THE GLOBAL LAND PROJECT INTERNATIONAL PROJECT OFFICE
GLP REPORT
GLP – A JOINT RESEARCH AGENDA OF IGBP & IHDP
NO. 6, 2012
Global dryland greenness
Trends, drivers and policy implications
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
© GLP International Project Office and authors 2012
GLP Reports publish land system relevant material from the GLP community.
GLP – The Global Land Project - is a joint research project for land systems for the
International Geosphere-Biosphere Programme (IGBP) and the International Human
Dimensions Programme (IHDP).
Published by:
GLP International Project Office
www.globallandproject.org
Refer to this publication as:
Langanke, Tobias, Rasmus Fensholt, Kjeld Rasmussen, Anette Reenberg, Stephen D.
Prince, Bob Scholes, Compton Tucker, Quang Bao Le, Alberte Bondeau, Ron Eastman,
Howard Epstein, Andrea E. Gaughan, Ulf Hellden, Cheikh Mbow, Lennart Olsson, Jose
Paruelo, Christian Schweitzer, Jonathan Seaquist, Konrad Wessels (2012). Global dryland
greenness. Trends, drivers and policy implications. GLP Report No. 6. GLP-IPO,
Copenhagen.
ISSN 1904-5069
Cover photo: In the outskirts of Dahra, Senegal, 2006. Photo: Rasmus Fensholt
GLP Repoort No. 6 - Glo
obal dryland ggreenness - Trrends, drivers and policy im
mplications
Glob
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Trend
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Langanke
e, Tobias, Rasmus
R
Fe
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ussen, Ane
ette Reenbberg, Steph
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Prince, Bo
ob Scholess, Compton
n Tucker, Q
Quang Bao
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astman,
Howard E
Epstein, An
ndrea E. Ga
aughan, U lf Hellden, Cheikh Mbow, Lennnart Olsson
n, Jose
Paruelo, C
Christian Schweitzer
S
, Jonathan
n Seaquist, Konrad Wessels
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GLP Repo
ort No. 6
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Contents
1. Introduction……………………………………………………………………………..
1
2. The detection of dryland vegetation change from space………………………….
3
3. Observed patterns and trends………………………………………………………..
6
3.1. Additional information on some of the most important regions…..
11
4. Causes, signatures and data demands for verification……………………………
15
5. Use of ecosystem process models in tandem with Earth Observation data……
17
6. Discussion and conclusions……………..………………………………….. ……….
18
7. Research needs and application in environmental policy………………………..
20
8. References……………………………………………………………………………..
23
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Preface
This report is based on discussions during a GLP workshop in Copenhagen with the topic
‘Vegetation productivity in drylands. Trends, Similarities, Differences, Causes & Research gaps’,
held 12-14th January 2009. A journal publication (with the title: ‘Greenness in semi-arid areas
across the globe 1981-2007 – an Earth Observing Satellite based analysis of trends and drivers’) is
published in Remote Sensing of Environment (Fensholt et al., 2012). This publication has a focus on
the more technical aspects of using the GIMMS NDVI for vegetation greenness monitoring of semiarid areas. The discussions during the workshop and in the following compilation of the material
brought about a number of issues that were going beyond what is described in the main publication;
the main part of these issues are summarized in the present report in order to make the preliminary
thoughts and findings available for interested individuals outside the group of participants.
The report briefly summarizes our approach to the detection of dryland vegetation change from
space, and the observed global patterns and trends. For more detailed methodology and results we
refer to the journal publication. The emphasis is instead laid on our discussion of suggested causes
of trends, as well as on the spatio-temporal signatures and data demands that would be necessary to
establish the basis for exploring causal relationships. In addition, a short section on the use of
ecosystem process models with EO data, as well as an extended discussion and policy implications
section are made available. We will like to stress, however, that most of the material presented is
primarily meant as a starting point or inspiration for further work, rather than a comprehensive and
final analysis of the topic.
1. Introduction
The workshop was based on the question of trends in vegetation greenness in global drylands for
the period of 1981-2007. For our purpose we limit what we mean by ‘drylands’ to the semi-arid
zones (see Text box 1). This area constitutes around 17.7% of the global land area (excluding
Antarctica and Greenland), and is home to approximately 15% of the global population (MEA,
2005). Semi-arid ecosystems provide vital ecosystem services such as food, grazing, energy and
forestry products. Since the Sahel drought of the 1970s and 80s and the UN Conference on
Desertification in 1977 (followed up by the United Nations Convention to Combat Desertification
(UNCCD) in 1992) there has been widespread international interest in the dynamics of drylands in
general and in particular in the Sahel region.
Text box 1: Drylands as delineated in this paper
Many different definitions exist for ‘drylands’. The World Atlas of Desertification (United
Nations Environment Programme et al., 1997) defines drylands as those areas with an aridity
index (AI, the ratio between the mean annual precipitation and the mean annual potential
evaporation) below 0.65. The MEA (2005) uses the same definition and adds four subtypes:
Hyper-arid (<0.05 AI), Arid (0.05-0.2 AI), Semi-arid (0.2-0.5) and Dry subhumid (0.5-0.65
AI). The areas investigated here, are confined to those with an AI between 0.2 and 0.5 (semiarid), based on the World Atlas of Desertification (Figure 1) and the MEA (WRI, 2002). This
limitation is due to the well-known uncertainty of NDVI estimates for sparse vegetation
(Huete, 1985) that can cause spurious trends depending on the sensor (Fensholt and Proud,
2012). This restriction to semi-arid areas for this report excludes hyper arid, arid and dry subhumid zones, which are included in wider dryland definitions.
1
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
The official UNCCD definition of desertification, equating it with land degradation in drylands (see
Text box 2), implies that change in vegetation productivity is a key indicator (but not the only one)
of land degradation. Furthermore, vegetation productivity is of great economic significance because
crop and livestock production in many arid and semi-arid regions is the most important economic
activity. Moreover, primary production is a key supporting ecosystem service, as defined by the
MEA-Millennium Ecosystem Assessment (2005). Therefore, spatially and temporally consistent,
long-term data on changes and trends in vegetation productivity are of great interest for the
assessment of environmental conditions and their trends in dryland regions. Only Earth Observation
(EO) satellite data provide suitable, global, temporally and spatially consistent data, with a mediumterm time-series covering the last 3 decades.
While many reports of declining vegetation productivity in semi-arid lands have been published
over the last decades, a recent and authoritative example being the MEA (2005), recent publications
have shown a more nuanced picture with both declines and increases (Bai et al., 2008), with several
papers focusing on the ‘greening of the Sahel’ (see e.g. Anyamba and Tucker et al., 2005; Hellden
and Tottrup, 2008; Hickler et al., 2005; Prince et al., 2007). These publications have been based on
a variety of different datasets and they use slightly different methods.
This report is based on work that aims at providing a global analysis of the trends in vegetation
greenness in Earth’s semi-arid lands, as assessed from the Advanced Very High Resolution
Radiometer (AVHRR) sensor on board the US NOAA series of satellites over the period from 1981
– 2007. The ‘Normalized Difference Vegetation Index’ (NDVI), computed from the AVHRR data,
is used as a proxy for vegetation productivity. Trends in NDVI may be related to climatic as well as
non-climatic causes of change, and it is obviously of great policy relevance to be able to infer more
specifically their causes and implications.
Text box 2: Land degradation and desertification
According to the UNCCD, ‘desertification’ means “land degradation in arid, semi-arid and dry
sub-humid areas resulting from various factors, including climatic variations and human
activities” (UNCCD homepage). Whereas, ‘land degradation’ means “reduction or loss, in
arid, semi-arid and dry sub-humid areas, of the biological or economic productivity and
complexity of rain-fed cropland, irrigated cropland, or range, pasture, forest and woodlands
resulting from land uses or from a process or combination of processes, including processes
arising from human activities and habitation patterns, such as: soil erosion caused by wind
and/or water; deterioration of the physical, chemical and biological or economic properties of
soil; and long-term loss of natural vegetation.”
According to the MEA (2005) some 10-20 per cent of drylands are already degraded (MEA:
637) although some influential reports claim much higher (Dregne and Chou, 1992), and some
lower totals (Lepers et al., 2005). Many reputable sources rank desertification among the
greatest environmental challenges today and a major impediment to meeting basic human
needs in drylands (MEA). It is however underlined that further studies are needed in order to
identify where the problems occur and their true extent (MEA: 625).
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GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
-120
-90
-60
-30
0
30
60
90
120
150
60
30
0
World Humidity classes
(the world atlas
of desertification)
Humid
Dry Sub-Humid
-30
Semi-Arid
Arid
Hyper-Arid
Cold
Fig. 1: World Humidity classes (The World Atlas of Desertification). This GLP Report considers
only the semi-arid areas.
2. The detection of dryland vegetation change from space
Vegetation indices (VI) measure the differential absorption and scattering by green leaf material of
solar radiation in the visible waveband (0.4 - 0.7 μm, although red wavelengths only are generally
used in VIs) and near infrared wavebands (0.8 - 1 μm). This difference is attributable to the
absorption of visible radiation by chlorophyll and its accessory pigments while near infrared,
although slightly absorbed, is strongly scattered by the complex air-water interfaces between the
cells within leaves (Gates et al., 1965). Thus the more green leaves in the field of view, the less
visible and the more near infrared radiation are measured by a satellite borne sensor. Several factors
other than leaf absorption and scattering also affect red and near infrared reflectance. These include
canopy structure, leaf angle distribution, non-green stems, branches and dead plant material (both
attached and on the soil surface), the soil surface itself, solar zenith angle, and sensor view angle.
Different soils and litter reflect differently in the visible and near infrared, so the reflectance of
sparse vegetation can be significantly affected by the non-green components (Huete et al., 1985).
Because of the central role of vegetation cover as an indicator of the land surface condition in
drylands, time series of vegetation indices are the main tool for trend detection using remote sensing
(a selection of the many papers includes Tucker et al., 1991, and Prince and Goward, 1995).
Net primary production (NPP) is the process whereby vegetation biomass is produced.
Traditionally, Earth Observation based production efficiency modelling (PEM) has been based on
the light use efficiency (LUE) concept originally proposed by Monteith (1972), who considered
biomass accumulation (g/m2) as an ongoing process correlated with the amount of
photosynthetically active radiation (PAR) absorbed or intercepted by green foliage (APAR in
MJ/m2). NPP depends on, among other factors, the fraction of photosynthetically active radiation
(fPAR) absorbed by the canopy, and VIs are proxies for fPAR (Sellers, 1985; Myneni et al., 1995).
The most widely used VI is the Normalized Difference Vegetation Index, NDVI = (NR– R)/(NIR +
R), where R and NIR are the surface reflectances in the red and near infrared, respectively (Tucker
1979). Measurements and modeling have shown that NDVI is linearly related to fPAR (Asrar et al.,
3
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
1984 and several other studies). When vegetation indices are used in areas of sparse vegetation, it is
necessary to correct for background effects to maximize the vegetation signal. This requires
location-specific corrections that cannot be applied elsewhere (e.g. Tucker et al., 1994). While other
vegetation indices have been proposed, to date, the only VI available globally from 1981 to present
is the NDVI. Properties of vegetation, in addition to fPAR, that have been estimated from NDVI
include Leaf Area Index (LAI), chlorophyll concentration, above-ground biomass, net primary
productivity, fractional vegetation cover, vegetation water content, and leaf nitrogen.
Unsurprisingly, the properties with a close biophysical relationship to the absorption and scattering
of solar radiation in the visible and near infrared (such as fAPAR, LAI and chlorophyll content) are
generally better estimated than more indirectly related properties such as total biomass. A single
observation of NDVI cannot be used to make absolute measurements of leaf biomass, productivity
or phenology since this is simply a one-time estimate of fPAR. For annual crops and herbaceous
vegetation in drylands, the sum of repeated NDVI measurements throughout the growing season
multiplied by the time intervals between measurements is correlated with annual NPP (Tucker et al.,
1985; Prince, 1991a; Paruelo et al., 1997). Since coarse resolution satellites observe the same place
on the ground almost daily, even after days of excessive cloud cover have been excluded,
measurements at 10-15 day intervals can be achieved in most drylands. Across different vegetation
formations, climate zones, or soil types, additional data such as temperature, soil moisture and
humidity become significant, and more complex models that incorporate these are necessary
(Prince, 1991b), however, simple summation of NDVI may outperform such complex models, since
these models have their own measurement errors. It should be noted that NDVI and all other
vegetation indices based on R and NIR cannot measure change in the composition of vegetation, for
instance a shift between palatable and unpalatable species. Long-term, consistent, and nonstationary satellite data records are needed to monitor and quantify inter-annual trends in vegetation.
The processing must be such that it does not inadvertently remove trend data. Several global and
regional NDVI time series have been created from the sequence of Advanced Very High Resolution
Radiometer (AVHRR) instruments on the NOAA satellites from 1981 to present. Apart from the
AVHRR record, there are no other global NDVI data with such a long record available. Only since
1995 have other instruments, often with improved sensor attributes, started to make NDVI time
series measurements. Thus for the purposes of trend detection, there is a trade-off between the
length of the record and the quality of the observations. Although AVHRR data are expected to
continue until they are replaced by the VIIRS instrument on the future NPOESS satellites, as data
from MODIS and other improved sensors become available (e.g., for MODIS, starting in 2000),
they can be added to the AVHRR record with appropriate harmonization (Pedelty et al., 2007)
The AVHRR was originally intended for visual analysis of meteorological conditions, and
applications to vegetation studies were never imagined (Cracknell, 2001). The realization of the
potential of AVHRR for vegetation studies came later, when modifications to optimize the sensor
for vegetation studies were not possible. Consequently there are aspects of AVHRR data that, while
not seriously affecting their meteorological uses, are not ideal for vegetation trend studies. These
include post-launch degradation in sensor calibrations and drift in the satellite overpass times which
have significant effects on VI time series, irrespective of ground conditions. Other changes include
the spectral properties of the sensors themselves. Numerous investigations have evaluated NDVI
continuity and have proposed inter-sensor translation methods among the different AVHRR
instruments as well as between AVHRR instruments and newer sensors, including the Moderate
Resolution Imaging Spectroradiometer (MODIS) (Pedelty et al., 2007), the Système Pour
l'Observation de la Terre (SPOT)-VEGETATION, the Sea-viewing Wide Field-of-view Sensor
4
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Table 1: Characteristics of available AVHRR data sets. The NOAA Global Vegetation Index, GVI,
product is excluded because, in these data, inter-annual trends are suppressed; thus GVI is useless
for studying trends or the response of vegetation to changing climatic conditions and other factors.
Data
Source
Level 1b (Raw‐top
of atmosphere)
Pathfinder AVHRRLand
PAL
NASAGIMMS‐g
Long-term Data Record
(LTDR, in progress)
Spatial
Extent
Global
Global
Global
Global
Data provided
Channels 1‐5
NDVI and channels 1‐5
NDVI, solar zenith angle,
channels 4 and 5.
No
No
No
No
Yes
Yes
No
No
No
No
No
Volcanic dust periods
Yes
Yes
Yes
Expected in next version
No
No
Yes (Pinzon et al., 2002)
Expected in next version
BRDF
No
No
No
Expected in
next version
Cloud screening
No
CLAVR (Stowe et al., 1991)
Temperatures from AVHRR
channel 5
CLAVR (Stowe et al., 1999)
Sensor
Calibration
No
Rao and Chen, 1995
Rao and Chen, 1995; Los, 1998;
and NOAA
Vermote and Kaufman, 1995;
Vermote and Saleous, 2006
Spatial
resolution
Length of
archive
Estimated
geolocation
error
From 1.1 km at nadir
8 km
8 km
0.05 deg (~5km at
equator)
1981‐present
1981‐2001
1981‐2007
1981‐2008
8km
8 km
0.025 deg (~2.5 km at
equator)
Compositing
period
None
10 days
Bi‐monthly
Not composited
Where to get
data
http://www.ncdc.no
aa.gov/oa/pod‐
guide/ncdc/docs/intro
.htm
http://www.ngdc.no aa.gov/ec
osys/cdroms/Pathfinder98/p
athfind.htm#land
http://www.landcover.org/data/g
imms/
http://ltdr.nascom.nasa.gov
/ltdr/ltdr.html
Key citation
Kidwell, 1991
James and Kalluri,1994
Tucker et al., 2005
Pedelty et al., 2007
Corrections
Rayleigh
Ozone
Water vapour
Aerosols
Solar Zenith
angle
(SeaWiFS), the Medium Resolution Imaging Spectrometer (MERIS) and the anticipated
Visible/Infrared Imager Radiometer Suite (VIIRS) on the future NPOESS series. It remains a
challenge, however, to produce long-term and consistent vegetation index time series across the
sequence of multiple sensor systems with their different spectral responses, spatial resolutions,
swath width and orbiting geometry.
Several archives of AVHRR data exist in which various types of processing have been applied to
the satellite data in order to minimize unwanted effects, some of which are specific to the satellite
sensor used and others of which are inseparable from measurements made from a satellite, such as
sun-sensor geometry and the effects of measurement through the atmosphere. The aim of the
processing is to estimate the NDVI that would be measured just above the vegetation surface, with
all other significant variables normalized. The corrections and methods used in the processing have
evolved through time and continue to be improved. Some archives have been reprocessed back to
1981 several times over, each time using improved techniques, while others continue to use the
methods adopted at the inception of the data series. Some details of the processing in publicly
available archives are summarized in Table 1.
5
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Sensor calibration is no longer a source of significant error (El Saleous et al., 2000). The greatest
remaining sources of error in AVHRR estimates of surface NDVI are caused by absorption and
scattering by atmospheric components (Nagol et al., 2009). Detection of partial cloud cover and
hence the decision of whether to use or omit data is also a major source of uncertainty. Seasonal
solar zenith angle variations and the effects of viewing vegetation at different angles (bi-directional
reflectance distribution function, BRDF) are further sources of error. As the atmospheric correction
schemes improve, the BRDF effect becomes more pronounced, thereby increasing the bias towards
the selection of NDVI values from the forward scatter viewing direction (Huete et al., 2002;
Fensholt et al., 2010). The selection of the maximum NDVI over a compositing period, so called
maximum value compositing, can be effective by selecting the least hazy and nearest to nadir
observations, but this is at the expense of the loss of any multiple, good observations in the
compositing period, and it must be borne in mind that a single composite may be made up of data
from any one of the overpasses in the compositing period. More recently a series of physicallybased corrections based upon those developed for the MODIS sensor has been suggested as a means
to improve AVHRR NDVI data (Pedelty et al., 2007), and the new LTDR data processing (Table 1)
apply these. Nevertheless, the AVHRR instruments lack the additional channels needed to make
corrections as good as those applied to MODIS, SPOT-VEGETATION or MERIS and envisaged
for VIIRS. Notwithstanding the caution needed in the interpretation of satellite measurements of
VIs discussed above, the data are invaluable for detecting trends in important aspects of vegetation.
In fact, there are no other data as spatially and temporally comprehensive for the past 29 years.
3. Observed patterns and trends
In Fensholt et al. (2012) linear and non-linear temporal trend indicators are applied for
characterization of changes in dryland vegetation greenness. The calculated trends are analysed
with gridded data on potential climatic constraints to plant growth (Nemani et al., 2003), to explore
possible causes. Figure 2 shows the greening trend in semi-arid areas (1981-2007) as based on the
GIMMS NDVI data. Figure 3 shows the trend in global precipitation (based on the GPCP dataset),
and Figure 4 the trend in air temperature (both for the same period and geographic extent). In
addition for selected regions an analysis of changes in the seasonal variation of vegetation
greenness and climatic drivers is conducted to understand the causes of observed inter-annual
changes.
The precipitation data used is the blended gauge-satellite GPCP 2.1 product (Adler et al., 2003)
provided in a 2.5 degree spatial resolution covering the full period of AVHRR data. Air temperature
data are the NOAA NCEP CPC GHCN_CAMS (National Oceanic and Atmospheric
Administration, National Centers for Environmental Prediction, Climate Prediction Center, Global
Historical Climatology Network, Climate Anomaly Monitoring System) 0.5 degree gridded global
monthly land surface air temperature (Fan and van den Dool, 2008). Precipitation and air
temperature data are resampled to preserve the 8 km spatial resolution of the NDVI data using a
nearest neighbour algorithm replicating the pixels without changing the original cell values.
When combining information from Figure 2 with information from Figure 3 and 4, it should be
noted that the spatial resolution of the NDVI trend data is 8 km (yet based on full resolution data
with a resolution of 1.1 km), while the original resolution of the GPCP data set is 2.5 degrees. In
Figure 3 and 4 the GPCP data has been resampled to 8 km, yet trends in rainfall and temperature at
scales finer than 250 km are not represented in the figures.
6
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Although there are regions of increased and decreased NDVI (see Figure 2) , on average semi-arid
areas at the global scale experience an increase in greenness, both in drylands limited by water and
by temperature. Overall 36% and 27% of the dryland pixels were characterised by significant
change values (both positive and negative) at the 0.05 and 0.01 significance levels respectively and
out of the pixels with a significant trend 77% and 78% was characterised by positive trends at the
0.05 and 0.01 significance levels. The changes in NDVI on a global scale varied a great deal from
region to region. However, inter-annual variation (Fensholt et al., 2012) reveals that the increases in
greenness may have widely different explanations. This implies that current generalizations,
claiming that land degradation is on-going in all semi-arid areas worldwide, are not supported by
the satellite based analysis of vegetation greenness.
Regionally, positive trends dominate in the Sahel-Sudan, India, West Australia and the northern
Great Plains of North America, while negative trends tend to be more widespread in eastern-central
Australia, parts of central Asia, parts of the south-western USA, and parts of South America.
The regional trends are further summarized in Table 2, as well as in an additional section with more
detailed regional information (chapter 3.1).
-120
-90
-60
-30
0
30
60
90
60
30
0
-30
Changes in NDVI (NDVI units)
over the total period
Jul. 1981 - Dec. 2007
High : 0.1
Low : -0.1
Not significant p>0.05
-60
Fig. 2: Median slope in NDVI for semi-arid areas: July 1981-December 2007.
7
120
150
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
-120
-90
-60
-30
0
30
60
90
120
150
60
30
0
Changes in precipitation
(mm/year/year)
High : 10
-30
Low : -10
Not significant p>0.05
-60
Fig. 3: Median slope in GPCP precipitation for semi-arid areas: July 1981-December 2007.
-120
-90
-60
-30
0
30
60
90
120
60
30
0
Changes in air temperature
(degree celcius/year)
-30
High : 0.1
Low : -0.1
Not significant p>0.05
-60
Fig. 4: Median slope in air temperature for semi-arid areas: July 1981-December 2007.
8
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GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Table 2: Summary information by region/country (for semi-arid areas) on water and/or temperature limitation for plant growth, NDVI
trend and short statement on possible causes. 0=no trend visible, +=increase visible, -=decrease visible.
For more detailed information on causes for these trends, see Fensholt et al., 2012 and the following chapter 3.1.
Region/country
with significant
semi-arid area
Vegetation
water or
temperature
limited
W
NDVI
increase
Precip.
trend
Temp.
trend
Indication for cause
+
+
+
NDVI increase consistent with increase in precipitation.
Southern Africa
Namibia
RSA
Botswana
W
W
W
0
0
+
0
0
0/+
0/+
0
0
Southern Zimbabwe
Southern Madagascar
W
W
-
0/+
0
0
+
Absence of clear NDVI trend consistent with lack of change in precipitation and temperature.
Absence of clear NDVI trend consistent with lack of change in precipitation and temperature.
Increase in NDVI in central Botswana partly consistent with precipitation increase, and can be linked
to four years of high greenness (2004-2007).
NDVI decrease in southern Zimbabwe not explained by climatic factors. Other causes likely.
Absence of clear NDVI trend consistent with lack of change in precipitation and temperature.
+
-
0
Increase in NDVI while decrease in precipitation. Other causes likely.
+
0
0/+
0
+
+
Slight NDVI increase consistent with precipitation trend.
Absence of clear NDVI trend consistent with lack of change in precipitation and temperature.
Lack of clear NDVI trend, while partly increased precipitation.
NDVI decrease while no clear precipitation change. Seasonal NDVI analysis shows decreases in
winter and spring corresponding to changes in precipitation seasonality (decreases in winter and
spring).
NDVI increase with no significant precipitation trend. NDVI increase is caused by increases during
southern hemisphere autumn and winter. Could be caused by increased April precipitation. However
anthropogenic influence from agriculture likely.
Sahel (Senegal to
Sudan)
Horn of Africa and East Africa
Central southern
W
Somalia
Central Kenya
W
Ethiopia
W
Australia
Northern A.
Central east and
south eastern A-
W
W
0
-
+
0
0
0/+
SW Australia
W
+
0/+
0
T and W
0/+
0
+
T and W
0/+
0/-
+
Central Asia
Kazakhstan,
Kyrgyzstan, southern
Russia
Mongolia and Inner
Mongolia
9
Absence of clear NDVI trend consistent with lack of change in precipitation and temperature. Small
area in in SW Russia shows consistent greening, related to greener conditions during early spring to
summer. This coincides with a moderate increase in precipitation and with an increase in air
temperature from February. Central Kazakhstan shows a long-term decrease in NDVI, caused by
decreasing summer NDVI values (possibly related to air temperature increase causing
evapotranspiration losses).
Absence of clear NDVI trend. Some positive trends in eastern Mongolia. Temperature increase could
be contributing factor.
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Region/country
with significant
semi-arid area
Vegetation
water or
temperature
limited
T and W
W
NDVI
increase
Precip.
trend
Temp.
trend
Indication for cause
0
+
0
+/0/-
+
0/+
Absence of clear NDVI trend consistent with lack of change in precipitation and temperature.
Significant NDVI increases correspond with only very regionally confined (and otherwise stable)
precipitation increases. Most of the NDVI increase seems to have non climatic, but instead
anthropogenic causes.
T and W
-
+/0
0
W
W
0
+
0
+
+
0/+
Decreases in NDVI despite stable temperatures and increased precipitation for southern Argentina.
Reasons unclear (could include problems with GIMMS dataset for this region).
Absence of clear NDVI trend consistent with lack of change in precipitation and temperature.
NDVI increase consistent with increase in precipitation.
0
0/+/-
0/0/-
+
+
T and W
0/+
0/-
+
T and W
W
+
-/+
0
0
+
+
Increase in NDVI, not reflected in any trends of the climatic constrains. Other causes likely.
Inconsistent NDVI trends, from slight greening to NDVI decreases in small areas of northern
Morocco and north-western Algeria. Trends are not reflected in trends of the climatic constrains.
Arabian Peninsula and West Asia
Turkey
T and W
+
0
+
Syria
Northern Iraq
W
W
0
-
0
0
+
+
Iran
Afghanistan
T and W
T and W
0
0/+
0
0
+
0/+
Turkey shows a NDVI increase, but no precipitation increase. The NDVI increase is the result of a
strong increase in spring and early summer (March-June). Anthropogenic causes are likely, e.g.
irrigation schemes.
Absence of clear NDVI trend consistent with lack of change in precipitation and temperature.
NDVI decrease in a small area of north-western Iraq, not reflected in any trends of the climatic
constrains. Other causes likely.
Absence of clear NDVI trend consistent with lack of change in precipitation and temperature.
Absence of clear NDVI trend consistent with lack of change in precipitation and temperature.
Northern China
Indian subcontinent
(India and Pakistan)
South America
Argentina, Chile,
Paraguay, Bolivia
NE Brazil
Venezuela and
Colombia
Central and North America
Mexico
W
SW USA and dry,
W
warm Great Plains
Northern and NW
USA (temperature
limited)
Mediterranean basin
Spain
North Africa
(Tunisia, Algeria,
Morocco)
10
Absence of clear NDVI trend consistent with lack of change in precipitation and temperature.
Some weak NDVI decreases in the southern USA partly overlap with a (larger) area of precipitation
decrease in New Mexico, Arizona, Utah and Colorado. The more temperature limited areas show a
slight NDVI increase consistent with a temperature increase.
A greening trend in Dakota, does not reflect any trends of the climatic constrains. Causes unknown.
3.1. Additional information on some of the most important regions1
a) Sahel. The map of avg. NDVI trends and the graph summarizing the trend for the
whole region clearly indicate an increase in vegetation productivity in the Sahel zone of
West Africa. This is also suggested by several other studies, including Anyamba and
Tucker (2005), who found “gradual and slow but persistent recovery from peak
drought conditions” (in 1984) as well as with Olsson et al. (2005) and Vlek et al.
(2008). Considerable spatial variability can be observed, yet the overall trend remains
clear for the central part of the Sahel-Sudan zone.
The general increase in vegetation productivity may not be surprising in view of the
decrease associated with the ‘Sahel-drought’ of the seventies and early eighties: rather,
it may be considered a ‘recovery’ in the sense that productivity has increased after the
drought, although it has not necessarily returned to pre-drought conditions. Since no
consistent data sets go back to before 1981-1982, it is difficult to know whether
vegetation productivity is approaching pre-drought levels, yet there is strong evidence
(Rasmussen et al., 2001) that tree cover and vegetation composition have changed
considerably at least in some parts of the Sahel.
As concerns the importance of various causal factors, Hellden and Tottrup (2008) as
well as Vlek et al. (2008) find that overall there is a positive correlation between avg.
NDVI and annual rainfall, yet the explanation of anomalies, may well require that nonclimatic factors are taken into account.
0.4
0.35
0.3
NDVI
0.25
0.2
0.15
0.1
0.05
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
0
Fig. 5a: Observed average monthly maximum value composites of GIMMS NDVI
(1981-2007) for the Sahel region.
1
This is a summary of feedback from those authors that have work experience in the regions, and the
result of a short literature review. It is by no means meant as a comprehensive review and we are aware
that the different regions are covered in very highly variable detail. The literature review has been done in
2009 and in most cases reflects the available literature around that time.
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GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
From Figure 5a there is no indication of 1983-1984 to be the only years characterised as
years of anomaly as compared to the rest of the time series. Rather a shift in NDVI-level
between the first 13 years (including 1994) and the last 14 years of the time-series could
be an unbiased description of the 27-year tendency shown for Sahel as a region.
The impact of human activities is difficult to discern at the scale of 8 km pixel
resolution, but there appears to be co-occurrence between areas of decreasing trends in
NDVI and some of the areas characterised by the largest increase in population density
(Figure 5b) in Western Senegal, around the Niger River in Mali and in south-western
Niger (population density data for 1990-2010 from Center for International Earth
Science Information Network (CIESIN), Columbia University; United Nations Food
and Agriculture Programme (FAO); and Centro Internacional de Agricultura Tropical
(CIAT). For the areas between Niger and Nigeria and in central Burkina Faso that have
undergone a significant increase in population density, there were less pronounced
tendencies of changes with regression slope values close to zero.
This is in broadly in line with global findings of Bai et al. (2008), who found no simple
pattern when comparing rural population density with land degradation. Globally, they
find, the correlation coefficient is even negative, although positive in some cases, such
as South Africa.
Fig. 5b: Change in NDVI and population for the Sahel region from July 1981 to
December 2007. (Top) Difference in median NDVI, (Bottom) Difference in population
density between 2010 and 1990 (Source: Gridded Population of the World, adjusted to
match UN totals; CIESIN, FAO and CIAT).
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GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
b) Southern Africa. There is great diversity of trends in the heterogeneous southern
African drylands, which is consistent with the findings of Hellden and Tottrup (2008).
The apparent positive trend in central Botswana is associated with four years of high
greenness (2004-7), while little change may be detected earlier. In addition, the interannual variability appears to be particularly high in this region. The weak decrease in
north-western Namibia is in line with the results of Xiao and Moody (2005).
Again, rainfall changes are likely to explain some features, as also suggested by Hellden
and Tottrup (2008), yet human factors certainly also play a role in this region, which has
been strongly affected by armed conflicts and rapid economic, demographic and social
change over the period studied. There is little long-term, large-scale data on vegetation
productivity that allow us to validate the results and provide detailed explanations
c) Horn of Africa and East Africa. Overall the region is characterized by a slightly
increasing trend in NDVI, yet there are large differences within the region. Examples of positive
trends may be found in the pastoral areas in the border zone between Somalia, Kenya and
Ethiopia (in line with the findings of Omuto et al., 2010) as well as in southern Ethiopia and
Sudan. Negative trends are prevalent in coastal and riverine areas of southern Somalia and
inland in Kenya. There is also some evidence of high rates of tree cutting (and absence of
regrowth) in the arid tiger bush area of north eastern Somalia, with intensive charcoal
production (Oduori et al., 2011)
d) Australia. While NDVI in Australia as a whole has increased slightly, there are marked
differences between different parts of the continent. Western Australia and the areas in inland
Queensland/eastern Northern Territory have experienced a consistent increase, areas in southern
Queensland/northern New South Wales display more complex, and slightly negative, patterns of
change. These findings correspond to those reported by Donohue et al. (2009), who also suggest
that changes are likely to be mostly driven by rainfall, associated also with changes in
ecosystem structure. Donohue et al. base their analysis on Pathfinder AVHRR Land and CSIRO
AVHRR Time Series (PAL and CATS). The Eastern Australia winter and spring decrease in
NDVI is reflected in changes in precipitation seasonality with decreasing winter and spring
values.
e) Central Asia. This huge region also displays great differences in NDVI trends, yet the
overall trend is neutral or slightly positive. Significant increases may be found in the western
and northern parts of the region, as represented by the Volga delta region in Russia on the NW
side of the Caspian Sea and in central Kazakhstan, we find a slightly negative trend. There is
little basis for speculating on the possible causes for the observed patterns and trends, yet it
should be noted that changes in the length of the growing season, associated with air
temperature increases, are likely to have marked effects in this region. The Central Kazakhstan
long-term decrease in NDVI, as also reported by de Jong et al. (2011) is caused by decreasing
summer NDVI values, corresponding with the driest period of the year. No changes in the
limited precipitation are observed over the period, but the air temperature has increased
noticeably during spring and early summer months which potentially could cause a larger water
deficit from evapotranspiration losses.
f) Mongolia and Inner Mongolia. The average trend for the region is slightly positive, and
the most significant positive trends may be found in westernmost Mongolia and in the southeastern part of the region of Inner Mongolia. This corresponds well to the findings of Hellden
and Tottrup (2008), who also point to increases in the length of the growing season as an
important factor in the region, along with changes in land management.
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GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
g) Indian Subcontinent. The semi-arid parts of the Indian sub-continent have experienced a
relatively consistent increase in avg. NDVI of about 10% over the 25 year period. According to
Jeyaseelan et al. (2007), this is likely to be the result of land cover transformation rather than
rainfall. The reversal of the trend since 1998, found by Jeyaseelan et al. (2007), cannot be seen
in this analysis.
h) NE Brazil. For NE-Brazil no consistent trend in average NDVI may be detected. In
contrast, Barbosa et al. (2006) find an increase in the period 1984-1990 and a decrease in the
1991-1998 period (attributed to extreme variability in response to tropical SST anomalies).
i) South America (South and West). Included in this area is Patagonia, the Monte
(Argentina), Mediterranean shrublands (Chile) and the Chaco and Espinal Xerophitic Forest In
this extremely heterogeneous region the average trend is negative, in the order of 10% over the
study period, but the spatial pattern is complex, with strong negative trends in Patagonia, which
is also characterized by extreme inter-annual variability. This was also found by Paruelo et al.
(2004), who associated it with rainfall change. In Central Argentina no clear trend can be
observed. In the southern Chaco area the negative trend observed is likely to be caused by low
rainfall over the last 4-5 years. Studies by Paruelo et al. (2004) and Baldi et al. (2008) support
the finding that there was no significant trend up to 1999.
j) Drylands Venezuela and Colombia. Trends in avg. NDVI in drylands of northern South
America vary greatly over the region with mainly positive trends in the western part. The
average trend is positive. Paruelo et al. (2004) described the invasion of African C4 grasses in
the Venezuela Llanos as a major reason for this trend.
k) Mexico, SW USA and dry Great Plains. In North America there is an average weak
positive trend. This is mostly due to a significant increase in the northern Great Plains (North
and South Dakota), in accordance with Neigh et al. (2008) who attributed this trend to increases
in precipitation. In central and western Texas and southern Arizona the trend is slightly
negative, in line with other studies (Bai et al., 2008; Kawabata et al., 2001; Piao et al., 2006;
Slayback et al., 2003), who found decreasing avg. NDVI in arid and semi-arid regions. Two
papers specifically on the Sevilleta desert-grasslands of southern New Mexico (Weiss et al.,
2004; Pennington and Collins, 2007) found; however, no trends in the 1985-2005 period.
l) Mediterranean basin. The Mediterranean basin displays a variety of different trends, and
the average trend is weakly positive. Negative trends may be found in north Africa (Morocco),
in parts of northernmost North Africa and in Israel. Consistent positive trends may be found in
Anatolia in Turkey, in eastern-central Spain and in the Nile delta. Most of the negative trends in
arid and semi-arid Spain correspond to the Gualdalquivir valley, perhaps due to land use
changes. In an analysis of the NDVI trends for Spanish national Parks, Alcaraz-Segura et al.
(2008) found positive trends in parks located in the semi-arid-sub-humid part of Central Spain
(less than 700 mm). Alcaraz-Segura et al. (2008) discussed that positive trends in national parks
would have followed the lengthening of the growing period due to temperature increases (Galán
et al., 2001), stronger in winter (Brunet et al., 2001) than in summer, despite the fact that
precipitation did not change significantly in central Iberia during the last decades studied
(although interannual variability increased slightly) (Galán et al., 1999). NDVI trends agree
with the observed general densification in evergreen woody vegetation in the last decades in the
Iberian matorral as a consequence of land use abandonment (Valladares et al., 2004). VicenteSerrano and Heredia-Laclaustra (2004) relate the NDVI decreases in SW Spain to higher
temperatures and lower precipitation associated with the trends in the winter North Atlantic
Oscillation (NAO). A recent article (Martinez and Gilabert, 2009) analysed NDVI trends in
Spain from the 1989-2002 period using the wavelet transform. Most of the area presented
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GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
positive trends. The authors related the lack of significant trends in south-eastern Spain (Murcia
and Almería) to degradation processes.
m) Arabian Peninsula and West Asia (Iraq, Eastern Turkey, Iran, Pakistan). Large
parts of this region display little change, but exceptions stand out: While a declining trend
(especially since 1999) can be observed in northern Iraq, large areas along the Euphrates and
Tigris are greening. Increases due to irrigation may be found in Central Saudi Arabia, and in
parts of Pakistan there are significant increases as well.
4. Causes, signatures and data demands for verification
While the long time series of satellite acquisitions provides a basis for the assessment of
the spatial and temporal changes in NDVI, it is more challenging to establish a solid
base for assessing the plausible causes of observed trends. In the following, some
possible ways of addressing this challenge is briefly addressed; first by suggesting a
systematic search for signs of causal effects of different types of causes of change, and
second, by drawing attention to possible improvement in the explanation of causal
relationships that could be offered by ecosystem modelling.
As summed up in Table 3, many different explanations of observed trends in the avg.
NDVI time series may be, and have been, suggested. In Table 3, they have been
categorized according to whether they are suggested to be mere technical artefacts,
effects of climatic change, effects of changed atmospheric composition, effects of
ecological change (which may in turn have been caused by a range of factors) or effects
of changes in land management. The table proposes a plausible ‘spatio-temporal
signature’ for each of the listed causes. The idea is that some causes will lead to changes
with distinct spatial and temporal patterns which can be recognized in an interpretation
of the maps and graphs. Hence, identification of such signatures can be a useful
complement to advance hypothetical, causal relations, yet in many cases the required
information will not be available to infer the cause. Also, different causal factors may
have similar ‘signatures’.
Table 3: Overview of suggested causes of observed trends in avg. NDVI, their specific
‘signatures’ (the spatio-temporal patterns of change in avg. NDVI and the relationships
between spatio-temporal patterns and the suggested causal factors), and the data
required to validate causal hypotheses.
Proximal cause
Spatio-temporal signature
Data demands for
verification/falsification
Technical artefacts
Systematic biases in
vegetation indices due
to changes in aerosol
content, atmospheric
water content, and other
atmospheric
constituents, not or
insufficiently corrected
for.
Spatio-temporal patterns of NDVI
variations reflect spatio-temporal
patterns of change in the
atmospheric constituents (which
differ strongly between aerosols and
well-mixed gases).
15
Long-term, high spatial and temporal
resolution data sets on atmospheric
concentrations of aerosols and relevant
gases.
Detailed information on atmospheric
correction procedures.
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Proximal cause
Spatio-temporal signature
Data demands for
verification/falsification
Changes in cloud
masking procedures,
and or systematic
change in cloud cover.
Changes in cloud masking
procedures may cause sudden
changes in NDVI statistics, while
systematic changes in cloud cover
may cause spurious trends.
NDVI values vary in parallel to
orbital drift of satellites and to
changes in calibration. In GIMMS
data, artefacts may be related to the
use of the EDM method.
Independent information on cloud
statistics.
Detailed information on cloud
masking procedures.
Spatio-temporal patterns of avg.
NDVI change reflect spatiotemporal patterns of rainfall and/or
evaporation change. The scale of
these changes will often be larger
than those associated with land
management changes.
Changes in phenology, as identified
in GIMMS and other EO data,
reflect changes in rainfall
distribution.
Earlier greening and later browning
of vegetation in temperature-limited
dryland biomes, reflecting changes
in temperatures.
GIMMS (temporal domain) and
MODIS (spatial domain) avg. NDVI
data.
Consistent and spatially detailed
rainfall and evaporation data, e.g. from
TRMM (rainfall) and EO data driven
hydrological models (evaporation).
High temporal resolution EO data, e.g.
from MODIS or MSG.
High resolution (spatial as well as
temporal) rainfall data.
High temporal resolution EO data, e.g.
from POES, MODIS, MSG, on
vegetation greenness.
High temporal resolution EO data on
temperature, e.g. from standard
meteorological observations or
MSG/GOES.
Systematic biases due to
orbital drift and acrosssensor calibration
inconsistencies.
Precise documentation of correction
and calibration methods used for the
different data sets.
Climatic factors
Inter-annual changes in
plant available water
(both rainfall and
potential evapotranspiration).
Changes in intra-annual
rainfall distribution.
Changes in temperature
causing changes in
length of the growing
season.
Atmospheric chemistry factors
Increasing CO2
concentration.
N-fertilization due to
increasing NOx
concentration.
Global scale increase in avg. NDVI,
yet the size of the increase may
vary, e.g. according to composition
of the vegetation/crop cover in terms
of C3/C4 distribution.
Local-to-global scale increase in
avg. NDVI reflecting spatiotemporal patterns of increase in NOx
concentration.
Information on the distribution of C3
and C4 plants, as an input to vegetation
models.
Increase in woody vegetation:
Reduced inter-annual variability,
and higher dry season minimum
NDVI.
More forbs: A rapid early season
green-up. Replacement of perennial
with annual grasses: Earlier NDVI
maximum.
Changes in avg. NDVI reflect
changes in the active fire and burnt
area statistics (frequency, location
and timing).
Independent information on ecosystem
change, e.g. from high resolution
satellite images and field work.
Information on the spatio-temporal
pattern of increase in NOx
concentration.
Information on vegetation response to
increased NOx concentration.
Ecological change
Changes in functional
type composition of
rangelands: Woody
plants versus grasses
versus forbs.
Changes in fire regime.
16
Burnt area and active fires may be
derived from AVHRR, MODIS and
MSG data.
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Proximal cause
Spatio-temporal signature
Data demands for
verification/falsification
Changes in management
Changes in agricultural
land use, crop choices,
agricultural practices
and cropping calendar.
Deforestation,
reforestation, forest
degradation and
recovery.
Changes in irrigated
area and in irrigation
management.
Changes in grazing
pattern.
Expansion of
settlements and
infrastructure.
Patterns of avg. NDVI change
reflect the scale of changes in land
use (and agricultural practice). Often
changes in land use have high
spatial variability at small scales.
Changes in woody cover will be
reflected in changes in intra and
inter-annual variations in NDVI and
avg. NDVI, e.g. lower inter-annual
variability and higher dry season
minimum NDVI.
Extension of the period of high
NDVI, and reduced inter-annual
variation.
Diffuse intra- and inter-annual
changes in NDVI, reflecting the
livestock pressure at times of the
year when vegetation is vulnerable.
Localized distinct changes in NDVI.
High resolution satellite data in
combination with statistics and field
observations.
Medium/high resolution satellite data
in combination with statistics and field
observations.
Medium/high resolution satellite data
in combination with statistics, field
observations and hydrological
information.
Temporally and spatially distributed
data on livestock numbers and
composition, e.g. from ILRI.
Maps, high resolution satellite images,
population statistics, lights-at-night
images.
5. Use of ecosystem process models in tandem with Earth
Observation data
The analysis of the output of ecosystem process models together with data from satellite
remote sensing is increasingly being used to provide new insights regarding the
underlying causes of the trends observed in many of the world’s drylands. The basic
approach reviewed here hinges on the direct comparison of model output (usually a
variable related to primary production) with EO data, either aggregated for a region or
spatially distributed on a cell-by-cell basis. The basic requirement is that these models,
driven by historical climate, atmospheric CO2, land use, and management data, are able
to mimic observed long-term trends as well as the inter-annual and spatial variability
observed in vegetation indices.
Hickler et al. (2005) tested the extent to which climate and atmospheric CO2
concentrations could account for the greening trend (NDVI from GIMMS) in the Sahel
observed in the EO data using the LPJ-DGVM (Lund Potsdam Jena-Dynamic Global
Vegetation Model, Sitch et al., 2003). As the LPJ-DGVM was able to simulate the
observed long-term greening trend (and its inter-annual variability) at the aggregated
level, they were able to identify rainfall as the primary cause of the trend, with
atmospheric CO2 concentrations causing only a slight linear increase. The greening of
the Mediterranean shrublands in the 1980s could also be attributed to changes in
precipitation as well as rising CO2 concentrations by using a mechanistic model of
vegetation growth (Osborne and Woodward, 2001). De Beurs and Henebry (2004)
related observed changes in PAL-derived NDVI between 1985-1988 and 1995-1999 to
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GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
shifts in agricultural practices in Kazakhstan, primarily an increase of fallow land
dominated by weedy species and by grasslands under reduced grazing pressure.
Spatial patterns of the differences in the output of ecosystem process models and
satellite observations have been analyzed in order to either generate new knowledge or
to help improve the representation of processes in the model. McCloy and Lucht (2004)
explored some of these issues by comparing global data sets of LPJ-DGVM-simulated
and satellite data-derived FPAR (Fraction of Absorbed Photosynthetically Active
Radiation) and concluded that a lack of correspondence occurred mostly for agricultural
areas and certain desert conditions. Seaquist et al. (2009) mapped the level of agreement
between net primary productivity (NPP) simulated by the LPJ-DGVM (potential NPP)
and satellite-derived estimates of greenness (NDVI) across the Sahel on a cell-by-cell
basis for the period 1982-2002. They provisionally rejected the hypothesis that people
have had a measurable impact on vegetation dynamics because there was a lack of
relationship between degree of data-model agreement to state-of-the-art data sets on
land use intensity and population. Moreover, they showed that the LPJ-DGVM is able
to faithfully capture vegetation dynamics for semi-arid grasslands, but not necessarily
other biomes.
Additional approaches include the parameterization of ecosystem process models with
variables derived from EO data in order to improve model confidence (e.g. Prince and
Goward, 1995; Brogaard et al., 2005; Seaquist et al., 2006) or the tuning of model
parameters using comparable variables derived from remote sensing, using optimization
procedures (e.g. Jarlan et al., 2008). The application of such assimilation methods in
regions where trends are observed would be instrumental for reducing uncertainties
regarding the causes. However, to the best of our knowledge, none of these approaches
has been used to these ends.
6. Discussion and conclusions
The results presented above demonstrate that broad generalizations on global trends in
vegetation productivity in drylands, using NDVI as a proxy, over the last 27 years are
not supported by empirical evidence. While the average trend is slightly positive on a
global scale, the local and regional trends reveal considerable variation in direction and
magnitude of change. To some extent, however, identification of regional trends in this
time period is possible, e.g. as concerns positive trends in the Sahel-Sudan zone, the
northern Great Plains in North America, Western Australia and parts of the Indian
drylands and negative trends in Central Asia, Central Australia and Patagonia.
Whether the observed trends in average values may be translated into evidence of
desertification/land degradation or the opposite is a more complex question. As noted in
Text box 2, the notion of desertification includes much more than vegetation
productivity. Changes in economic productivity and in biological diversity of drylands,
for example, are also included in the UNCCD definition of desertification/land
degradation. Thus, undesirable changes in species composition (e.g. towards more
drought sensitive species or species less suitable for grazing/browsing) and in the
relative proportion of biomass in trees, shrubs and grasses may be considered to
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GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
constitute land degradation. Such changes may well go hand in hand with increases in
vegetation productivity, as detected by the NDVI analysis documented here. Hence,
there is an obvious need for incorporating in-depth local scale monitoring of other
ecosystem characteristics than biological productivity. This points to a fundamental
challenge associated with the UNCCD definitions of desertification and land
degradation in arid and semi-arid environments and the prospects of monitoring them.
On the one hand, not every short-term reduction in vegetation productivity can be
translated into land degradation as inter-annual climatic variations cause fluctuations in
biological production; on the other hand, a wide range of external stressors (climatic or
human) may also impair the resilience of the dryland ecosystem and lead to a downward
spiral of degradation and hence desertification (MEA, 2005). On this background, it is
no easy task to devise a suitable monitoring and assessment framework for
desertification/land degradation that can take advantage of available global scale data
while at the same time taking local as well as temporal characteristics and variability
into account.
The identification of causes of change poses a special challenge. Recent literature (e.g.
Reynolds et al., 2007; Stafford Smith, 2008) has asserted that the physical and societal
components of dryland environments may have characteristics which are fundamentally
different from many other land systems, as regards climate variability, vegetation
resources, population densities, remoteness, market access, local culture and knowledge.
The corresponding complexity of driving forces of change makes simple explanations
of dryland system development a difficult task. It has been specifically noted that the
critical dynamics of drylands may be determined by ‘slow’ variables (Reynolds et al.,
2007) which control long-term changes, whereas ‘fast’ variables control the fluctuating
characteristics of the drylands. ’Slow’ variables, such as soil fertility or household
capital wealth, are suggested to be particularly useful for gaining insight in the longterm trends and dynamics of the human-environment systems in drylands, whereas
‘fast’ variables, such as crop yields and vegetation productivity are seen as poor
indicators of land degradation. As to the important discussion of seminal characteristics
of the long-term processes of change in dryland systems, it has been stressed that the
notion of thresholds may be particularly relevant because regional scale systems have a
number of potential regime shifts (in ecological, social or economic domains) with
corresponding land use and land cover impacts. Examples could be shifting relative
prioritization of grazing and cultivation (Reenberg, 2009). The type of analysis carried
out here, spanning 27 years and based on identification of linear trends, does not allow
for efficient separation of impacts of ‘slow’ and ‘fast’ processes and variables, and it is
not geared to the identification of non-linear behaviour, which is associated with
‘thresholds’.
The complex dynamics of dryland (agro-) ecosystems makes it difficult to uncover
direct causal explanations of the trends observed. The identified weak increase in NDVI
for the drylands in general conforms to the expectations, supported by model studies,
that the increase in CO2-concentration in the atmosphere would cause a slight increase
in vegetation productivity, yet other explanations cannot be ruled out. The analysis that
this GLP Report is based on, has found positive correlations between changes in rainfall
and trends in NDVI for certain regions, yet reliable global, long-term, high-resolution
rainfall data sets do not exist, allowing a spatially and temporally consistent analysis of
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GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
the relationship between trends in vegetation productivity and rainfall. Analysis at
regional and local scale in many parts of the world have found relationships between
NDVI trends and various ‘human factors’, not least changes in land use, yet
generalizing from these studies remains difficult due to their heterogeneous data basis
and methodologies.
In spite of the incontestable shortcomings, the NDVI trends might still be a useful
platform for analysis of dryland changes. Their obvious advantage is that they represent
a unique data set, covering a consistent monitoring over a time period of 27 years.
While they do not directly lend themselves to easy documentation of plausible causal
relationships, they might be combined with other sources of information in a way that
allows for more advanced integrated assessment of dryland systems and their change. It
has for example been demonstrated by Hill et al. (2008) how rule-based approaches to
interpreting times series of satellite data can be used to map land use change syndromes,
and this GLP Report has pointed to opportunities for revealing spatial patterns of trends
or shifts. Such ‘spatio-temporal signatures’, i.e. observable temporal and spatial patterns
of NDVI change, are proposed as a possible way to proceed in the exploration of local
and regional causal mechanisms, e.g. in order to separate changes in ‘slow’ and ‘fast’
variables or regional ‘regime shifts’ in the human-environmental systems. This heuristic
approach is, however, only briefly sketched out, together with some reflections
concerning ‘data & methodological approaches’ that would be required for
identification and verification of such signatures; further elaboration is required along
with independent information on the causal factors.
7. Research needs and application in environmental policy
Implications for Earth Observation based dryland monitoring
The findings reported on in this GLP Report (for more details see Fensholt et al., 2012),
point to some important research perspectives as regards EO techniques as well as to the
ways in which EO data might be employed in combination with other information
sources to enhance insight in the direction and rate of change in human-environmental
systems in drylands:
• While we have based our analysis on what we find to be currently the best
publicly available global data set on NDVI/vegetation productivity trends,
covering the whole period from 1981 to 2007, further developments of the
processing methods, including atmospheric and BRDF correction, are feasible.
The LTDR data set (see Table 1) is a candidate to replace the GIMMS data set.
• In order to clarify the role of rainfall trends in explaining vegetation
productivity trends in drylands, there is a need to develop an authoritative,
global long-term rainfall data set with high spatial resolution, combining
information from rain gauges with information derived from optical, thermal
and microwave EO.
• More sophisticated methods of analysis (including an enhanced pre-processing)
may be applied to detect non-linear trends of vegetation productivity, and to
detect changes in seasonality of photosynthesis, associated with structural
changes in ecosystems. Methods like the TIMESAT procedure (Eklundh and
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GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
•
•
•
•
Olsson, 2003) or the TiSeG (Colditz et al., 2008) provide good support
enhancing the pre- and post processing in this direction.
To move on from estimation of trends in vegetation productivity (expressed by
a greenness index; NDVI) to monitoring of changes in a wider range of
ecosystem properties at the global scale, other data sources need to be brought
into use. Changes in woody cover might be particularly important, and use of
high resolution optical data and (future) polarimetric SAR would be relevant for
this purpose.
Likewise, non-linear approaches would be required to separate the impacts of
‘slow’ and ‘fast’ variables, and to identify ‘regime shifts’ associated with
thresholds (Kinzig et al., 2006)
Validation of the NDVI based vegetation productivity global maps needs to be
more systematic in order to assess the value of NDVI as a proxy for the dryland
vegetation productivity trends. This requires a systematic collection of
comparable ground truth data on vegetation productivity and species
composition from a large number of sites around the globe, preferably over long
period of time. Few historical data sets fulfilling these requirements are
available. Future collection of such data should be organized within the
framework of global-scale ecosystem research programs, e.g. ‘transect
programs’.
The research into the causal mechanisms must be carried out on a regional and
local scale involving a wide range of analytical methods, making systematic and
optimal use of whatever data is available from the realms of e.g. case studies,
mapping, diagnostics, etc. As suggested in this paper, encircling of the plausible
causes of NDVI-change may be facilitated by identification of their ‘spatial
signatures’.
Synergies between earth observation data and ecosystem process models
Capitalizing on the synergies between EO data and ecosystem process models for
understanding the mechanisms of change is underexploited in the context of drylands
research. EO data on vegetation productivity should increasingly be integrated with
vegetation models. EO data provides an important input for validating and enhancing
vegetation models (e.g. in terms of phenological timing), and vegetation models may
contribute to explaining the vegetation productivity trends derived from EO data. With
this in mind, we consider the following to be research priorities:
•
Reconciling disparities in the spatio-temporal resolution between satellite
observations and ecosystem process models, including a systematic global survey
of data-model differences for the most widely used data sets and models.
•
Addressing the extent to which ecosystem process models can capture observed
non-linear or abrupt change patterns in land cover/land use, and how such models
can be altered to capture more realistic change trajectories.
•
Further development of a consistent, standardized land cover/use data set in order
to support data model comparison studies.
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GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
•
Further development of assimilation methods for spatio-temporal resolutions and
a generalization level appropriate to global analyses – this is important for
capturing the spatio-temporal heterogeneity for reliable scaling and for
understanding the impacts of extreme events.
Policy implications
Since drylands are assumed to be particularly vulnerable to climate change, and given
the growing political attention to climate adaptation and to community resilience to
climate change, there are strong reasons to inform national and international policies in
the best possible way. For dryland systems it seems crucial to improve our
understanding of their functioning and directions of change. As biological production is
important to livelihood systems in most dryland regions, monitoring, modelling and
projection of changes in vegetation productivity will provide crucial knowledge in
preparing for the adaptation measures required.
Since results show that there are no universal negative or positive trends in vegetation
productivity in the drylands of the world, policy recommendations must be region
specific in nature and hence call for place-based assessment approaches. While global
generalizations concerning desertification/land degradation do not hold, it is, however,
not necessary to treat every local situation as unique. There are good reasons to believe
that many region-scale patterns are sufficiently coherent and causally consistent to form
the basis of national and international policy. Simplistic explanations should, on the
other hand, be avoided. Even though a greening trend may be observed for several
regions, this does not mean that other indicators do not suggest that land degradation is
ongoing, or that the supply of ecosystem services is adequate or improving. Integration
of data from diverse sources, including both various types of EO systems and ecological
surveys, into ‘integrated monitoring systems’ would be a very useful step forward
towards integration of the biophysical and human dimension in assessing the directions
of change and the underlying causes.
In spite of the caution needed in the interpretation of satellite measurements of trends in
NDVI, they do provide valuable information which is not available from other sources.
The value, seen from a policy perspective, of the analyses of these data would be greatly
enhanced if placed in the proper context of other information sources, not least, the
results of long-term ecological research. Further, more comprehensive analysis of causal
factors than possible at the global scale would be valuable in a regional and national
policy context. This would also include the separation of the impacts of ‘fast’ and
‘slow’ variables, which have significantly different policy implications (Reynolds et al.,
2007).
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8. References
Adler, R.F., Huffman, G.J., Chang, A., Ferraro, R., Xie, P.P., Janowiak, J., Rudolf, B.,
Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P. and Nelkin, E.,
2003. The version-2 global precipitation climatology project (GPCP) monthly
precipitation analysis (1979-present). Journal of Hydrometeorology 4: 1147-1167.
Alcaraz-Segura, D., Cabello, J., Paruelo, J.M. and Delibes, M., 2008. Trends in the
surface vegetation dynamics of the national parks of Spain as observed by satellite
sensors. Applied Vegetation Science 11(4): 431-440.
Anyamba, A. and Tucker, C.J., 2005. Analysis of Sahelian vegetation dynamics using
NOAA-AVHRR NDVI data from 1981-2003. Journal of Arid Environments 63: 596614.
Asrar, G.M., Fuchs, M., Kanemasu, E.T. and Hatfield, J.L., 1984. Estimating absorbed
photosynthetically active radiation and leaf area index from spectral reflectance in
wheat. Agronomy Journal 87: 300-306.
Bai, Z.G., Dent, D.L., Olsson, L. and Schaepman, M.E., 2008. Proxy global assessment
of land degradation. Soil Use and Management 24: 223-234.
Baldi, G., Nosetto, M.D., Aragón, R., Aversa, F., Paruelo, J.M. and Jobbágy, E.G.,
2008. Long-term satellite NDVI data sets: evaluating their ability to detect ecosystem
functional change in South America. Sensors 8: 5397-5425.
Barbosa, H.A., Huete, A.R. and Baethgen, W.E., 2006. A 20-year study of NDVI
variability over the Northeast Region of Brazil. Journal of Arid Environments 67: 288307.
Brogaard, S., Runnstrom, M. and Seaquist, J.W., 2005. Primary production of Inner
Mongolia, China, between 1982 and 1999 estimated by a satellite data-driven light use
efficiency model. Global and Planetary Change 45(4): 313-332.
Brunet, M., Aguilar, E., Saladíe, O., Sigró, J. and López, D., 2001. The Spanish
temperature series. Time variations and trends over the last 150 years. Geophysical
Research Abstracts 3: 5333-5376.
Center for International Earth Science Information Network (CIESIN), Gridded
Population of the World, Version 3 (GPWv3), 2005. CIESIN, Columbia University.
http://sedac.ciesin.columbia.edu/gpw/index.jsp
Cracknell, A.P., 2001. The exciting and totally unanticipated success of the AVHRR in
applications for which it was never intended. Advances in Space Research 28(1):233240.
Colditz, R.R., Conrad, C., Wehrmann, T., Schmidt, M. and Dech, S., 2008. TiSeG: A
Flexible Software Tool for Time-Series Generation of MODIS Data Utilizing the
23
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Quality Assessment Science Data Set. IEEE Transactions on Geoscience and Remote
Sensing 46(10): 3296-3308. DOI: 10.1109/TGRS.2008.921412
De Beurs, K.M. and Henebry, G.M., 2004. Land surface phenology, climatic variation,
and institutional change: Analyzing agricultural land cover change in Kazakhstan.
Remote Sensing of Environment 89(4): 497-509.
de Jong, R., de Bruin, S., de Wit, A., Schaepman, M.E. and Dent, D.L., 2011. Analysis
of monotonic greening and browning trends from global NDVI time-series. Remote
Sensing of Environment 115: 692-702.
Donohue, R.J., Vicar, T.R. and Roderick, M.L., 2009. Climate-related trends in
Australian vegetation cover inferred from satellite observations, 1981-2006. Global
Change Biology. DOI: 10.1111/j.1365-2486.2008.01746.x
Dregne, H.E. and Chou, N-T., 1992. Global desertification dimensions and costs. In
Degradation and restoration of arid lands. Lubbock, Texas Tech. University.
El Saleous, N.Z., Vermote, E.F., Justice, C.O., Townshend, J.R.G., Tucker, C.J. and
Goward S.N., 2000. Improvements in the global biospheric record from the Advanced
Very High Resolution Radiometer, International Journal of Remote Sensing 21(6-7):
1251-1277.
Eklundh, L. and L. Olsson, 2003. Vegetation index trends for the African Sahel 19821999. Geophys. Res. Lett. 30(8): 1430. DOI:10.1029/2002GL016772.
Fan, Y. and van den Dool, H., 2008. A global monthly land surface air temperature
analysis for 1948-present. Journal of Geophysical Research 113: D01103.
DOI:10.1029/2007JD008470.
Fensholt, R. and Proud, S.R., 2012. Evaluation of Earth Observation based global long
term vegetation trends: Comparing GIMMS and MODIS global NDVI time series.
Remote Sensing of Environment 119: 131-147.
Fensholt, R., Sandholt, I., Proud, S.R., Stisen, S. and Rasmussen, M.O., 2010.
Assessment of MODIS sun-sensor geometry variations effect on observed NDVI using
MSG SEVIRI geostationary data. International Journal of Remote Sensing 31(23):
6163-6187.
Fensholt, R., Langanke, T., Rasmussen, K., Reenberg, A., Prince, S.D., Tucker, C.,
Scholes, R.J., Bao Le, Q., Bondeau, A., Eastman, R., Epstein, H., Gaughan, A.E.,
Hellden, U., Mbow, C., Olsson, L., Paruelo, J., Schweitzer, C., Seaquist, J. and Wessels,
K., 2012. Greenness in semi-arid areas across the globe 1981-2007: an Earth Observing
Satellite based analysis of trends and drivers. Remote Sensing of Environment 121:
144-158. Doi:10.1016/j.rse.2012.01.017
Galán, E., Cañada, R., Rasilla, D., Fernández, F. and Cervera, B., 1999. Evolución de
las precipitaciones anuales en la Meseta meridional durante el siglo XX. Pp. 169-180 in:
24
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Raso, J.M. and Martín-Vide, J. (eds.). La climatología española en los albores del siglo
XXI. Oikos-Tau, Barcelona.
Galán, E., Cañada, R., Fernández, F. and Cervera, B., 2001. Annual temperature
evolution in the Southern Plateau of Spain from the construction of regional climatic
time series. Pp. 119-131 in: Brunet, M. and López, D. (eds.). Detecting and modelling
regional climate change. Springer-Verlag, Berlin.
Gates, D.M., Keegan, H.J., Schleter, J.C. and Weidner, V.R., 1965. Spectral properties
ofplants. Applied Optics 4: 11-20.
Hellden, U. and Tottrup, C., 2008. Regional desertification: A global synthesis. Global
and Planetary Change 64(3-4): 169-176.
Hickler, T., Eklundh, L., Seaquist, J., Smith, B., Ardö, J., Olsson, L., Sykes, M.T. and
Sjöström, M. 2005. Precipitation controls Sahel greening trend. Geophysical Research
Letters 32: L21415.
Hill, J., Stellmes, M., Udelhoven, T., Röder, A. and Sommer, S., 2008. Mediterranean
desertification and land degradation. Mapping related land use change syndromes based
on satellite observations. Global and planetary Change 64: 146-157.
Huete, A.R., Jackson, R.D. and Post, D.F., 1985. Spectral response of a plant canopy
with different soil backgrounds. Remote Sensing of Environment 17(1): 37-53.
Huete, A., Didan, K., Miura, T. and Rodriguez, E., 2002. Overview of the Radiometric
and Biophysical Performance of the MODIS Vegetation Indices. Remote Sens. Environ.
83(1): 195-213.
James, M.E. and Kalluri, S.N.V., 1994. The Pathfinder AVHRR land data set: an
improved coarse resolution data set for terrestrial monitoring. International Journal of
Remote Sensing 15(17): 3347-3363.
Jarlan, L., Mangiarotti, S., Mougin, E., Hiernaux, P., Mazzega, P. and Le Dantec, V.,
2008. Assimilation of SPOT/VEGETATION NDVI data into a sahelian vegetation
dynamics model. Remote Sensing of Environment 112(4): 1381-1394.
Jeyaseelan, A.T., Roy, P.S. and Young, S.S., 2007. Persistent changes in NDVI between
1982 and 2003 over India using AVHRR GIMMS (Global Inventory Modeling and
Mapping Studies) data. International Journal of Remote Sensing 28(21): 4927-4946.
Kawabata, A., Ichii, K. and Yamaguchi, Y., 2001. Global monitoring of interannual
changes in vegetation activities using NDVI and its relationships to temperature and
precipitation. International Journal of Remote Sensing 22: 1377-1382.
Kidwell, K., 1991. NOAA Polar Orbiter Data User's Guide. U.S. Dept. of Commerce,
Washington, DC.
25
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Kimenta, J., 2004. Elements of Econometrics. The University of Michigan Press.
Kinzig, A.P., Ryan, P., Etienne, M., Allison, H., Elmquist, T. and Walker, B.H., 2006.
Resilience and Regime Shifts: Assessing Cascading Effects. Ecology and Society 11(1):
20. URL: http://www.ecologyandsociety.org/vol11/iss1/art20/
Lepers, E., Lambin, E.F., Janetos, A.C., DeFries, R., Achard, F., Ramankutty, N. and
Scholes, R., 2005. A Synthesis of Information on Rapid Land-cover Change for the
Period 1981-2000. BioScience 55(2): 115-124. DOI: http://dx.doi.org/10.1641/00063568(2005)055[0115:ASOIOR]2.0.CO;2
Los, S.O., 1998. Estimation of the ratio of sensor degradation between NOAA AVHRR
channels 1 and 2 from monthly NDVI composites. IEEE Transactions on Geoscience
and Remote Sensing 36: 206-213.
Martinez, M. and Gilabert, M.A., 2009. Vegetation Dynamics from NDVI time series
analysis using wavelet transform. Remote Sensing of Environment 113: 1823-1842.
McCloy, K.R. and Lucht, W. 2004. Comparative evaluation of season patterns in long
times series of satellite image data and simulations of a global vegetation model. IEEE
Transactions on Geoscience and Remote Sensing 42: 140-153.
MEA-Millennium Ecosystem Assessment, 2005. Ecosystems and Human Wellbeing:
Desertification Synthesis. World Resources Institute, Washington DC.
Monteith, J.L., 1972. Solar Radiation and Productivity in Tropical Ecosystems. Journal
of Applied Ecology 9: 747-766.
Myneni, R.B., Hall, F.G., Sellers, P.J. and Marshak, A.L., 1995. The meaning of
spectral vegetation indices. Trans. Geosc. Remote Sens. 33: 481-486.
Nagol, J.R., Vermote, E.F. and Prince, S.D., 2009. Effects of atmospheric variation on
AVHRR NDVI data. Remote Sensing of Environment 113: 392-397.
Nemani, R.R., Keeling, C.D., Hashimoto, H., Jolly, W.M., Piper, S.C., Tucker, C.J.,
Myneni, R.B. and Running, S.W., 2003. Climate-driven increases in global terrestrial
net primary production from 1982 to 1999. Science 300: 1560-1563.
Neigh, C.S.R., Tucker, C.J. and Townshend, J.R.G., 2008. North American vegetation
dynamics observed with multi-resolution satellite data. Remote Sensing of Environment
112: 1749-1772.
Oduori, S.M., Rembold, F., Abdulle, O.H. and Vargas, R., 2011. Assessment of
charcoal driven deforestation rates in a fragile rangeland environment in North Eastern
Somalia using very high resultion imagery. Journal of Arid Environments 75(11): 11641172. DOI:10.1016/j.jaridenv.2011.05.002.
26
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Olsson, L., Eklundh, L. and Ardö, J., 2005. A Recent Greening of the Sahel – trends,
patterns and potential causes. Journal of Arid Environments 63: 556-566.
Omuto, C.T., Vargas, R.R., Alim, M.S. and Paron, P., 2010. Mixed-effects modelling of
time series NDVI-rainfall relationship for detecting human-induced loss of vegetation
cover in drylands. Journal of Arid Environments 74(11): 1552-1563.
DOI:10.1016/jaridenv.2010.04.001.
Osborne, C.P. and Woodward, F.I., 2001. Biological mechanisms underlying recent
increases in the NDVI of Mediterranean shrublands. International Journal of Remote
Sensing 22(10): 1895-1907.
Paruelo, J.M., Garbulsky, M.F., Guerschman, J.P. and Jobbágy, E.G., 2004. Two
decades of Normalized Difference Vegetation Index changes in South America:
identifying the imprint of global change. Int. J. Remote Sensing
http://www.informaworld.com/smpp/content~db=all?content=10.1080/0143116031000
1619526
Paruelo, J.M., Epstein, H.E., Lauenroth, W.K. and Burke, I.C., 1997. ANPP estimates
from NDVI for the Central Grassland Region of the United States. Ecology 78(3): 953958.
Pedelty, J., Vermote, E.F., Devadiga, S., Roy, D., Schaaf, C., Privette, J., Pinheiro, A.,
Prince, S., Justice, C., Nagol, J., Masuoka, E.B., Brown, M., Pinzon, J.M., Tucker, C.J,
Ju, J. and Liu, J., 2007. Generating a long-term land data record from the AVHRR and
MODIS Instruments. Geoscience and Remote Sensing Symposium, 2007. IGARSS
2007. IEEE International. Pp.1021−1025.
Pennington, D.D. and S.L. Collins., 2007. Response of an aridland ecosystem to
interannual climate variability and prolonged drought. Landscape Ecology 22: 897-910.
Piao, S., Friedlingstein, P., Ciais, P., Zhou, L. and Chen, A., 2006. Effect of climate and
CO2 changes on the greening of the Northern Hemisphere over the past two decades.
Geophysical Research Letters 33: L23402.
Pinzon, J., 2002. Using HHT to successfully uncouple seasonal and interannual
components in remotely sensed data. In SCI 2002 Conference Proceedings, 14-18 July,
Orlando, Florida.
Prince, S.D. and Goward, S.N., 1995. Global primary production: A remote sensing
approach. Journal of Biogeography 22(4-5): 815-835.
Prince, S.D., 1991a. Satellite remote sensing of primary production: comparison of
results for Sahelian grasslands 1981-1988. International Journal of Remote Sensing 12:
1301-1311.
Prince, S.D., 1991b. A model of regional primary production for use with coarseresolution satellite data. International Journal of Remote Sensing 12: 1313-1330.
27
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Prince, S.D., Wessels, K.J., Tucker, C.J. and Nicholson, S.E., 2007. Desertification in
the Sahel: a reinterpretation of a reinterpretation. Global Change Biology 13(7): 13081313.
Rao, C.R.N. and Chen, J., 1995. Inter-satellite calibration linkages for the visible nearinfrared channels of the Advanced Very High Resolution Radiometer on NOAA-7, -9
and -11 spacecraft. International Journal of Remote Sensing 16: 1931-1942.
Rasmussen, K., Fog, B. and Madsen, J., 2001. Desertification in reverse? Observations
from northern Burkina Faso. Global Environmental Change 11(4): 271-282.
Reenberg, A., 2009. Embedded flexibility in coupled human-environmental systems in
the Sahel: Talking about resilience. Pp. 132-158 in: Hastrup, K. (ed). The Question of
Resilience Social implications of environmental changes. The Royal Danish Academy
of Sciences and Letters, Copenhagen.
Reynolds, J.F., Stafford Smith, D.M., Lambin, E.F., Turner II, B.L., Mortimore, M.,
Batterbury, S.P.J., Downing, T.E., Dowlatabati, H., Fernández, R.J., Herrick, J.E.,
Huber-Sannwald, E., Jiang, H., Leemans, R., Lynam, T., Maestre, F.T., Ayarza, M. and
Walker, B., 2007. Global desertification: building a science for dryland development.
Science 316: 847-851.
Seaquist, J.W., Olsson, L., Ardö, J. and Eklundh, L. 2006. Broad-scale increase in NPP
quantified for the African Sahel, 1982-1999. International Journal of Remote Sensing
27: 5115-5122.
Seaquist, J.W., Hickler, T., Eklundh, L., Ardö, J. and Heumann, B.W., 2009.
Disentangling the effects of climate and people on Sahel vegetation dynamics,
Biogeosciences Discussions 5: 3045-3067.
Sellers, P.J., 1985. Canopy reflectance, photosynthesis and transpiration. International
Journal of Remote Sensing 6(8): 1335-1372.
Sitch, S., Smith, B., Prentice, I.C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J.O.,
Levis, S., Lucht, W., Sykes, M.T., Thonicke, K. and Venevsky, S., 2003. Evaluation of
ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic
global vegetation model. Global Change Biology 9(2): 161-185.
Slayback, D.A., Pinzon, J.E., Los, S.O. and Tucker, C.J., 2003. Northern hemisphere
photosynthetic trends 1982-99. Global Change Biology 9: 1-15.
Stafford Smith, M., 2008. The ‘desert syndrome’ – causally-linked factors that
characterise outback Australia. The Rangeland Journal 30: 3-14.
Stowe, L.L., 1991. Cloud and Aerosol Products at Noaa Nesdis. Global Planet Change
90: 25-32.
28
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
Stowe, L.L., Davis, P.A. and McClain, E.P., 1999. Scientific basis and initial evaluation
of the CLAVR-1 global clear/cloud classification algorithm for the Advanced Very
High Resolution Radiometer. J. Atmos. Oceanic Technol. 16: 656-681.
Tucker, C.J., 1979. Red and near-infrared linear combinations for monitoring
vegetation. Remote Sensing of Environment 8(2): 127-150.
Tucker, C.J., Dregne, H.E. and Newcomb, W.W., 1991. Expansion and Contraction of
the Sahara Desert from 1980 to 1990. Science 253(5017): 299-300.
Tucker, C.J., Brown, M.E., Pinzon, J.E., Slayback, D.A., Mahoney, R., Saleous, N.E.
and Vermote, E.F., 2005. An extended AVHRR 8-km NDVI dataset comparable with
MODIS and SPOT Vegetation NDVI data. International Journal of Remote Sensing 26:
4485-4498.
Tucker, C.J., Newcomb, W.W. and Dregne, H.E., 1994. AVHRR data sets for
determination of desert spatial extent. International Journal of Remote Sensing 15(17):
3547-3566.
Tucker, C.J., Van Praete, C.L., Sharman, M.J. and van Ittersum, G., 1985. Satellite
remote sensing of total herbaceous biomass production in the sengalese Sahel 19801984. Remote Sensing of the Environment 17: 233-249.
United Nations Environment Programme, 1997. World Atlas of Desertification.
Middleton, N., and Thomas, D.S.G. (eds.). Arnold, London.
Valladares, F., Camarero, J.J., Pulido, F. and Gil-Pelegrín, E., 2004. El bosque
mediterráneo, un sistema humanizado y dinámico. Pp. 13-26 in: Valladares, F. (ed.).
Ecología del bosque mediterráneo en un mundo cambiante. OAPN-MMA, Madrid.
Vermote, E. and Kaufman, Y.J., 1995. Absolute calibration of AVHRR visible and
near-infrared channels using ocean and cloud views. International journal of remote
sensing 16(13): 2317-2340.
Vermote, E.F. and Saleous, N.Z., 2006. Calibration of NOAA16 AVHRR over a desert
site using MODIS data. Remote Sensing of Environment 105(3): 214-220.
Vicente-Serrano, S.M. and Heredia-Laclaustra, A., 2004. NAO influence on NDVI
trends in the Iberian Peninsula (1982-2000). International Journal of Remote Sensing
25: 2871-2879.
Vlek, P., Bao Le, Q. and Tamene, L., 2008. Land decline in Land-Rich Africa: A
creeping disaster in the making. CGIAR Science Council Secretariat. Rome, Italy.
Weiss, J.L, Gutzler, D.S., Coonrod, J.E.A. and Dahm, C.N., 2004. Long-term vegetation
monitoring with NDVI in a diverse semi-arid setting, New Mexico, USA. Journal of
Arid Environments 58: 249-272.
29
GLP Report No. 6 - Global dryland greenness - Trends, drivers and policy implications
WRI, 2002. World Resources Institute. Drylands, People, and Ecosystem Goods and
Services: A Web-based Geospatial Analysis. http://www.wri.org
Xiao, J. and Moody, A., 2005. Geographical distribution of global greening trends and
their climatic correlates: 1982–1998. International Journal of Remote Sensing 26(11):
2371-2390.
30