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Remote Sensing of Environment 87 (2003) 371 – 375
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Short communication
A spectral reflectance-based approach to quantification of grassland
cover from Landsat TM imagery
Yong Zha a, Jay Gao b,*, Shaoxiang Ni a, Yansui Liu c, Jianjun Jiang a, Yuchun Wei a
b
a
College of Geographic Science, Nanjing Normal University, Nanjing 210097, China
School of Geography and Environmental Science, University of Auckland, Private Bag 92019, Auckland, New Zealand
c
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Received 18 February 2003; received in revised form 1 May 2003; accepted 31 May 2003
Abstract
In this paper, a reflectance-based method is proposed to accurately quantify percent grass cover from TM data for a semiarid grassland in
western China. In situ measured percent grass cover was sampled over 1 m2 plots at 68 sites. Their ground coordinates were logged with a
global positioning system (GPS) receiver and their spectral reflectance measured with a spectrometer. Normalized difference vegetation index
(NDVI) was derived from both in situ measured spectral reflectance and radiometrically calibrated Landsat Thematic Mapper (TM) bands 3
and 4. It was found that the NDVI derived from in situ measured spectral reflectance was closely correlated with percent grass cover
(R2 = 0.74), but not with its counterpart derived from the satellite image. After standardization of the latter with the former, the TM-derived
NDVI bore a close regression relationship with the in situ measured samples (R2 = 0.74). This relationship enabled the successful
quantification of grass cover from the satellite image at an overall accuracy of 89%. This reflectance-based method can be used to reliably
quantify grass cover from TM imagery.
D 2003 Elsevier Inc. All rights reserved.
Keywords: Grassland cover; Quantitative remote sensing; Spectral reflectance; TM imagery
1. Introduction
Quantification of percent grass cover by means of remote
sensing is usually accomplished through an empirical relationship between grass cover and the value of its
corresponding pixels on a satellite image (Friedl, Michaelsen, Davis, Walker, & Schimel, 1994). Prior to the quantification, the satellite image is usually transformed into
various indices, one of the most popular being the normalized difference vegetation index (NDVI) (Dymond, Stephens, Newsome, & Wilde, 1992; Paruelo & Golluscio,
1994), a very useful parameter for distinguishing vegetation
when it is green. This relationship may be established
through regression analysis of the index against in situ
sampled grass cover within a plot. Ideally, ground sampling
and recording of the satellite image should occur simultaneously in order to avoid any variations in illumination
caused by changing atmospheric conditions. Accurate quan-
* Corresponding author. Tel.: +64-9-373-7599; fax: +64-9-373-7434.
E-mail address: [email protected] (J. Gao).
0034-4257/$ - see front matter D 2003 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2003.05.001
tification also requires that the use of satellite imagery is
preceded by radiometric calibration of the image to account
for atmospheric radiance.
The quantification of percent grass cover by means of
remote sensing in a spatially patchy environment is challenging if in situ sampling size is considerably smaller than
the spatial resolution of the satellite image. Logistic difficulty in the field means that grass samples can be collected
from within a limited spatial extent that is much smaller than
the pixel size of most earth resources satellite images (e.g.,
Landsat TM’s 30 m by 30 m). In this case, ground sampling
over such a small area is seldom representative of the cover
within 1 pixel size on the ground. It has been speculated that
such differential sampling sizes made up the largest source
of error in the estimates of assigning point-based ground
data to area-integrated measurements from satellite (Friedl
et al., 1994). Unsurprisingly, no statistically significant
relationship could be established between vegetation indices
and estimates of dried green biomass using this sample point
approach (Anderson, Hanson, & Hass, 1993). The difficulty
in the estimation using a sample point method stems
partially from sample point location error.
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J. Gao et al. / Remote Sensing of Environment 87 (2003) 371–375
The objective of this study is to develop a new approach
by which the aforementioned limitation in estimating grass
cover from Landsat Thematic Mapper (TM) imagery is
overcome. This spectral based approach was tested in the
grassland in western China with satisfactory results.
2. Study area
The study area is located in northeastern Qinghai Province, western China (37jN and 99.5jE). Its annual precipitation mounts to 381.4 mm, much less than the annual
evaporation of 857.7 mm. Elevation in the study area ranges
from 3200 to 3800 m above sea level. Grass cover inside the
study area exhibits a distinct transition at 3300 m, above
which it is meadow or bushy meadow (Chen, Peng, Zhou, &
Zhao, 1994). At such a high elevation, temperature tends to
be cooler with less evaporation. Owing to the moist air, grass
generally exceeds 90% in its coverage. Below this elevation
is semiarid steppe where grass cover varies considerably from
below 20% to over 90%.
3. Research method
3.1. In situ sampling
Fieldwork was carried out on 21 and 22 July 2000. Spectral reflectance of grass was measured with a Japan-made
PM-12A spectrometer over the 0.4 – 1.05 Am wavelength
range within which the standard error of measured reflectance stood at 0.35– 1.32% after many repetitive tests. In
total, measurements were made at 68 randomly selected
sites, of which 13 were deemed unuseable because their
values were out of range (caused by reading errors) or two
sites falling within the same pixel on the satellite image. At
each site the percentage of grass cover within 1 m2 sampling
plot was visually estimated by two to three experienced
rangers whose estimates were within 5% of one another. The
location of the sampling site was determined with a Garmin
International 12XLC portable global positioning system
Fig. 1. Scatter plot between in situ measured grass cover and its
corresponding pixel value on the TM-derived NDVIi image.
Fig. 2. Relationship between in situ measured grass cover and its
corresponding value on the reflectance-derived NDVIr image.
(GPS) receiver. It has 12 channels with a horizontal accuracy of 10 m. Coordinates at six other sites known as ground
control points (GCPs) were also logged with the GPS
receiver.
3.2. TM data and their processing
A half-scene Landsat TM image recorded on 17 July
2000 was collected, from which a subscene of 2200 by 2200
pixels was identified. It was radiometrically corrected using
the Pons and Solé-Sugrañes’ (1994) method, and geometrically rectified to the Gausse – Krüger coordinate system
using the six GCPs at a residual of 0.1454 pixels. Afterwards, TM3 and TM4 were used to derive an NDVI image.
3.3. Data analysis
The spectral reflectance values at around 0.85 Am (R0.85)
and 0.65 –0.675 Am were analyzed further because they
correspond approximately to the wavelength (range) of
TM4 and TM3, respectively. After the reflectance at 0.65
and 0.675 Am was averaged, the mean (R0.6625) was used as
the spectral value for TM3. The two reflectance values in
TM3 and TM4 were used to derive pseudo-NDVI called
Fig. 3. Scatter plot of NDVIr calculated from in situ measured spectral
reflectance against raw pixel values on NDVIi image derived from TM3
and TM4.
J. Gao et al. / Remote Sensing of Environment 87 (2003) 371–375
373
4. Results
Fig. 4. Regression relationship between grass cover estimated on the ground
and TM-derived NDVIi that has been standardized with in situ measured
reflectance.
reflectance-based NDVI or NDVIr (Eq. (1)), against which
in situ collected percent grass cover was linearly regressed.
Percent grass cover sampled on the ground does not
appear to bear any statistically significant relationship with
the value of its corresponding pixel on the NDVIi image
derived from TM3 and TM4 (Fig. 1). There are three
explanations for this absence. The first and the most
important reason is that the in situ grass cover was measured
within a sampling plot of 1 m2 while the NDVIi pixel value
is based on a ground area of 30 by 30 m 2. Spatial
heterogeneity of grass cover makes it very unlikely that
the spot at which the ground cover was sampled is representative of the situation over the entire pixel area. The other
two minor factors are the effects of soil reflectance on the
Landsat TM image, and errors in measuring cover on the
ground. Therefore, it is impossible to quantify percent grass
cover using this sample point method directly.
Grass cover ¼ 108:18 NDVIr þ 6:3616 ðR2 ¼ 0:74Þ
R0:85 R0:6625
R0:85 þ R0:6625
ð1Þ
ð2Þ
Additionally, the radiometrically calibrated and geometrically corrected TM3 and TM4 images were used to derive
a ‘‘true’’ NDVI (NDVIi) image. Pixels corresponding to the
position of in situ sampling plots were located on this newly
derived image under the guidance of their GPS coordinates.
The values of these pixels were regressed against the in situ
sampled grass cover data to examine whether percent grass
cover can be quantified using this spectral reflectance
method.
Nevertheless, grass cover at a given sampling plot is
closely associated with its reflectance-derived NDVIr value
(Fig. 2). Their regression relationship (Eq. (2)) has an R2
value of 0.74. This relationship suggests that grass cover can
be quantified adequately so long as the TM-derived NDVIi
value can be associated with its spectral reflectance-derived
counterpart. However, there does not appear to be any
correlation between these two sets of NDVI values (Fig.
3). Upon a closer scrutiny, it is found that they are not
recorded to the same numeration scale. For instance, the
NDVIr ¼
Fig. 5. Distribution of quantified percent grass cover at 10 levels. The ground area covered is 9 km by 9 km. Top faces north.
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J. Gao et al. / Remote Sensing of Environment 87 (2003) 371–375
NDVIi values derived from the satellite image have a range
from 0.4067 to 0.5805 (Fig. 1), much narrower than that
from in situ measured reflectance which varies from 0.2898
to 0.6283 (Fig. 2). This difference in value range is probably
a consequence of the atmospheric impact. Therefore, it was
decided to standardize the TM-derived NDVIi with the
NDVIr obtained from in situ measured reflectance at every
sampling plot j. This standardization was accomplished
using Eq. (3).
correct level, resulting in an overall accuracy of 89% (Table
1). The accuracy for an individual percentage level varies
between 75% and 100%. There is no definite relationship
between the accuracy level and the percent cover. In
general, those covers between 40% and 80% tend to be less
accurately quantified than other covers.
NDVIrj NDVIrmin
NDVIr max NDVIr min
ðNDVIimax NDVIimin Þ þ NDVIimin
Because of differential sampling sizes on the ground and
from space, percent grass cover cannot be directly quantified from TM imagery based on concurrently collected
samples over 1 m2 plots. However, a statistically significant
relationship (R2 = 0.74) exists between in situ measured
grass cover and NDVI derived from in situ measured
spectral reflectance. After the NDVI results derived from
the TM image were calibrated with the in situ measured
spectral reflectance, their statistically significant relationship
with ground sampled percent grass cover was established
through regression analysis. The application of this empirical relationship transformed the TM-derived NDVI image
into a map of percent grass cover which was subsequently
visualized at 10 percentage levels. Assessed against 100
randomly selected check points, this map had an accuracy of
89%. It is concluded that assisted by in situ measured
spectral reflectance, TM imagery, in conjunction with concurrent sampling of grass cover on the ground, can be used
to reliably quantify percent grass cover in an environment
where grass cover is spatially heterogeneous. It should be
applicable to any grassland where grass cover is spatially
heterogeneous.
NDVIij ¼
5. Conclusions
ð3Þ
Where NDVIimax and NDVIimin stand for the maximum
and minimum NDVIi derived from the TM image, respectively; NDVIrmax and NDVIrmin are the maximum and
minimum NDVIr derived from the in situ measured spectral
reflectance, respectively. After the maximum and minimum
NDVIr and NDVIi values were plugged into Eq. (3), it was
simplified as:
NDVIij ¼ 0:513733 NDVIr j þ 0:257757
ð4Þ
This standardization is essentially a process of scaling up
radiometric values of the TM image pixels. After standardization the relationship between grass cover estimated on the
ground and NDVIi derived from the TM bands became
much closer at an R2 value of 0.74 (Fig. 4). It represents a
drastic improvement over the relationship shown in Fig. 1.
Such a close relationship demonstrates the possibility of
quantifying grass cover from TM imagery. This relationship
was then used to transform the TM-derived NDVIi image
into a map of grass cover. This map was later visualized at
10 percentage levels at an interval of 10% (Fig. 5). The
distribution of the mapped cover closely resembles the
pattern shown on the original image.
The produced map was quantitatively evaluated for its
accuracy. In total, 100 points were selected randomly for
this purpose. A comparison of the visually estimated percent
grass cover with the mapped results indicated that of these
100 random check points, 89 had been quantified at the
Acknowledgements
We are grateful for the valuable comments made by two
anonymous reviewers on the former version of this manuscript. This research was supported by a grant from the
National Natural Science Foundation of China (Grant No.
49971056). It also received funding from the Knowledge
Table 1
Confusion matrix for the grass cover map assessed with 100 check points (row: mapped results; column: in situ measured results)
Cover
V10
11 – 20
V10
11 – 20
21 – 30
31 – 40
41 – 50
51 – 60
61 – 70
71 – 80
81 – 90
91 – 100
Sum
5
1
2
21 – 30
8
2
5
3
10
31 – 40
18
1
19
41 – 50
20
1
21
51 – 60
2
15
17
61 – 70
1
6
1
8
71 – 80
81 – 90
91 – 100
Accuracy (%)
9
9
83.3
100
100
90
87
88.2
85.7
75
100
90
89
1
3
4
3
1
4
J. Gao et al. / Remote Sensing of Environment 87 (2003) 371–375
Innovation Project of IGSNRR at the Chinese Academy of
Science (Grant No. CXIOG-E01-05-03), and from the Key
Science and Technology Project of the Ministry of Land
and Resources, People’s Republic of China (Grant No.
20010102).
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