INT. J. REMOTE SENSING,
VOL.
25,
NO.
20 OCTOBER, 2004,
20, 4177–4189
Assessment of grassland degradation near Lake Qinghai, West
China, using Landsat TM and in situ reflectance spectra data
Y. LIU
Institute of Geographical Sciences and Natural Resources Research, Chinese
Academy of Science, Beijing 100101, China; e-mail: [email protected]
Y. ZHA
College of Geographic Science, Nanjing Normal University, Nanjing 210097,
China; e-mail: [email protected]
J. GAO*
School of Geography and Environmental Science, University of Auckland,
Private Bag 92019, Auckland, New Zealand; e-mail: [email protected]
and S. NI
College of Geographic Science, Nanjing Normal University, Nanjing 210097,
China
(Received 25 February 2003; in final form 22 December 2003 )
Abstract. The severity of grassland degradation near Lake Qinghai, West
China was assessed from a Landsat Thematic Mapper (TM) image in conjunction
with in situ samples of per cent grass cover and proportion (by weight) of
unpalatable grasses (PUG) collected over 1 m2 sampling plots. Spectral reflectance
at each sampling plot was measured with a spectrometer and its location determined
with a Global Positioning System (GPS) receiver. After radiometric calibration, the
TM image was geometrically rectified. Ten vegetation indices were derived from TM
bands 3 and 4, and from the spectral reflectance data at wavelengths corresponding
most closely to those of TM3 and TM4. Regression analyses showed that NDVI and
SAVI are the most reliable indicators of grass cover and PUG, respectively.
Significant relationships between TM bands-derived indices and in situ sampled
grass parameters were established only after the former had been calibrated with
in situ reflectance spectra data. Through the established regression models the TM
image was converted into maps of grass cover parameters. These maps were merged
to form a degradation map at an accuracy of 91.7%. It was concluded that TM
imagery, in conjunction with in situ grass samples and reflectance spectra data,
enabled the efficient and accurate assessment of grassland degradation inside the
study area.
1.
Introduction
Grassland in China is distributed mostly in Inner Mongolia, the Loess Plateau,
and the Qinghai-Tibetan Plateau, totalling 313.33 Mha. These areas have a
*Corresponding author.
International Journal of Remote Sensing
ISSN 0143-1161 print/ISSN 1366-5901 online # 2004 Taylor & Francis Ltd
http://www.tandf.co.uk/journals
DOI: 10.1080/01431160410001680419
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Y. Liu et al.
semi-arid to arid climate that is highly vulnerable to degradation, desertification,
and salinization. Such destructive processes have affected about one third of the
grassland. Moreover, degraded grassland is expanding at an annual rate of
667 000 ha (Bao et al. 1998).
Grassland degradation refers to the overall reduction in grassland productivity
as a consequence of human activities and natural processes. It may manifest as a
reduction in the extent of grass cover, density of grass cover, and output of forage,
or as an increase in unpalatable grass species, and even as denudation of underlying
soil. Grassland degradation is rather severe in Qinghai Province with moderately
and severely degraded land (e.g. density of grass cover v31% and proportion of
unpalatable grass (PUG) species w51%) totalling 7.266 million ha. This accounts
for nearly 20% of the entire grassland. Grassland degradation has caused land
carrying capacity to decrease from supporting 1.49 goats or their equivalent grass
consumption per hectare in the 1950s and 1960s to 0.75 goats per hectare in 1998
(Bao et al. 1998). Grassland degradation is also responsible for causing a loss of
12 million tons of hay (Wang and Li 1999).
Grassland degradation in this region is caused primarily by human factors, and
secondly by environmental factors. In this predominantly grazing region, excessive
reliance on animal husbandry under a growing population has exerted great
pressure on the land (Wei et al. 1997). The degradation problem has also been
exacerbated by attacks of locusts and rats, and by droughts (Shi 2000). In order to
bring grassland degradation under control and make grazing sustainable, it is very
important to study its severity and ascertain its spatial distribution.
Grassland degradation is conventionally studied through field investigation
during which its contributing factors are identified. Each of them is then graded,
and the overall severity of degradation is derived by combining the effect of all
contributors (Li 1997). This method of assessment is inefficient and costly because
grassland usually covers a large spatial extent in remote and inaccessible areas
(Asrar et al. 1986). In addition, the results obtained from the traditional method of
assessment are unreliable due to the contributing factors being difficult to be
mapped at a high level of accuracy. By comparison, remote sensing is much more
efficient in assessing grassland degradation (Liu et al. 2001).
Remotely sensing grassland degradation is achievable either indirectly or
directly. In the indirect method a productivity model is constructed from climate
and vegetation variables, some of which can be derived from satellite images. These
variables include trends in average vegetation cover with distance from water at the
end of every wet period, and trends in cover variance with distance from water
(Pickup et al. 1994). The role of remote sensing in the assessment is to supply
information on vegetative cover. However, grassland degradation cannot be
assessed based solely on remotely sensed data. Thus, this indirect method falls
outside the scope of this paper. Instead, this study focuses on the assessment from
grass parameters such as per cent cover, biomass, and proportion of unpalatable
grass species, all of which can be estimated directly from satellite data, usually
through the use of a Vegetation Index (VI) (Williamson and Eldridge 1993, Hobbs
1995, Purevdorj et al. 1998). VI is an arithmetic disparity between pixel values in
two or more spectral bands of the same imagery, aiming to maximize the vegetative
signal while minimizing other effects (e.g. soil and other background covers) in the
resultant image. Therefore, VIs are able to reveal potential and current status of
grassland degradation. In particular, a new index called Moving Standard
Deviation Index (MSDI) has been proposed to study degradation (Tanser and
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Palmer 1999). This landscape diversity index is a powerful adjunct to Normalized
Difference Vegetation Index (NDVI) in monitoring degradation patterns in a semiarid heterogeneous landscape.
Several researchers have assessed severity of grassland degradation using
satellite imagery (Shi et al. 1999, Liu et al. 2001). Shi et al. (1999) produced a map
of alpine grassland degradation through visual interpretation of Landsat Thematic
Mapper (TM) and SPOT images. Liu et al. (2001) mapped the severity of grassland
degradation using both unsupervised and supervised classification of TM data.
Wang et al. (2002) first classified NDVI images derived from Landsat Multispectral
Scanner (MSS) and TM images using an unsupervised classification method, and
then evaluated the severity of grassland degradation based on the classified results.
Eve et al. (1999) took advantage of this computer-based automatic method to
classify Advanced Very High Resolution Radiometer (AVHRR) data into several
classes, one of which corresponded to irreversibly degraded grassland. These studies
demonstrate that both visual interpretation and supervised classification have been
applied to map grassland degradation. However, both methods are relatively slow.
Although the unsupervised method is much faster, its results are not so useful.
The aim of this study is to devise an efficient and simple method for quickly
assessing grassland degradation by means of remote sensing. The specific objectives
are: (a) to identify grassland degradation indicators that are critical to animal
husbandry and that can be studied by means of remote sensing; (2) to determine the
vegetation indices that are most effective in assessing grassland degradation; and (3)
to develop an automatic method for efficiently and timely mapping severity of
grassland degradation in West China.
2.
Study area
At approximately 37‡N and 99.5‡E, the study area is located in the northeast of
Qinghai Province, western China (figure 1). It has a semi-arid climate with an
annual temperature ranging from 21.5‡C to 3.4‡C. Terrain in this alpine province
has an elevation between 3200 and 3800 m above sea level (asl). The amount of
annual precipitation totals about 381.4 mm, most of which falls during the summer
months between June and September. The frigid climate associated with the high
altitude makes the growing season rather short. Consequently, grassland productivity is rather low at 144.9 g m22 during wet years, but only at 60.7 g m22
during dry years (Wei et al. 1997). Grass cover exhibits a clear distinction at around
3300 m asl, above which grassland is meadow or bushy meadow. Owing to the
moist air, grass cover generally exceeds 90% without obvious traces of degradation.
Below this elevation grassland is semi-arid steppe where grass cover varies
considerably from below 20 to over 90%.
This area was selected for study because it is one of the most important bases of
animal husbandry in Qinghai Province and because grassland degradation has
become a serious concern here. Overgrazing by yaks and sheep, together with
locust- and rat-induced destruction, has resulted in grassland degradation (Chen
et al. 1994). Grazing-induced degradation is especially severe during early winter
and late spring (Ni et al. 1999). Monitoring of grassland degradation over such a vast
region requires efficient mapping of grass cover parameters from satellite imagery.
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Y. Liu et al.
Figure 1.
Location of the study area and its vicinity.
3. Methodology
3.1. Remotely sensed data
A half-scene Landsat TM image recorded on 17 July 2000 was acquired, from
which a subscene of 2200 by 2200 pixels was delimited. This subscene image was
radiometrically processed using the Pons and Solé-Sugrañes’ (1994) model, and
geometrically rectified to the Gausse-Krüger coordinate system using six ground
control points at a residual of 0.1454 pixels. All image processing was undertaken in
ENVI1 (version 3.2).
3.2. In situ sampling
Fieldwork was carried out on 21 and 22 July 2000 within five days of satellite
overpass. In the field a total of 60 sampling plots of 1 m2 in size were randomly
selected. Per cent grass cover within each plot was visually estimated by two to
three experienced local rangers to the nearest 5%. Of these 60 sites, 55 estimates
were useable. The remaining five sites either fell into the same pixel or were blocked
by cloud on the satellite image. Grass within each plot was then clipped and
immediately weighed. The process was repeated twice, first for unpalatable grass
species and then for all remaining ones. Grass samples were collected at 22 of the 60
sampling plots. Their precise location was determined with a Garmin International
12XLC portable global positioning system (GPS) receiver. It has 12 channels with a
horizontal accuracy of 10 m. At each site two consecutive readings were logged with
their average as its final coordinates.
In addition, the spectral reflectance of grass cover at the sampling plots was
Grassland degradation in China
4181
measured at 17 wavelengths over the range of 400–1050 nm with a Japan-made PM12A spectrometer. The spectrometer was mounted on a tripod about 1 m above the
ground. The intensity of the returned electromagnetic radiation from the target
within its 10‡ field of view was converted into reflectance. Reflectance was measured
twice at every sampling site, first for the grass cover and shortly afterwards for the
reference board.
3.3. Data analysis
Of the 17 measured spectral reflectance data, only three at 650, 675, and 850 nm
were further analysed. The reflectance at 650 and 675 nm was averaged first and the
averaged reflectance corresponds most closely to the approximate wavelength range
of TM3 while the reflectance at 850 nm corresponds to that of TM4. These
reflectance data were considered the equivalent spectral response of TM3 and TM4,
from which more than 10 vegetation indices were derived. The in situ measured per
cent grass cover and PUG were considered as the dependent variables. They were
linearly regressed against these indices (independent variables) separately. Of the 10
established univariate regression models, the one with the largest R2 value was
regarded as the most accurate, and the VI in the regression model was considered as
the most effective estimator of the dependent grass variables, namely, per cent grass
cover and PUG.
The geometrically rectified and radiometrically calibrated TM bands 3 and 4
were used to derive VIs that had been found most effective in estimating grass cover
parameters. On these resultant index images, pixels at positions corresponding to
the ground sampling plots were determined. Regression analyses were undertaken
to construct an empirical relationship between these raw index values and the grass
cover parameters measured on the ground. The same regression analysis was
repeated using calibrated index values, also. Calibration of the raw index derived
from the TM bands for sampling plot j (denoted as Ij) to its reflectance counterpart
Rj was achieved using equation (1) (Zha et al. 2003):
Rj {Rmin
Ij ~
|ðImax {Imin ÞzImin
ð1Þ
Rmax {Rmin
where Imax and Imin stand for the maximum and minimum index values derived
from TM bands 3 and 4, respectively; Rmax and Rmin are the maximum and
minimum index values derived from the in situ measured reflectance data,
respectively. The established regression models were used to transform the TM
image data to maps of degradation distribution. After being assigned an equal
weight, results from the two grass parameters were merged to form an overall
degradation map that was visualized at four severity levels to show the spatial
pattern of degradation risk.
4. Results
4.1. Effectiveness of VIs
In order to find out which VI is the most suitable for studying grassland
degradation in this semi-arid area, a number of VIs were derived and used in the
univariate linear regression analyses. These indices included Difference VI (DVI),
ratio VI (RVI), NDVI, Soil Adjusted VI (SAVI), Modified SAVI (MSAVI),
Infrared Percentage VI (IPVI), and Modified Simple Ratio (MSR) (refer to
Davidson and Csillag 2001 for their calculation formulas). In the analyses, two
indicators of grassland degradation (per cent grass cover and PUG) were used as
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the independent variables (table 1). The R2 value of these equations ranges from
0.33 to 0.74 for per cent grass cover. In general, the prediction from a single band is
less accurate than from multiple bands. This outcome demonstrates that the use of
more spectral bands will produce more reliable results for grassland. All R2 values
are extremely similar to one another if the same number of spectral bands is used in
deriving the index. Three indices (NDVI, IPVI and MSR) achieved the same largest
R2 value of 0.74. We decided to use NDVI because it is relatively easy to derive,
most commonly used, and hence most understood.
The univariate regression models in which the dependent variable is PUG have
an R2 value between 0.41 and 0.73. Four indices (DVI, SAVI, RDVI and MSAVI)
achieved the same maximum R2 value, all of which are quite different from those
for per cent grass cover. Therefore, the same VI may not be the best estimator for
all grass parameters. Among these four indices, SAVI was selected because it is
most commonly used.
4.2. Degradation from grass cover
The in situ measured per cent grass cover varies widely from 35 to 75% with a
mean of 58.1%. These values have a standard deviation of only 10.2. It seems that
those covers outside this range are under represented in the 55 samples. This could
have been caused by the limited spatial extent of sampling plots in the field.
Grass cover percentages do not bear any definite relationship with the value of
their corresponding pixels on the NDVI image derived from TM3 and TM4 as
revealed by a scatter plot. However, grass cover at a given sampling plot is closely
associated with its spectral reflectance-derived NDVI value. The R2 value (0.74) of
Table 1. Accuracy of predicting per cent grass cover and proportion (by weight) of
unpalatable grasses (PUG) from various vegetation indices judged by the R2 value of
the regression equation.
Regression model
Grass parameter –
dependent variable
Per cent cover
PUG
Vegetation Index –
independent variable
Coefficient
Interception
R2
RED
IR
DVI
RVI
NDVI
SAVIL~0.5
RDVI
MSAVI
IPVI
MSR
RED
IR
DVI
RVI
NDVI
SAVIL~0.5
RDVI
MSAVI
IPVI
MSR
2291.29
106.9
122.38
13.85
108.18
115.29
118.95
107.63
216.36
37.13
370.33
2252.26
2210.12
217.29
2137.26
2165.39
2174.14
2155.66
2274.52
246.59
104.41
9.63
22.10
17.56
6.36
12.57
13.53
16.52
2101.82
22.56
227.08
141.78
90.77
81.11
96.29
95.24
95.06
90.01
233.55
75.16
0.52
0.33
0.60
0.72
0.74
0.70
0.69
0.69
0.74
0.74
0.41
0.60
0.73
0.62
0.68
0.73
0.73
0.72
0.68
0.65
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the regression relationship suggests that grass cover can be mapped adequately
from TM image data if the TM-derived NDVI values can be associated with their
reflectance-derived counterparts. Nevertheless, these two sets of NDVI values do
not appear to be correlated in any way. Upon further examination it was found
that those NDVI values derived from the in situ measured reflectance have a broad
range from 0.2 to 0.7 while those from the TM image have a narrow range of
0.4–0.6. Therefore, the TM-derived NDVI data were calibrated to the NDVI
obtained from in situ measured reflectance. After calibration the relationship
between grass cover visually estimated on the ground and TM image-derived NDVI
values became much closer at an R2 value of 0.74 (figure 2). Such a close
relationship (equation (2)) demonstrates that grass cover could be mapped from the
TM data accurately. The NDVI image was later converted to a map of per cent
grass cover based on equation (2).
ð2Þ
Grass Cover ð%Þ~210:57 NDVI{47:915 R2 ~0:74
4.3. Degradation from PUG
The 22 PUG observations have a range from 13.0 to 60.0 with a mean of 29.9
and a standard deviation of 13.2. Again, there does not seem to be a huge variation
among them. This could be explained by the fact that most of them were sampled
within a limited spatial extent.
The establishment of a regression model for PUG followed the same procedure
for per cent grass cover except that NDVI was replaced by SAVI. The raw SAVI
data derived from the TM bands, and those SAVI indices derived from in situ
measured reflectance spectra data were plotted separately against PUG. A
significant relationship was found to exist for the reflectance-derived SAVI only,
but not for the TM image-derived SAVI. The SAVI data were calibrated to the
former. After calibration, PUG had a linear and statistically significant correlation
with the image-derived SAVI (figure 3).
ð3Þ
Unpalatable Grass ð%Þ~{318:11 SAVIz152:13 R2 ~0:73
The high R2 value of 0.73 (equation 3) suggests that PUG can be estimated from the
Figure 2. Regression relationship between grass cover (%) estimated on the ground at
sampling plots and its TM-derived NDVI that has been calibrated with 55 in situ
measured reflectance values. GC~210.576NDVI247.915 (R2~0.74).
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Y. Liu et al.
Figure 3. Regression relationship between proportion of unpalatable grass (%) and its TMderived SAVI that has been calibrated with 22 in situ measured reflectance samples.
PUG~2318.116SAVIz152.13 (R2~0.73).
TM image data at a reasonably high accuracy level. This equation was subsequently
used to transform the TM image into another degradation map.
4.4. Assessment of overall degradation
The two degradation maps independently produced from the two grass cover
parameters were combined to form an overall map so that degradation could be
assessed more efficiently. This was achieved through linearly adding the two
variables up with a weight of 0.5 assigned to each (equation 4).
Risk of degradation~0:5 ð1{grass coverÞz0:5 PUG
ð4Þ
This map contained continuously varying pixel values that had to be categorized
into a few groups in order to effectively visualize the spatial distribution of
degradation risk. In the literature several criteria have been proposed to define a
specific level of degradation severity. In terms of per cent grass cover, less than 10%
is considered severe, less than 30% moderate, and less than 50% slight (Shi et al.
1999). Similarly, Shi and Wang (1994) defined a reduction of 10% in per cent grass
cover as slight, 10–35% as moderate, and over 35% as severe. For PUG, the
thresholds are more than 70% as severe, 50–70% as moderate, 30–50% as slight. In
2002 the Chinese Ministry of Agriculture proposed the most authoritative criteria
for the entire country of China. They are w50% as severe for both reduction in
grass cover and PUG, 21–50% as moderate, 6–20% as light for grass cover change
and 11–20 for PUG. Apparently, there are no universally accepted criteria. Based
on these commonly used ones, we proposed and adopted a set of new criteria for
the study area (table 2). Coincidentally, the threshold values were identical for both
per cent grass cover and PUG.
The overall risk map (figure 4) shows four levels of degradation severity: severe,
intermediate, slight, and intact. Roughly three quarters of the study area has been
affected by degradation to various levels of severity (table 2). Severely degraded
areas make up the smallest class at 13.37%. Patches of denudated and compacted
soil have emerged in these areas (figure 5(a)). Their overall productivity has dropped
by 60% (Anonymous 1999). Intermediately degraded areas compose the largest
class of the four at 35.32% (table 2). They have shown clear signs of change in grass
species composition and appearance (figure 5(b)), and their overall productivity has
Grassland degradation in China
Table 2.
Indicators of grassland degradation, criteria for degradation severity, and statistics
of degraded grasslands.
Severity level
Severe
Moderate
Slight
Intact
4185
Per cent cover
Unpalatable grasses (%)
Area (km2)
%
f30
31–50
51–70
w70
w70
51–70
31–50
f30
10.39
30.72
21.49
18.40
12.83
37.92
26.53
22.72
dropped by 30–60%. The exposed soil is compact with a dry surface. Slightly
degraded areas (28.61%) make up the second most common severity class at an area
of 23.17 km2. They have not shown profound changes in grass species composition
and appearance (Figure 5(c)). The reduction in its overall productivity is less than
30% (Anonymous 1999). The areas that have not been affected by degradation yet
(figure 5(d )) make up nearly a quarter of the study area. Consequently, they have
not shown any void space between grasses.
5. Discussion
5.1. Criteria for selecting degradation indicators
It is not easy to determine what criteria should be used for realistically assessing
grassland degradation. Ludwig (1986) argued that estimates of net primary
Figure 4. A map showing the distribution pattern of severity of grassland degradation. It
has been median filtered with a widow size of 3 by 3 pixels. Ground area covered is
9 km by 9 km.
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Y. Liu et al.
(a)
(b)
(c)
(d )
Figure 5. Typical grassland areas that have been affected by degradation at various severity
levels. (a) An example of severe degradation. Grass is sparsely distributed with a
large proportion of unpalatable grasses. The denudated soil is compacted with a low
fertility. (b) An example of moderate degradation. Bare and compacted soil is
widespread with unpalatable grasses clearly visible. (c) An example of slight
degradation. Patches of bare soil are clearly visible. (d ) An example of no
degradation. Grass cover is mostly continuous.
Grassland degradation in China
4187
production based on VI cannot be used reliably to indicate degradation status
because degradation does not necessarily result in slowed plant growth. Instead,
diversity should be used as the indicator. Unlike this view, we assessed grassland
degradation from the productivity perspective because it is more relevant to animal
husbandry. From this perspective, the identified criteria include above-ground
biomass, ground cover, soil, structure of ecosystem, and recovery (Li 1997). Shi and
Wang (1994) used only grass cover, productivity, and proportion of palatable
grasses for the same purpose. In this study only two of these critical indicators
(grass cover and PUG) were used, both of which can be estimated from remotely
sensed data. Although the number of assessment criteria used is not comprehensive
in this paper, the obtained results have practical values.
5.2. Importance of data calibration
In this study we did not find a strong relationship between in situ sampled grass
parameters and the value of their corresponding pixels on the TM bands-derived VI
image. The absence of such a correlation is attributed to the atmospheric effect,
even though the satellite image has been radiometrically rectified. It is speculated
that the atmospheric effect was tackled during radiometric rectification, but was not
eliminated completely. Residual atmospheric effect (e.g. atmospheric attenuation)
suppressed the range of satellite data. Their correct range was restored after the
satellite data-derived indices were calibrated with in situ measured reflectance
spectra data which were closely correlated with in situ measured grass parameters.
Data calibration based on equation (1) essentially stretches the range of satellite
data-derived VIs. Data stretching enables the establishment of a strong correlation
between in situ measured grass parameters and their corresponding pixel values on
the satellite image. Thus, the combination of in situ measured reflectance spectra
with a Landsat TM image appears to be a viable method in retrieving grassland
parameters from satellite data (Zha et al. 2003), and hence in assessing grassland
degradation.
5.3. Accuracy of assessment
The accuracy of the produced degradation map (figure 4) was verified during a
field visit in July 2002. In total, 36 evaluation points were selected for this purpose.
Field-observed severity of degradation at 33 of these points was consistent with its
counterpart shown in the map, leading to an accuracy of 91.7%. Admittedly, this
high accuracy level is related to the fact that degradation was mapped at only four
categories of severity. The accuracy would certainly be lower had the severity been
visualized at more categories.
The fact that a higher accuracy of assessment was not achieved and that the
highest R2 value achieved in all regression analyses was only 74% is caused by a
variety of factors, the most important being differential sampling sizes on the
ground and from space. Recovery of physical features such as per cent grass cover
and PUG from satellite data requires concurrent sampling. Logistic difficulty makes
it impossible to carry out in situ sampling over an extensive area. Besides, PUG
estimates become increasingly unreliable over a larger sampling plot. Therefore, it is
rather common for the in situ sampling size to be smaller than that from space. As a
matter of fact, the in situ sampling size of roughly 1 m2 adopted in this study is
much smaller than the sampling size of 30 m by 30 m on the TM image. Friedl et al.
(1994) speculated that such differential sampling intervals on the ground and from
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Y. Liu et al.
space made up the largest source of error in the Kauth-Thomas greenness-based
estimates of assigning point-based ground data to area-integrated measurements
from satellite. This is certainly applicable to this study, even though the exact
contribution by this factor is impossible to quantify. Nevertheless, the high
accuracy level of 91.7% achieved demonstrates that differential sampling sizes did
not invalidate the assessed distribution of grassland degradation.
6.
Conclusions
In this study two common degradation indicators (per cent grass cover and
PUG) were used to assess severity of grassland degradation near Lake Qinghai in
Qinghai Province, West China. The best VI for studying per cent grass cover in this
semi-arid environment is NDVI whose performance is very similar to IPVI and
MSR. The VI most suitable for estimating PUG from TM imagery is SAVI, even
though three other indices achieved equally accurate results. It is impossible to map
these two degradation indicators directly from the TM image data with the
assistance of in situ grass samples collected over sampling plots of 1 m2 in size.
However, statistically significant regression models were established between the
two variables at an R2 of 0.74 after the TM bands-derived index values were
calibrated with the in situ measured reflectance spectra data. The use of in situ
measured spectral reflectance data successfully reduced the errors in the assessment
caused by differential sampling sizes on the ground and from space. A simplified
degradation map of four levels of risk was generated at an accuracy of 91.7%. It
shows that overall, three quarters of the area under study has been affected by
degradation at various levels of severity. This VI based method of assessment, in
conjunction with in situ measured grass cover parameters and spectral reflectance
data, enables grassland degradation to be assessed efficiently and accurately.
Acknowledgments
This research was partly funded by grants from the National Natural Science
Foundation of China (grant no. 49 971 056 and 40 171 007). Additional financial
support was received from the Key Science and Technology Project at the Key
Land Use Laboratory, the Chinese Ministry of Land and Resources (grant
no. 20 010 102). We appreciate the valuable comments from two anonymous
reviewers on an earlier version of this manuscript.
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