LUCID’s Land Use Change Analysis as an Approach
for Investigating Biodiversity Loss and Land Degradation Project
An Approach to Dual Land Use and Land Cover Interpretation of
2001 Satellite Imagery of the Eastern Slopes of Mt. Kenya
LUCID Working Paper Series Number: 16
by
Bilal Butt
and Jennifer M. Olson
Department of Geography
Michigan State University
East Lansing, MI 48824 USA
October 2002
Address Correspondence to:
LUCID Project
International Livestock Research Institute
P.O. Box 30709
Nairobi, Kenya
E-mail: lucid@cgiar.org
Tel. +254-20-630743
Fax. +254-20-631481/ 631499
An Approach to Dual Land Use and Land Cover Interpretation of 2001
Satellite Imagery of the Eastern Slopes of Mt. Kenya
The Land Use Change, Impacts and Dynamics Project
Working Paper Number: 16
by
Bilal Butt
and Jennifer M. Olson
Department of Geography
Michigan State University
East Lansing, MI 48824 USA
October 2002
Address Correspondence to:
LUCID Project
International Livestock Research Institute
P.O. Box 30709
Nairobi, Kenya
E-mail: lucid@cgiar.org
Tel. +254-20-630743
Fax. +254-20-631481/ 631499
Copyright © 2002 by
Michigan State University Board of Trustees,
International Livestock Research Institute, and
United Nations Environment Programme/Division of Global Environment Facility
Coordination.
All rights reserved.
Reproduction of LUCID Working Papers for non-commercial purposes is encouraged.
Working papers may be quoted or reproduced free of charge provided the source is
acknowledged and cited.
Cite working paper as follows: Author. Year. Title. Land Use Change Impacts and Dynamics
(LUCID) Project Working Paper #. Nairobi, Kenya: International Livestock Research
Institute.
Working papers are available on www.lucideastafrica.org or by emailing lucid@cgiar.org.
LUCID Working Paper 16
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TABLE OF CONTENTS
List of Tables ..........................................................................................................................iv
List of Figures .........................................................................................................................iv
List of Appendices ..................................................................................................................iv
A. INTRODUCTION .............................................................................................................1
B. THE LAND USE AND LAND COVER CLASSIFICATION APPROACH .....................1
C. IMAGE INTERPRETATION ............................................................................................3
1. Study site characteristics affecting interpretation .........................................................3
2. Automated vs. visual interpretation ..............................................................................3
3. Stages of interpretation .................................................................................................4
a. Georectification .........................................................................................................4
b. Initial land cover interpretation ..................................................................................5
c. Ground truthing ..........................................................................................................7
d. Correction of land cover attributes and generation of land use attributes ..................8
e. Calculation ..............................................................................................................10
D. CONCLUSIONS AND RECOMMENDATIONS ..........................................................10
E. REFERENCES ................................................................................................................16
Appendices .............................................................................................................................17
LUCID Working Paper 16
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LIST OF TABLES
1. Land use and land cover classes for the Eastern Mt. Kenya study site ...............................2
2. Calculated area and perimeter for each of the land use and land cover categories ...........12
LIST OF FIGURES
1. Vector digitising of the Shamba System polygon at a scale of 1:100,000 ...........................6
2. Map catalogue with the digitised polygons overlaid onto satellite imagery .........................7
3. Attribute data recorded from ground truthing with the use of the design template and the
GPS unit ..........................................................................................................................8
4. Island polygon overlaying another polygon and after intersection, two separate polygons
created ...........................................................................................................................10
5. Land cover map of the Eastern Mt. Kenya LUCID study site ...........................................13
6. Land use map of the Eastern Mt. Kenya LUCID study site ...............................................14
7. Land cover draped over a 250m resolution DEM ..............................................................15
LIST OF APPENDICES
1. Definitions of Land Use, Land Cover and Land Use/ Cover Change ...............................17
2. Waypoint Sheet for Ground Truthing ...............................................................................18
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A. INTRODUCTION
A dual land use/ land cover mapping exercise was undertaken to identify, interpret and
analyse the landscapes of Mt. Kenya and the surrounding lowlands using Landsat ETM+
imagery acquired for 11 March, 2001. Mt. Kenya and its surrounding lowlands are
characterized by extreme variations in both the physical landscape, ranging from glaciers to
dryland savannahs, and in the human landscape, ranging from large paddy rice schemes to
small crop/livestock farms. Due to this heterogeneity, automated supervised and unsupervised
classification resulted in the misclassification of much of the study area. As a result, the
LUCID team adopted a mixed approach using supervised classification and visual
interpretation. The land use and land cover mapping activities were largely based on visual
interpretation and transformed into a GIS through the vector digitisation of polygon features.
The interpretations were corrected after ground truthing and consulting secondary sources.
The team assigned separate land use and land cover classes to each polygon to better link the
spatial patterns of use/ cover changes to socio-economic and biophysical driving forces and to
record the vegetative characteristics for associated analyses of land degradation, biodiversity
and carbon sequestration.
This working paper was written as a general guide to conducting land use/cover interpretation
of satellite imagery in heterogeneous areas where the results of automated classification
systems are unsatisfactory. The paper details the conceptual approach to the classification and
interpretation strategies, outlines the stages of interpretation and analysis and provides
recommendations on how to adapt the approach for similar studies in Africa. The IGBPLUCC definitions of land use, land cover, land use change and land cover change, which the
team used to develop its classification scheme, are listed in Appendix 1 (IGBP, 1997).
B. THE LAND USE AND LAND COVER CLASSIFICATION APPROACH
The land use/cover interpretation of the March 11, 2001 ETM+ satellite image of Mt. Kenya
is part of the wider LUCID research project that is examining the relationship between
changing land use, biodiversity and land degradation. As such, it was critical that the
interpretation of the image provides an accurate rendition of the location and distribution of
land use and cover types, including as much information about the type of agricultural cover.
The cover information will be important for examining, for example, the relationship between
changing vegetative classes, fauna, and soil characteristics. It will also be required for future
examination of above ground carbon storage. The wider project is also identifying the
underlying socio-economic and biophysical driving forces of land use/cover change in order
to better project future changes. For this, it is vital to differentiate areas under different land
ownership and management types, a differentiation that could be captured in a land use
classification. Both land cover and land use classifications, therefore, are required for the
purposes of this study. A hierarchical system of nomenclature was utilized for the land use/
cover classification scheme and is listed in Table 1. The land cover scheme has been adapted
from the Food and Agricultural Organization of the United Nations (FAO) (Latham, 2001)
and the Biosphere-Atmosphere Transfer Scheme (BATS) classifications (Dickinson et al.
1996). The FAO land use classification scheme was refined to include land ownership and
management variations.
LUCID Working Paper 16
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Table 1: Land use and land cover classes for the Eastern Mt. Kenya study site
Land Use Code
General Land Use Type
1000
1100
1110
1120
1130
1140
1200
1300
2000
2100
2200
2300
2400
2500
3000
3100
3200
3300
4000
4100
4200
4300
5000
6000
7000
7100
7200
8000
8100
8200
Agriculture - Small Scale
Land Cover Code
General Land Cover Type
1000
2000
2100
2200
2300
2400
2500
2600
2700
3000
4000
4100
4110
4120
4130
4140
4150
4200
4210
4220
5000
6000
6100
6200
7000
Tundra/Mooorland/Glaciers/Grasses
Forest
Specific Land Use Type
Sub-Specific Land Use Type
Rainfed Cropping
Tea
Maize Dominant
Mixed Bush/Crops
Coffee
Irrigated Cropping (Horticulture Dominant)
Grazing Land (Bush and Grassland)
Agriculture - Large Scale
Rainfed Cropping (Wheat Dominant)
Irrigated Cropping (Horticulture Dominant)
Shamba System (Mix of Crops and Tree Plantations)
Ranches
Wheat and Grazing Pasture)
Protected Areas
National Parks
National Reserves
Forest Reserves
Institutional Land Uses
University Research Plot
KenGen Land
Don Bosco Farms
Tree Plantations
Urban and/or Built-up Areas
Water Bodies
Dams
Lakes
Non-Protected Forest Areas
Non-Degraded
Degraded
Specific Land Cover Type
Sub-Specific Land Cover Type
Bamboo Forest
Afro-Montane Forest
Woodland (Open Canopy, mostly Dryland Forests)
Tree Plantations
Shamba System
Degraded Forest
Degraded Woodland
Bush
Cultivated Land
Rainfed Cultivation
Tea
Maize Dominant
Mixed Bush/Cultivation (Grains Dominant)
Wheat and Pasture
Coffee
Irrigated Crops
Rice
Horticulture
Urban and/or Built-up Area
Water Bodies
Dams
Lakes
Grassland
In this dual land use/ land cover classification system, every polygon is assigned both a land
use and land cover attribute, resulting in separate use and cover spatial layers. More than one
polygon may share the same land use code but may have different land cover codes. For
example, the land use polygon ‘national park’ on Mt. Kenya includes two land cover
polygons: the tundra/moorland/glaciers zone and the afro-montane and bamboo forest zone.
Similarly, areas under the same land cover may have different land use designations; such
forest cover areas with land use codes of national park, national forest reserve or unprotected
forest. This dual land use/ cover coding allows for flexibility in the spatial analyses.
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C. IMAGE INTERPRETATION
C.1. Study site characteristics affecting interpretation
Mt. Kenya is the second highest mountain in Africa and is located in central Kenya. Its wellwatered slopes provide critical high potential agricultural conditions in the predominately
semi-arid nation, and the mid-slopes have been intensely farmed for many years. The
mountain is surrounded by a semi-arid lowland plateau. The project’s study site consists of
the eastern slopes of Mt. Kenya and encompasses a steep ecological gradient from the glaciers
on the mountaintop at 5,199 metres above mean sea level (AMSL) to dryland grasslands at
600 metres AMSL elevation. The site covers 11,670 km2, or approximately one third of an
ETM+ Landsat satellite image.
The heterogeneity of the landscape of the site is extreme both between and within land use
and cover classes. For example, the natural vegetation ranges from sparse tundra vegetation
and afro-montane rainforest, to sparse grasslands in the lower elevation, dryland area. The
human managed landscape includes irrigated paddy rice, tea and tree plantations, coffee and
maize farms and scattered fields of millet and sorghum within bush. Most of the landscape is
heavily influenced and closely managed by humans. Land managers include small-scale
farmers whose farms are typically less than 2 hectares each, private wheat farms and sheep
ranches of up to 300 hectares in size, large agricultural parastatals and parks and reserves
managed by local and national governments.
C.2. Automated vs. visual interpretation
The heterogeneity in land uses and covers led to our inability to rely on automated
classification schemes. Traditional supervised and unsupervised classification techniques
tended to produce either too many classes differentiating areas that were actually similar, or,
when the number of classes was reduced, to join radically different areas such as irrigated
areas with mixed bush and crops. An attempt to reduce the noise by dividing the image into
separate elevation bands and classifying within those bands, was useful in defining the
boundaries of some classes (e.g., forest types on the mountain, grasslands within a park, tea),
but was not helpful for most of the study area such as those characterized by small scale
agriculture.
In the drylands, which are a mosaic of small and large cultivated fields, fallowed fields,
grassland, and bush of varying heights, the automated classification schemes produced
speckled results without differentiating broader regions, for example areas with medium
versus low intensity agriculture. In the drylands, it was necessary to interpret high resolution
aerial photographs (1:20,000) in sample sites to obtain estimates of the area under cultivation
and under other uses (Olson, 1998). At the lower resolution (1:100,000) of the ETM+ satellite
imagery, it was possible to delineate only a class of mixed bush and farms with no finer
detail. The scale of 1:100,000 was arrived at by zooming to the raster resolution.
To better understand the societal restrictions and processes in order to project future land
use/cover change, it was necessary to differentiate land tenure and management types.
Differentiating between large and small-scale tea producers, or between government and
community managed bush land areas, for example, is critical in predicting how the use of the
land will change, and whether the area under that class will expand or contract. This
information was gathered from a variety of sources including ground truthing, group
interviews, maps and GIS layers from different sources and by consulting experts
knowledgeable of the area. The land use class information was saved as a separate variable
from the land cover information. Each polygon was thus assigned a land use as well as a land
cover attribute. Where the land cover and use boundaries differed (e.g., due to agricultural
incursion into a protected forest), the land cover boundary has been used as the initial land use
LUCID Working Paper 16
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boundary (protected area and other ownership boundaries will later be added).
The basic approach adopted, therefore, was to conduct visual interpretation of the image and
to identify both the land use and land cover of each polygon. The LUCID team also identified
the boundaries of some land covers, such as between forest types using an automated
classification technique known as ‘seeding’.
The vectorized digitising of the raster image file (also known as heads-up digitising) is similar
to manual digitising of paper sources in that lines or polygons are traced by hand, but the
interpreter works directly on the computer screen using the image as backdrop. With the help
of the display tools of ArcView GIS, such as zoom in and out, the operator can work at the
resolution of the raster data and thereby digitise at a higher accuracy level. However, the
accuracy is still highly dependent on the interpreter. The tracing method automates the
process by creating one line or polygon at a time on the image displayed on the computer
screen. This is a significant improvement in accuracy and speed over manual digitising of
interpretations placed first onto paper. The improvement is especially pronounced when fully
automatic raster to vector conversions cannot be applied in cases such as low image quality or
complex layers. These include, but are not limited to instances of cloud cover, or when a
segment of the image contains a number of different land use or cover classes such as Shamba
system interspersed with plantation and afro-montane forests.
C.3. Stages of interpretation
The various stages of interpretation that were utilized as part of the land use and land cover
change mapping assessment included georectification of the satellite image, initial stages of
visual interpretation, ground truthing, and finally the correction of land use/land cover
attributes.
C.3.a. Georectification
The first stage in the interpretation of the image was to geographically rectify the raster
ETM+ image so that it conforms to existing spatial data. This was conducted in Erdas ©
Imagine software (ERDAS, 2001) using the following parameters: the datum was set to WGS
84 and referenced to the Universal Transverse Mercator (UTM) Zone 37 South. The image
was referenced to a number of Ground Control Points (GCP’s) taken from a 1:50,000
topographic maps produced by the Survey of Kenya (1962-1997). The entire Landsat ETM+
Scene was georectified using approximately 90-100 GCP’s distributed across the image. The
GCP’s were selected to be features that were visible on both the image and the topographic
map sheets, such as roads, forest boundaries, towns, and other key features. The resulting
output was saved as an Erdas Imagine *.img file and then opened in ESRI © ArcView as an
image data source. ArcView GIS version 3.2 was used throughout most of this project (ESRI,
1999)
The next stage was to overlay selected vector GIS data layers to assist in the land use/cover
interpretation of the image. The team consulted layers such as roads, towns and market
centres, rivers, administrative and protected area boundaries, and agro-ecological zones
(ILRI, 2002, Jaetzold and Schmidt 1983). These data sets had been originally prepared at the
national scale and, for this project, clipped to a bounding polygon of the study area.
Following the projection conversion from decimal degrees to UTM Zone 37 South, most of
the layers became spatially incorrect with errors ranging from as little as 100 metres to as
much as 1 kilometre. A possible method by which this error could be reduced would be to
transform the polygon with the boundary of the study area to geographic decimal degrees and
a WGS 84 datum, and then use this boundary to clip the additional GIS data layers. The
clipped layers should then be transformed to UTM coordinates. In this project, the
supplemental GIS data layers were only used a guide to help in identification and
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interpretation. The scale that the supplemental data was displayed and interpreted was a
minimum of 1:100,000 to maintain consistency with the scale of interpretation of the image.
Only features recognizable at that scale are thus mapped. As a result, any features that are too
small to clearly visualize at that scale were not digitised, resulting in a de facto minimum
mapping unit of 30 hectares.
C.3.b. Initial land cover interpretation
The next stage of the mapping activity was to label each polygon with its land cover
identifiers. A new attribute record was edited to the attribute table and labelled as LC_Code.
The new fields were added as a numeric variable with sufficient character spaces for the land
cover type labels.
Each general land cover type was assigned a 4 digit numeric code, for example, a land cover
class of cultivated land was assigned a code of 4000, within this land cover class there are a
number of specific land cover types such as rainfed cultivation. Rainfed cultivation also has
specific land covers such as tea. Therefore a land cover code for tea was thereby assigned a
code of 4100. The full code list is given in Table 1.
A variety of combinations of the 30 metre spatial resolution imagery bands were used to
assist in the identification and interpretation process. The combinations that were most
commonly used were bands [4,3,2] [5,4,3] and [7,4,2] [R,G,B]. These were used in
combination with the 15-meter panchromatic band, which was added as a separate layer
(typically the 15- meter panchromatic is viewed in ArcView as bands 9,9,9 unless the band
has been created as a separate file as in other remote sensing software packages). The 4,3,2
band combination detects vegetation through chlorophyll content, while bands 5,4,3 reflect
moisture content and bands 7,4,2 reflect irrigated surfaces. The 4,3,2 band combination was
commonly used to differentiate forests and degraded forests, the tea and coffee zones, and the
large farms and ranches. The combination 5,4,3 was used to examine dry woodland and
riverine forests. Finally the combination 7,4,2 was used to identify large and small-scale
irrigated crops such as rice and horticulture. The 15 metre panchromatic band was especially
useful for identifying features distinguished by texture or shape such as the boundaries of
square fields indicating the presence of farms, or rectangles indicating buildings in urban
areas.
The next stage of the mapping exercise was to visually interpret and digitise the boundaries
of the land cover polygons. Figure 1 illustrates how a feature representing the Shamba
systems (a government scheme where a mix of forests and cash crops are grown together) on
the upper slopes of Mt. Kenya was digitised at a scale of 1:100,000. The polygon that has
been digitised is then given a label identifier (ID) with a generic name representing the land
cover type and a land cover code.
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Figure 1. Vector digitising of the Shamba System polygon at a scale of 1:100,000
This same process was repeated until the entire image was interpreted and a new layer created
of unique polygons each with their own label identifier. Each polygon was thus surrounded
by other unique ID polygons. The general and interactive snapping tolerances were enabled
and set at 50 meters to permit adjacent polygons’ vertexes to be joined. Snapping vertexes to
nodes is an important procedure to minimize errors and to avoid polygon area
miscalculations.
The initial interpretation did not differentiate land uses from land covers, but instead
determined just the cover types, as the mapping exercise was to provide a general
identification of landscape boundaries. The initial interpretation contained twelve land cover
classes, including large scale farms, small scale maize farming, small scale tea/coffee
farming, tundra, afro-montane forest, riverine woodlands, shrub land, bush, deforested or bare
soil, urban, and water. An additional category labeled ground truth contained polygons in
which the visual interpretation was difficult and required ground truthing and/or consulting
additional sources.
A critical component of the initial land use/cover classification involved the use of the ‘seed
tool’, a procedure available in the image analysis extension of ArcView GIS 3.2. The seed
tool identifies a contiguous area of an image with spectral characteristics similar to a training
area that the interpreter selects. The seed tool was mostly used to delineate the boundary
between land covers. Examples of how the seed tool was used include differentiating the tea,
coffee and maize small-scale agriculture zones, and bounding forests and water bodies.
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C.3.c. Ground truthing
Once the polygons had been digitised and each assigned a land cover code, the resultant maps
were prepared for ground truthing. The study area was divided into blocks, each
corresponding to the extent of a 1:50,000 scale topographic map. The blocks, with their land
cover interpretation displayed over the image, were printed and compiled into a field
notebook. See Figure 2.
Figure 2. Map catalogue with the digitised polygons overlaid onto satellite imagery. Each
map zone was printed separately for ground truthing.
Two key points that enhance the utility of the ground truthing maps are: 1) grid coordinates
should be displayed in the correct coordinate system (e.g., in UTM they should be in metres
and tic lines spaced at 5,000-metre intervals), and 2) the polygons should be displayed such
that they are ‘transparent’ with only their boundaries visible so that the satellite image
remains visible underneath the polygon boundaries. During ground truthing, the observer is
then able to easily identify and correct the boundaries. Each printed map was placed in plastic
sheets and compiled into folder. This permitted changes to the boundaries and identified
fields to be drawn or written directly on the maps while in the field.
During the ground truthing, Global Positioning System (GPS) units were used to identify
where we were, to document the location of waypoints and to track line features such as roads
or tracks. The correct parameters need to be programmed into the GPS unit, ideally the same
parameters as those used to geo-reference the image, to ensure that the GPS recordings
correspond to the GIS data layers. For example, the datum was set at WGS 84 and projection
parameters set to UTM/UPS. When recording waypoints in the GPS, it is important to
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average the recordings to reduce the x and y coordinate errors by remaining at the same place
for approximately 60-90 seconds.
The point and line data recorded on the GPS was downloaded onto a laptop computer every
night to prevent the accidental overwriting of the data in the GPS. The downloading was done
using OziExplorer GPS Software (downloadable at www.oziexplorer.com). This easy-to-use
software exports data to ESRI format shape files, which can be read by ArcView. The coordinates should first be exported in decimal degrees or latitude/longitude coordinates, and
then if necessary transformed into UTM co-ordinates using either the projection utility tool.
Data sheets (see Appendix 2) were completed that documented each waypoint’s surrounding
land covers and uses, including details such as plant species and degree of deforestation.
Interviews with people near the waypoints and in nearby towns helped to clarify land
ownership, management and causes of use/cover change. The team also used still
photography to document various observations. The information from the data sheets and the
roll and frame numbers of the photographs were recorded as attribute data of the waypoints
shape file as shown in Figure 3.
C.3.d. Correction of land cover attributes and generation of land use attributes
Information from the field, including notes on the notebook maps, interviews, photographs
and the attribute data from the GPS, as well as secondary sources, were used to verify and
correct the original land cover interpretation and use these secondary sources to determine the
general, specific and sub-specific land use categories. Secondary sources included the
1:50,000 scale topographic maps, national and regional GIS layers for Kenya and its
provinces, and the Mt. Kenya Aerial Survey Report and GIS dataset (SoK, 162-1997, ILRI,
2002, Gathaara 1999). By switching between the attribute table and the shape file (overlaid
onto the image) the interpreter could identify and correct the land use and land cover codes.
Figure 3: Attribute data recorded from ground truthing with the design template and the GPS
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Although few corrections in the location of boundaries between polygons were necessary,
several polygons had originally been assigned incorrect land covers. Examples of omission
and commission resulting in misclassification included:





Several large farms and institutional land in the semi-arid area had been misclassified
as small scale agriculture;
A large region in a forest reserve had been classified as tree plantation but was found
to be under the Shamba system (a governmental scheme of rotating planted trees and
crops);
A forest reserve (Imenti) had been classified as non-degraded forest when actually it
was vigorous secondary growth following complete clearance of a mature forest a
few years earlier;
Some hills in the semi-arid zone had been misclassified as bush when they were
actually degraded woodland, having been thinned out by grazing and cutting for
charcoal;
The area under irrigated agriculture had been underestimated because the interpreters
were not expecting to find irrigation in zones where it had been only recently
developed.
These examples illustrate the importance of ground truthing and consulting supplemental
sources, due to the limited information discernable from remotely sensed imagery and errors
made by the interpreters due to a lack of complete knowledge. Areas with similar spectral
characteristics may have very different covers (e.g., degraded woodland versus bush) or uses
(large versus small scale agriculture). Land management systems (e.g., tree plantation versus
Shamba system) are not visible on the image yet define how the land is used and how it will
change.
The next stage was to clean the data layer and correct any ‘island’ polygons. Island polygons
are created when digitising a small polygon on top of a larger feature (see Figure 4). They
cause problems during interpretation and analysis because that area has been assigned two
attributes. There are a variety of methods to clean island polygons. One method is to
download the ‘shape clean’ ArcView extension from the Internet and use the ‘intersection’
command. For small areas, an alternative is to digitise the island feature and then the larger
polygon. A different method is to convert the layer containing the island polygons to a grid
file (provided that the spatial analyst extension is loaded in ArcView) and then re-convert the
grid file to a shape file. The island polygons will then be clipped out of the larger polygons.
One disadvantage of the latter technique is that the land use or cover string label will be
assigned a numeric code and you loose the text label, for example all plantation polygons will
be converted to a code of 2. The observer will then need to manually correct the numeric grid
codes to be text. Another disadvantage is that island polygons may be lost and need to be redigitised.
Through trial and error, the LUCID team found that the output grid cell size specification
should be set to about 50 meters to preserve the shape of smaller polygons. Finally, another
method is to draw a line splitting the larger polygon, and digitise the island polygon adjacent
to the line. The split polygon can then be deleted and a new polygon drawn. Snap the edges of
the previously split polygon to the new polygon using the general and interactive snapping.
Through any of these procedures, the cleaned island polygons should resemble the left portion
of Figure 4.
Once all the polygons had been digitised, each polygon assigned a land use and cover code
(noting the land use categories had been derived from the initial land cover interpretation and
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subsequently from secondary data sources), and the interpretation corrections completed; the
resultant shape file was then built and cleaned. The build command creates or updates the
attribute tables, whereas the clean command generates coverages with correct polygon
topology. The clean command also edits and corrects geometric coordinate errors, assembles
arcs into polygons and creates feature attributes for each polygon. This was accomplished by
converting the shape file to an Arc/Info coverage using the SHAPEARC command in
Arc/Info (if Arc/Info is not available, the X-Tools ArcView extension may be downloaded
from the Arc Scripts menu on the ESRI website at http://www.esri.com).
Figure 4. Island polygon overlaying another polygon and after intersection, two separate
polygons created
Forest
Mixed Bush/Cultivation
E. Area
C.3.e. Calculation
The X-Tools extension in ArcView calculates the area of each polygon in square meters,
acres or hectares. To match the identification code with the text description, double click the
legend and add the appropriate labels. Save the land use and land cover legend files under
separate names in the working directory to prevent confusion between the land uses and cover
codes. Tables and maps created with these files then include the text descriptors. The
calculation of area (in meters squared, acres and hectares) and perimeter (in meters) is
conducted in the table properties and the calculate menu is selected. Tables of area and
perimeter are then constructed automatically. Table 2 shows the area and perimeters of each
land use and land cover code that have been generated from this interpretation.
D. CONCLUSIONS AND RECOMMENDATIONS
The land use and land cover mapping procedure described above effectively represented the
heterogeneous mix of human and natural landscapes of the Mt. Kenya area. The refinement of
the land cover classification consisted mostly of adding classes that are important
economically (e.g., irrigated agriculture) or for plant biodiversity and land degradation (e.g.,
degraded versus non-degraded forest). The refinement of the land use classification scheme
was based on knowledge of the drivers of land use change in the area derived from previous
fieldwork and data analysis. In the land use classification, the major addition was
differentiating between land management and ownership types.
This information will be critical in the process of projecting how the land use may change in
the future. For example, large-scale farmers and agricultural institutions are much more likely
to keep their land under pasture, or invest in irrigation technology, than small-scale farmers.
Small-scale farmers may respond more quickly than parastatals to changes in the market, for
example by switching from tea to maize when prices change. Similarly, the Kenya Wildlife
Service (KWS) and the Forest Department (FD) have different policies regarding the
harvesting of trees in forests.
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This type of land use and cover interpretation and analysis requires information about the area
that can be only obtained from a variety of supplemental sources including maps, literature,
interviewing people and ground truthing. Interpretation based only on the image’s spectral
characteristics is fraught with limitations and the resultant errors would be compounded
during a change analysis. Automated classification based on the spectral characteristics was,
in this image, not helpful due to the heterogeneous landscape and very small land
management units.
The LUCID team recommends that for future land use and land cover analysis of such
heterogeneous landscapes:



A Mix of automatic classification and visual interpretation o Automatic is useful for delimiting homogeneous vegetation zones, such as
forests and tree or agricultural plantations.
o Visual interpretation is useful to reduce interpretation errors in heterogeneous
natural landscapes as well as in complex human-managed landscapes. In
human managed landscapes, supplemental information is often required to
differentiate, for example, between extensive areas of grain crops versus
natural savannah, or to correctly identify zones of intense agriculture. In our
area, for example, much of the landscape was covered by tiny fields under a
variety of crops (perennial crops such as tea and coffee interspersed with
seasonal maize and horticultural crops, with fields separated by planted
trees).
Adopting a dual land use and land cover classification scheme, to provide critical
information on land use drivers and constraints in projecting future changes in use,
and to provide information on the biophysical characteristics of the landscape for a
variety of environmental analyses.
Fuzzy boundaries exist between some of the largest and most important land
use/cover classes in tropical agricultural settings, but it is nevertheless important to
attempt their rough delimitation. The transition from tea/coffee to cropping/maize
dominant to mixed maize/ bush, for example, is gradual and not necessarily visible on
imagery. The biophysical and socio-economic differences, however, are significant
and important to recognize. Identifying changes between in their spatial extent using
imagery, however, may not be possible.
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Table 2: Calculated area and perimeter for each of the land use and land cover categories
LAND USE
Land Use Code
1110
1120
1130
1140
1300
2100
2200
2300
2400
2500
3100
3200
3300
4100
4200
5000
6000
7100
7200
8100
8200
Land Cover Code
1000
2100
2200
2300
2400
2500
2600
3000
4110
4120
4130
4140
4150
4210
5000
6100
6200
7000
Count
3
5
303
2
41
1
11
6
1
7
9
6
59
1
1
8
6
4
1
21
5
Count
1
1
15
30
9
6
35
35
3
5
313
6
2
11
6
4
1
18
Area
634,837,037.25
1,337,136,352.55
5,236,709,395.65
868,175,221.14
316,942,599.31
18,825,909.06
110,116,218.05
19,233,075.62
63,168,376.27
161,425,273.62
1,079,733,572.68
486,881,765.78
1,137,655,210.34
7,646,988.50
66,618,737.93
51,393,392.80
3,796,692.52
29,624,850.02
418,595.76
32,651,974.09
7,456,017.06
Perimeter
Acres
Hectares
269,630.10
375,565.50
1,674,895.80
356,518.33
675,841.16
29,288.54
116,727.57
55,240.79
43,979.53
106,145.51
241,700.36
269,872.65
832,009.54
15,718.63
74,201.41
111,540.50
18,831.15
101,257.87
2,455.74
179,893.92
31,228.70
156,871.02
330,412.27
1,294,013.90
214,529.91
78,317.91
4,651.97
27,210.20
4,752.58
15,609.18
39,888.89
266,806.91
120,310.62
281,119.60
1,889.60
16,461.78
12,699.53
938.18
7,320.43
103.44
8,068.45
1,842.42
63,483.70
133,713.64
523,670.94
86,817.52
31,694.26
1,882.59
11,011.62
1,923.31
6,316.84
16,142.53
107,973.36
48,688.18
113,765.52
764.70
6,661.87
5,139.34
379.67
2,962.49
41.86
3,265.20
745.60
LAND COVER
Area
Perimeter
720,922,999.34
149,074.84
445,076,764.09
229,313.51
807,686,784.55
524,897.35
117,681,520.30
272,327.48
51,538,752.74
113,374.11
19,233,075.62
55,240.79
238,246,065.85
278,933.42
628,878,990.84
773,095.77
634,837,037.25
269,630.10
1,337,136,352.55
375,565.50
5,256,982,095.66
1,721,025.30
161,279,913.68
107,979.12
868,175,221.14
356,518.33
110,116,218.05
116,727.57
3,796,692.52
18,831.15
29,624,850.02
101,257.87
418,595.76
2,455.74
238,815,871.04
222,968.45
Acres
178,143.24
109,980.42
199,582.95
29,079.62
12,735.45
4,752.58
58,871.65
155,398.76
156,871.02
330,412.27
1,299,023.37
39,852.98
214,529.91
27,210.20
938.18
7,320.43
103.44
59,012.45
Hectares
72,092.30
44,507.68
80,768.68
11,768.15
5,153.88
1,923.31
23,824.61
62,887.90
63,483.70
133,713.64
525,698.21
16,127.99
86,817.52
11,011.62
379.67
2,962.49
41.86
23,881.59
LUCID Working Paper 16
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Figure 5.
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Figure 6.
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Figure 7. Land cover draped over a 250m resolution DEM with location of study transect
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E. REFERENCES
ESRI (Environmental Systems Research Institute). 1999. ArcView GIS version 3.2. Redlands,
CA, USA.
ERDAS Inc. 2001. Erdas Imagine 8.5 Software - Atlanta, GA, USA.
Gathaara, Gideon N.. 1999. Aerial Survey of the Destruction of Mt. Kenya, Imenti and Ngare
Ndare Forest Reserves. Kenya Wildlife Service (KWS): Nairobi, Kenya.
Geist, H. and Lambin, E. 2002. Proximate Causes and Underlying Driving Forces of Tropical
Deforestation. BioScience 52(2): 143-150.
Jaetzoldt, R. and Schmidt, H. 1983. Farm Management Handbook of Kenya: Part A, Eastern
Kenya. Ministry of Agriculture, Government of Kenya: Nairobi, Kenya.
IGBP (International Geosphere-Biosphere Project). 1997. LUCC Report Series No. 3. 1997.
LUCC Data Requirements Workshop: Survey of Needs, Gaps and Priorities on Data
for Land Use/Land Cover Change Research. Organized by IGBP/IHDP-LUCC and
IGBP-DIS. Barcelona Spain, 11-14 September 1997.
ILRI (International Livestock Research Institute). 2002. ILRI GIS Database. Available from:
http://www.ilri.cgiar.org/gis/.
Latham, J. 2001. AFRICOVER East Africa. LUCC Newsletter (7):15-16
Olson, Jennifer M. 1998. A Conceptual Framework of Land Use Change in the East African
Highlands. Paper read at Earth's Changing Land: Joint Global Change and Terrestrial
Ecosystems and Land Use and Land Cover Change Open Science Conference on
Global Change, at Barcelona, Spain.
Survey of Kenya (SoK). 1962-1997. 1:50,000 Scale Topographic Map Sheet Index Chuka,
Embu, Embu North, Gatunga, Irereni, Ishiara, Ithanga, Karatina, Kiambere,
Kimangau, Kindaruma, Makuyu, Marania, Masinga, Maua, Meru, Mitunguu, Mt.
Kenya, Muranga, Mwingi, Nanyuki, Naro Moru, Nkubu, Siakago, and Tseikuru.
Ministry of Lands and Settlement, Government of Kenya. Government Printer:
Nairobi, Kenya.
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APPENDICES
Appendix 1. Definitions of Land Use, Land Cover and Land Use/ Cover Change
Below are the land use and land cover definitions adopted by LUCC-IGBP-IHDP (quoted
from LUCC Report Series No. 3, 1997: 19-20).
Land cover refers to the physical characteristic of earth’s surface, captured in the distribution
of vegetation, water, desert, ice, and other physical features of the land, including those
created solely by human activities such as mine exposures and settlement.
Land use is the intended employment of and management strategy placed on land cover type
by human agents, or land managers. Forest, a land cover, may be used for selective logging,
for resource harvesting, such as rubber tapping, or for recreation and tourism. Shifts in intent
and/or management constitute land-use changes.
Land-cover and land-use changes may be grouped into two broad categories: conversion or
modification. Conversion refers to changes from one cover or use type to another. For
instance, the conversion of forests to pasture is an important land-use/land-cover conversion
in the tropics, while abandonment of once permanently cultivated land and the regeneration of
forests is taking place in parts of the mid-latitudes. In contrast, modification involves
maintenance of the broad cover or use type in the face of changes in its attributes. Thus a
forest may be retained while significant alterations take place in its structure or function (e.g.,
involving biomass, productivity, or phenology). Likewise, slash-and-burn agriculture, a use,
may under-go significant changes in the frequency of cropping, and use capital and labor
inputs while retaining the rotation, cutting, and burning that constitute such uses.
Land-cover conversion operates through many pathways, the constellations of which form
specific processes. For instance, deforestation leads to many types of land cover, but one
common conversion process entails cutting, burning, and even planting of grass to create a
pasture.
In turn, site abandonment may lead in succession to a secondary forest. These pathways and
processes, such as deforestation, desertification, wetland drainage, or agricultural
intensification mediate the conversion or modification of land cover. Thus they can be
envisioned as forcing functions, which have direction (forest to pasture or pasture to forest),
magnitude (amount of change), and pace (rates of change). In turn, these pathways are
typically triggered by changes in the use of the land, specific operating strategies (e.g., labor,
capital, crops), which are linked to changes in the purpose of land management (e.g., for
subsistence, market, occupation, or recreation). Thus changes in the controlling land agents or
the context in which they operate affect land use and, ultimately, land cover.
It is important to recognize that many land-use/land-cover change pathways exist and are
differentiated globally and over time. The study of LUCC focuses much of its effort and
emphasis on understanding the specific conditions and controls - both biophysical and social
which determine these pathways.
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Appendix 2. Waypoint Sheet for Ground Truthing
Collector: __________ Map Catalogue #: _______ Sheet Name: ____
Date: __/__/02 Northing:_________ Easting: _______ EPE:_____
Photographic Roll #: ______ Frame #: ______ Slope: ___________
Photographic Description: __________________________________
Cover Type at Present Position:______________________________
_______________________________________________________
Dominant Plant Species at Present Position:____________________
_______________________________________________________
Status of Vegetation Degradation:____________________________
_______________________________________________________
Cover Type Looking East: __________________________________
_______________________________________________________
Cover Type Looking West: _________________________________
_______________________________________________________
Cover Type Looking North: _________________________________
_______________________________________________________
Cover Type Looking South: _________________________________
_______________________________________________________
Additional Notes: _________________________________________
_______________________________________________________
_______________________________________________________
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