Temporal Analysis of Agricultural Land Use in the Semi Arid
Trophics of India Using IRS Data
Satoshi Uchida
Environmental Resources Division
Japan International Research Center for Agricultural Sciences
(JIRCAS)
I-2, Ohwashi, Tsukuba, Ibaraki 305, Japan
Tel: (81)-298,38-6355 Fax : (81)-298-38-6651
E-mail : uchidas@jircas.affrc.go.jp
Abstract
Indian Remote Sensing Satellite (IRS) data was examined to
analyze temporal as well as spatial characteristics of agricultural land use in
the semi-arid tropics of India. Because of the high probability of acquirement
of cloud free data, one of the major cropping season, Rabi (post rainy), were
selected to monitor its agricultural activity. In the Rabi season normalized
vegetation index (NDVI) value of cropped area showed higher amount than that of
forest and also those of other categories. The pattern of temporal change of
NDVI of each land use category could be approximated as a linear decrease in the
latter period of Rabi season. This formation was applicable to correct the
difference of date of observation of satellite data to discriminate the cropped
area from other land use by NDVI value. The results of application of this
method showed that the cropped area in Rabi season had increased double in the
period of 1989 to 1996 and the higher rate of increase was indicated at the part
of higher land suitability for agricultural purpose estimated by IRS data.
Introduction
Agricultural land use system in the semi-arid tropics been
developed to meet the demands of food supply for increasing population. The
cropped area has been expanded over the land where the physical conditions of
cultivation might be suitable. However in this area the land is prone to degrade
its productivity and the distribution of cropped area may change temporally. In
order to investigate the temporal change of agricultural land use satellite
remote sensing data would be the most effective data source. Because the
accuracy of published statistic information about agricultural land use is often
skeptical and the land unit of statistics would not satisfy to examine the
behavior of local land use changes.
Satellite remote sensing can observe the same area repeatedly
with a certain intervals. From this advantageous feature a number of study have
been performed on monitoring land cover or land use using remote sensing data.
The study using low spatial resolution data such as NOAA AVHRR would be
effective to monitor the seasonal change of land cover by its high frequency of
observation in case of representing widely homogeneous land cover features. On
the other hand high spatial resolution data such as LANDSAT TM and SPOT HRV
would not be applicable to the study on detailed temporal change of agricultural
land use.
International Crops Research Institute for the Semi-Arid
Tropics (ICRISAT) in India and JIRCAS started a collaborative research program
on the evaluation of environmental changes using remote sensing techniques in
1994. This program includes the study of temporal and spatial characteristics of
change of agricultural land use in the semi-arid tropics of India using Indian
Remote Sensing Satellite (IRS) data, which is categorized into high spatial
resolution data . This paper shows a method to compare the state of agricultural
land use between different years based on inconsistent date of observation and a
tendency of land use change in correlation with land suitability estimated from
IRS data.
Objectives
There are three major objectives in this study. The first one
is to characterize the seasonal change of normalized vegetation index (NDVI)
calculated from IRS data for each land use category. The second is to estimate
the distribution of agricultural land use for the specific cropping season by
applying a model to represent changing tendency of NDVI. And the last one is to
examine the relationship between the rate of change of agricultural land use and
the estimated land suitability.
Study Area
This study area includes the campus of ICRISAT located at the
western side of Hyderabad city in the middle of Deccan plateau, where the amount
of mean annual rainfall is about 800 mm. This area is characterized by the
double cropping season in a year, which are Kharif (rainy) and Rabi (post
rainy). Soil types dominantly appeared in the study area are Vertisols (black
soil) and Alfisols (red soil) and both of them are representative soils of
semi-arid tropics (Kanwar (1982).
Methods
In this study the area used for agricultural purpose would be
estimated from NDVI values. For the case of Indian semi-arid tropics the
quantative criteria of NDVI is considered as a better of discriminator of
agricultural land use than usual supervised classification method. Because the
cropping system in terms of combination of species is too complex and also
sampling technique of training data in usual classification method differs
individually.
The satellite data analyzed in this study is IRS LISS-II
which consists of 4 spectral bands. The spatial resolution of IRS LISS-II is 36
meters and recurrent period is 22 days. Table 1 shows the list of acquired data,
which is the entire sample of obtainable cloud-free data.
Table 1 List of Acquired IRS LISS-II Data
|
Year (June-May) |
Kharif |
Rabi |
Hot & Dry |
|
1988-1989 |
none |
6 Feb |
none |
|
1989-1990 |
none |
24 Jan. |
none |
|
1990-1991 |
none |
2 Feb. |
None |
|
1991-1992 |
none |
none |
none |
|
1992-1993 |
21 Oct. |
26 Dec., 17 Jan |
24 Mar., 29 May |
|
1993-1994 |
none |
13 Dec., 4 Jan. |
24 Apr. |
|
1994-1995 |
none |
30 Nov., 4 Feb. |
11 Apr. |
|
1995-1996 |
none |
22 Jan. |
none |
In order to examine the temporal change of NDVI unchangeable
land use in Rabi season through the analyzed period is determined by overlaying
3 times of classification data. The date of these data are 24 January 1990, 17
January 1993 and 22 January 1996, when the physiological phase of crops is
represented between flowering and harvest.
Before calculation NDVI the minimum value in the histogram of
each band data is subtracted to correct radiometrically biased amount. The same
treatment is performed in the process of obtaining other indices calculated from
IRS multi-spectral data in this study. These indices are redness index (RI) and
brightness index (BI), of which the formula of calculation using band
i
(B
i) data is expressed as follows, respectively.
RI =
(B3-B1)(B3+B1) BI =
Φε(Bi)
2
RI and BI are effective to discriminate black soil and red
soil. It is also noted that these two indices could evaluate the land
suitability for agricultural purpose as shown later in figure 5 (Uchida(1997)
Results and Discussions
1) Temporal change of NDVI
Land cover is classified into 5 categories, which are cropped
area (C), forest or bush (F), degraded vegetation (D), bare land (B) and water
(W), from 3 times of IRS data. Table 2 shows the constitution matrix of land
cover changes between observed years.
Table 2 Constitution Rate (%) of Land Cover from 1990 to 1993
(left) and 1993 to 1996 (right)
|
1990/93 |
C |
F |
D |
B |
W |
|
C |
3.74 |
3.04 |
1.31 |
0.41 |
0.01 |
|
F |
8.07 |
13.31 |
3.62 |
0.75 |
0.01 |
|
D |
3.05 |
7.70 |
27.22 |
2.49 |
0.00 |
|
B |
1.56 |
3.61 |
10.60 |
6.34 |
0.01 |
|
W |
0.51 |
0.52 |
0.59 |
0.36 |
1.10 |
|
1993/96 |
C |
F |
D |
B |
W |
|
C |
8.16 |
5.12 |
2.26 |
0.95 |
0.46 |
|
F |
6.62 |
13.10 |
6.85 |
1.19 |
0.42 |
|
D |
2.15 |
5.31 |
27.34 |
8.11 |
0.45 |
|
B |
0.47 |
0.99 |
3.29 |
5.31 |
0.29 |
|
W |
0.01 |
0.01 |
0.00 |
0.08 |
1.03 |
Table 2 indicates that the almost half of the total area has
changed its land cover types in a interval of three years. Cropped area also has
changed its distribution widely and considerable acreage has been converted from
forest or bush area to cropped area. This result suggests that agricultural land
use has varied its distribution in these years and is necessary to monitor
yearly characteristics of land use to recognize the agricultural activity in
this area.
Here was assume that the land use type has been unchanged in
the period from 1990 to 1996 if the land cover type is identical through three
classified data. Figure 1 shows the relation between mean NDVI value of each
land use type and days after October 1st for every IRS data. This future
describes the following significant features. One is the value of NDVI lines
from high amount in order as cropped, forest or bush, degraded vegetation and
bare land for every samples. Another feature is a linear tendency of decrease of
NDVI against passed days for each land use type in a period from November to
February.
Figure 1 Temporal Change of Normalized Vegetation Index by Land use Type
2) Estimation of cropped area using NDVI
The relation shown in Figure 1 may lead a model to
discriminate land use type using NDVI data. Figure 2 describes lines of boundary
of land use types. These lines are obtained by a linear regression method for
the middle points weighed by standard deviation for the samples of which the
observation date is between November and February and year from 1990 to 1996.
The correlation coefficients of each regression lines are -0.86, -0.92 and -0.90
from top to bottom, respectively.
This concept of land use discrimination is based on the
presence of specific curve of observed vegetation index or leaf area index of
cropped area (Huda et al.(1984), Fischer (1994). Figure 3 illustrates the case
of this study an applicable period in the figure would coincide with late
December to early February.
Figure 2 Land Use Discrimination by NDVI
Figure 3 Representative Temporal Characteristics of
Vegetation Index by Land Use Type
Figure 4 Estimated Cropped Area Using NDVI and Rainfall in
October to December
Figure 4 shows the rate of estimated cropped area using the
formation described in Figure 2. In this figure it is also represented the
amount of rainfall in October to December, which may contribute the soil
moisture content around the sowing stage. This figure indicates that cropped
area tends to small in case of scarce rainfall around sowing stage. It is also
noted that the cropped area has increased almost double in the analyzed period.
Although there is no satisfactory statistics data to evaluate the reliability of
estimation, these results could express the general tendency of land use change
at the study area.
3) Relation between Land Use Change and Land Suitability
Uchida (1997) has examined the method to evaluate the land
suitability for agricultural purpose using indices of soil color derived from
IRS LISS-II data. Figure 5 illustrates the relation between land suitability and
calculated indiced. In order to show the characteristics of change of cropped
area by land suitability soil class unit is obtained by clustering of
multi-spectral data of the least vegetation imagery (29 May 1993). Figure 6
represents the trend of change of cropped area by each soil class, where solid
lines denotes the units or black soil and dashed lines on red soil.
The most suitable lass on black soil is numbered 8 and the
next is 5. It is evident that the more suitable area has more percentage of
cropped area. Another notable characteristics is the higher rate of increase at
the more suitable area in the period from 1990 to 1994.
Figure 5 Relation between Land Suitability and Induced
Derived from IRS Data
Figure 6 Temporal Change of Estimated Cropped Area by Soil
Class.
Conclusions
This study has examined the applicability of NDVI of high
spatial resolution satellite data for estimating cropped area in specific season
of a year. In case of the semi-arid tropics of India the latter half of Rabi
season would be available to employ a method of estimating cropped area using
standard tendency of change of NDVI. The results of estimation show that the
scarcity of rainfall around sowing stage would influence the extent of cropped
area and the high rate of areal increase would be appeared at the place of
higher suitability. Although it is required to evaluate the accuracy of
estimation in detail, the method adopted in this study has a potential to
accumulate the information on land use change which is indispensable in
environmental problems.
The author is deeply grateful to Dr. F. T. Bantilan Jr. and
Mr. Srinivas at GIS unit of ICRISAT for their cooperative performance. He should
express thanks to Drs. Y. Yamamoto and H. Sasaki of National Grassland Research
Institute for supporting projector activity.
References
- Fischer, A. (1994) : A Model for the Seasonal
Variation of Vegetation Indices in Coarse Resolution Data and Its Inversion
to Extract Crop Parameters, Remote Sens. Environ. 48, 220-230.
- Huda, A. K. S., et al. (1984): Problems and Prospects
in Modeling Pearl Millet Growth and Development, Agrometerology of Sorighum
and Millet in the Semi-Arid Tropics, ICRISAT, 297-306.
- Kanwar, J. S. (1982): Problems and Potentials of
Vertisols and Alfisols - The Two Important Soils of SAT-ICRISAT Experience, Tropical
Agriculture Research Series, 15, 119-138.
- Uchida, S. (1997): Analysis of Location Environment of
Agriculture in the Semi-Arid Area of India Using IRS Data, Proc. Annual
Conf. Jap. Soc. Photogram. Remote Sens., 301-306. (in Japanese)
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