as a function of the initial (precultivation) carbon contents. He found
most soils lost at least 20% of their organic carbon after cultivation. Further, the
loss of soil carbon content was strongly dependent on the length of time a soil had
been cultivated and the initial carbon content. For soils under cultivation for 70-
100 years the current organic carbon present in the soil is given by the following
equation:
SOC, = (SOC; * 0.76) + 0.88
(in t/ha)
(1)
(R = 0.77, n = 129)
in which,
SOC, = final or current soil organic carbon
SOC, = initial carbon content of the soil.
Equation 1 was applied to each initial soil carbon content and an estimate was
made of current soil organic carbon in the agricultural soils of the FSU. These
estimates are presented in Table 1 (columns 5 and 6). The current estimate of soil
carbon in agricultural soils of the FSU is 32.4 gigatons. The productive chernozem
soils account for more than 60% of this total. On average there has been a 24%
decline in soil carbon and a total loss of 10.2 gigatons of carbon since the onset of
cultivation.
Continued Conventional Tillage
It is generally assumed that over time cultivated soils reach a new equilibrium
at a lower level of organic carbon (Hobbs and Brown, 1965; Haas et al., 1957;
Unger, 1968; Mann, 1985). The time required to reach equilibrium and the carbon
content under the new equilibrium condition depends on the soil type, climatic
conditions and agricultural management practices (i.e., crop rotations, tillage,
fertilizer usage, etc.). Long-term investigations of soil properties at the Rothamsted
(England) Experiment Station indicate soil organic carbon has long term stability.
Jenkinson (1991) studied the soil carbon content of unfertilized test plots that have
been cultivated for over 150 years and observed that organic carbon appears to
3
reach equilibrium after approximately 45 years. However, most researchers report
that soils cultivated less than 100 years tend to show a continued slow loss of soil
carbon to the atmosphere (Hobbs and Brown, 1965; Haas, 1957; Unger, 1968).
It is difficult to predict the slow rates of soil carbon loss for soils approaching a
new equilibrium condition. Since 1913 there has been no significant expansion of
agricultural lands. Considering this long period of cultivation it is reasonable to
assume, notwithstanding ongoing erosion, that an equilibrium condition has been
reached. An exception to this assumption is the 33 million hectares (approximately
15% of all agricultural land) of the so called "virgin lands", which were first
converted to agricultural use in the mid 1950's (Medvedev, 1987). At this time it
is not possible to quantify the condition of soil carbon in the "virgin lands".
However, these soils should continue to lose carbon to the atmosphere up to the
beginning of the next century.
Technical Management Options and Soil Carbon
The type of agricultural management employed can significantly effect the
equilibrium level of organic carbon in soil. Conventional tillage practices encourage
the oxidation of soil organic carbon by mixing crop residues into the soil, adding
air into soil macropores, and exposing the soil to direct solar radiation. Large
amounts of available food in an oxygen rich environment which is heated by solar
radiation produce higher metabolic rates in soil microbial communities, thus
increasing the efflux of carbon from the soil. Erosion rates for conventional tillage
can be quite high. The soil is usually completely inverted, the structure broken
down through secondary tillage operations, and then left totally exposed to the
action of wind and water for extended periods of time.
One approach suggested for reducing the rates of carbon release from
agricultural soils is conservation tillage. Conservation tillage management practice
leaves most or all of the crop residues on the soil surface. Conservation tillage is
generally defined as tillage practices that leave more than 30% of the crop residue
on the soil surface. No-till management leaves virtually 100% of the organic
residues undisturbed on the surface of the soil. No-till is the most extreme form of
conservation tillage where all residues are left on the surface and the soil is
disturbed only the minimum amount necessary to seed the next crop. A number of
benefits accrue from this management strategy including lower rates of carbon
oxidation and erosion.
The cover of crop residue left on the surface of the soil acts as a mulch,
reducing soil temperatures and increasing soil moisture. Cool moist soils have been
shown to have significantly slower rates of carbon loss and in some cases act as a
long term sink for atmospheric carbon. Conservation tillage reduces the frequency
and magnitude of mechanical disturbance. This, in turn, reduces the creation of air
filled soil macropores and slows the rate of carbon oxidation. Further, less mixing
of crop residue into the soil, reduces the rate of biological and chemical interactions
(Dick, 1983).
No-till management has been shown to significantly reduce the rates of soil
erosion (Phillips et al., 1980). Reduction in the rate of erosion tends to preserve
the upper carbon rich layers of the soil. It has been estimated that up to 50% of the
33
soil organic carbon removed by erosion is later oxidized and enters the atmosphere
(Kern and Johnson 1991).
Defining the Climatic Limits
The existence of climatic limits for no-till management in the northern areas of
the United States have been reported by a number of authors (Phillips et al., 1980;
Fenster, 1977; Gebhardt et al., 1985). In general terms, those areas where crop
production is limited by cold wet soils and short growing seasons are considered
unsuitable for no-till management. It is reasonable to assume, based on the
latitudinal extent of most of the agricultural areas of the FSU (the most southern
extremes of agricultural land in the FSU are roughly equivalent to the northern
Great Plains of the United States), that a significant portion of the agricultural land
in the FSU will not be climatically suitable for no-till management.
The agricultural areas of the FSU are characterized by a strong continental
climate with long cold winters and short warm summers. In significant portions of
the FSU agricultural production is limited by short growing
seasons
and high soil
moisture that restricts tillage operations in the spring (Medvedev, 1987). In the
northwestern parts of the FSU "the intense winter cold ... (is) less important than
the shortness of the summers and the lateness of spring..." (Symons, 1972). It is
reasonable to assume that areas where crop production is limited by low soil
temperatures, low rates of evapotranspiration, relatively high winter precipitation, and
a short growing season will not be suited for no-till management, an agricultural
management practice that produces cooler, moister soil.
A standard unit of measurement for estimating the heat balance of agricultural
lands is the growing degree-day. Data from Symons (1972) concerning the climatic
production limits for various crops in the FSU suggest that areas of less than 1600
growing degree-days (above 10°C or 50°F) would be too cool for no-till manage-
ment. Maps with climatic factors for the FSU are contained in the USSR Atlas of
Agriculture (1960). Based on these maps, areas with less than 1600 growing
degree-days were removed from further consideration.
Areas having a significant moisture surplus were assumed to be located in
regions with a precipitation to potential evapotranspiration ratio greater than 1.33.
These areas were assumed to be generally unsuited to no-till management and were
also removed.
Generally, the areas eliminated by these assumptions were
characterized in descriptive terms as poorly drained, and limited in agricultural
production by cold wet soils and short growing seasons (Symons, 1972; Medvedev,
1987).
The areas of each soil type that were included within the general agro-climatic
limits suitable for no-till management are summarized in Table 2 (column 3). The
results presented indicate the amount of agricultural land suitable for no-till
management is 181 million hectares. This represents a reduction of 29 million
hectares (or almost 14% of the total agricultural lands) when the climate limits
previously discussed were applied. Figure 3 shows the location and extent of arable
lands (>30% arable) and the portions of those lands suitable for no till management
in the PSi T
Potential Effect of Conservation Tillage on Soil Carbon
An increase in soil organic carbon has been reported in agricultural soils where
conservation tillage has been practiced (Unger, 1968 and 1991; Arshad et al, 1990;
Blevins et al., 1983; Dick, 1983), with few exceptions (e.g., Carter and Rennie,
1982).
Further, a significant change in the distribution of organic carbon in the soil
profile occurs with conservation tillage. Reduced mixing of crop residues into the
soil column produces a high concentration of organic carbon at or very near the soil
surface.
Kern and Johnson (1991) developed regression equations predicting increases in
organic carbon resulting from no-till management in agricultural soils of the United
States.
Their equations were based on published research results from 15 different
studies of no-till management. Their work resulted in two general equations for
predicting the response of soil carbon to the no-till management option. Research
results concerning the effects of conservation tillage short of complete no-till (such
as ridge tillage and mulch tillage) on soil carbon were inconclusive. Therefore, in
the present study the effect of conservation tillage on soil carbon has been restricted
to the no-till management option.
In the uppermost eight centimeters of the soil column the response of soil
carbon to no-till is predicted by the following equation (Kern and Johnson, 1991):
SOC, = (1.283 * SOCC) + .0510
(in t/ha)
(2)
(R = 0.75, n = 15)
in which,
SOC, = the current soil organic carbon (re. equation 1)
SOC , = the soil carbon equilibrium condition for the no-till management option.
In the 8-30 centimeter portion of the soil column the rate of carbon
accumulation is less under no-till management because the tillage operations no
longer transport organic matter from the surface into the lower portions of the soil
profile. The equation for this portion of the soil column is (Kern and Johnson,
1991):
SOCK, = (1.16 * SOCC) - 0.018
(in t/ha)
(3)
(R = 0.89, n = 34)
Below 30 centimeters, no significant differences in the rates of carbon
accumulation or loss were observed between no-till management and conventional
tillage practices.
To use equations 2 and 3 it was necessary to have carbon profiles for each soil
type and distribute the carbon contents through each profile. Organic carbon
profiles for soil types in the FSU reported by Glazovskaya (1972) were used to
calculate the percent distribution of carbon in each soil profile. These profiles and
percent distribution of carbon with depth are presented in Figure 2 and Table 4,
respectively.
The distributed organic carbon contents and the current soil carbon equilibrium
(SOCC from Table 1 (column 4) for each soil type were used with equations 2 and
3 to predict potential carbon sequestration for each agricultural soil type in the FSU.
The changes in soil carbon for each soil type are presented in Table 3 (columns 46). The carbon pool for a no-till management scenario is the product of the
climatically suitable area of each soil type, the appropriate percent distribution of
carbon in the soil profile, and the coefficients in equations 2 and 3. Comparison of
the estimated current carbon pool for climatically suitable lands now under
conventional cultivation (Table 2 (column 7)) and the no-till carbon pool (Table 3
(column 8)) suggests that a complete conversion of all climatically suitable land to a
no-till management practice would sequester 3.3 gigatons of carbon (Table 3
(column 9)). The overall increase in carbon would be slightly more than 10% of
the total carbon presently stored in agricultural soils. This estimated accumulation
of carbon in the soil represents the new equilibrium for carbon in agricultural soils
under no-till management. Sequestration of additional atmospheric carbon in the
soil produced by a conversion from conventional to no-till is expected to take at
least 10 years (Kern and Johnson, 1991). Accumulation of carbon in the soil
produced by this change in management strategy is not a continuing process; once a
new carbon equilibrium has been reached under no-till management no additional
atmospheric carbon will be sequestered by continued no-till management (IPCC,
1992).
Benefits and Problems Associated with No-Till Management
No-till management represents a useful agricultural management option that
when properly applied produces a number of benefits. Aside from the long-term
benefits of carbon sequestration, conservation tillage has been proven to reduce soil
erosion. The amount of soil that is saved by implementing conservation tillage was
estimated to be from 30 to 90% (Amemiya, 1977; Phillips et al., 1980; Gebhardt,
1985). While accurate data for soil erosion are not available for the FSU
considerable anecdotal evidence indicates that soil erosion may be an even greater
problem in the FSU than it is in the United States (Brown and Wolf, 1984;
Medvedev, 1987). For example, the rich chemozems which produce 80% of the
grain crops in the FSU have been especially hard hit by soil erosion and the loss of
organic matter (carbon) (Priputina, 1989).
Additionally, crop residues reduce wind speed at the soil surface and act as a
sponge and buffer for incoming precipitation. The mulch of crop residues also
increases the rate at which water infiltrates the soil (Fenster, 1977). The
introduction of no-till management has been shown to increase the aggregate
stability and raise the percentage of organic matter in the soil (Blevins et al., 1983).
These increases have a high positive correlation with increased root growth and
production in agricultural crops. The increase in organic matter also results in
higher storage of carbon in the soil.
In the United States 6.48 million hectares (6% of agricultural lands) are
currently under no-till management (Kern and Johnson, 1991) with the percentage
increasing each year. Agricultural lands managed with the no-till option are
projected to reach 45% of all crop lands in the United States by 2000 (Phillips et
al., 1980). Projecting the same percentages for no-till management in the FSU, the
amount of carbon sequestered in agricultural lands would be .18 and 1.35 gigatons
of carbon at 6 and 45%, respectively.
A number of additional factors must be considered when forecasting changes in
the agricultural sector of the FSU. Even though conversion to no-till management
requires lower capital investment than do other management practices that have been
proposed to sequester carbon in agro-ecosystems, no-till management requires
different or specially modified agricultural equipment.
In the United States the cost
of new equipment often prohibits the introduction of conservation tillage. It is
unlikely that individual farmers (there are few in the FSU) or individual collective
farms will be able to finance the cost of new equipment, at least in the next decade.
It is also unlikely that the governments of the republics in the FSU will be able to
spend their foreign currency on equipment that may produce little short-term
increase in yield.
A number of authors (Philips et al., 1980; Kern and Johnson, 1991; Hinkle,
1983) suggest that no-till management is more complex than conventional tillage
with respect to the control of weeds, insects and other pests. For example, crop
residues on the surface prohibit mechanical removal of weeds. Annual plowing
does not bury the previous season's weed seeds nor can mechanical cultivators be
used to remove weeds between the developing crops. Increased (or complete)
reliance on chemical weed control my result from the adoption of no-till
management. The production and timely delivery of an increased number of
agricultural chemicals is probably beyond the capability of the current agricultural
infrastructure in the FSU (Chistobayev, 1990).
Summary and Conclusions
One-hundred and eighty-one million hectares of agricultural land in the FSU are
climatically suitable for no-till management. Complete conversion of all climatically
suitable land to no-till management would sequester 3.3 gigatons of carbon. This
represents a 10% increase in carbon in the agricultural soils of the FSU.
Accumulation of carbon in the soil produced by a conversion from conventional to
no-till is expected to take at least 10 years.
All successful agricultural operations require energetic, involved and educated
operators who have a wide variety of skills and often have the ability to recognize
the long-term benefit of a particular management practice. Up to the present time,
these traits have not been encouraged in the FSU. Medvedev (1987) gave the
following analysis of the agricultural population of the FSU:
"... in the Soviet Union ... the most active and productive part of the rural
population has been destroyed ... Thus while only the most active, able,
and devoted farmers remain on the land in the United States and Western
Europe, in the Soviet Union it is the least able, the most passive, and the
oldest who stay voluntarily."
Dramatic changes in infrastructure, education, and social structures will be
necessary in the agricultural sector of the FSU before no-till management will be
implemented to sequester carbon in the terrestrial biosphere.
—
-
-
-
-
-.
-
—- ——'
-
-
-
-
I
oGpa
-
S w (I (IC II
—
-.--
•
--
-
L
-
- --•---.--
stp
i:a
Russia
ç
Arable Lands (Greater than 30% A able)
Lands Suitable for No-Till Manag anent
LNon-Arable Lands (Less than 30%Ira4e)
L
-:
I
—
Kazaka
e
Iran
/
Afgani
s
ki
s
an
an
j
islan
China
;Kor*a
Figure 1. Agricujtgral Soils in the Former Soviet Union Suitableift lip-Till Management
IL
E
(
°
L
G
11
U
Desert
"
Q
0
O
N
+.
O
C/)
1015
5 1015
2
4
1
4
6--8
N..
1,
Id.
60,;.
-C
Chestnut;
O
E
N
2
3
1
40
E
U
50
Q
60
(1)
O
70
L
80
Organic Matter, percent by weight
Figure 2. Soil Organic Carbon versus Depth (after Glazovskaya, 1972)
Table 1. Carbon Content of Agricultural Soils of the Former Soviet Union
Soil Type
Total Area
Initial or
Initial or
Cultivated
of
Precultivation
Carbon Conten t
Precultivation
Carbon Pool
(109 t)
Equilibrium
C arbon Conte nt
SOCc
(4)
(t/ha)
(5)
Current
Carbon Pool
for
Aagricultural So ils
(109 t)
(6)
Agricultural S oils
(106 Ha)
SOC1
(t/ha)
(1)
--T
(2)
(3)
51.7
120.0
6.2
91.3
4.7
3.9
190.0
.7
144.5
.6
Gray-Forest
44.1
160.0
7.1
121.7
5.4
Chernozem
78.0
333.0
26.0
253.8
19.8
Chestnut
17.5
110.0
1.9
83.7
1.5
Solonetz
6.7
42.0
.3
32.0
.2
Gray-Brown Desert
8.3
45.0
.4
34.3
.3
Podzol
Floodplain-Meadow
Totals
210
42.6
32.5
I
It
10
Table 2. Carbon Content of Agricultural Soils Climatically Suitable for
.. il u, i',
No-Till Manage ment
I
Soil Type
Total
Area
Area
of
Decrease
in
of
Agricultural
Soils
Climaticall y
(106 Ha)
Soils
(106 Ha)
Area
Percent
Current
Equilibrium
Carbon
Reduction
in
Area
Content
SOCK
(t/ha)
Suitable
for
Conservatio n
Tillage
Podzol
(2)
(3)
Suitable
for
Conservation
Tillage
(10 t)
(106 Ha)
(1)
Current
Carbon Pool
for Soils
Climatically
(4)
(5)
(6)
(7)
51.7
31.4
20.3
39
91.3
2.9
3.9
1.9
2.0
52
144.5
.3
Gary-Forest
44.1
40.1
4.0
10
121.7
4.9
Chernozem
78.0
76.5
1.5
2
253.2
19.4
Chestnut
17.5
16.9
.6
36
83.7
1.4
Solonetz
6.7
6.7
4.0
32.0
.2
Gray-Brown Desert
8.3
7.6
.7
34.3
.3
Floodplain-Meadow
Total
210
181
29
.6
8
29.4
13.8
Ii
I
10
Table 3. Soil Carbon Content Under No-Till Management
.
t.
Soil Type
Area
Current
(l)
of
Equilibrium
Soils
Carbon
Climatically
Suitable
Content
(SOCd
for
(t/ha)
Conservation
Tillage
(3)
Carbon
Content
0-8 cm
(t/ha)
(4)
Carbon
Content
8-30 cm
(t/ha)
(5)
Carbon
Content
> 30 cm
(t/ha)
(6)
Ii
ICI
).
"No-Till"
Total
Increase
Equilibrium
"No-Till"
in
Carbon
Content
(t/ba)
(7)
Carbon
Current
Equilibrium
Carbon
in
Carbon
Pool
(109 t)
(9)
from
"No-Till"
Pool
(109 t)
(8)
(ha)
Percent
Increase
Pool
Management
(10)
(2)
Podzol
31.4
91.3
35.6
24.7
31.0
105.3
3.3
1.9
144.5
30.3
56.4
57.8
162.2
.3
Gray-Forest
40.1
121.7
12.2
35.3
74.2
130.9
5.3
.4
Chernozem
76.5
253.2
38.0
114.0
101.2
21.5
2.1
ll
Chestnut
16.9
83.7
23.4
54.4
5.9
99.1
1.7
.3
21
6.7
32.0
13.8
18.2
0.0
38.9
.3
1
21
7.6
34.3
11.3
23.0
0.0
41.2
.3
Floodplain-Meadow
Solonetz
Gray-Brown Desert
Total
282
181
32.7
11
It P
1
P
.4
0
15
0
0
3.3
8.6
0
Table 4.
Percent Distribution of Soil Carbon
> 30 cm
0-8 cm
8-30 cm
Podzol
39
27
34
Acidic Floodplain-Meadow
21
39
40
Gray-Forest
10
29
61
Chemozem
15
45
40
Chestnut
28
65
7
Solonetz
43
57
0
Gray-Brown Desert
33
67
0
Soil Type
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Identification of Carbon Quantifiable Regions in the Former Soviet Union
Using Unsupervised Classification of
AVHRR Global Vegetation Index Images.
Greg G. Gaston
Ted S. Vinson 2
Philip L. Jackson '
Tatayana P. Kolchugina 2
1. Department of Geosciences
Oregon State University
Corvallis, Oregon
2. Civil Engineering Department
Oregon State University
Corvallis, Oregon
Please Address Correspondence to
Greg Gaston
U.S. EPA Research Lab
200 S.W. 35th
Corvallis, Oregon 97333
Abstract
Global VegetationIndex (GVI) data from the Advanced Very High Resolution
Radiometer (AVHRR) was used to identify macro-scale vegetation/land cover
regions in the former Soviet Union (FSU). These regions are a better representation
of surface vegetation and land cover than can be obtained from existing thematic
maps of the FSU. Image classes were identified through cluster analysis using the
ISODATA clustering algorithm and a maximum likelihood classifier. Qualitative
analysis of the image variants produced with different input parameters indicated
that an image with 42 classes best represented significant details in vegetation and
land cover patterns without producing uninterpretable levels of detail that represent
artifacts of the clustering algorithm. Initial identification of image classes has been
made by considering the weight of evidence provided by quantitative and qualitative
analysis of existing maps, analytical tools from class statistics, ancillary data from a
variety of sources and expert assessment by Russian scientists with extensive field
experience in the FSU. Overall, this method of image classification using GVI data
appears to accurately describe regions with similar vegetation and land cover across
the FSU. Some questions regarding the identification of wetlands and potential
problems with classification in the Russian high arctic are discussed. The products
of this research will help improve carbon budget estimates of the FSU by providing
accurate delineation and definition of carbon quantifiable regions.
Introduction
To date, direct assessment of the carbon pools and fluxes (i.e. carbon budget) in
terrestrial ecosystems in the former Soviet Union (FSU) has not been performed.
The best current estimates of the carbon budget for this vast land area (one sixth of
the earth's land surface) which contains the largest expanse of boreal and tundra
ecosystems in the world are based exclusively on extrapolation of site specific
carbon estimates to the regions displayed on a limited number of thematic maps
(Kolchugina and Vinson, 1993; Vinson and Kolchugina, 1993).
However, there are serious questions about the information content of these
maps. Each map, at best "...is a snapshot of the situation seen through the particular
filter of a given surveyor, in a given discipline at a certain moment in
time"(Burrough, 1987). Cartographic data are generally the result of observations
taken over a very short time, thus maps are static sources of information. There are
problems with scale changes between maps, significant differences in the level of
detail between maps, and changes in classification schemes between maps and
across political boundaries. The placement of boundary lines between areas that
grade into each other over long distances is an arbitrary decision of the individual
cartographer. Additionally, the size and inaccessibility of much of the land area of
the FSU necessitates cartographic generalization, introducing interpolation errors..
Political restrictions on availability and even purposeful inaccuracy of USSR maps
are believed to be limited to large scale maps. Continental scale maps, used in this
research, are likely to be as accurate as field sampling and cartographic
representation allows.
There is wide agreement that satellite data can be used to overcome the
limitations of thematic maps, providing timely, consistent and reliable information
on the areal extent of, and conditions in, terrestrial ecosystems (Norwin and
Greegor, 1983, Janssen et al., 1990, Justice et al.1985, Hall et al., 1991). The
purpose of this research is therefore to utilize satellite data to identify macro-scale
vegetation regions across the FSU. These regions should improve the accuracy of
carbon budget estimates.
An Appropriate Remote Sensing System
The Advanced Very High Resolution Radiometer (AVHRR) appears to be the
most useful satellite remote sensing system presently available for continental scale
studies of terrestrial ecosystems. The AVHRR is a part of NOAA's meteorological
satellite program which is designed to provide daily coverage of atmospheric
conditions over the entire globe. The orbit and resolution of this sensor system
while designed for meteorological data collection, has been successfully used to
monitor conditions in terrestrial ecosystems (Tucker et al., 1983, Hayes, 1985,
Townshend et al., 1987, and Fung et al.,1987). A comparison of landcover mapping
based on the accuracy of 1.1 km AVHRR data to landcover maps based on 80 m
Landsat MSS indicated the accuracy for classifications based on AVHRR data was
71.9% compared to 76.8% for MSS. This accuracy is considered acceptable for
most research and given the disparity in spatial resolution, the accuracy of the
AVHRR is exceptional (Gervin et al.,1985).
For analysis of conditions in terrestrial ecosystems the most commonly used
product from the AVHRR is a normalized ratio of channel 1 (0.58-0.68 microns)
and channel 2 (0.725-1.1 microns). This normalized ratio of visible and near infra
red reflectance is commonly termed the Normalized Difference of Vegetation Index
(NDVI).
The equation used to calculate NDVI is as follows (Kidwell, 1990):
CH.2 (Near IR) - CH.1 (Visible)
NDVI = --------------------------------CH.2 (Near IR) + CH.1 (Visible)
(1)
Normally active photosynthetic vegetation absorbs the visible wavelengths
(AVHRR Ch.1) (esp. red) and reflects near infra-red radiation (AVHRR Ch.2).
Higher photosynthetic activity results in increased red absorption and even higher
near infra-red reflection. NDVI values range from -1 to +1, with most vegetation
clustering around +.6 NDVI; higher values are characteristic of higher photosyn-
thetic activity. This index has been related to Primary Productivity (Tucker et
al.,1983), Leaf Area Index (LAI)(Spanner et a1.,1990), Percent Vegetative Cover
(Townshend et a1.,1987) and Biomass and Absorbed Photosynthetically Active
Radiation (PAR or IPAR)(Goward et al.,1990). There appears to be a strong
correlation between NDVI and the vegetation in terrestrial ecosystems. Most
research using AVHRR data concentrates on this index to evaluate vegetation
attributes in the analysis of terrestrial ecosystems.
The highest spatial resolution for the AVHRR scanner is 1.1 km at the nadir.
As the scan widens to acquire data across the entire 112 degrees of the sensor
swath width, the spectral and spatial resolution degrades due to increases in
atmospheric path length,curvature of the earth, and changes in reflection angles.
The wide scan angles and daily repeat of orbits produce a tremendous amount
of data. A lack of on-board storage and limited down-link connections require on-
board pre-processing and sampling of the data (Goward et al.,1990). The first of
the pre-processed products is known as Global Area Coverage (GAC). GAC data
are produced on a daily basis by averaging the first 4 pixels in a 5 x 3 pixel array,
and assigning this average value to the entire array, producing a nominal 4 x 4 km
pixel (Goward et al.,1987).
GAC data are further processed by NOAA, by selecting the "greenest" pixel (the
highest NDVI value) in a 4 x 4 pixel array. The resulting product, known as the
Global Vegetation Index (GVI) reflects the highest NDVI value occurring in a seven
day period over a nominal 16 x 16 km pixel (Kidwell,1990).
The presence of clouds (on an average day one half the surface of the globe is
obscured by clouds), shadows, off nadir 'look' angles, and different reflection ratios
all tend to reduce NDVI values. In order to reduce the effects of these factors a
temporal compositing routine is used. The maximum NDVI is usually obtained in an
unobscured near nadir view. A maximum value composite is constructed by taking
the maximum value for a given pixel in a given time frame. Weekly and monthly
maximum value composites are regularly produced using a set of georeferenced
AVHRR images (Kidwell, 1990). This process appears to remove most of the
problems associated with clouds, off nadir views, and other factors that reduce
NDVI (Holben,1986). Goward et al. (1990) maintain that errors can be reduced to
± 10% (.1 NDVI unit) using a maximum value compositing process on a monthly
time step.
The maximum value compositing procedure assumes that the surface features are
homogenous over the sampling area. This procedure may overestimate the actual
extent of photosynthetically active vegetation. In choosing the "greenest" pixel it is
possible that an oasis would be chosen to characterize an otherwise barren desert
(Townshend and Justice, 1986).
The temporal signature of a heterogenous landscape may also be confused using
this procedure. For example, regenerating broadleaf vegetation in a disturbed area in
a conifer forest may produce "greenest" pixels that represent these areas of
regeneration rather than the bulk of the conifer forest. Further, the temporal
compositing procedure which simply assigns the value of a single 4 x 4 km GAC
pixel to the entire 16 x 16 km GVI pixel, regardless of time or the spatial location
of each pixel, makes locating the exact geographic position of individual pixels
impossible.
Data and Methods
Research Background
The use of AVHRR derived products to characterize the location, areal extent,
and characteristics of land cover (terrestrial eco-systems) has been reported by a
number of authors.
Goward et al. (1985), used three week composites of GVI data to identify and
characterize vegetation patterns of North America. They selected 3 x 3 pixel plots
58
of "known" surface conditions. The change in NDVI with time for these plots was
graphed. The area of the temporal NDVI signature was determined for each test
area and this statistic was used to relate each class to measures of biomass
productivity. Their research demonstrated that major land cover regions could be
identified using seasonal patterns of NDVI change.
Justice et al. (1985) using, in part, GVI data concluded that the extent and
seasonal
dynamics of global vegetation could be successfully mapped on a global
scale. A variety of classification schemes were tested by Townshend et al. (1987)
in research directed towards classifying vegetation complexes in South America.
They concluded that a maximum likelihood classification produced the most
satisfactory delineation of South American vegetation complexes, when compared to
training sites selected from the UNESCO 1981 vegetation map.
Loveland et al. (1991) utilized unsupervised classification of monthly maximum
value composites (1 km resolution) to characterize the land cover of the
conterminous United States. They reported that large homogenous land cover
regions were well identified, citing forest ecosystems as prime examples of
successful classification.
GVI Data Used To Identify Carbon Quantifiable Regions
The goal of this research was to use Global Vegetation Index data to identify
macro-scale vegetation/land cover regions across the FSU. The motivation for
identifying these regions is an improvement of carbon budget estimates in the FSU.
The use of a single year time series could produce bias in regional identification
due to variations from climatic norms occurring in a single year. In order to reduce
the possible effects of inter-annual variation, monthly GVI composites from 1985
through 1988 were averaged, creating a single set of monthly observations that
represented conditions over a 4 year period.
The GVI composites used in this research are slightly different in construction
from a simple maximum value composite. It is possible that the use of a single
maximum value from a single observation may cause further compression of the
temporal resolution. For example, the maximum NDVI value for the month of June
may occur on the 30th, and the maximum July may occur on the 1st, consequently
two "monthly" observations may be separated by a single day. The GVI data used
in this research are constructed from weekly maximum value composites for the
weeks occurring in a calendar month, the maximum and minimum values are
removed and a root mean square (RMS) average is calculated from the remaining
weekly values (NOAA-EPA,1992). This RMS average data set reduces the chances
of temporal compression possible with the use of single observations while
preserving most of the advantages found in a true maximum value compositing
scheme.
At high latitudes, with low winter sun angles, anomalously high NDVI values
are commonly found in AVHRR images. This phenomenon (known as the
"terminator effect") is apparently caused by the different transmission rates of red
and near infra-red radiation (Justice et al.,1985). Examination of the GVI
composites of the FSU in the winter months displayed extremely low (usually 0)
NDVI values, with the exception of the high arctic which appeared to be vigor-
ously photosynthetic throughout December, January and February. Of course, the
actual levels of vegetative growth are negligible between October and March-April
over most of the FSU. Consequently, the NDVI data used in the present study
were restricted to composites from March through October.
Problems with Weekly GVI Composites
The problems previously discussed (e.g. the oasis effect and lack of spatial
certainty) are inherent to the sampling scheme used to create GVI data. It is also
recognized that utilizing a monthly time step for maximum value composites may
result in the loss of temporal detail. Examination of GVI data sets at a weekly
time step (1988 only)
suggests
that in the extremely continental climates of the FSU
the rapid northward advance of the "green wave" in the spring takes place at a rate
much faster than can be captured in a monthly time step
.
However, the use of weekly composites is not without problems. Our
experiments with the 1988 weekly time step data showed what are believed to be
significant problems with cloud contamination. Since the Russian high arctic is one
of the most cloud covered regions on earth (Parker,1983), a weekly compositing
period does not appear to be sufficient time to significantly reduce the problem of
cloud cover. Holben (1986) discusses the problem of temporal compositing period,
and suggests that the best guide is the quantity of information desired and a priori
knowledge of the phenological response curves of the vegetation. He states that
longer compositing periods significantly reduce problems with clouds, etc., but that
more observations make identification of vegetation complexes based on
phenological change easier. A four year average of monthly maximum value
composites represents a compromise in data selection, and in the author's opinion
are the most appropriate data for initial identification of carbon quantifiable regions
across the entire FSU.
Clustering and Classification
Unsupervised classification is a means of analyzing data that allows the inherent
structure of the data to be seen (Anderberg,1973). Unsupervised classification was
recommended by Townshend and Justice (1986) as the most effective way to
identify vegetation communities using GVI data. This method was used by
Loveland et al. (1991) with eight monthly maximum composites from 1990 as a
first step in creating a landcover map of the conterminous United States. An
unsupervised classification requires two steps: (1). identification of "clusters" within
the data (cluster analysis), and (2). a maximum likelihood grouping of the entire
data set around these "cluster centers".
Combined multi-year data for each of the seven months in the growing season
of the FSU were clustered using the ISODATA algorithm imbedded in the GRASS
geographic information system (GIS)(U.S Army CERL,1992). ISODATA is the
most common clustering algorithm found in image processing systems (Bryant,1978).
ISODATA requires a number of input parameters, most significantly the initial
number of classes and the class separation distance. These parameters define the
level of generalization in the resulting image. The initial number of classes sets the
maximum number of classes that can be generated. The class separation distance
defines a threshold distance below which pixel clusters will be grouped together.
Changes in these parameters produce some very noticeable differences in the
resolution and level of detail in the resulting image. Most discussions of cluster
analysis suggest that extensive experimentation and expert assessment are the
primary means of selecting appropriate input parameters and the most useful
resulting image (Swain and Davis,1978, Anderberg,1973, Harmon and Shapiro,1990).
A number of quantitative methods have been proposed to determine the number
of "natural" clusters present in a data set. Milligan and Cooper (1985) compared 30
methods for estimating the natural number of clusters in a test data set, and
identified the Calinski/Harabaz pseudo F statistic as the most reliable. This statistic
is supplied by the SAS module 'FASTCLUS' (SAS/STAT,1990). This module was
executed with the GVI data for the FSU. The results of this analysis suggest that
the "natural" number of clusters present in the combined monthly maximum value
composites from March-October for the FSU is only two. The authors suspect this
statistic represents a vegetation/no vegetation split, which is much too simplistic for
a detailed analysis of the FSU.
The most effective method of identifying the appropriate number of clusters
(image classes) was based on expert assessment and visual comparison of experimental images. This was an iterative process that involved changing cluster
parameters to create a large number of image variants. Examination of the images
revealed some stable overall patterns with varying levels of detail in class
separation. The fundamental problem became the identification of the image variant
that successfully identified important vegetation/landcover complexes without
creating an unnecessarily large number of classes as an artifact of processing.
63
Key to the selection of the most useful image variant was the identification of a
set of features that should be seen on a detailed vegetation/landcover map of the
FSU. This set of features was identified with the aid of maps, ancillary data
sources, and expert assessment provided by scientists with extensive field experi-
ence in the FSU . Image variants were examined to see if these details were
successfully identified while minimizing confusion in the overall patterns seen in the
balance of the image.
Images produced using a smaller number of initial clusters (i.e. 15) were
smoother and more generalized with a high correlation with the classes displayed on
a generalized soil/vegetation complex map of the FSU (Ryabchikov, 1988).
However, significant detail was lost on this image, with areas of very different
characteristics lumped together into a single image class. Images produced with a
large number of classes (i.e. 75) were extremely noisy with overall patterns
degraded with unacceptable (and uninterpretable) detail.
The image variant chosen
for this research (42 classes) had sufficient detail to locate the specified features and
at the same time preserved the overall patterns described and mapped for the
vegetation communities of the FSU.
The image map used to classify macro-scale vegetation/land cover patterns of
the FSU is shown in Figure 1. There are 42 image classes each reflecting a
different timing and magnitude of photosynthetic activity as recorded by NDVI. The
image is a simple latitude - longitude projection.
Interpreting the Image
Quantitative Tools
The critical step in utilizing an unsupervised classification scheme is trans-
forming the image classes into "informational classes" (Swain and Davis,1978).
A number of quantitative tools can be brought to bear on the problem of image
interpretation. Computerized GIS tools make it extremely simple to produce
coincidence tabulations, showing the percentage of image classes present in each
class of a thematic map or maps. In this research, for example, image classes were
compared with the best available digitized vegetation map of the FSU
(Sochava,1960), giving some indication of vegetation present in each image class.
For example, image class 2 which has been identified as mountain tundra/arctic
tundra contains the following mapped vegetation classes; northern mountain tundra
(43.3%), arctic tundra (12.9%), arctic desert (9.7%), southern mountain tundra
(5.8%) and typical tundra (3.2%). The remainder of this image
class
(25.1%) is
represented by very small percentages of a number of mapped vegetation
classes.
Overall, this image class has a high correlation with the vegetation map. In
contrast, class 21, identified as predominantly agriculture contains 49 mapped
vegetation classes scattered with no apparent pattern or dominant characteristic.
Direct comparison of image classes to thematic map classes is a useful
technique but does not provide a complete explanation of class characteristics.
Goward et al. (1987, p.29) states that "...comparison of AVHRR observations to
'known' distributions produce varying results based on the map selected".
It is possible that vegetation patterns displayed by classified remote sensing data
may show significant spatial patterns independent of previous classification schemes.
Townshend (1987) asserts that because existing maps of vegetation and land use
have been developed using different methods they may not be acceptable standards
of reference for AVHRR classifications.
Another useful interpretative tool is the construction and comparison of temporal
NDVI response curves (TNRC). These can provide a great deal of information
about individual classes and the relationships between classes. TNRC's provide
useful information for identifying similarities between classes and allow one to
separate classes with significantly different reflectance response.
It may be possible to develop additional analytical tools based on TNRCs.
Goward et al. (1985 and 1987) and Tucker et. al. (1985) suggested the use of the
area under the TNRC could be used to classify vegetation types and that this
statistic might be related directly to biomass estimates of terrestrial ecosystems (at
least in North America). It may also be possible to develop additional statistical
tools to identify patterns of class groupings and to quantify differences between
classes.
Realistically, however, image interpretation is not done solely with quantitative
tools. The combined weight of evidence from quantitative tools, information from a
wide variety of maps, descriptive geographies, and the assessment provided by
experts with field experience must all be used by the interpreter to convert image
classes into information classes.
Qualitative Tools
Table 5 lists the maps that were used to aid visual interpretation of the image
classes and in the case of the vegetation map (Sochava 1960) were used in a
coincidence tabulation to provide quantitative information for each image class.
In addition to the
maps
presented in Table 1, several descriptive geographies by
Russian geographers were extremely useful (e.g. 'Physical Geography of Asiatic
Russia' by S.P. Suslov (1961), and 'Natural Regions in the USSR' by L.S. Berg
(1950)). A variety of other ancillary data sources were also consulted to provide
additional information for image interpretation.
Expert assessment was provided primarily by two Russian scientists with
extensive field experience in the FSU. These scientists are identified and their
credentials are presented in Table 5.
Basic Geographic Patterns in the FSU
Successful interpretation of the image created through unsupervised classifica-
tion of GVI images which represents the land cover of the FSU is facilitated by an
understanding of the climatic and physiographic factors that influence vegetation and
human settlement patterns. In simple physiographic terms the FSU can be described
as a plateau, open to the north and bounded on the south and east by high
mountains. The Russian platform and the Siberian Platform are "typically flat,
almost planar" (Nalivkin,1973) and the lowlands of western siberia are "flat as a
67
floor" (Lydolph,1970). The Ural mountains, the Putorana mountains and other small
ranges in the central portions of the FSU do not provide a significant topographic
barrier to climatic events.
The high complex mountain ranges of the Kopet Dag, the Pamir, Tien Shan,
Altai, Sayans and other ranges across the south and east of the FSU "shield" the
center of the country from tropical and marine tropical air masses. This shielding
from moisture in the south, coupled with the distance that marine air masses from
the Atlantic must travel over land and the frozen surface of most of the arctic
ocean, produce moisture deficit conditions in much of the FSU (Parker,1983)
Physiographically, the FSU is predominantly a stage on which a strong
continental climate with a limited moisture regime at high latitudes shapes the
vegetation complexes, producing definite zones of soil and vegetation. Physiographic and climatic factors produce pronounced latitudinal zones of vegetation.
Along a north to south transect the vegetation transitions from arctic tundra through
shrubby tundra to northern taiga forest. This north to south transition is marked by
vegetation communities with increasing biomass. Further south, moisture deficits
produce a broadleaf forest transition, steppe, semi-desert, and desert, an order
corresponding to vegetation communities with decreasing biomass. An additional
shift in vegetation communities generally occurs across a west-east transect, from
European species to predominantly Asian species.
The Image Classes
The patterns of the image classes reflect the expected patterns in vegetation that
result from climatic factors (re. Figure 3). Latitudinal banding in the image classes
reflects the effects of extreme cold and moisture deficits. Image class differences
reflect species change from west to east. From north to south there is an increase
in biomass as related to NDVI "greenness" with an increase in the percentage of
woody vegetation which is represented in image classes 2, (3-5), 4, 6, 8, 10. These
classes represent arctic (high mountain) tundra (image class 2) transitioning through
shrubby tundra (image class 6, 8, 10) to the forest/tundra (image class 12) boundary
where the sparse forests of the northern taiga begin to dominate the vegetation
communities. A similar pattern may be observed in the elevational gradient of the
Burranga mountains, the Putorana mountains and the mountain ranges of eastern
siberia.
In the south, the effects of moisture deficits on vegetation communities are
reflected in image classes 13 (desert) through 14 (28), 17, 26 (23), 35 (32) which
represent increasing vegetation grading from semi-desert steppe (image class 14 and
28) into forested meadows (image classes 35 and 32). The Northern taiga forest of
the FSU are identified by classes 11, 15, 16, 18, 20, and 23. The dwarf and
shrubby forests of the pacific coast are identified by image classes 9, 19, and 22.
Middle taiga forests are found in image classes 24, 25, 29, 30, and 31. Southern
taiga forests are represented by image classes 33, 36, and 37. Mixed conifer and
broadleaf forests are identified by image classes 38, 39, and 40. Broadleaf forests
69
with a smaller percentage of conifers and agricultural lands are identified by image
classes
41 and 42.
Table 6 presents a summary description of all image classes. A complete
description of the identification process for all 42 image classes is beyond the scope
of this paper. However, some examples of the identification
process
are provided
for clarity.
In the forest regions of the FSU a number of
classes
have been generated.
There is some question concerning the exact species composition of several
classes
but most maps and ancillary data sources describe the central siberian plateau as an
unbroken, unmixed larch forest. This expanse of larch is differentiated only by the
density of larch trees and in some cases the composition of the understory. Image
classes 11 and 16 appear as divisions in this unbroken siberian larch forest.
Examination of the TNRC for each class reveals that these image classes are
virtually identical in temporal patterns with the only differences occurring in the
magnitude of the NDVI peak (Figure 4). A large body of evidence, including that
provided by comparing TNRCs, aided the identification of these
classes
as larch
forest of varying density.
In the Amur river region class 42 has been identified as a high biomass
mixed broadleaf forest. Detailed maps of the vegetation communities in this area
(Suslov, 1961; Sochava 1960) are very similar in their location and description of
broadleaf and mixed broadleaf "Ussurian" taiga vegetation (re. Figure 5). A
comparison between the mapped distribution of broadleaf and mixed "Ussuri" taiga
forests and the distribution of class 42 show a very high correlation (re. Figure 6).
70
In the mountainous region of south central Asia, image classes reflect very clear
delineations of vegetation communities (re. Figure 7). Vegetation in this region is
strongly influenced by elevation and the presence of water for irrigation. The
distribution of irrigated agriculture is very well identified by image class 21,
concentrated along the Amu-Darya and Syr-Darya rivers and indicating the course of
the Hi river flowing into Lake Balkash. Class 21 also locates very accurately the
Fergana valley, a fertile agricultural valley that has been under irrigation for several
centuries. Detailed inspection of this region helps illustrate the accuracy of the
methods used in this research. The Aral sea, Lake Balkash, and Lake Issul-kul all
appear as image class 1. An extremely small area of class 1 (two or three pixels
east of Lake Balkash) locate and identify Lake Ala-kul, one of the brackish
remanents of a much larger Lake Balkash that once filled this synclinal valley
(Suslov,1971).
The semi-desert transitional zones are represented in the region by both class
14 and class 28. Suslov (1971, p.569) describes strong differences in the "origin,
composition and ecology... (of the) predominate vegetation" in these semi desert
steppes. The image classes show clear differences that appear to correspond to the
differences described by Suslov. Comparison of the TNRCs for each class reveals
the similarity of these classes in timing, development and magnitude of vegetation
growth confirming the identification of these classes as a complex that represent
semi-desert steppe.
7
Agricultural Land
Locating the areas where agriculture is a significant factor to the character of
the landscape is an important consideration in interpreting the image classes. The
maps available to aid in interpreting the image classes are "potential natural"
vegetation maps that do not reflect actual agricultural land use. Maps that
accurately show current agricultural land use were not available at this time.
Currently, information on the location and extent of agricultural lands is provided by
the arable lands map of Cherdantsev (1961). The first three categories of this map
(greater than 60% arable, 30-60% arable, and less than 30% arable) appear to
accurately identify agricultural lands in the FSU. Assigning appropriate factors (eg.
80%, 45%, 15%) for each of these three classes, Gaston et al. (1993) calculated the
area of agricultural lands in the FSU at 210 million hectares. This estimate
compares very favorably with USDA estimates of 211 million hectares (USDA,
1990).
A similar method was used to estimate the area of arable lands in each image
class. Image classes 41, 38 and 35 appear to correlate well with the arable lands
mapped by Cherdantsev (1961). A coincidence tabulation between image classes
and arable lands indicated that 50% (approximately 100 million hectares) are located
in these three image classes. The estimated area and percent coverage of
agricultural land in each image class are presented in Table 7. Agricultural land
uses are a significant factor in a limited number of image classes. The percentage of
arable land in each image class (re. Table 7) were considered when defining and
describing each image class.
7
Discrepancies in Classification
A number of examples where the classification procedures appear to work very
well have been described. It is also possible to offer examples of discrepancies
between the spatial patterns in the image classes and the conditions described and
mapped in other sources. For example, the tundra of the Tamir peninsula is divided
on almost all vegetation maps into three nearly equal divisions. The dividing lines
for the tundra divisions closely follow lines of latitude. The image classes of the
Tamir do not exactly follow these mapped patterns (re. Figure 8). There is a more
complex north to south distribution of tundra as displayed in image classes 2, 3 and
5. The image classes appear to accurately reflect the GVI data and are not an
artifact of clustering. The reason for this disagreement is unknown. Perhaps the
four year average of monthly maximum value composites used in this study does
not completely remove the influence of clouds or extreme off nadir look angles in
the remote sensing data.
It is possible that the thematic
maps
tend to over
generalize the patterns of vegetation distribution. In order to successfully answer
these questions additional information should be provided by higher resolution
sensor data (such as Landsat Thematic Mapper) or by field reconnaissance.
It appears that wetlands are not well identified in the image. For example the
west coast of the Kamchatka peninsula is known to be covered with coastal marshes
and "blanket bogs" (Sochava 1960). While the image classes in this region appear
to clearly identify vegetation especially as influenced by elevational gradients (r.e
Figure 9). The image displays only a narrow band of classes 7 and 21 along the
western shore of the Kamchatka peninsula. The image classes are much more
narrow than the bogs mapped by Sochava (1960)(r.e. Figure 10) or described by
Botch (personal communication, 1992). The extensive wetlands of the western
siberian lowlands are also not well identified. Under representation of wetland areas
may be a product of the "oasis effect" inherent in GVI sampling. A large island of
trees in a wetland matrix could represent the maximum NDVI which is used to
characterize the entire GVI pixel.
Summary and Conclusions
The research presented provides an example of the use of satellite data to
identify and characterize vegetation/land cover complexes over one sixth of the
earth's land surface. The image described in this paper represents a first attempt to
utilize satellite data to identify carbon quantifiable regions across the FSU. This
effort should be viewed as the beginning of a process that improves the
identification and characterization of vegetation/land cover classes over the FSU.
The use of the ISODATA clustering algorithm and a maximum likelihood
classification of monthly maximum value GVI composites has produced a clear
image of the FSU in which terrestrial vegetation/land cover complexes are clearly
separated. The separation of image classes is based on "greenness" and not on
species composition. The timing, magnitude, and duration of photosynthetic activity
as reflected by NDVI are the critical factors that influence class separation.
There
often appears to be a high correlation between images classes and species
composition (eg. class 42 in the Amur river region). However, some of the
divisions described by different image classes across the boreal forest may not
74
reflect species changes. The differences in image classes appear to be a result of a
number of factors including: species composition of all vegetation present in a pixel,
the density of vegetation, and the latitudinal position of the image pixel. It is not
possible at this time to fully isolate and explain the individual influence of all these
factors when characterizing the image classes.
Initial identification of class characteristics was possible through the combined
use of qualitative and quantitative tools. Previous research helps confirm the
conclusions from this research that while quantitative comparisons with existing
maps are useful, cartographic data does not fully describe the image classes
generated from satellite data. The conversion of image classes to "information"
classes must remain the result of qualitative analysis. It may be possible to use the
image classes generated in this research to create a stratification for sampling higher
resolution sensor data and provide structure for ground reconnaissance. Higher
resolution data (both temporal and spatial) would provide support for classification
refinement and definition.
Acknowledgement
The work presented herein was funded by the U.S. Environmental Protection
Agency (EPA)- Environmental Research Laboratory Corvallis, Oregon, under
Cooperative Agreement CR820239 to Oregon State University. Jeffery J. Lee is the
Project Officer for the project entitled "Carbon Cycling in Terrestrial Ecosystems of
the Former Soviet Union". The work presented is a component of the U.S. EPA
Global Climate Research Program, Global Mitigation and Adaption Program, Robert
K. Dixon, Program Leader. This paper has not been subjected to the EPA's review
and, therefore, does not necessarily reflect the views of the U.S. EPA, and no
official endorsement should be inferred.
Table 5
Maps used as an aid in image interpretation
'Vegetation of the USSR', 1960, Sochava V.B.(ed.), Ministry of Interior, Moscow
'Forest Tree Species of the USSR', 1960, Kruchinin A.P.(ed.), Ministry of
Interior, Moscow
'Landscape Units of the USSR', 1988, Isachenko A.G. Inst.of Geog. Leningrad State
University
'Geographic Belts and Zonal Types of Landscapes of the World' 1988,
Ryabchikov A.M., School of Geography, Moscow State Univ.
'Arable Lands of the USSR in 1954', 1961, Cherdantsev G.N., In: A Geography of
the U.S.S.R.- Background to a Planned Economy ed. J.P. Cole and F.C. German
'Permafrost Regions in the USSR', 1985, Ministry of Interior, Moscow
Scientific Experts
Dr. Marina Botch
Dr. Botch is a professor and senior research scientist at the Komarov Botanical
Institute in St. Petersburg, Russia. She is the Chairperson of the Russian committee
on peatlands which is the highest appointment related to peatlands in Russia. She
has extensive field experience and is the author of over 300 papers.
Dr. Kira Kobak
Dr Kobak is a professor and senior research scientist at the State Hydrologic
Institute in St. Petersburg, Russia. She has extensive experience in plant physiology,
soil processes, carbon cycling and climatic influences on ecological systems.
Table 6
A Brief Description of Image Classes
Image Class
1
Description
Water / No Vegetation
2
High Mountain Tundra (Arctic Tundra)
3
Arctic Tundra / Mountain Tundra
4
Mountain Tundra
5
"Typical" Tundra
6
Shrubby Tundra of Siberian Mountains
7
Bunch Grass Steppe
8
"Typical" / Shrubby Tundra
9
Dwarf Shrub Pine / Alder (Riparian Grass and Willows of Ob river)
10 Shrubby Tundra
11 Sparse Northern Taiga Larch
12 Forest Tundra
13 Desert
14 Semi-Desert (Artemisia and Bunch Grass)
15 Scattered Larch on Mountain Slopes
16 Northern Taiga Larch
17 Artemisia and Bunch Grass: Transition from Semi-Desert to Grassy Steppe
18 Northern Taiga Spruce / Larch Forest
19 Mountain Shrubs of Siberia; Pinus pumila, Alnus spp.
20 Northern Taiga Spruce Forest
21 Irrigated Agriculture (Some northern steppe east of Ural Mountains and
Kamchatka)
22 Mixed Dwarf Forest of Pacific Coast; Pinus pumila, Betula ermani
23 Scattered Forest (probably Pine/Aspen in the south; Spruce/Birch in the north)
24 Middle Taiga Forest; Larch / Pine
25 Open Forest: Larch in Vilyuy basin and Birch / Pine / Aspen in central
Siberia
26 Grassy Steppe
27 Pinus pumila / Betula ermani in Kamchatka
Table 6: (contd.)
28 Semi-Desert Steppe in Kopet-Dag and Pamir Mountains. ("Iranian in
character")
29 Middle Taiga Pine and Spruce
30 Middle Taiga Spruce / Fir / Pine Forest
31 Conifer Forest on Mountain Slopes
32 Forest Meadow Steppe: (Boggy Larch Forest in Vilyuy and Lena basins)
33 Coniferous Forest of Russian Plain (Pine / Spruce)
34 Grassy Steppe on Mountain Slopes of Central Asia;
Agricultural land use is
significant part of land cover matrix
35 Forest Meadow Steppe ; steppe portions are in agricultural crops
36 Larch / Pine (spruce) Forests on Mountain Slopes of Southern Siberia
37 Southern Taiga Forest of Southern Russia (predominantly mixed conifer)
38 Mixed Conifer / Broadleaf Forest (spruce/oak) (West of Ural Mountains)
agricultural crop patterns significant part land cover matrix
39 Mixed Conifer / Broadleaf Forest (aspen-birch/spruce-pine) East of Urals)
40 Mixed Forest of Southern Siberia (birch and spruce-larch)
41 Broadleaf (mixed) forest of Russia, Beyloruss, and the Ukraine
agricultural crop patterns significant part of land cover matrix
42 Highly Productive mixed forest (the species mix changes with location)
Table 7
Arable Lands located in Image Classes
Image Class
Area of Arable Land (million ha)
Percentage
35
43.7
64%
38
29.5
43%
41
27.7
42%
26
14.25
36%
37
13.3
27%
34
10.2
44%
33
10.0
14%
32
7.8
13%
23
5.5
11%
in Arable Land
total 162 million ha.
*Image classes 35, 38, and 41 account for over 50% of all arable lands estimated
for the FSU
Table 8
Descriptions of Figures
Figure 3.
Unsupervised classification of four year average Global Vegetation Index (GVI)
images for the seven months of the growing
season
(March-October). The image
has 42 classes each representing areas with similar timing, duration and peak
"greenness", as recorded by NDVI values. The image is displayed in a simple
latitude-longitude grid array.
Figure 4.
Temporal NDVI response curves (TNRC) of class 11 and 16. These classes
represent Larch forests of northern and middle siberia. These classes appear to be
separated only by the peak greenness. Classes of Larch forest are discriminated
only by density of the forest.
Figures 5 and 6.
Vegetation map (Sochava 1960) of the Amur/Primorski region. Class 55 is
Ussurian Taiga, Class 261 is Broadleaf forest, and Class 221 is Mixed Broadleaf
Forest. These mapped vegetation classes correspond very well to image classes 39
and 42. Further examination of Figure 4 demonstrates a good correspondence of
image classes to elevation.
Table 8 (contd.)
Figure 7.
Image details in south central asia. The distribution of image
classes
appears to
correspond well to elevation (inset) and to vegetation communities described in
ancillary data sources. For example class 28 along the slopes of the Kopet Dag
represent semi-desert steppe. The timing and magnitude of greenness for this class
is different (earlier and higher) than for other semi-desert classes (class 14). This
differentiation between areas of semi-desert steppe is well described by Suslov
(1961).
The snow covered peaks of the Pamir mountains are clearly located by image
classes 1 (no vegetation), 2 and 4 (high mountain tundra). In this region class 21
appears to clearly locate irrigated agriculture along the Amu Darya and Syr Darya
rivers. The Fergana valley, the irrigated slopes near Tashkent and the Ili river that
flows into Lake Balkash are also identified as irrigated agriculture in image class
21.
Figure 8.
Comparison of soil/vegetation association map (Ryabchikov 1988) to image class
distribution in the vicinity of the Yamal Peninsula. The nearly equal divisions in
vegetation distribution displayed on the map have been confirmed by expert
assessment. Image classes 3 and 5 are not distributed equally along latitude lines.
Examination of all GVI images used in the classification display a persistent pattern
of values that reflect the distribution of image classes 3 and 5. The distribution of
image classes may reflect persistent cloud cover or extremely steep look angles
inherent in GVI data for this region.
Figure 9 and 10.
Image class distribution in Kamchatka. The distribution of image classes clearly
reflect the effects of elevation on the distribution of vegetation.
Overall, the image
classes correspond to mapped vegetation classes. However, class 271 on the
vegetation map is "blanket bogs" or wetlands along the western shore of the
Kamchatka peninsula. The greater extent of wetlands on the vegetation map has
been confirmed by expert assessment. Only a narrow band of image classes 7 and
21 appears to reflect the distribution of bogs on the west coast of the Kamchatka
peninsula. The under representation of wetlands may be a result of the sampling
inherent in the creation of GVI.
uotun laTAOS iauuo3 JO 08EWi pa JissEID
II
Figure 3
Cl
7
—t
-Th
OZL
09
Oti
OZ
class 11
class 16
0
April
May
June
July
August
September
October
Figure 4
09
0
Temporal NDVI Response Curves for Class 11 and 16
OOL
T --------' --------
Figure 5
Vegetaton Map (Sochava, 1960): Amur Region
z
JJ
85
Figure 6
Classified Image: Amur Region
Inset: Elevation
/
86
LSO
I
A
n
Y
WP'erTana Valley
.N f
Y
7
iq
Pa,ur Mts.
!
C':
N_
I.
Figure 8
and Classified Image in Yamal Region
as
Comparison of Soil/Vegetation Complex Map (Ryabichikov, 1988)
tic
Unsupervised
Classification
4
(Otto lPerr
+C
Figure 9
Inset: Elevation
Classified Image: Kamchatka
7_'
S
'a I•
is4J-
ft
'p.
9
S
I—
-
S
SS
-
N
UI".
'p
I•
I
Figure 10
Classified Image: Kamchatka
Inset: Vegetation (Sochava, 1960)
19:
9
•
•
9
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Vinson, T.S., T.K. Kolchugina, Pools and Fluxes of Biogenic Carbon in the Former
Soviet Union. Journal of Water, Air and Soil Pollution. (accepted for
publication).
The Use of Global Vegetation Index (GVI)
To Estimate Phytomass and Net Primary Productivity in Terresterial
Ecosystems of the Former Soviet Union
Greg G. Gaston' *
Tatayana P.Kolchugina2
1.
Geosciences Dept.
Oregon State University
Corvallis, OR 97331
2.
Dept. of Civil Engineering
'Oregon State University
Corvallis, OR 97331
* Please Direct Correspondence to:
Greg Gaston
U.S. EPA Research Lab
200 SW 35th
Corvallis, OR 97333
(503)754-4496
(503)754-4338 FAX
gregg@russia.cor2.epa.gov (internet)
Abstract
A limited number of functions in a terrestrial carbon budget can be assessed
by satellite remote sensing systems. One approach for estimating phytomass
accumulation and net primary productivity (NPP) using Global Vegetation Index
(GVI) is to identify relatively homogenous vegetation and landcover regions.
Regions with similar vegetation and land cover in the former Soviet Union (FSU)
were identified using unsupervised classification of four year average monthly
maximum value GVI composites. Using a variety of information and techniques,
each of the 42 image classes was identified in terms of vegetation and land cover
(i.e., % forest, % agricultural, and % natural non-forest). Accurate delineation and
description of most vegetation and land cover complexes in the FSU was possible
using classified GVI imagery. This method provided more accurate and precise
descriptions for most ecosystems than is available on general thematic maps. There
was good comparison between reported area statistics from the FSU and land cover
estimates for image classes. The area of forest lands were estimated at 1,330 Mha
with the actual area of forest ecosystems estimated to be 875 Mha. Arable lands
were estimated to be 211 Mha. Using the classified image with the Bazilevich data
base of phytomass and NPP for vegetation in the FSU and calculated estimates of
phytomass and NPP in agricultural lands, the total phytomass in the FSU was
estimated at 97.1 Gt C, with 86.8 in forest vegetation and 0.6 Gt C in arable lands.
The total NPP for the FSU was estimated at 8.6 Gt C/yr, with 3.2 and 4.8 Gt C/yr
in forest and natural non-forest ecosystems. The total phytomass of natural
ecosystems compared well with estimates which used thematic maps to identify
ecosystems.
The phytomass estimated for forest ecosystems was higher than
another assessment which considered the age-class distribution of forest stands in the
FSU. The total NPP of natural ecosystems estimated in this study was 23 %
greater than previous estimates which used thematic maps to identify ecosystems.
The NPP densities estimated in this study were greater than a published
where NDVI data was correlated to NPP parameters.
assessment
Introduction
Natural processes in the oceans and terrestrial ecosystems, together with
human activities, have caused a measurable increase in the atmospheric
concentration of CO2. In 1988 the atmosphere contained 748 gigatons (Gt) of
carbon, the largest amount during the last 160,000 years (Post et al., 1990). On
average, the CO2 concentrations in the atmosphere appear to be increasing by 3.0
Gt/yr (Keeling, 1983; Tans et al., 1990)
The long-term ecological consequences of change in the chemical
composition of the atmosphere are not fully understood; however, a warmer global
climate is possible (PPIGW, 1992). If CO2 concentrations were to double, the
earth's temperatures could rise between 1 and 5° C (Schneider, 1990). Global
warming may disrupt the equilibrium of the natural carbon cycle by accelerating the
rates of plant respiration (Keeling et al., 1989) and decay of organic matter (Dixon
and Turner, 1991; Raich and Schlesinger, 1992).
It may be possible to manage terrestrial ecosystems to help offset increased
amounts of atmospheric CO2 (PPIGW, 1992). Before any global strategy for
environmental management aimed at mitigation of climate change can be formulated,
accurate assessment of the carbon cycle within the national boundaries is necessary
(Vinson and Kolchugina, 1993). The quantification of the carbon cycle following
an assessment of carbon pools and fluxes is generally referred to as the carbon
budget.
The Former Soviet Union (FSU) was the largest country in the world,
occupying one-sixth of the land surface of the earth. An understanding of the
100
carbon budget of the FSU is essential to the development of a global strategy aimed
at mitigation the potential negative impacts of climate change.
A Carbon Budget
The biogenic carbon cycle consists of a combination of pools and fluxes.
The pools are carbon stored in soil and vegetation. Effluxes are carbon emissions
resulting from plant respiration and decomposition of organic matter. Influxes of
carbon are represented by formation of new organic matter in soil and vegetation.
The annual accumulation of organic matter in vegetation after carbon is expended
for autotrophic respiration is known as net primary production (NPP).
To calculate the carbon budget of any region, a geographic area within which
carbon cycle parameters can be quantified must be isolated. The term ecoregion has
been applied to the boundaries and areal extent of the geographic area and the term
ecosystem has been applied to the soil-vegetation associations within an ecoregion
(Vinson and Kolchugina, 1993).
Carbon budget parameters may be expressed in terms of carbon content (for
pools) or rate per hectare (for fluxes) for a variety of soil-vegetation associations.
If the soil, vegetation, and land cover characteristics are accurately portrayed on the
thematic maps used to identify ecosystems, then the carbon budget for an ecoregion
can be established simply by multiplying the area of the individual ecosystems by
the associated carbon contents. The carbon contents and fluxes for all ecoregions
may be summed to arrive at the carbon budget for a larger region, or nation.
Kolchugina and Vinson (1991) and Vinson and Kolchugina (1993) used this
approach to assess the carbon budget of the FSU (including Russia, Ukraine,
101
Byelorussia, Kazakhstan, and the Baltic States). The southern republics (Turkmenia,
Tadjikistan, Kyrgizia, and Uzbekistan), and the Trans-Caucasian republics (Armenia,
Azerbaijan, and Georgia) were not included. Maps containing information on
distribution of soil-vegetation associations within the FSU (Ryabchikov, 1988),
wetlands (Isachenko, 1988), and arable lands (Cherdantsev, 1961) were used to
isolate ecoregions. The areal coverage of the ecoregions was integrated with the
carbon content and flux data for major vegetation and soil types in the FSU
(Bazilevich, 1986; Kobak, 1988) to establish the carbon budget within each
ecoregion.
The key map in this study was the "Geographical Belts and Zonal Types of
Landscapes of the World" (Ryabchikov, 1988). This global map represents a
synthesis of soil, vegetation, and climatic parameters. The use of this very general
map with the detailed Bazilevich data base required equating a range of carbon
parameters from the data base to map legend categories. For example, the map
legend describes only a single division in the coniferous forests in the FSU, namely
"light-crowned coniferous" and "dark-crowned coniferous" forests. No other
descriptions are provided. A range of carbon values from the Bazilevich data base
were assigned to each map class, representing the range of possible vegetation types
and phytomass and NPP densities possible within map legend categories.
There was concern, however, about the information content of the maps used
to identify the carbon quantifiable ecoregions. Each map, at best "...is a snapshot of
the situation seen through the particular filter of a given surveyor, in a given
discipline at a certain moment in time" (Burrough, 1987). Cartographic data are
generally the result of observations taken over a very short time, thus maps are
102
static sources of information. There are problems with scale changes between maps,
significant differences in the level of detail between maps, and changes in
classification schemes between maps and across political boundaries. The placement
of boundary lines between areas that grade into each other over long distances is an
arbitrary decision of the individual cartographer. Additionally, the size and
inaccessibility of much of the land area of the FSU necessitates cartographic
generalization, introducing interpolation errors.
There is wide agreement that satellite data can be used to overcome the
limitations of thematic maps, providing timely, consistent and reliable information
for the entire globe on the areal extent and conditions of terrestrial ecosystems
(Norwin and Greegor, 1983, Badwhar, 1986, Janssen et al., 1990, Justice et al.1985,
Hall et al., 1991).
Satellite Remote Sensing Systems
The abilities of satellite sensor systems to measure ecosystem functions are
limited as these systems generally record only reflected radiation from the surface.
Surface conditions represent a complex mixture of vegetation, exposed soil or rock,
water and shadows. The species composition, structure, density and levels of
photosynthetic activity of both the dominant vegetation and understory vegetation are
all a significant part of the signal recorded by satellite remote sensing systems
(Spanner et al., 1990; Knipling, 1970). Other conditions such as the "brightness" or
color of the exposed soil (rock) and the amount and density of shadows present are
all integrated into a satellite observation of terrestrial ecosystems (Huete et al.,
1985). Not only are the data recorded by a satellite sensor a result of complex
surface interactions but many important ecosystem processes cannot be directly
measured. Soil processes, litter accumulation, and mortmass accumulation for
example are not part of the signal received by satellite remote sensing systems.
Before further discission of how remote sensing data from satellite systems
can be used to estimate components of the terrestrial carbon cycle it is important to
define the system most appropriate for regional, contentional, and global scale
investigations of the carbon cycle.
The Advanced Very High Resolution Radiometer (AVHRR) appears to be the
most useful satellite remote sensing system presently available for continental scale
studies of terrestrial ecosystems. The AVHRR is a part of NOAA's meteorological
satellite program which is designed to provide daily coverage of atmospheric
conditions over the entire globe. The orbit and resolution of this sensor system
while designed for meteorological data collection, has been successfully used to
monitor conditions in terrestrial ecosystems (Tucker et al., 1983, Hayes, 1985,
Townshend et al., 1987, and Fung et al.,1987). A comparison of landcover mapping
based on the 1.1 km AVHRR data to landcover maps based on 80 m Landsat MSS
indicated the accuracy for classifications based on AVHRR data was 71.9%
compared to 76.8% for MSS. This accuracy is considered acceptable for most
research and given the disparity in spatial resolution, the accuracy of the AVHRR is
exceptional (Gervin et al.,1985).
A normalized ratio of channel 1 (0.58-0.68 microns) and channel 2 (0.725-
1.1 microns) is the most commonly used product from the AVHRR for analysis of
conditions in terrestrial ecosystems. The normalized ratio of visible and near infra
red reflectance is commonly termed the normalized difference of vegetation index
(NDVI).
The equation used to calculate NDVI is as follows (Kidwell, 1990):
CH.2 (near IR) - CH.1 (visible)
NDVI = ---------------------------------
(1)
CH.2 (near IR) + CH.1 (visible)
Normally active photosynthetic vegetation absorbs the visible wavelengths
(AVHRR Ch.1) (especially red) and reflects near infra-red radiation (AVHRR Ch.2).
Higher photosynthetic activity results in increased red absorption and even higher
near
infra-red reflection. The NDVI values range from -1 to +1, with most
vegetation clustering around +0.6 NDVI; higher values are characteristic of higher
photosynthetic activity. This index has been related to primary productivity (Tucker
et al.,1983), leaf area index (LAI) (Spanner et al.,1990), percent vegetative cover
(Townshend et al.,1987) and biomass and absorbed photosynthetically active
radiation (PAR or IPAR) (Goward et al.,1990). There appears to be a strong
correlation between NDVI and the vegetation in terrestrial ecosystems. Most
research using AVHRR data concentrates on the NDVI to evaluate vegetation
attributes in the analysis of terrestrial ecosystems.
The highest spatial resolution for the AVHRR scanner is 1.1 km at the nadir.
As the scan widens to acquire data across the entire 112 degrees of the sensor
swath width, the spectral and spatial resolution degrades due to increases in
atmospheric path length, curvature of the earth, and changes in reflection angles.
The wide scan angles and daily repeat of orbits produce a tremendous
amount of data. A lack of on-board storage and limited down-link connections
require on-board pre-processing and sampling of the data (Goward et al.,1990). The
first of the pre-processed products is known as global area coverage (GAC). The
GAC data are produced on a daily basis by averaging the first 4 pixels in a 5 x 3
pixel array, and assigning this average value to the entire array, producing a
nominal 4 x 4 km pixel (Goward et al.,1987).
The GAC data are further processed by NOAA, by selecting the "greenest"
pixel (the highest NDVI value) in a 4 x 4 pixel array. The resulting product,
known as the global vegetation index (GVI) reflects the highest NDVI value
occurring in a seven day period over a nominal 16 x 16 km pixel (Kidwell,1990).
The presence of clouds (on an average day one half the surface of the globe
is obscured by clouds), shadows, off nadir `look' angles, and different reflection
ratios all tend to reduce NDVI values. In order to reduce the effects of these
factors a temporal compositing routine is used. The maximum NDVI is usually
obtained in an unobscured near nadir view. A maximum value composite is
constructed by taking the maximum value for a given pixel in a given time frame.
Weekly and monthly maximum value composites are regularly produced using a set
of georeferenced AVHRR images (Kidwell, 1990). This process appears to remove
most of the problems associated with clouds, off nadir views, and other factors that
reduce NDVI (Holben,1986). According to Goward et al. (1990) errors can be
reduced to ± 10% (0.1 NDVI unit) using a maximum value compositing process on
a monthly time step.
106
The maximum value compositing procedure assumes that the surface features
are homogenous over the sampling area. This procedure may overestimate the
actual extent of photosynthetically active vegetation. In choosing the "greenest"
pixel it is possible that an oasis would be chosen to characterize an otherwise
barren desert (Townshend and Justice, 1986).
The temporal signature of a heterogenous landscape may also be confused
using this procedure. For example, regenerating broadleaf vegetation in a disturbed
area in a conifer forest may produce "greenest" pixels that represent these areas of
regeneration rather than the bulk of the conifer forest. Further, the temporal
compositing procedure which simply assigns the value of a single 4 x 4 km GAC
pixel to the entire 16 x 16 km GVI pixel, regardless of time or the spatial location
of each pixel, makes locating the exact geographic position of individual pixels
impossible.
For identification of vegetation and land cover in the FSU, GVI monthly
maximum value composites from 1985 through 1988 were selected as the most
appropriate remote sensing data presently available.
Approaches for the use of AVHRR data
Two basic approaches can be taken to the use of remote sensing data from
satellite sensors to estimate carbon budget dynamics: (1) direct quantitative
calculation of ecosystem characteristics using remote sensing data as a primary
indicator, or (2) use of the unique signal created by various ecosystem
characteristics to identify homogenous vegetation/land cover regions. These regions
107
should be an accurate representation of surface conditions which can be linked with
plot level field studies to extrapolate carbon budget estimates.
A strong correlation between NDVI ("greenness") and biomass has been
reported by a number of authors, Tucker et al. (1981,1983), Kimes et al. (1981) and
Goward et al (1985, 1987). Many of the studies have focused on homogenous
agricultural crops or relatively simple grassland plant canopies (Tucker, 1983). In
these environments a linear relationship appears to exist between LAI (calculated as
a function of NDVI) and NPP. In more complex ecosystems a relationship exists
but its form is less predictable. Goward et al. (1985, 1987) identified the apparent
linkage of integrated area under the curve produced by plotting NDVI values as
they change with time against biomass estimates for different ecosystems. Fung et
al. (1987) reported a linkage between NDVI in terrestrial vegetation and measured
changes in the concentration of atmospheric CO2. Runyon et al. (1991) however,
reported that NDVI (as a surrogate for intercepted radiation) was not a good
predictor of higher biomass levels (greater above-ground NPP). Badwhar et al.
(1986) emphasizes that the use of ratios calculated for agricultural crops or range
land may not be suitable for estimating the biomass of a deciduous forest canopy.
Rather than attempting to isolate factors in the overall remote sensing signal
which can be related to ecosystem productivity, a second approach, utilizing all the
unique factors that make up the signal to identify complexes of vegetation and
landcover has been used by a number of authors. For example, Goward et al.
(1985), used three week composites of GVI data to successfully identify and
characterize vegetation patterns of North America.
lob
Justice et al. (1985) using, in part, GVI data concluded that the extent and
seasonal dynamics of global vegetation could be successfully mapped on a global
scale. A variety of classification schemes were tested by Townshend et al. (1987)
in research directed towards classifying vegetation complexes in South America.
The research concluded that a maximum likelihood classification produced the most
satisfactory delineation of South American vegetation complexes, when compared to
the UNESCO 1981 vegetation map.
Loveland et al. (1991) used unsupervised classification of monthly maximum
value composites (1 km resolution) to characterize the land cover of the
conterminous United States. They reported that large homogenous land cover
regions were well identified, citing forest ecosystems as prime examples of
successful classification.
The Image-Map of the FSU
Unsupervised classification of monthly GVI composites was used to create an
image that delineates regions of similar vegetation and landcover in the FSU.
In
order to reduce the possible effects of inter-annual variation, monthly GVI
composites from 1985 through 1988 were averaged, creating a single set of monthly
observations that represented conditions over a four-year period. Unsupervised
classification required two steps: (1). identification of "clusters" within the data
(cluster analysis) and (2) a maximum likelihood grouping of the entire data set
around these "cluster renter-,"
Clusters were identified on the basis of timing, magnitude, and duration of
"greenness" as recorded by NDVI change with time. The complete signature of
understory vegetation, geometric structure of vegetation communities, and land cover
differences within the instantaneous field of view (IFOV) of the AVHRR
sensor,
were integrated into the remote sensing observations. While it was nearly
impossible to separate the individual influences of each factor, it was possible to
identify regions with similar signatures. Figure 11 presents the NDVI response with
time for four sample image classes. The shape and magnitude of these curves form
the signature of each class and were the basis for identifying clusters in the data.
These signatures were very useful in defining the vegetation and land cover matrix
that comprise each image class.
Using a variety of quantitative and qualitative methods including comparison
with existing vegetation maps and consultations with scientists with extensive field
experience in the FSU' it was possible to describe each of the 42 spectral/temporal
classes on the image in terms of vegetation and land cover. The image map of the
FSU created by clustering eight months of four year average GVI data is presented
in a simple latitude-longitude format in Figure 3 (page 83).
The patterns of the image classes reflect the expected patterns in vegetation
that result from climatic factors (re. Figure 3, page 83). Latitudinal banding in the
image classes reflects the effects of extreme cold and moisture deficits. Image class
differences also reflect species change from west to east. From north to south there
'M.S. Botch, Senior Researcher, Komarov Botanical Institute, St. Petersburg, Russia,
and
K.I. Kobak, Senior Researcher, State Hydrological Institute, St. Petersburg, Russia
is an increase in biomass as related to NDVI "greenness" with an increase in the
percentage of woody vegetation which is represented in image classes 2, 3 and 5, 4,
6, 8, 10. These classes represent arctic (high mountain) tundra (image class 2)
transitioning through shrubby tundra (image class 6, 8, 10) to the forest/tundra
(image class 12) boundary where the sparse forests of the northern taiga begin to
dominate the vegetation communities. A similar pattern may be observed in the
elevational gradient of the Burranga mountains, the Putorana mountains and other
mountain ranges of Eastern Siberia.
In the south, the effects of moisture deficits on vegetation communities are
reflected in image classes 13 (desert) through 14 and 28, 17, 26 and 23, 35 and 32,
which represent increasing vegetation grading from semidesert steppe (image class
14 and 28) into forested meadows (image classes 35 and 32). The northern taiga
forests of the FSU are identified by classes 11, 15, 16, 18, 20, and 23. The dwarf
and shrubby forests of the Pacific coast are represented by image classes 9, 19, and
22. The middle taiga forests are identified by image classes 24, 25, 29, 30, and 31.
The southern taiga forests are represented by image classes 33, 36, and 37. The
mixed-conifer and broadleaf forests are identified by image classes 38, 39, and 40.
The broadleaf forests with a smaller percentage of conifers and agricultural lands are
identified by image classes 41 and 42.
Underestimation of wetlands appears to be a problem with these image
processing techniques.
Areas on the image known to contain extensive wetlands
(e.g. the western shore of the Kamchatka Peninsula and the lower Ob basin) are not
well identified by image class differences. This underestimation of wetlands is also
a problem with thematic maps of the FSU (M.S. Botch, 1992, personal
communication).
Linking Phytomass and NPP Data to Image Classes
Image classes described in terms of vegetation, agricultural land use, and
geographic location were correlated to the data base compiled by Bazilevich (1986).
The Bazilevich data base contains phytomass and NPP estimates for all major
vegetation formations in the FSU. Along with the descriptions of vegetation and
land cover provided for each image class, the image class signature curves were a
useful tool for assigning phytomass and NPP estimates from the Bazilevich data
base.
In order to further refine the carbon assignments for each image class using
Bazilevich data base it was necessary to estimate some of the individual components
that make up the vegetation and land cover of each image class. For example,
image class 35 was characterized as "forest-meadow-steppe, with significant
agricultural land use". Forest vegetation has a very different phytomass and NPP
than meadow-steppe vegetation, both of which are very different from the phytomass
and NPP found in agricultural land.
The percent forest cover for the taiga forests in the FSU averages 65 %
(Kolchugina and Vinson, 1993). It was assumed that even the most dense of forests
112
identified in an image class contained non-forested patches that made up at least 20
percent of the area identified as forest. Using available published data on percent
forest cover of forest land in the FSU (Vorobyov, 1985; State Forestry Committee,
1990), areal extent of forest ecosystems was identified in each image class. For
example, the forest-steppe ecosystems represented by image classes 32 and 35
(forest-meadow-steppe) average 15 % forest cover according to Vorobyov (1985).
In the absence of published data an expert assessment of forest density was made
for each image class. This assessment considered the geographic location of image
classes and the density of "greenness" as recorded by NDVI and graphed in the
signature of each image class. The total estimates of area for each land cover type
in all image classes are presented in Table 9.
The non-forest areas in each image class were either considered agricultural
lands or natural non-forest vegetation. If the natural non-forest vegetation types
found in each image class were not specifically described in the Bazilevich data
base, carbon values which reflect the natural non-forest vegetation most likely to be
found in each geographic location were used.
For example, image class 18 (i.e.,
northern taiga spruce/larch) was assigned a percent forest cover of 80 % (re. Table
1).
Phytomass and NPP values for forest meadows which most likely represent the
character of non-forested vegetation in this region were used.
Agricultural statistics provide data which may be used to estimate phytomass
and NPP of agricultural lands. Most agricultural statistics are expressed in terms of
total yield by crop, or yield per unit area (t/ha). Equations that convert crop yield
to total phytomass and NPP were calculated by Sharp et al. (1976). These
equations were used together with production data reported in USDA agricultural
113
statistics for the FSU (USDA, 1990).
Both phytomass and NPP were calculated for
each crop reported (phytomass is equivalent to NPP in the case of annual crops).
The coarse resolution of the remote sensing data and the classification limits
imposed by the diversity of the data set prevented the separate identification of
individual cropping patterns. Therefore, all production data were combined and a
single weighted average NPP calculated for all agricultural lands in the FSU. This
single value (2.85 t C/ha) was used to estimate the phytomass and NPP for
agricultural lands in the FSU.
Estimates of Area
Information on the location and extent of agricultural lands was provided by
the arable lands map of Cherdantsev (1961). When compared to all available
descriptions of agricultural land in the FSU, the first three categories of this map
(greater than 60 % arable, 30-60 % arable, and less than 30 % arable) appeared to
accurately identify agricultural lands in the FSU. Assigning appropriate factors
(e.g., 80 %, 45 %, 15 %) for each of these three classes it was possible to calculate
the area of agricultural lands in the FSU at 211 Mha. This estimate was identical
to USDA estimates of agricultural land in the FSU (USDA, 1990). Sokolovskiy et
al. (1989) report the agricultural lands in the FSU to be 217 Mha, which compares
favorably with the 211 Mha estimated in this study. It must be acknowledged that
this excellent comparison in total area may be the result of a number of errors that,
in the final calculation, cancel each other out.
The estimates for forest and natural non-forest landcover (re. Table 9)
correspond well to published data for each land cover type in the statistical reports
from the FSU. The forest statistical data base for the FSU (Alimov et al., 1989)
reports the total area of forest land in the FSU (area of forest biomes as defined in
Kolchugina and Vinson, 1993a) at 1,254.2 Mha (36.6% of the total land area in the
FSU). The total area of image classes described as forested with no allowance
made for forest density (percent forest cover) is 1,330 Mha (39 % of the total land
area in the FSU).
Forest statistical data of the FSU report the area of commercial
forests at 811 to 814 Mha.
This compares favorably with the estimates of 875
Mha of forest ecosystems calculated for the image classes using the percentages of
actual forest cover provided by an expert assessment (re. Table 9). The higher
estimate of forest ecosystems from image classification, may result from: 1.
Exclusion of forests with no commercial value from the forest statistical data of the
FSU, while image class descriptions included all types of forest cover. For
example, in the vicinity of the Taimir peninsula a significant area of forest-tundra
was identified by image class descriptions. However, the forest statistical data for
the Taimir oblast (an administrative division), report no commercial forests in this
area, or 2. The "oasis effect" inherent in GVI sampling. It is possible that isolated
patches of forest with high NDVI values incorrectly characterize entire GVI pixels
producing an overestimation of forest ecosystems. Kolchugina and Vinson (1993a)
estimated area of forest ecosystems in the FSU at approximately 800 Mha.
Evaluating Phytomass and NPP Estimates
In order to compare results based on image classification to the estimates
made by Kolchugina and Vinson (1991) and Vinson and Kolchugina (1993), it was
necessary to (1) "mask out" areas not evaluated by Kolchugina and Vinson (1991)
and Vinson and Kolchugina (1993), and (2) group image classes into the same
general biomes used in their analysis. The term biome is applied to the complex of
ecosystems within a climatic region. Kolchugina and Vinson (1991) and Vinson
and Kolchugina (1993) identified nine biomes in the FSU: polar desert, tundra,
forest-tundra/sparse taiga, taiga, mixed-deciduous forest, forest-steppe, desert-
semidesert, and mountainous subtropical woodlands. Figure 12 presents a
comparison of area by biome.
Overall, the comparisons between area estimates based on thematic maps and
the image based estimates are good. The most significant differences between the
image based classification and the estimates made by Kolchugina and Vinson (1991)
and Vinson and Kolchugina (1993) resulted from the differences in the biome
description. For example image class 11 described as "sparse northern taiga larch"
forest was assigned to the taiga biome, while in the previous assessment this
vegetation type was assigned to the forest- tundra/sparse taiga biome.
Phytomass and NPP
Table 10 presents the phytomass and NPP estimates based on image
classification. The total phytomass in the FSU was estimated at 97.1 Gt C, with
86.8 Gt C in forest vegetation, and 0.6 Gt C in crops (re. Table 10). Thus the total
phytomass of natural ecosystems was estimated at 96.5 Gt C, which compared
favorably with the estimate made by Vinson and Kolchugina (1993) (91.0 Gt C).
Estimated forest phytomass compared well with the estimate of Kolchugina and
Vinson (1993b) (87.7 Gt Q. The image based estimate of forest phytomass was
116
twice as large as an estimate made for forests considering age class distribution of
forest stands in the FSU (Kolchugina and Vinson, 1993c).
Total NPP was estimated at 8.6 Gt C/yr (re. Table 10), with 3.2, 4.8, and
0.6 Gt C/yr NPP in forest, natural non-forest, and arable ecosystems, respectively.
The NPP of non-arable land was 8.0 Gt C/yr, which was 23 % greater than
estimated by Vinson and Kolchugina (1993). Forest NPP was 34 % greater than
the estimate made for forest ecosystems (2.1 Gt C/yr) which considered
age-class
distribution of forest stands (Kolchugina and Vinson, 1993c).
The phytomass and NPP densities as depicted in Figures 13 and 14 reflect
generally good agreement between the present assessment an earlier estimates, with
the exception of desert-semidesert biome. Vinson and Kolchugina assigned higher
values of phytomass and NPP to the desert-semidesert biome reflecting a variation
of vegetation types from low productive scattered herb cover to the woody shrub
formations with well developed root structure and high values of phytomass and
NPP. Present
assessment
considered relatively low values of GVI of the desert-
semidesert classes when correlating them to Bazilevich data base. In addition,
irrigated agriculture is a significant feature in this region. Vinson and Kolchugina
(1993) did not separate this ecosystem from their estimate of desert-semidesert
biome. Inclusion of irrigated agriculture into the desert-semidesert biome produced
higher estimate of NPP made for the desert-semidesert biome (re. Figure 14).
NPP estimates were also compared to estimates based on Fung et al. (1987)
(re. Figure 15). In almost every case the image based estimates were greater than
those based on Fung et al. (1987). This disagreement reflects the overall
differences in NPP parameters of the Bazilevich data base and NPP data of Fung et
117
al. (1987). The Bazilevich data base tends to emphasize mature (established) forest
ecosystems with higher phytomass and NPP than developing forests. It is possible
that Fung et al (1987) used NPP data which underestimated the production of
below-ground and woody parts of plants.
The exception to this pattern, again, was in the desert-semidesert biome
where image based estimates were lower than those of Fung et al. (1987). This
difference may be a result of generalization of the desert and semi-desert ecoregion
by Fung et al. (1987). By including isolated ecosystems with high annual NPP, a
higher average NPP value for the entire class may be generated. It also may be
possible that in relying on the image classes to accurately identify ecosystems and
using the temporal NDVI response curves to help select representative phytomass
and NPP values from the Bazilevich data base, there was a tendency to select
values that underestimate the phytomass and NPP for these ecosystems.
Summary and Conclusion
Accurate delineation and description of most vegetation and land cover
complexes in the FSU was possible using classified GVI imagery. This method
provided more accurate and precise descriptions for most ecosystems than is
available on general thematic maps. Good comparison was found between reported
area statistics from the FSU and land cover estimates for image classes, The area
of forest biomes was estimated at 1,330 Mha, the area of forest ecosystems was
estimated at 875 Mha. Arable land was reported by the USDA (1990) at 211 Mha,
and Sokolovskiy et al. (1989) reported the agricultural lands in the FSU to be 217
Mha, which compares favorably with the 211 Mha estimated in this study.
Using the Bazilevich data base for natural ecosystems and a weighted
average calculated from yield data for agricultural systems, image class based
estimates for the total phytomass in the FSU were 97.1 Gt C, with 86.8 and 0.6 Gt
C in forest vegetation and arable land, respectively. Total NPP was estimated at
8.6 Gt C/yr, with 3.2, 4.8, and 0.6 GtC/yr of forest, natural non-forest, and arable
ecosystems, respectively. The total phytomass of natural ecosystems compared well
with the published estimates based on the use of thematic maps to identify
ecosystems. The phytomass estimated for forest ecosystems was significantly higher
than a previous assessment which considered the age-class distribution of forest
stands in the FSU. The total NPP of natural ecosystems was 23 % greater than
estimates which used on thematic maps to identify ecoregions. NPP densities
estimated for nine biomes in the FSU in this study were greater than an assessment
where NDVI data was correlated to NPP parameters. The differences in estimated
NPP may be a result of differences in the data base used to estimate carbon storage
and flux.
Acknowledgment
The work presented was funded by the U.S. Environmental Protection
Agency (EPA) - Environmental Research Laboratory, Corvallis, Oregon, under a
cooperative Agreement CR820239 to Oregon State University. Jeffrey J. Lee is the
Project Officer for the project entitled "Carbon Cycling in Terrestrial Ecosystems of
the Former Soviet Union." This paper has not been subjected to the EPA's review
and, therefore, does not necessarily reflect the views of the EPA, and no official
endorsement should be inferred.
Table 9
Landcover, Phytomass and NPP Densities for Image Classes
Phvtomass (1/ha)
NPP (t/ha/vr)
NonDescription
Class Ih
Non-
Area (Mha)
3 Arctic tundra
% Fores % Arabic Non-forest Forest Arabic forest Forest Arabic forest
25.74106399 0.00°/
0 00%
0 00 2.8!
O.OC
100.00°/
0.0(
0 OC
0.00
31.10372839
0.00°/
0.00%
100.00%
0 00 2.8!
O.80
00( 2 85 0.01
35.63446643 0.00%
0 00%
100 00%
O.OC
2.8!
2.95 0.00
2.85 0.7!
4 Mountain tundra
29.39552146
0.00%
1.70%
98.30%
O.OC
2.85
4 8C
0.00
2.85
1.1!
46.82851701
0.00%
0.00%
100 00%
O.OC
2 85 11.2C
0.00
2 85
1.3!
46.40196614
0.00°/
0.00%
100.00%
0 OC
2.85 11.50
0.00
2 85
2.65
42.6740631
0.00°/
11.25%
88 75%
0.00
2.85
3 75
0.00
2.85
2.90
5 "Typical"/shrubby tundra
37.84658304
0.00%
000%
100.00%
0 OC
2.85 120C
0.00
2.85
2.70
9 Dwarf shrub (NE coast)
27.71765332
0.00%
0.00%
100.00%
0 OC
2.85
490(
0.00
2.85
1.45
1C Shrubby tundra
44.58128004
0.00%
0.00%
100.00%
2.85 11.9!
0.00
2.8!
2.70
11 Northern taiga larch (sparse)
80.59291861
50.00%
0.000%
00(
5000% 306(
2.85 11.20
1.75
2.8!
1.50
12 Forest-tundra
48.89610815
20.00%
0.00%
80.000%
30.7!
2.85 11.20
23C
2.85
1.50
13 Desert
109.7763894
0.00%
0.680%
99.320%
00(
2 85
1.00
0.00
2.85
0.50
14 Semidesert steppe
71.60893556
0.00%
0.89%
99.11%
0.0(
2.85
4 90
0.00
2.8!
3.30
15 Larch on mountain slopes
66.67558662
50.00%
2.90%
47.10%
31.0(
2 85
6.70
18(
2.85
7.30
16 Northern taiga larch
78.98773814 80.00%
0.00%
20 00%
32.45
1 Water
2 Mountain tundra
5
"Typical'/ tundra
6 Shrubby mountainous tundra
7 Solonchack
6.70
1.8!
2.85
7.30
0.00%
8.26%
91.74%
0 00
2.8!
7.30
00(
2 8!
6.85
l8 Northern taiga spruce/larch
82.79395443
8000%
1.96%
18040/4
61.90
2 8!
19 Mountain shrub (Asia)
22.41326032
0.00%
0000/4
100.00%
0.00
20 Northern taiga spruce
46.41969123
80.00%
0000/4
20.00%
52.64188225
0.00%
8.000%
22 Dwarf forest (Pacific Coast)
55.09691881
70.00%
23 Scattered forest
57.10990325
24 Middle taiga larch/pine
25
21 Irrigated agriculturbr t
I
it
6 7C
24(
2.85
7.30
2.8! 40.50
O.0C
2.8!
2.15
61.50
2 8!
6 7C
2.4(
2.85
7.30
92.00%
32.50
2 8!
7.45
2.85
2.8!
2.85
0.00%
30.00%
42.45
2 8!
6 70
3.4!
2 85
73C
50.00%
17.46%
32.54%
65.25
2.8!
7.45
3 55
2.85
6.55
52.2949575E
80 00%
0.00%
20.00%
70.45
2 8!
6.70
2 05
2.85
7.3(
75.39311802
50.00%
5.20%
44.808/4
33.5!
2.85
6 70
12!
2.85
7.3(
119
2 85
68.7003375
11 Artem. and grass steppe
I
Table 9 (contd.)
Landcover, Phytomass and NPP Densities for Image Classes
Phytomaa3
Class 11
Description
26 Grassy -steppe
2111 Kamchatka forests
28 Semidesert steppe
29 Middle taiga pine/sprucc
30 M:JJI_
wuuic Taiga sprucevnr/pme
(t/ha)
NPP (t/ha/vr)
Non-
Non
Area (Mha) % Fores I % Arable Non-forest Forest
Arable forest Forest Arable foxes
1
39.72402402
0.00% 36000/1
64 00% 151.30
2 85 6 95 5.50
2 8! 7.4.
23.89714841 70.00%
2 07%
27.93°/, 1181(
2 85 6.70 3.85
2.85 7.3(
21.3125764E
0.00%
7.10%
92.90°/ 103.4(
2.85 8.10
2.9(
2 85 8.9(
67.31948441 65 001/4
6.20%
28.800/, 95.30
2.85
6.70
2.60
2.8.)
74 27422755
73(
70.00%
2 20%
27.806/, 128 7C
2.85
6.70
3.1(
2.85
7.3(
51.1174943E
70.006%
3.05%
26.96%
64 9C
2.85
6.70
2.6(
2.85
58.13327201
7.3(
15.00%
13 00%
72.00% 118 7C
2.85
8.10
4.15
2.85
74.00376915 65.004%
23.12878312 10.00%
8.90
14 00%
21.00% 118 25
2.85
7.45
3.15
44.00°,1
46.000% 144.2C
2.85
7.45
7.40
68.67734205
2.85 10.4(
2.85 10.4(
15 008/c
64.008/4
21 000/, 151.3C
2.8!
8.10
7.00
72.4816053!
2.85
80.004%
2.580/1
37 Southern taiga mixed forest (Russia)
17.42% 128 OC
2.85
7.45
41C
48.72437352
2.85 10.4(
50.0061,
38 Mixed conifer/b roadleaf
z7.00%
23 00%
99.45
2 85
8 10
3.05
69.3674436E
2 85
56.90%
43.000/1
39 Mixed conifer/broadleaf (Asia)
0 10% 160 85
2 85
7.45
7.2C
56.50604175
2.85 10.4(
70 00%
40 Mixed forest (southern taiga)
9 25%
20 75% 160 85
2 85
7.45
7 20
48 52276405
2 85 10 4C
80 00°/
2.45%
17.55% 210.50
2 85
81C
4.20
66.29212437
2 85
8.9C
5800°4
4200%
000°/ 2160(
2 85
8 10
6 50
28.21865235
2 85
8.9C
80.000%
736%
12.64% 235.5(
2 85
8 10 12.45
2 85
8 9C
31 Middle taiga conifer on moanrains
37 Forest-meadow-s
teppe
33 Southern taiga pine/spruce
34 Grassy-steppe (mountain)
35 Forest meadow steppe (agriculture)
3E
Southern taiga conifer forest on mountains
41 Broadleaf forest (agriculture)
42 Mixed forest (very productive)
i
8.9(
8.9(
Table 10
Total Phytomass and NPP for Image Classes and Landcover Type
Area (Mha)
Class #
1
Forest
Arable
Total Phytomass (Mt C)
Non-Forest
Arable
Forest
Non-forest
Total NPP (Mt C)
Forest
Arable Non-forest
0.00
0.00
0.00
25.74
0.OC
0.00
0.00
0.0C
0.00
0.00
0.00
31.10
0.00
0.00
24.8E
0.00
0.00
0.31
0.00
26.72
33.23
0.00
0.00
35.63
0.00
0.00
105.12
0.00
0.00
0.5C
28.90
0.00
1.42
138.70
0.00
1.42
0.00
0.00
46.83
0.00
0.00
524.48
0.00
0.00
63.22
0.00
0.00
46.40
0.00
0.00
533.62
0.00
0.00
122.91
0.00
4.8C
37.87
0.00
13.68
142.02
0.00
13.68
109.83
8
0.00
0.00
37.85
0.00
0.00
454.16
0.00
0.06
102.19
9
0.00
0.00
27.72
0.00
0.00
1358.17
0.00
0.00
40.19
10
0.00
0.00
44.58
0.00
0.00
532.75
0.00
0.00
120.37
11
40.30
0.00
40.30
1233.01
0.00
451.32
70.52
0.00
60.44
12
9.78
0.00
39.12
300.71
0.00
438.11
22.49
0.00
58.68
13
0.0C
0.75
109.03
0.00
2.13
109.03
0.00
2.13
54.51
14
0.00
0.64
70.97
0.00
1.82
347.76
0.00
1.82
234.21
229.25
4
6
33.34
1.93
31.40
1033.4 7
5.51
210.41
60.01
5.51
16
63.1S
0.00
15.80
2050.52
0.00
105.84
116.90
0.00
115.32
17
0.00
5.67
63.03
0.00
16.17
460.09
0.00
16.11
431.73
18
66.24
1.62
14.94
4099.96
4.62
100.01
158.96
4.62
109.03
15
0.00
0.00
22.41
0.00
0.00
907.74
0.00
0.00
48.19
2C
37.14
0.00
9.28
2283.85
0.00
62.20
21
0.00
4.21
48.43
0.00
12.00
360.81
0.00
22
38.51
0.00
16.53
1637.20
0.00
110.74
28.42
138.4!
23
28.55
9.91
24
41.84
25
37.70
26
Iq,
15
0.00
67.77
12.00
138.03
133.0E
0.00
120.64
101.37
28.42
121.72
89.1
1863.21
0.00
10.4E
2947.34
0.00
70.0E
85.76
3.92
33.7E
1264.72
11.17
226.30
47.12
11.1
246.57
0.00
14.30
25.42
0.00
40.7E
176.65
0.00
40.76
189.40
27
16.73
0.49
6.61
1975.51
1.41
44.72
64.40
1.41
48.72
28
0.00
1.51
19.80
0.00
4.31
160.38
0.00
-4.31
176.21
25
43.76
4.11
19.35
4170.11
11.90
129.90
113.77
11.90
141.53
30
51.95
1.63
20.65
6691.31
4.66
138.34
161.18
4.66
150.73
31
35.78
1.5E
13.78
2322.21
4.44
92.32
93.03
4.44
100.58
32
8.72
7.5E
41.86
1035.06
21.54
339.03
36.19
21.54
372.52
33
48.10
10.36
15.54
5688.11
29.53
115.78
151.52
29.53
161.62
34
2.31
10.1E
10.64
333.52
29.00
79.26
17.1
29.00
110.65
35
10.30
43.95
14.42
1558.63
125.27
116.82
72.11
125.21
128.36
36
57.99
1.81
12.63
7422.12
5.33
94.07
237.74
5.33
131.31
31
24.36
13.16
11.21
2422.82
37.49
90.71
74.30
37.49
99.74
38
39.41
29.83
0.07
6348.76
85.01
0.52
284.18
85.01
0.72
39
39.55
5.23
11.73
6362.30
14.85
87.361
284.79
14.89
121.95
40
38.82
1.19
8.52
8171.23
3.35
68.98
163.04
3.35
75.79
41
38.45
27.84
0.00
8305.08
79.35
0.00
249.92
79.35
42
22.57
2.08
3.51
5316.39
5.92
28.891
281.06
5.921
31.74
875.54
210.93
1142.56
86837.40
9676.671 3169.6E
601.141
4773.05
-- .
18.58
0.00
76.3!
1,
E
601.14
--=i =-4 _
D
Total
0.00
1 I v9
Ii"
f A'A!;a.
119.
I.'
Class 16
Clas-g 2
zim
a iu
IN
in
n
1.M
".r
Apd
Max
MY
Ions
dro
A.pV..
- Sid Dev _ Avcragc - Min
0 I..J
u-
1
Ap.a
-
I.I
A.. ..
Mn a- Sid Dcv _ _ Avcraee __ M1;,I
Iw.
1.1.,
S.p.... r
o...
S.p. u .
0o
Northern Taiga Larch
Mountain Tundra
Class 39
3M)
Class 14
60
Im
-in
0
App
A1.r
Mar
I.ry
Ialr
A.p..x
Lp.nM.
Sid I)- _._ Avclagc , MIn
Mixed Broadleaf/Coniter''IFlirest
Mb.'-
Apa
16p
-.-.. Max
1-
IQ,
Aup..
_+ - SW Dcv __ Avcragc -0- Min
Semi-Desert Steppe
Figure 11. Comparison of the Signatures of Sample Image Classes
.Arable; irrigated.
land and meadow
(237 Mha, I 1%)
Tundra
(261 Mha, 13%)
Desert, semi-desert
(126 Mha, 6%)
Fairest tundra,
shrubland and
dwarf forest
(153 Mha, 7%)
Steppe
(123 Mha,
6%)
Arable
(191 Mha, 10%)
Tundra
(226 Mha, I1%)
Desert, semi-desert
(174 Mha, 9%)
Forest tundra
(340 Mha, I 7%)
Stcppe
(1-17 Mha, 6%) : as,
Forest
steppe
Forest steppe-
(144 Mha,
7%)
(11.0 Mha, 5%)
Mixed deciduous
(196 Mha, 10%)
Mixed deciduous
(141 Mha, 7%)
Taiga
(822 Mha, 40%)
Total: 2065. Mha
Present :Study
(Note: Non-arable land inc.ludes forest
and non-forest vegetatio+n in class)
I
Taiga
(709 Mha, 35%)
Total: 2008 Mha
Vinson and Kolchugina (1993)
If
Figure 12. Area of Biomes Identified by Image Classes and Thematic Maps
a
'II
1,0
VIII
II
.0"0
(1993):
Figure 13. Comparison of Phytomass Densities
MOR
: Al
sei>::x::c. .
O
C
O
ba
saftsuOU ddNI JO uosLmduuoD
C
rO3
ftl
1'25
1i ain2t3
meadows
land/ Arablelimgated
Forest-steppe
Mixed-deciduous
Taiga
forest dwarf
Forest-tundra/shrubland/
0-4
LA
ao
M
9ZT
References
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Class 1. Water / No Vegetation
Well Identified class found in the Aral Sea, Lakes Balkash,Onega,Lagoda,and
Baikal. Of note are the Pamir mountains, and Lake Ala-Kul in South Central Asia.
This class also appears along the eastern shore of the Caspian Sea, almost
completely surrounding the Kara-Bogaz-Gol. This area according to Suslov (p.485)
"... The shore is covered with hill sand amid which there are numerous salt lakes..."
Coincidence with Vegetation Map Classes:
Water 38.81%
Desert Marshes 10.20%
Class 1 (water
% Arable Lands:
NA
Overall Mean: 28.409
Area under TNRC:
SO
March-October 190.8
M-k
Apd
M.,
. Max
April-September 126.38
J
.
Std Dev
My
Average
A.y
bfin
Closely Related to Class:
13 Desert
Summary Statistics for Class:
Minimum
v
Maximum
Mean
Std Dev
-------------------------------------------------------------__________
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
1588
1588
1588
1588
1588
1588
1588
1511
17.0000000
11.0000000
0
0
1.0000000
0
0
17 nnnnnnn
54.0000000
52.0000000
53.0000000
56.0000000
52.0000000
53.0000000
50.0000000
99 nnnnnnn
36.8520151
27.9628463
24.4534005
22.5510076
24.0724181
25.7424433
29.5642317
19 0741071
6.0265232
5.1165871
7.9601066
9.7661655
9.5135188
10.3045514
7.1338119
1014575
ti
Class 2. High Mountain Tundra / Arctic Desert
Well identified class seen in Burranga Mountains, Putoran Mountains,
Verkohansk, Chersky, and North Ural Mountains. Also located in the Pamir
Mountains in the south. This class also appears in the Lena River delta, and along
the Arctic coast. The confusion between low elevation Arctic tundra and mountain
tundra should not be critical for carbon quantification.
Coincidence with Vegetation Map Classes:
Northern Mountain Tundra 43.27%
Arctic Tundra 12.91%
Class 2
% Arable Lands in Class:
NA
Overall Mean: 39.398
lm F
Area under TNRC:
too
March-October 270.717
April-September 195.477
A1.d
Closely Related to Class:
M.,
y.. Max
,®.
,d,
-
-.- Std Dev _,_ Average -.0- Min
2-4-6 in the mountains
3-5-8 on the arctic coasts
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
Std Dev
2745
2745
2745
2745
2745
2745
2745
2745
29.0000000
19.0000000
11.0000000
8.0000000
28.0000000
26.0000000
28.0000000
33.0000000
59.0000000
39.0000000
40.0000000
61.0000000
69.0000000
73.0000000
58.0000000
51.0000000
45.5253188
30.7697632
24.2848816
30.5737705
49.6069217
51.2255009
39.7854281
43.4163934
5.4315409
2.6801349
4.3795519
7.7115762
7.7735082
8.5273829
4.1948105
2.6775891
Class 3. Arctic Tundra/Mountain Tundra
Closely related to class 2 and 5; tundra vegetation, more dense vegetation
than class 2. Located on the lower slopes of the Arctic mountains. Patterns in the
Yamal Peninsula do not fit very well mapped distribution.
Coincidence with Vegetation Map Classes:
Northern Mountain Tundra 30.84%
Arctic Tundra 19.55%
Typical Tundra 12.05%
Class 3
% Arable Lands in Class:
NA
230
Overall Mean: 46.073
200
150
Area under TNRC:
:ao
March-October 323.525
April-September 248.308
so
Closely Related to Class:
Apnl
MW
-.- Max
J-
I*
A{oa
sue.
t Std Dev -&-Average .¢ Min
2 and 5
Summary Statistics for Class:
Variable
N
Minimum
Maximum
Mean
MAR
3213
APR
MAY
JUN
JUL
AUG
3213
3213
3213
3213
3213
SEP
3213
OCT
3213
34.0000000
23.0000000
11.0000000
11.0000000
40.0000000
52.0000000
32.0000000
37.0000000
56.0000000
38.0000000
32.0000000
48.0000000
91.0000000
101.0000000
74.0000000
50.0000000
45.9203237
30.1596639
21.8972923
32.4749455
71.9489574
75.5144725
46.4718332
44.1954560
Std Dev
4.3843093
2.1355886
2.9577824
6.1020594
7.3666989
7.0718730
6.0648914
2.1078674
Class 4. Mountain Tundra
Very closely related to class 2. Located in small amounts below class 2 in
the Siberian high country. With the exception of a core area of class 1, the Pamir
mountains are characterized by class 2 and 4.
Coincidence with Vegetation Map Classes:
Northern Mountain Tundra 45.95%
Class 4
% Arable Lands in Class:
1.7 %
Overall Mean: 47.11
=
Area under TNRC:
March-October 332.965
April-September 216.897
M.W
M.y
Apnl
. Max
I-
t Std Dev
Idy
Angoe
sq-.ce
Average .¢. Min
Closely Related to Class:
2 and 6 (Classes 2-4-6 form a mountain tundra complex)
Summary Statistics for Class:
N Obs
Variable
Minimum
1903
MAR
APR
26.0
22.0
MAY
12.0
JUN
JUL
AUG
SEP
OCT
28.0
38.0
29.0
30.0
38.0
Maximum
59.0
45.0
58.0
75.0
86.0
85.0
75.0
55.0
Mean
43.97
32.56
31.08
50.47
65.93
61.06
47.8
44.17
Std Dev
6.24
3.30
6.8
7.38
7.93
7.53
6.63
2.91
136
Class 5. "Typical" Tundra
A more dense tundra type vegetation, located primarily in the Yamal and
Tamir peninsulas with smaller areas scattered across the Arctic coast.
Coincidence with Vegetation Map Classes:
"Typical Tundra" 29.49%
Northern Mountain Tundra 16.66%
Arctic Tundra 15.67%
Class 5
% Arable Lands in Class:
NA
Overall Mean: 51.522
Area under TNRC:
March-October 366.562
April-September 290.8
AW9
Closely Related to Class:
. Max
M"
I®.
Ian
_ Std Dev -,Average
Min
3 and 8
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
v
Minimum
Maximum
Mean
Std Dev
4291
4291
4291
4291
4291
4291
4291
4291
34.0000000
24.0000000
11.0000000
17.0000000
70.0000000
74.0000000
39.0000000
36.0000000
56.0000000
36.0000000
30.0000000
51.0000000
105.0000000
117.0000000
66.0000000
51.0000000
46.6047541
30.1479842
21.0109532
36.7720811
89.0426474
93.8673969
50.1069681
44.6222326
3.8073559
1.9812072
2.8764977
5.6530845
5.6012652
6.0429224
4.3267451
2.0256306
Class 6. Shrubby Tundra of Siberian Mountains
Part of the 2-4-6 class complex that identifies the Siberian mountain ranges,
Burranga, Verkohansk, etc. Higher biomass suggests shrubby tundra vegetation.
Also located in Boggy areas of western Siberia.
Coincidence with Vegetation Map Classes:
Northern Mountain Tundra 40.96%
Northern Mountain Scattered Larch 15.11%
Bogs 7.52%
Class 6
% Arable Lands in Class:
NA
2"
Overall Mean: 52.832
I
x4
150
Area under TNRC:
100
March-October 377.927
4
April-September 301.235
Apa
Closely Related to Class:
Mq
_41- Max
,m.
Jay
_._Std Dev -*.-Average
Aopa
..Min
2-4
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
t:
Minimum
Maximum
Mean
Std Dev
3322
3322
3322
3322
3322
3322
3322
3322
29.0000000
23.0000000
12.0000000
28.0000000
67.0000000
57.0000000
33.0000000
36.0000000
56.0000000
40.0000000
43.0000000
83.0000000
109.0000000
97.0000000
77.0000000
53.0000000
44.8642384
31.9659843
27.9936785
58.4840458
85.6035521
76.9981939
52.1550271
44.5888019
3.7693103
2.7399130
4.6112619
8.7860524
6.0554220
5.5933670
6.8493744
2.5755845
Class 7. Bunch Grass Steppe (Solenchuck)
Generally located east of Ural mountains, very similar to class 17 except
peak in NDVI occurs in July rather than in June. There is no good information
available right now to positively identify the vegetation/landscape represented by this
class. However, considerable evidence points to Solenchuck soils with high salt
content.
Coincidence with Vegetation Map Classes:
Grassy Steppe 19.18%
Grassy Steppe w/Herbaceous plants 12.0%
% Arable Lands in Class:
Class 7
11.25%
Po
Overall Mean: 59.462
Area under TNRC:
March-October 433.378
IOD
April-September 352.695
Closely Related to Class:
0
ApI
M.y
y.. Max
Jr
1dy
_. Std Dev -t- Average
Ao{ar
SW--b-
Min
17 and 23
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
2212
2212
2212
2212
2212
2212
2212
2212
26.0000000
21.0000000
29.0000000
46.0000000
50.0000000
40.0000000
39.0000000
35.0000000
63.0000000
58.0000000
78.0000000
104.0000000
117.0000000
101.0000000
96.0000000
63.0000000
38.5682640
38.3801989
53.9945750
77.4534358
87.2373418
74.5212477
59.4882459
46.0547016
_-
Std Dev
6.8958358
4.9103797
8.7925469
8.7188407
10.7727313
9.3051685
7.9007495
3.9578501
Class 8. "Typical" / Shrubby Tundra
This class represents part of the transition from Arctic deserts to northern
Taiga forest. This image class covers large areas of the Tamir Peninsula and the
plains west of the Yamal peninsula. This class appears to identify a region of
mixed "typical" and shrubby tundra. Representing a transition from class 5 to
classes 10-12.
Coincidence with Vegetation Map
Classes:
"Typical" Tundra 28.66%
Shrubby Tundra 20.68%
Forest Tundra Larch 16.61%
% Arable Lands in Class:
NA
Class 8
780
Overall Mean: 56.615
700
Area under TNRC:
ISO
March-October 408.680
1W
April-September 334.939
50
Closely Related to Class:
Fwd
t Max
5, 10 and 12
w,
7®e
7WY
Au{=
srymes
_.s._ Std Dev -A.-Average -.0- Min
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
Std Dev
N
Minimum
Maximum
Mean.
3231
3231
3231
3231
3231
3231
3231
3231
33.0000000
25.0000000
11.0000000
23.0000000
85.0000000
91.0000000
40.0000000
37.0000000
53.0000000
36.0000000
33.0000000
62.0000000
131.0000000
125.0000000
73.0000000
50.0000000
44.6771897
2.7915346
29.4986072
2.2150445
4.0486505
21.3249768
45.2732900
104.1770350
108.0922315
56.0718044
43.8074899
-
6.4105101
7.8135537
5.4056075
4.8469882
1.9694461
Class 9. Dwarf Shrub Pine and Willow (Riparian grass and willows in lower Ob)
This is a problem class as it is seen both in the Ob river plain and more
extensively on the Pacific coast near the Anadyr depression.
Evidently, the dwarf
shrub Pine/Alder in a shrubby/forest tundra type environment described by Suslov
(pg 224) in the Anadyr-Penzhina depression has a temporal /NDVI signature that
overlaps riparian grass and willow of the lower Ob. Carbon content evaluation will
split this class calculating the carbon contents for each separately.
Coincidence with Vegetation Map Classes:
Northern Mountain Tundra 29.94%
Pinus pumila 21.11%
Class 9
% Arable Lands in Class:
NA
Overall Mean: 57.550
Area under TNRC:
March-October 416.598
April-September 343.245
April
M.)
-a- Max
Closely Related to Class:
J
My
A"-
t Std Dev -&- Average _0.. Min
Statistically related to class 8 and 11
Geographically related to class 19 and 22
Summary Statistics for Class:
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
1788
1788
1788
1788
1788
1788
1788
1788
li-
maximum
Mean
29.0000000
54.0000000
22.0000000
41.0000000
12.0000000
45.0000000
21.0000000
89.0000000
53.0000000
120.0000000
69.0000000
123.0000000
52.0000000
102.0000000
37.0000000
62.0000000
- - - - - - - --- - - -
40.9882550
29.5503356
25.7063758
53.1923937
94.0369128
96.3199105
73.9893736
46.6168904
minimum
-Variable
=
Std Dev
3.0620110
3.2698509
5.7152565
12.5710030
10.1287615
9.0020520
6.8451085
3.1452943
14
Class 10. Shrubby Tundra
This class falls between 8 and 12-20; this class is part of the transition
towards more trees. In the Far East, this class (without class 10) reaches almost to
the Arctic ocean. According to Suslov (pg 181) ..."In eastern Siberia, tundra
meadows become more scarce. Under the hot, dry summer conditions, woody
vegetation reaches to within 39 miles of the shore".... "the sharply continental climate
(east of the Lena River) contributes to the northward expansion of woody plants".
Coincidence with Vegetation Map Classes:
"Typical" Tundra 15.63%
Northern Taiga Larch 11.78%
Class 10
% Arable Lands in Class:
NA
2O
Overall Mean: 56.363
2W
Area under TNRC:
13D
March-October 406.380
Im
April-September 331.283
Closely Related to Class:
AFQ
IMP
-a-MU
IStd Dev
My
Aug-
_r Average
Sq-.b.
on
Min
12 and 20 on the southern edge
8 to the northern edge
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
Std Devi
3597
3597
3597
3597
3597
3597
3597
3597
35.0000000
25.0000000
12.0000000
38.0000000
87.0000000
73.0000000
40.0000000
38.0000000
55.0000000
39.0000000
35.0000000
75.0000000
118.0000000
44.1887684
- 3.4229492
2.0812813
3.5111002
6.0404215
5.2017977
111.0000000
70.0000000
52.0000000
30.5752016
24.3914373
57.5376703
101.4931888
93.2571587
54.6035585
44.8554351
5.8294048
5.7743698
2.3149431
Class 11. Sparse Northern Taiga Larch
Light crowned Taiga forest. Very sparse trees predominantly located in the
plateaus of eastern Siberia. In this region, the first of the forest classes. Very
closely related to class 15 and 16.
Coincidence with Vegetation Map Classes:
Northern Taiga Larch 49.8%
Northern Mountain Tundra 14.11%
% Arable Lands in Class:
class 11
NA
Overall Mean: 58.704
X0
Area under TNRC:
150
March-October 424.669
ou
April-September 346.603
50
0
M-b
Closely Related to Class:
ApN
- Max
Nry
J..
-r_ Std Dev
Icy
A.Cm
Average
Sqembc
. Min
16 and 15
Summary Statistics for Class:
-Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
Std Dev
5996
5996
5996
5996
5996
5996
5996
5996
37.0000000
26.0000000
18.0000000
63.0000000
82.0000000
64.0000000
37.0000000
37.0000000
54.0000000
40.0000000
46.0000000
95.0000000
119.0000000
103.0000000
70.0000000
54.0000000
45.6976318
33.1032355
31.2815210
76.7510007
100.9929953
86.4683122
51.1089059
44.2284857
2.3958125
1.9803496
3.9873192
5.3122868
5.8193503
5.8559434
6.0408104
2.4723652
Class 12. Transitional area of tundra
Increasing percentage of trees concentrated mainly in favorable microclimates.
See comments for class 10 regarding the distribution of this class east of the Lena
River.
Coincidence with Vegetation Map Classes:
Northern Taiga Larch 16.39%
N.Mt. Larch on Slopes 14.01%
N.Mt. Tundra 14.9%
Forest Tundra 10.6%
Class 12
% Arable Lands in Class:
NA
iso
200
Overall Mean: 61.901
Area under TNRC:
mo
March-October 451.055
50
April-September 374.823
M-h
Ap.
M.y
Max
Closely Related to Class:
-
I
Joy
AogM
Std Dev -d, Average -.0.- Min
11 and 20 in the northern forest classes
8 and 10 shrubby tundra
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
3642
3642
3642
3642
3642
3642
3642
3642
Minimum
Maximum
Mean
Std Dev+
36.0000000
25.0000000
16.0000000
47.0000000
98.0000000
80.0000000
43.0000000
38.0000000
53.0000000
42.0000000
40.0000000
87.0000000
138.0000000
129.0000000
80.0000000
55.0000000
43.6861614
32.0826469
27.2803405
70.5345964
115.0024712
101.2583745
60.7468424
44.6139484
2.9079196
3.0080969
3.9001818
7.0919364
5.9518109
6.6403647
6.7547086
2.6068599
Class 13. Desert
The geographic extent of this class matches very well with the limits of the
deserts in South Central Asia as described by both Berg and Suslov.. This class is
well identified, examining the NDVI/time plot. The spring "bloom" of vegetation
shows clearly in April-May.
Coincidence with Vegetation Map Classes:
Desert (all subdivisions) 81.3%
Class 13 (desert)
% Arable Lands in Class:
0.68%
Overall Mean: 43.303
Area under TNRC:
I5D
March-October 305.226
April-September 218.027
30
Closely Related to Class:
0
Apf
Max
f®
,Wr
w,
AMM
t Std Dev .. Average -0-Min
see.
1 (in shape of TNRC)
Summary Statistics for Class:
Variable
N
Minimum
Maximum
Mean
Std Dev
MAR
4389
40.0968330
4.9456906
4389
4389
4389
4389
4389
4389
4389
25.0000000
26.0000000
32.0000000
26.0000000
25.0000000
24.0000000
26.0000000
30.0000000
62.0000000
APR
MAY
JUN
JUL
AUG
71.0000000
67.0000000
46.0009114
49.5026202
45.3533835
39.9346093
40.7664616
42.4700387
42.2998405
6.1673708
7.0079916
SEP
OCT
61.0000000
52.0000000
54.0000000
54.0000000
Si 0000000
5.8698064
4.7753124
4.5814721
3.7499714
3.4898410
Class 14.
This class is well described by Berg and Suslov as sparse Artemisia with
widely scattered bunch grass.
Coincidence with Vegetation Map Classes:
Semi-desert Bunchgrass 21%
Desert, Artemisia 14%
Desert, Wazzi 25%
% Arable Lands in Class:
Class 14
0.89%
Overall Mean: 53.438
Area under TNRC:
Ib
March-October 385.180
IOD
April-September 289.859
M
Closely Related to Class:
0
Apd
t Max
28
Ji
,w
AYP-0. Std Dev -. Average -0. Min
M.,
sue.
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
3063
3063
3063
3063
3063
3063
3063
3063
Minimum
Maximum
Mean
Std Dev
24.0000000
29.0000000
34.0000000
44.0000000
38.0000000
38.0000000
36.0000000
32.0000000
66.0000000
80.0000000
101.0000000
84.0000000
74.0000000
71.0000000
69.0000000
71.0000000
36.3251714
52.9951028
69.5563173
63.7502449
53.9249102
51.5710088
51.0564806
48.3258244
8.2512024
7.8130701
9.6877783
6.5478178
5.4309011
4.6699110
4.3694216
4.4570974
Class 15. Scattered Larch on Mountain Slopes
The lower slopes of the Trans-Baikal mountains are spotted with this class,
this class is also located in the North Ural mountains and significantly in the
identified wetland area of western Siberia.
Coincidence with Vegetation Map Classes:
Northern Mountain Larch 16%
Northern Mountain Larch on Slopes 14%
Northern Mountain Tundra 13%
% Arable Lands in Class:
Class 15
2.9%
__T
Overall Mean: 64.403
Area under TNRC:
150
March-October 469.255
ioo
April-September 387.119
Closely Related to Class:
10
0
H.,
Aped
Max
Jim.
t Std Dev
l.y
A.p m
Average
s gombv
Min
11 and 16
Summary Statistics for Class:
-Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
4121
4121
4121
4121
4121
4121
4121
4121
Minimum
Maximum
Mean
28.0000000
21.0000000
22.0000000
61.0000000
78.0000000
73.0000000
47.0000000
37.0000000
58.0000000
52.0000000
61.0000000
108.0000000
127.0000000
119.0000000
89.0000000
56.0000000
45.1778694
36.1681631
40.9012376
84.5964572
102.0412521
92.5020626
67.0778937
46.7573405
-
Std Dev
4.2217255
3.3851641
7.1099343
7.0640603
7.0166063
6.6404584
7.1674154
2.7442500
Class 16. Larch Forest (Light Crowned Conifer Forest)
Restricted to large areas of Central and Eastern Siberia. From the literature,
almost pure stands of Dahurian and Siberian larch. The TNRC of this class is
virtually identical to class 11.
Coincidence with Vegetation Map Classes:
Northern Mountain Larch on Slopes 37%
Northern Taiga Larch 14%
Northern Mountain Tundra 13%
% Arable Lands in Class:
NA
class 16
2O
Overall Mean:64.770
Area under TNRC:
2W
March-October 473.250
170
April-September 395.044
Im
50
Closely Related to Class:
0
April
11, 15, 25, and 32
-}- Max
1®
1*
Agsa
UP-4.- Std Dev _ Average -0- Min
M.,
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
Std Dev
5679
5679
5679
5679
5679
5679
5679
5679
37.0000000
27.0000000
18.0000000
77.0000000
97.0000000
79.0000000
42.0000000
38.0000000
52.0000000
42.0000000
49.0000000
113.0000000
133.0000000
114.0000000
73.0000000
54.0000000
44.8265540
33.2923050
33.5323120
92.4602923
114.4627575
97.8688149
56.7198450
45.0005283
2.0793588
1.9781643
5.1864806
6.2690751
5.2643382
5.9944096
5.6441573
2.4142569
Class 17. Brush and Bunch Grass: Transition from Semi-Desert to Grassy Steppe
This class is located adjacent to class 14; semi-desert. It appears to
represent a zone of increasing grass, predominantly bunch grass, in the desert shrub
zone.
Coincidence with Vegetation Map Classes:
Grassy Steppe 35.49%
Semi-desert: Artemisia and Bunch Grass 32.39%
Semi-desert: Bunch Grass 12.86%
Class 17
% Arable Lands in Class:
8.26%
270
Overall Mean: 61.659
Area under TNRC:
March-October 452.433
too
April-September 362.551
0
Closely Related to Class:
Apil
nt.,
-a- Max
i.
-4- Sid Dev
toy
Average
AupW
s.p-e.
Min
7, 23, and 26
forms the northern border of class 14
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
Std Dev
3102
3102
3102
3102
3102
3102
3102
3102
25.0000000
26.0000000
52.0000000
62.0000000
49.0000000
40.0000000
38.0000000
35.0000000
52.0000000
84.0000000
110.0000000
115.0000000
94.0000000
82.0000000
74.0000000
70.0000000
32.3913604
49.0415861
80.2295293
87.5119278
72.0228885
64.2163121
58.5699549
49.2901354
3.8775555
7.8630169
8.2340124
8.2427899
7.7609266
6.4980799
4.5685971
4.6863190
Class 18. Northern Taiga Spruce / Larch Forest
This class is located in a rough band from the White Sea near Archanglsk
to the wetlands east of the Ob River. Class 18 is also located in a band south of
class 16 near the Vilyuy River in Central Siberia.
Coincidence with Vegetation Map Classes:
Middle Taiga Larch 31.4%
Northern Taiga Spruce 18%
Class 18
% Arable Lands in Class:
1.96%
750
Overall Mean: 71.269
700
Area under TNRC:
ISO
March-October 523.424
too
April-September 435.015
50
0
Closely Related to Class:
Apa
Ma
-... Max
M.,
J
_. Std Dev
,m,
A SWebr
onobw
Average --o-Min
statistically to 22 and 26
geographically to 20, 23, 29, 16, and 25
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
5175
5175
5175
5175
5175
5175
5175
5175
27.0000000
21.0000000
36.0000000
80.0000000
85.0000000
79.0000000
44.0000000
38.0000000
59.0000000
59.0000000
70.0000000
119.0000000
148.0000000
126.0000000
84.0000000
55.0000000
47.1426087
41.6805797
51.8021256
101.0564251
115.8977778
100.6823188
65.5764251
46.3142029
-
Std Dev
3.9528227
4.9504112
6.0035001
6.2663705
7.4006357
6.2587200
5.9890301
2.1868243
1
Class 19. Mountain shrubs of the Far East: Pinus pumila and Scrub Alder
Seen in the Kamchatka and Kolyma ranges. The southern tip of Kamchatka
is class 19. This area is described by Suslov (pg 397) as moss-scrub alder/ash and
Pinus pumila.
This class is also seen in the middle region of the Ob River. Apparently,
riparian grass and willows in the flood plain have signatures that correspond to class
9 in the north and class 19 in the mid-course of the Ob. Upstream from class 19,
class 27 appears to correspond to the course of the river.
Coincidence with Vegetation Map Classes:
v
Pinus pumila 40%
Northern Mountain Tundra 20%
Cotton Grass Tundra 10.27%
Class 19
% Arable Lands in Class:
NA
270
Overall Mean: 68.398
200
Area under TNRC:
170
March-October 501.535
I00
April-September 425.950
30
Closely Related to Class:
Apd
Mq
_..- Max
,m.
Jm,
Ao{m
_*. Std Dev t Average
spec
Min
-9, 22, and, 27
Summary Statistics for Class:
Variable
Minimum
maximum
Mean
Std Dev
----------------------------------------------------------------------MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
1299
1299
1299
1299
1299
1299
1299
1299
31.0000000
24.0000000
16.0000000
23.0000000
63.0000000
62.0000000
56.0000000
38.0000000
51.0000000
42.0000000
59.0000000
102.0000000
162.0000000
181.0000000
143.0000000
71.0000000
41.0084681
29.9368745
29.1593533
66.4965358
116.9022325
120.4603541
92.9314858
50.2871440
-----------------
2.4740419
2.8312617
7.7700986
12.4715002
12.6902345
15.0692472
13.3373008
4 71SA4Q1;
Class 20. Northern Taiga Spruce Forest
This class is located in a discontinuous band from near the Kanin Peninsula
to the wetlands east of the Ob River. This class picks up in Siberia south of the
Putorana mountains. This class is closely related to class 12, the forest tundra zone,
and class 18. The eastern end of this class occurs in central Siberia. This class is
not located in eastern Siberia.
Coincidence with Vegetation Map Classes:
Northern Taiga Spruce 29.58%
Northern Taiga Larch 19.79%
Middle Taiga Larch 12.25%
% Arable Lands in Class:
Class 20
NA
Overall Mean: 69.472
Area under TNRC:
March-October 509.998
April-September 425.218
Closely Related to Class:
12, 18, and, 24
Apd
y- Max
M -Y
J-
-4.. Std Dev
t
J*
Average
A-
s,pembv
. Min
Summary Statistics for Class:
Variable
N
Minimum
Maximum
Mean
=
Std Dev
----------------------------------------------------------------------MAR
3190
30.0000000
64.0000000
47.1225705
4.0912711
APR
3190
27.0000000
57.0000000
39.0018809
4.9105511
MAY
3190
17.0000000
55.0000000
36.7542320
5.6774744
JUN
3190
58.0000000
114.0000000
87.8426332
9.1019624
JUL
3190
102.0000000
155.0000000
126.2473354
6.6970892
AUG
3190
87.0000000
147.0000000
110.5304075
7.3319054
SEP
3190
46.0000000
83.0000000
63.8435737
5.7936434
OCT
3190
38.0000000
51.0000000
44.4338558
1.7128024
Class 21. Irrigated Agriculture / Grasslands?
A most interesting class. This class appears along the Amu-Darya and SyrDarya in south central Asia. A detailed view shows this lass in the Fergana valley,
along the Iii River. This class also appears in the Caucuses mountains and east of
the Urals. Two small patches appear in the Kopet Dag along the Kara Kum canal.
This class also appears along the Mongolian border in what Suslov "cereal steppes".
A few of these pixels also appear as the blanket bogs on the west coast of the
Kamchatka peninsula. This scattering of this class warrants care in assigning carbon
values for the carbon budget. This class is obviously irrigated agriculture however
in the Amur region class 21 is located on the middle slopes of the interior mts.
This pattern appears to occur throughout southern siberia.
Coincidence with Vegetation Map Classes:
The vegetation map used for this research is a 'potential' natural vegetation map
and thus may be inappropriate for use in identifying class 21. (there is no real
major class to identify)
% Arable Lands in Class:
8%
Class 21
iw
Overall Mean: 73.710
Area under TNRC:
hm
March-October 541.856
lh
April-September 451.025
0
Closely Related to Class:
M.y
Apnt
-
Max
J.
_ Std Dev
!my
A
gm
Average -0-Min
Summary Statistics for Class:
-Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
2572
2572
2572
2572
2572
2572
2572
2572
Minimum
Maximum
Mean
Std DevI
28.0000000
24.0000000
38.0000000
57.0000000
68.0000000
73.0000000
61.0000000
37.0000000
69.0000000
76.0000000
88.0000000
120.0000000
140.0000000
150.0000000
127.0000000
83.0000000
43.6924572
43.0093313
59.7892691
92.4568429
107.0260498
106.8405910
84.9121306
51.9517885
7.8666377
7.5537173
7.7637667
10.1470367
9.6603794
9.6732024
10.5217106
6.7446053
Class 22. Dwarf Forests of the Pacific Coast
(Pinus pumila, Betula ermanii, Larch and Spruce depending on site)
Wind sculpted shrubs; short trees of higher elevations in Kamchatka, and
along the shore of Okhotsk sea. This area is described by Suslov on pgs 371-372.
A significant area of this class is also located at the base of the Kanin Peninsula.
Coincidence with Vegetation Map Classes:
Scattered Larch on Mountain Slopes 25.97%
Larch and Pine (Far East) 19.03%
Japanese Stone Pine (Pinus pumila) 15.66%
% Arable Lands in Class:
Class 22
NA
Overall Mean: 70.905
Area under TNRC:
ISO
March-October 521.293
Itb
April-September 441.980
So
Closely Related to Class:
Ape]
. Max
9and19
Mq
,me
Mm,
Aug-
SepemhQ
.4- Std Dev -t- Average -o- Min
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
Std Dev
3368
3368
3368
3368
3368
3368
3368
3368
30.0000000
24.0000000
18.0000000
69.0000000
97.0000000
91.0000000
64.0000000
40.0000000
54.0000000
45.0000000
56.0000000
122.0000000
141.0000000
136.0000000
110.0000000
63.0000000
43.3785629
33.3643112
35.6125297
94.9833729
118.7111045
110.7131829
81.9596200
48.5201900
2.8766510
3.2850875
6.3432963
7.9013646
7.8701578
7.3836070
7.6852338
3.5040232
----------------------
Class 23. Scattered Forest (Pine / Aspen in the South)
(Pine / Birch in the North)
One of the few classes that shows a north/south confusion. This class is
located both in the Karalean peninsula, the northern Taiga forest, and in the North
Urals and in the steppes of Khazaldstan, north of Lake Balkash. The evidence
would suggest scattered trees with a strong herbaceous matrix. This north/south
confusion may require adjustment in the carbon budget of the FSU for this class.
Coincidence with Vegetation Map Classes:
Grassy Steppe 21.17%
Grassy Steppe w/ herbs 17.96%
Northern Taiga Pine 9.23%
Middle Taiga Spruce 8.41%
% Arable Lands in Class:
Class 23
17.46%
Overall Mean: 68.398
Area under TNRC:
Ib
March-October 506.625
too
April-September 419.120
50
Closely Related to Class:
APB
May
Max
18 and 29 in the north
7 and 26 in the south
J*
M.
t Std Dev
A.{.a
see.
Average -0-Min
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
Std Dev
3069
3069
3069
3069
3069
3069
3069
3069
26.0000000
26.0000000
48.0000000
76.0000000
67.0000000
56.0000000
47.0000000
38.0000000
68.0000000
70.0000000
93.0000000
127.0000000
125.0000000
104.0000000
85.0000000
57.0000000
40.6203975
43.5008146
70.3558162
99.3365917
101.1000326
83.2479635
65.0791789
47.3880743
7.8313956
7.0917632
7.4062469
7.8056013
9.3902827
7.4023918
5.9991242'
3.1397691
Class 24. Mixed Conifer Forest (Larch, Pine,
Spruce)
This class is located in central Siberia. From the head waters of the Taz
River to the head waters of the Lena River. The band of class 24 is located
between class 20, 15, 18 and on the north, and class 30, 31, and 36-39 on the
south.
Coincidence with Vegetation Map Classes:
Middle Taiga Larch 30.76%
Larch-Pine of Baikal zone 22.39
Northern Mountain: Spruce / Fir 15.61%
Middle Taiga Spruce 11.89%
% Arable Lands in Class:
NA
Class 24
2O
Overall Mean: 79.944
Area under TNRC:
March-October 585.846
100
April-September 473.516
Closely Related to Class:
Apnl
_...Max
25
M"
,®.
,m,
A
s
>
tStdDev ..,-Average -.0-Min
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
Std Dev
2971
2971
2971
2971
2971
2971
2971
2971
48.0000000
44.0000000
45.0000000
72.0000000
88.0000000
80.0000000
54.0000000
40_000nnnn
73.0000000
82.0000000
90.0000000
130.0000000
151.0000000
130.0000000
98.0000000
59.3527432
58.6273982
66.2204645
103.9229216
119.2790306
106.8004039
77.2935039
4A ns717in
4.2026256
6.2949536
7.4876913
8.8053655
9.4541545
7.0779841
6.8913175
7 A97RA19
tin nnnnnnn
Class 25. Open Forest: Larch in Vilyuy Basin
Aspen / Birch in Central Siberia
Located along the Vilyuy and middle Lena Rivers surrounding class 32. A
narrow strip of this class is located south of class 32 in central Siberia. In central
siberia This class is representative of forest meadow steppes.
Coincidence with Vegetation Map Classes:
Middle Taiga Larch 33.8%
Larch and Pine of Baikal zone 18%
% Arable Lands in Class:
Class 25
5.2 %
Overall Mean: 77.176
Z!M
Area under TNRC:
Iw
March-October 570.599
loo
April-September 482.554
50
Closely Related to Class:
M-
Apnl
_._Max
32
wr
,_
tStdDev
my
flog-
Sq-.be
.Average -_Min
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
Std Dev
4361
4361
4361
4361
4361
4361
4361
4361
27.0000000
24.0000000
32.0000000
88.0000000
102.0000000
92.0000000
50.0000000
40.0000000
63.0000000
58.0000000
73.0000000
134.0000000
156.0000000
132.0000000
108.0000000
54.0000000
46.5441413
41.2371016
55.1595964
113.6489337
127.2098143
112.9701903
73.5652373
47.0726898
5.5598032
4.5360138
5.8826623
6.8499902
6.8338241
5.8273344
8.4272615
2 n1R4Ani
Class 26. Grassy Steppe
This class is located only west of the Ural monutains, between class 35 and
class 17.
This area appears to represent the classic grassy steppes of central asia.
East of the Urals the equivalent area is represented by a complex of class 23 and 7.
Coincidence with Vegetation Map Classes:
Grassy Steppe 22.1%
Semi-desert: Sage and grass 16.7%
Grassy Steppe w/ forbs 14.7%
% Arable Lands in Class:
Class 26
36 %
Overall Mean: 72.565
Area under TNRC:
UD
March-October 532.685
April-September 423.446
70
Closely Related to Class:
Mq
Apnl
Max
I
Jd,
Apmbs
M$
_.,..Std Dev ..i.. Average
oc.ow
Min
23 and 7
Summary Statistics for Class:
4
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
Std Dev
1712
1712
1712
1712
1712
1712
1712
1712
27.0000000
26.0000000
59.0000000
58.0000000
52.0000000
51.0000000
42.0000000
39.0000000
77.0000000
106.0000000
129.0000000
136.0000000
129.0000000
106.0000000
95.0000000
73.0000000
39.8580607
61.4088785
91.9620327
99.5747664
88.4667056
76.4830607
66.9591121
55.8037383
9.2812572
11.6300811
9.9308695
11.4473978
10.2431030
9.6442722
7.7804900
5.9543390
Class 27. Pine
/
Birch Forests of Kamchatka
Suslov, (pg 395) described this area as "Taiga forest with birch groves".
Again like class 9 and 19. By far, the largest area of this class is located in the
Far East,Kamchatka. However, in the Ob river basin, the lower reaches of the river
are class 9, the middle regions class 19, and the upper reaches of the Ob are
identified as class 27.
Coincidence with Vegetation Map Classes:
Stony Birch (Betula ermanii) 32.3%
Pinus pimula 12.9%
% Arable Lands in Class:
NA
Class 27
2O
Overall Mean: 86.167
Area under TNRC:
tm
March-October 641.552
IC0
April-September 559.633
30
Closely Related to Class:
0
Y-b
April
_.-Max
19
Mq
Jme
July
_...StdDev _a-Aveage
Ao-m
Sq--h-
.Min
Summary Statistics for Class:
-variable
Minimum
Maximum
Mean
Std Dev
--------------------------------------------------------------------MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
1209
1209
1209
1209
1209
1209
1209
1209
30.0000000
26.0000000
22.0000000
52.0000000
93.0000000
107.0000000
71.0000000
40.0000000
55.0000000
63.0000000
94.0000000
146.0000000
188.0000000
189.0000000
152.0000000
80.0000000
41.5690653
34.1364764
49.1877585
102.1993383
151.4400331
146.1397849
110.6658395
53.9975186
3.4698898
5.2893496
14.0069868
15.4319420
14.8699635
14.1036659
12.2170289
5.6413939
Class 28. Semi-Desert Steppe in Kopet Dag and Pamir Mountains
This class is located on the southern border of the desert. Suslov (pg 569)
describes this steppe differently from the balance of the semi-desert steppes in
central Asia as being more "Iranian" in character. Also reference the video series
"Realms of the Russian Bear: the Red Deserts" which describes this region very
well. The early spring bloom, the rapid senescence due to moisture deficits etc. In
terms of carbon values, it is unlikely that this class is significantly different from
class 14.
Coincidence with Vegetation Map Classes:
Ephemeral Shrubs and Artemisia 11.7%
Desert 11.07%
Class 28
% Arable Lands in Class:
7.10%
Overall Mean: 64.239
:a)
Area under TNRC:
DSO
March-October 454.342
too
April-September 306.720
Closely Related to Class:
`A
0
M-h
Ap-i
...- Max
M.y
J®
-e-Std Dev
July
Auu.
SW-.b-
Average .. Min
14
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
Std Dev
840
840
840
840
840
840
840
840
29.0000000
24.0000000
43.0000000
28.0000000
19.0000000
23.0000000
18.0000000
31.0000000
142.0000000
155.0000000
154.0000000
101.0000000
91.0000000
106.0000000
103.0000000
69.0000000
71.7928571
88.0523810
76.5690476
63.2250000
57.0607143
55.7464286
54.1190476
47.3452381
20.6080384
20.4972656
19.8794338
11.7971772
10.6887877
11.1652977
9.2131491
4.6631312
Class 29. Middle Taiga Spruce and Pine
This class is located west of the Ob River between class 18 and class 3233. This class appears to be very similar to class 30 located east of the Ob River.
Class 23 appears as discontinuous patches in class 29. The location of the Ural
mountains is well defined by class 23 in the division of the zonal distribution of
class 29.
Coincidence with Vegetation Map Classes:
Middle and Southern Taiga Pine 25.26%
Middle Taiga Picea 16.57%
% Arable Lands in Class:
6.2 %
Class 29
Overall Mean: 77.599
No
Area under TNRC:
IM
March-October 572.492
100
April-September 475.865
so
Closely Related to Class:
April
30 and 23
t Max
may
Jr
t Std Dev
hur
Apa
-.r- Average -.0-Min
Summary Statistics for Class:
Variable
N
Minimum
Maximum
Mean
Std Dev
----------------------------------------------------------------------MAR
3948
29.0000000
68.0000000
48.2558257
5.5226373
APR
3948
26.0000000
70.0000000
48.3267477
6.2226344
MAY
3948
59.0000000
102.0000000
76.6263931
6.7807969
JUN
3948
85.0000000
133.0000000
110.1790780
6.3739635
JUL
3948
84.0000000
140.0000000
116.0590172
8.3903266
AUG
3948
74.0000000
124.0000000
99.9442756
7.6459491
SEP
3948
49.0000000
91.0000000
73.0564843
6.2664509
OCT
3948
37.0000000
63.0000000
48.3437183
3.3986306
Class 30. Middle Taiga Conifer (Spruce-Fir-Pine) Forest
This class is located east of the Lena River and east of Lake Bakail, across
the Trans-Bakail mountains to the northern 1/3 of Sakhalin island. A small patch
of this class is located in the center of the Kamchatka valley indicating the small
pocket of middle taiga that can grow at lower elevation in the peninsula.
Coincidence with Vegetation Map Classes:
Mountain Larch and Pine 25.6%
Middle Taiga Spruce 16.57%
Class 30
% Arable Lands in Class:
2.2%
Overall Mean: 82.529
M
Area under TNRC:
March-October 611.345
100
April-September 516.582
so
0
MW&
Closely Related to Class:
Apn,
M.,
t Max
le
M,
_._Std Dev __ Average
M
Sq_ftW
ooae.
Mm
29 in Europe and 36 in southern siberia
I
Summary Statistics for Class:
Variable
1
Minimum
Maximum
Mean
Std Dev
----------------------------------------------------------------------MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
3918
3918
3918
3918
3918
3918
3918
3918
29.0000000
29.0000000
52.0000000
88.0000000
91.0000000
96.0000000
64.0000000
40.0000000
64.0000000
64.0000000
93.0000000
135.0000000
149.0000000
137.0000000
115.0000000
65.0000000
48.1199592
45.8736600
73.1835120
112.6607963
127.5104645
116.3328229
86.8945891
49.6590097
4.6143492
5.4369095
6.8249766
6.9865877
6.9303230
6.6339808
7.1989040
3.5361815
Class 31. Conifer Forests on Mountain Slopes of Southern Siberia.
This class is located only in southern Asia near Lake Bakail. This class is
also located in the mountains of the Amur/Primoriski region and appears to be
closely related to elevation.
Coincidence with Vegetation Map Classes:
Spruce-Fir in Central Mountains 33.96%
Southern Taiga Larch-Pine 18.37%
% Arable Lands in Class:
3.05 %
Class 31
Overall Mean: 89.158
:Go
Area under TNRC:
170
March-October 652.773
:OD
April-September 526.612
!0
Closely Related to Class:
n+.,
Ap d
Max
24, 30, and, 36
,_
,m,-
IWy
_._ Std Dev ..r Average
s.,.
Min
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
14
2506
2506
2506
2506
2506
2506
2506
2506
Minimum
Maximum
Mean
33.0000000
44.0000000
65.0000000
71.0000000
81.0000000
81.0000000
75.0000000
41.0000000
92.0000000
95.0000000
108.0000000
143.0000000
148.0000000
148.0000000
125.0000000
89.0000000
63.8431764
65.6679968
82.6360734
115.3084597
121.8172386
112.4397446
94.4102155
57.1444533
Std Dev!
7.7339846
7.3344884
6.6398693
9.3236026
9.2316884
8.3314904
6.8179140
7.2559070
Class 32. Forest Meadow Steppe / Boggy Larch in Vilyuy Basin
This class has two areas of very different structure as part of the same class.
The forest meadow steppes east of the Ural mountains are represented as class 32.
These have been described as mixed conifer/birch groves in grassland. (Suslov, pg
36; Berg, pg 80). East of Altai mountains, four isolated patches of forest meadow
steppe appear to correspond to the isolated patches of forest meadow steppe in
south central Siberia described by Suslov (pg 204).
The other area of this class is located in the Vilyuy and Lena basins. This
corresponds to larch with pothole swamps class on the vegetation map. A
coincidence tabulation restricted to this region show 45.24% larch with pothole
swamps, plus 33.6% middle taiga larch. Obviously the NDVI signature of swampy
larch forest is very similar to that of forest meadow steppe in western Siberia.
Coincidence with Vegetation Map Classes:
Meadow and Forest Steppe 22.9%
Middle Taiga Larch with Pothole Swamps 19.2%
Aspen 10.58%
Middle Taiga Larch 14.66%
% Arable Lands in Class:
Class 32
=m
13 %
Overall Mean: 82.903
Area under TNRC:
IT
March-October 618.506
50
April-September 533.117
D.
Closely Related to Class:
M-
APO
Mq
-a- Max
J®r
1A,
Aapa Z. Saprbr
Ocher
t Sid Dev _ Average -0.. Mia
25 and 35
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
sN.
3198
3198
3198
3198
3198
3198
3198
3198
Minimum
Maximum
Mean
Std Dev
26.0000000
22.0000000
42.0000000
110.0000000
120.0000000
101.0000000
56.0000000
40.0000000
60.0000000
60.0000000
85.0000000
159.0000000
165.0000000
137.0000000
104.0000000
57.0000000
42.6932458
40.6751094
64.0434647
128.7141964
140.4878049
120.5819262
79.2892433
46.7357724
4.9601501
4.5797230
6.3618093
7.1575674
6.9336420
5.9018146
7.8275807
2.2629551
Class 33. Coniferous Forest of Russian Plain
This class is located across the center of the Russian plain, bordered by class
29-30 on the north, and class 37 on the south. Evidence suggests that this is a
region of mixed conifers.
Coincidence with Vegetation Map Classes:
Middle Taiga Spruce 23.5%
Southern Taiga Spruce 22.5%
Middle and Southern Taiga Pine 21.8%
% Arable Lands in Class:
Class 33
14%
210
Overall Mean: 84.167
.00
Area under TNRC:
130
March-October 624.734
IDD
April-September 527.372
Closely Related to Class:
M.,
Ap.d
Max
,®.
-4._ Std Dev
Jy
Aopr
s.,amnc
Average ..Min
Summary Statistics for Class:
Variable
N
Minimum
Mean
Std Dev
Maximum
----------------------------------------------------------------------MAR
47.9333959
3198
29.0000000
55.0000000
5.7523387
APR
49.0240775
3198
30.0000000
6.1936517
66.0000000
MAY
3198
63.0000000
82.3242652
5.7134345
97.0000000
JUN
3198
110.0000000
152.0000000
128.5056285
6.9709033
JUL
3198
105.0000000
128.5659787
151.0000000
6.7492647
AUG
112.9186992
3198
91.0000000
135.0000000
6.5310514
SEP
75.0572233
3198
52.0000000
5.7335403
91.0000000
OCT
3198
38.0000000
48.7426517
60.0000000
3.3634590
-------------I
Class 34. Grassy Steppe on Slopes of Caucasus and Pamir Mountains and Crimea
Well described in Crimea by Berg. Closely related to class 26 and classes
This class appears to be related to elevation. Examination of detailed maps
shows strong correlation with lower slopes of Pamir mountains.
23-7.
Coincidence with Vegetation Map Classes:
Grassy Steppe w/ herbs 22.51%
Grassy Steppe 11.86%
% Arable Lands in Class:
44 %
Class 34
Overall Mean: 90.192
:m
Area under TNRC:
March-October 668.128
April-September 533.804
50
Closely Related to Class:
A.d
. Max
26 and 35
-
,.y
,m.
-4-Std Dev
A.00
_ Average
serer
Min
n
Summary Statistics for Class:
Variable
Minimum
Maximum
Mean
Std Dev
------------------------------------------------------------------
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
942
942
942
942
942
942
942
942
-
30.0000000
46.0000000
92.0000000
75.0000000
68.0000000
59.0000000
55.0000000
44.0000000
105.0000000
142.0000000
169.0000000
189.0000000
159.0000000
140.0000000
123.0000000
82.0000000
49.5169851
80.9161359
121.5254777
123.4957537
111.0721868
97.0774947
80.6337580
57.2972399
11.8971517
15.6294218
12.3815917
16.1139501
15.0261936
12.0787947
10.4475130
5.9133643
Class 35. Forest Meadow Steppe
This class is located only in the western FSU, between the grassy steppe
of class 26 and the mixed forest of class 38. This class is related to class 32 on
the east side of the Ural mountains. The forest groves west of the Urals are
predominantly oak; east of the Urals, birch and aspen. According to Berg (pg 83),
"The steppe (parts) of the forest steppe are almost entirely under cultivation." From
the Arable Lands Map, class 35 has the greatest amount of high percentage arable
lands.
Coincidence with Vegetation Map Classes:
Grassy Steppe w/herbs 43.92%
Meadow and Forest Steppe 22.4%
% Arable Lands in Class:
Class 35
64 %
Overall Mean: 79.975
Area under TNRC:
IS
March-October 595.128
Iro
April-September 504.114
m
Closely Related to Class:
M
Apnl
..... Max
M"
h.
1d,
-... Std Dev -. Average
Min
falls between 38 and 26
the same structure appears to be class 32 east of the Ural Mtns.
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
Std Dev
3179
3179
3179
3179
3179
3179
3179
3179
26.0000000
33.0000000
72.0000000
99.0000000
89.0000000
76.0000000
53.0000000
40.0000000
50.0000000
68.0000000
121.0000000
151.0000000
141.0000000
121.0000000
91.0000000
70.0000000
35.6744259
46.3444479
95.4970116
122.2632903
117.3000944
97.1273986
71.9263919
53.6653036
3.8061108
6.4590453
7.9742885
8.4887410
8.1231419
7.4614049
6.6158356
4.8545192
Class 36. Larch / Pine (Spruce) Forests in Southern Siberia
This class is located from the Ob to Sakhalin Island across souther Siberia.
This class appears to be closely related to class 30-31 on the north, and class 40 to
the south. A correlation with elevation may be present. This connection is not
completely clear however.
Coincidence with Vegetation Map Classes:
Mountain Larch/Pine 17.4%
Southern Taiga Larch/Pine 12.21%
Middle Taiga Spruce 11.39%
% Arable Lands in Class:
Class 36
2.58 %
20
Overall Mean: 90.123
Area under TNRC:
IM
March-October 669.825
April-September 565.875
so
Closely Related to Class:
A,nl
31 and 40
M"
Max
J.
_*__ Std Dev
)m,
AoSM
i.. Average -.o. Min
sser
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
:N'
Minimum
30.0000000
33.0000000
53.0000000
97.0000000
112.0000000
103.0000000
74.0000000
41.0000000
- - - --- - - - -
3613
3613
3613
3613
3613
3613
3613
3613
Mean
Std Dev
52.1228896
75.0000000
52.0669803
74.0000000
75.7879878
95.0000000
125.3617492
148.0000000
140.9288680
170.0000000
127.2637697
149.0000000
96.5327982
133.0000000
51.6421257
70.0000000
----------
5.7132822
5.7826494
6.3598616
7.1951171
8.1692154
6.6464594
6.9618356
4.1937526
Maximum
Class 37. Southern Taiga Forests of Southern Russia
This class represents very productive spruce forests mixed in some places
with oak and hornbeam (Berg, pg 54). In the southern Ural mountains, there is a
strong correlation with mid to lower slopes and the distribution of class 37.
Coincidence with Vegetation Map Classes:
Southern Taiga Spruce 35.3%
Mixed Forest 13.5%
Broadleaf Forest 11.25%
% Arable Lands in Class:
Class 37
:50
27 %
Overall Mean: 90.991
Area under TNRC:
March-October 679.229
April-September 583.781
70
Ma
Closely Related to Class:
A,nl
MW
-._ Max
J
fw
Std Dev
- Sq.".
Average -o-Mm
38, 39 and 33
Summary Statistics for Class:
Variable
Minimum
Maximum
Mean
Std Dev
----------------------------------------------------------------------
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
2576
2576
2576
2576
2576
2576
2576
2576
26.0000000
30.0000000
66.0000000
125.0000000
123.0000000
96.0000000
56.0000000
39.0000000
_--_----
66.0000000
65.0000000
129.0000000
180.0000000
166.0000000
152.0000000
102.0000000
64.0000000
47.1708075
46.7488354
92.7239907
146.7585404
140.6358696
123.1164596
80.5465839
50.2259317
7.1419136
5.5678166
9.0258906
7.8729674
6.9311299
7.8844338
7.7267009
4.1786563
Class 38. Mixed Conifer / Broadleaf Forests
This class is located only in the European plains of Russia and the Ukraine.
This class represents oak/pine forest mixed with agricultural lands.
Coincidence with Vegetation Map Classes:
Mixed Forest 27.12%
Broadleaf Forest 23.43%
% Arable Lands in Class:
Class 38
43%
Overall Mean: 89.838
Area under TNRC:
to
March-October 667.716
April-September 562.001
Closely Related to Class:
Apd
MW
Max
35 and 41
1_
. Sod Dev
J*
A.P.
Avcage .4. Min
Sepe.C-
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
Maximum
Mean
Std Dev
30.0000000
63.0000000
34.0000000
80.0000000
102.0000000
109.0000000
88.0000000
67.0000000
80.0000000
128.0000000
158.0000000
151.0000000
132.0000000
109.0000000
75.0000000
43.1688539
54.7305337
103.8932633
130.3753281
129.0282881
112.1073199
86.5969670
58.7999417
5.2484713
8.1084937
6.5464620
8.1712625
6.6211944
N
Minimum
3429
3429
3429
3429
3429
3429
3429
3429
------------
45.0000000
----------
5.8942080
6.3530652
5.4633274
Class 39. Mixed Conifer / Broadleaf (Aspen-Birch) Forest
This class is located between class 32 and 36 in the Ob basin. This class
is also located in the Altai mountains in the Amur River. Like class 36, this class
is located only in souther Siberia from the Tom to the Amur. I suspect this class
represents fairly open forests with meadow openings. Class 38 and 39 should be
very similar in structure with oak west of the Urals (class 38) and birch/aspen east
of the Urals. Class 39 and 42 may be closely related.
Coincidence with Vegetation Map Classes:
4L
Meadow Forest Steppe 18.6%
Aspen 15.10%
Southern Taiga Pine 14.44%
Class 39
% Arable Lands in Class:
9.25%
.00
Overall Mean: 93.686
Area under TNRC:
March-October 701.451
50
April-September 608.724
0
Aprd
Mey
_45_. Max
Closely Related to Class:
1me
My
A"-
_*..Std Derv t Average -: Min
36 and 42
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
Std Dev
2833
2833
2833
2833
2833
2833
2833
2833
29.0000000
24.0000000
38.0000000
105.0000000
139.0000000
115.0000000
76.0000000
41.0000000
68.0000000
62.0000000
94.0000000
177.0000000
189.0000000
163.0000000
124.0000000
63.0000000
46.3490999
44.6911401
68.8545711
140.6156018
162.3780445
137.8736322
99.0024709
49.7214966
5.9096224
6.8449007
7.6820670
10.6211133
7.0482627
6.8815329
7.1667237
3.4701746
Class 40. Mixed Forest (Birch and Spruce/Laich)
This class is located near Lake Baikal, and along the Amur River across the
southern tier of southern Siberia. The Amur-Primoriski region is basically
characterized by class 40, 42, 39, and 31.
Coincidence with Vegetation Map Classes:
Picea and Abies of Southern Mountains 17.61%
Larch/Pine Forests of Southern Taiga 10.69%
% Arable Lands in Class:
Class 40
2.45 %
Overall Mean: 98.087
200
Area under TNRC:
150
March-October 728.115
April-September 614.496
a
Closely Related to Class:
0
ApN
36, 39, and 42
Max
M.,
,..
AI,
Aq..
s.p.6r
.t Std Dev _ Aveaage ... Min
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
Minimum
Maximum
Mean
Std Dev
----------=------''-----------------------------------2271
31.0000000
91.0000000
55.5112285
8.6216985
2271
32.0000000
94.0000000
57.0413915
7.8886555
2271
63.0000000
107.0000000
84.0546015
6.9738729
2271
109.0000000
168.0000000
140.9735799
8.3092331
2271
112.0000000
170.0000000
146.6574196
7.8142948
2271
101.0000000
164.0000000
134.1523558
8.2552155
2271
83.0000000
139.0000000
108.6583003
7.8810776
2271
43.0000000
88.0000000
57.6433289
6.7564000
Class 41. Broadleaf (Mixed) Forest of Russia, Beylorruss and the Ukraine
This class is located only in the western part of the FSU. This class is
almost completely surrounded by class 38, with large islands of class 38 in the
middle. Class 41 could almost be called islands inside a class 38 matrix.
Coincidence with Vegetation Map
Classes:
Mixed Forest 34.05%
Broadleaf Forest 31.46%
Class 41
% Arable Lands in Class:
ss
42 %
Overall Mean: 97.838
Area under TNRC:
March-October 728.974
April-September 614.863
Apnl
Closely
Related to Class:
.y.. Max
w,
r®.
July
An.
sM-Me>
. Std Dev -- Average -.0-Min
38
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
nrT
3206
3206
3206
3206
3206
3206
3206
R2nr.
Minimum
Maximum
Mean
29.0000000
36.0000000
77.0000000
112.0000000
103.0000000
99.0000000
76.0000000
4F nnnnnnn
59.0000000
108.0000000
148.0000000
173.0000000
168.0000000
142.0000000
136.0000000
RR nnnnnnn
41.4401123
60.3833437
114.4915783
141.5973175
139.5389894
120.8228322
98.4123518
rA ni4rAnn
-- ----------------------------
=_-
-
Std Dev
3.8339155
8.2035697
8.1823079
8.6060084
7.1288701
6.3222206
7.4159013
q 9232299
--- ----------------
Class 42. Highly Productive Mixed Forest
This class represents the "greenest" regions in the FSU. These regions are
located on the middle slopes of the Carpathians, Caucasus, and Altai mountains. A
small amount of this class is also located in the southern Ural mountains. In these
regions, we have mixed conifer/broadleaf forest with extensive and complex shrub
and herb understory.
In the Amur region, class 42 corresponds very well to the"Ussuri taiga". A
mixture of Manchurian oak and other broadleaf trees with a complex understory of
shrubs and herbaceous plants. Suslov's map (pg 859) shows this correspondence
very well.
Coincidence with Vegetation Map Classes:
Picea and Abies Central and Southern Highlands 20.35%
Broadleaf Forest 16.55%
Ussuri Taiga (Broadleaf/Conifer Forest) 14.93%
Broadleaf Forest of Far East 11.21%
% Arable Lands in Class:
7.36 %
Class 42
Overall Mean: 109.847
Area under TNRC:
March-October 820.074
April-September 701.344
JO
Closely Related to Class:
0
Myth
Aped
M -Y
Max
39 and 40
Jnn
July
,... Std Dev
Augeue
Sepem6r
Qm6r
Average -.0-Min
Summary Statistics for Class:
Variable
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
N
1236
1236
1236
1236
1236
1236
1236
1236
Minimum
Maximum
Mean
Std Dev
-----------------------------------------------------28.0000000
87. 0000000
51.1674757
10.1242617
29.0000000
123. 0000000
60.0315534
15.8419259
47.0000000
168. 0000000
105.3244337
19.2602652
1-13.0000000
198. 0000000
164.0121359
13.1211366
103.0000000
201. 0000000
160.9182848
14.9639444
112.0000000
172. 0000000
146.2192557
9.4046066
90.0000000
162. 0000000
124.8697411
13.9678065
43.0000000
107. 0000000
66.2305825
13.6757501
-
-------------