Monitoring Cropping Pattern Changes Using Multi-temporal WiFS Data

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1 Monitoring Cropping Pattern Changes Using Multi-temporal WiFS Data D. R. Rajak, M.P. Oza, N. Bhagia, and V. K. Dadhwal Crop Inventory and Modelling Division Agricultural Resources Group (RESA) Space Applications Centre (ISRO) Ahmedabad , India Abstract Cropping pattern is a basic element of cropping system. For the better management of cropping systems, up-to-date information on present cropping pattern and changes in cropping pattern is essential. Remote sensing data has shown its great potential in agricultural applications. Wide Field Sensor (WiFS) data available from IRS-1C, -1D and -P3 satellites provide an excellent opportunity for spatio-temporal monitoring of crops on a regional scale because of its large swath and high revisit capability. The present study demonstrates the use of multi-year multi-date WiFS data for monitoring changes in cropping pattern in Kota-Baran districts in Rajatsthan (India) from to Depending upon the availability of satellite data two approaches have been developed. Change in cropping pattern from mustard to wheat (of the order of 45,140 ha) from season to season was observed; while in season change from wheat to mustard acreage (of the order of 69,867 ha) has been detected and mapped. The cropping pattern changes (from to season) mapped using only two date WiFS data (for each season) are 11% higher than those mapped using complete data set spanning from early November to March. Introduction Never before in human history has the need been greater for better management of agricultural systems. In order to achieve such an aim it is absolutely necessary to have up-todate information regarding present cropping pattern and changes in cropping pattern in a region. Cropping pattern, a basic element of cropping system of any region, is the yearly sequence and spatial arrangement of crops or of crops and fallow on a given area. Cropping pattern of a region plays a vital role in determining the level of agricultural production and reflects the agricultural development of that area. The interplay of complex social, economic and physical factors is responsible for cropping pattern changes (Singh 1980). A change in the cropping pattern implies a change in the proportions of area under different crops, which depends to a large extent on the resources available to raise crops in the given agro-climatic settings (Palaniappan, 1985). Mustard/rapeseed, soybean and groundnut are major oilseeds produced in India. The production of mustard keeps on fluctuating; in it reached to 6.66 Mt from 5.76 Mt of In , India produced 5.77 Mt of mustard/ rapeseed, and the Rajasthan state contributed 43% of it. Demand of edible oils being higher than the production of oilseeds in India, it becomes necessary to import massive edible oils. State Trading Corporation of India imported 151,343 tone of edible oil in Geocarto International, Vol. 17, No. 3, September 2002 Published by Geocarto International Centre, G.P.O. Box 4122, Hong Kong. Use of remote sensing data in making crop inventory has been demonstrated in many parts of the world. Large Area Crop Inventory Experiment (LACIE ), covering USA, USSR, Brazil, Argentina, India etc. was one such study (Mc-Donald and Hall, 1980). Monitoring Agriculture with Remote Sensing (MARS), since 1988 has been conducted in Europe (Sharman, 1993; De Roover et al., 1993). In India, Crop Acreage and Production Estimation (CAPE) project since 1988 has demonstrated the potential of remotely sensed data for crop inventory (Navalgund et al., 1991). In CAPE, crop acreages across the years are estimated but spatial distribution of crops or crop change detection was not done. To monitor the dynamic phenomenon like cropping pattern changes over large regions huge amount of spatial and temporal data is required. Getting multi-date, cloud free, medium spatial resolution satellite data over crop cycle is very difficult. Wide Field Sensor (WiFS) data available from IRS-1C, -1D and -P3 satellites have been extremely useful for such spatio-temporal studies because of its large swath and high revisit frequency over a region. Main characteristics of the sensor are shown in Table 1. It is specifically designed for crop monitoring applications. Its high revisit frequency over an area is because of its wide swath. Multi-date WiFS data have been used for assessing crop growth within a season and comparison across seasons. It has been used to provide pre-harvest multiple forecasts of wheat production over large region (Oza et al., 53

2 1996; Rajak et al., 2000). The present study illustrates use of multi-year WiFS data for monitoring and mapping large shifts in cropping pattern in a study region in Rajasthan by two approaches. Study Area and Satellite Data Used The study area covers Kota and Baran districts in eastern Rajasthan, India (Fig 1). Mustard and wheat are two major crops of the area. Data from Wide Field Sensor (WiFS) onboard Indian Remote Sensing Satellites IRS- 1C, -P3. and - 1D were used in this study. Details of data used are given in Table 2. District boundary of Kota district (as per 1991) has been used in this study. Currently this comprises Kota and Baran districts. Methodology The methodology consists of three major steps: (l) Multiyear multi-date data geo-referencing; (2) Crop specific temporal NDVI pattern generation; and (3) Cropping pattern change images generation. Multi-year Multi-date Data Geo-referencing WiFS data registration with Survey of India topographic maps ( 1:250,000 scale) and then multi-date image to image registrations were carried out in National Wheat Production Forecasting project (Oza et al., 1999; Oza et al., 2000). Second order polynomials were applied for map-to-image and imageto-image registrations. Map-to-image geo-referencing root mean square (rms) errors were within ±180 m, while imageto-image registration rms errors were ±90 m. From the same multi-date (early November to March) data set for two seasons i.e and , and two-date data were taken for developing the methodology in this study. There were seven date WiFS data in , six date data in , and two date data in seasons (See Table 2). Crop Specific Temporal NDVI Pattern Generation With a-priori knowledge of major crops in area, the profile segments of 5 x 5 km size in wheat dominant cropped area (segment A) and mustard dominant cropped area (segment B) were identified and extracted. Temporal Normalized Difference Vegetation Index [NDVI=(NIR- RED)/(NIR+RED)] patterns for mustard and wheat crops were generated for the two seasons using multi-date WiFS data (from November to March). A sample area of 5 x 5 km (segment C) was identified where dominant crop wheat in has changed to mustard in The spectrotemporal patterns of wheat and mustard (segment A and B) for season are shown in Fig 2. Temporal spectral behavior of wheat has single peak pattern while spectral pattern for mustard has two peaks. The first peak is before the crop canopy turns yellow due to flowers and the second peak corresponds to seed filling stage when no flowers are found in the plants (Kar and Chakravarty, 2000). The temporal spectral profiles of segment C for and seasons are also shown in Fig 2. Temporal NDVI gradient values for mustard and wheat crops in Jan- Feb months were also calculated. Cropping Pattern Change Images Generation Keeping in mind the requirement of accuracy and timeliness for cropping pattern change mapping and availability of satellite data, two approaches have been developed for generation of cropping pattern change images. The first approach uses complete multi-date WiFS data set from November to March for each season and the second approach uses only two-date WiFS data during January and February for each season. Table 1 Major characteristics of Wide Field Sensor on board IRS satellites Table 2 Multi-date WiFS data used Parameter IRS-1C IRS-P3 IRS-1D Launch Date 26 Dec Mar Sep 1997 Repetivity (days) Spectral bands (m) Spatial resolution (m) Swath (km) Quantisation levels (bits) SNR (at saturation radiance) >128 >128 >128 (Source: Josheph et al., 1996; NDC, 1997) S.NO. SATELLITE PATH / ROW DATF OF PASS 1 IRS-P3 96/55 JAN 16, IRS-P3 96/53 FEB 09, IRS-1C 94/53 NOV 11, IRS-P3 94/53 DEC 08, IRS-1D 93/53 JAN 01, IRS-1C 93/53 JAN 17, IRS-P3 96/51 FEB 04, IRS-P3 94/53 FEB 18, IRS-P3 94/53 MAR 14, IRS-P3 94/53 NOV 09, IRS- 1C 94/53 NOV 30, IRS-P3 94/53 DEC 27, IRS- 1C 94/53 JAN 17, IRS- 1D JAN 30, IRS- 1C 94/53 MAR 05,

Figure 1 Location map showing Kota and Baran districts in Rajasthan (India) and locations of 5 x 5 km segments used for crop temporal spectral patterns Figure 2 Temporal Spectral patterns for crops

3 Figure 1 Location map showing Kota and Baran districts in Rajasthan (India) and locations of 5 x 5 km segments used for crop temporal spectral patterns Figure 2 Temporal Spectral patterns for crops from WiFS data over 5 x 5 km segments (Segment A: Wheat in ; Segment B: Mustard crop in and; Segment C: Shift in temporal pattern due to change from Wheat in to Mustard in Using Complete Multi-date WiFS Data Set (Approach-I) The study area was classified for two seasons ( and ) using multi-date WiFS data set spanning from early November to March. With a-priori knowledge of various land use/land cover in the area, the signatures were extracted and crop specific temporal spectral patterns were generated. Temporal spectral pattern for mustard and wheat crops for season averaged over a sample segment of 5 x 5 km are shown in Fig 2. Classified image for a season was prepared from complete data set using the knowledge of temporal spectral patterns in hierarchical decision tree rule classifier (Oza et al., 1996), where an unknown sample is 55

classified into a class using one or several decision functions in a successive manner. The decision tree consists of a root node, a number of interior nodes, and a number of terminal nodes.

4 classified into a class using one or several decision functions in a successive manner. The decision tree consists of a root node, a number of interior nodes, and a number of terminal nodes. The terminal nodes represent final classifications, while the interior nodes are linked into decision stages. Each node consists of a set of classes to be discriminated, the set of features to be used, and the decision rule for performing the classification. From root node to terminal node decision tree consists of multi-stages. In the first stage, the data loss and cloud covers/cloud shadows are identified and removed. Then snow/ice, water bodies, sand, built-up areas, forests, plantations, scrubland etc. are masked out in successive stages. In the final stage different crops are discriminated based on the knowledge of their temporal spectral responses and growth stages. The areas under cloud cover on one or more dates were classified using remaining cloud free data set. Crop change images were generated from classified images of the two seasons. Non-agricultural areas and other changes were masked out and change in crop from wheat to mustard only has been shown ( Fig 3). The change in cropping pattern in from wheat to mustard is 69,867 hectares, when compared to season. Using Two-Date WiFS Data Set (Approach-II) Temporal spectral profiles of wheat and mustard are distinct and different. NDVI gradient for wheat between mid-january and mid-february is different than that of mustard. Based on this criterion decision rules were prepared and six images of three seasons i. e. Jan 16, 1998; Feb 09, 1998; Jan 17, 1999; Feb 18, 1999; Jan 17, 2000 and Jan 30, 2000 were analyzed to identify changes in cropping pattern. Change image from to season was prepared using Jan 16, 1998; Feb 09, 1998; Jan 17, 1999 and Feb 18, 1999 WiFS images (Fig 4); while to prepare the change image from to season, Jan 17, 1999; Feb 18, 1999; Jan 17, 2000 and Jan 30, 2000 WiFS data were used (Fig. 5). Non-agricultural areas, the areas under cloud cover on any date, and other changes were masked out. Some parts of study area are under cloud cover on Jan 17, Table 3 shows a typical sequence of decision rules applied for estimating the cropping pattern changes. Change from mustard to wheat crop in with respect to was found Table 3 Sr. No. Decision Rule Class 1 MAX NDVI (full data-set)<0.1 Non-agricultural area (NDVI_D2S1 - NDVI_D1S1 ) < N1 and Crop pattern change 2 (NDVI_D2S2 - NDVI D1S2) > N2 (Mustard to wheat) ( NDVI_D2S1 - NDVI_D1S1) > N3 and Crop pattern change 3 (NDV1_D2S2 - NDVI_D1S2) < N4 ( Wheat to mustard) ABS {[NDVI_D2S1 -NDVI_D1S1) - No-change in crop type 4 (NDVI_D2S2-NDVI_D1S2)} < N5 ( Agricultural Area) D1S2: First date for Second season D2S1: Second date for First season N1... N5: NDVI Threshold values Typical hierarchical decision rules Figure 3 Cropping pattern change using Remote Sensing data for complete crop cycle (early November to March). 56

to be of the order of 45,140 hectares; while in 1999-2000 season, change from wheat to mustard crop was 77,595 hectares with respect to 1998-1999.

5 to be of the order of 45,140 hectares; while in season, change from wheat to mustard crop was 77,595 hectares with respect to Results and Discussion The temporal spectral patterns of mustard and wheat are distinct and different (Fig. 2). This criterion has been used in the first approach to discriminate mustard and wheat crops. Also, the different NDVI gradients for wheat and mustard crops between two dates (Jan-Feb months) provide the basis of discrimination in the second approach. Wheat growth pattern exhibits single peak while two peaks characterize spectral pattern of mustard. The spectral patterns of wheat and mustard averaged over sample areas of 5 x 5 km are shown in Fig. 2. Cropping pattern change images (Fig 3, 4 and 5), generated by two approaches, shows the large patches of seasonal shifting in crop from mustard to wheat in and wheat to mustard in The total area in Kota and Baran districts where cropping pattern has changed from mustard to wheat in season is 45,140 hectares (with respect to season); while in the cropping pattern change from wheat to mustard is of the order of 69,867 hectares (with respect to season). Acreage estimates of mustard and wheat crops by Board of Revenue (BOR), Rajasthan, in Kota and Baran districts for last four years are given in Table 4. Traditional estimates like BOR estimates do not give any information about spatial distribution of crops or changes in cropping pattern. However, the BOR estimates show decrease in mustard acreage by 25.2% and increase in wheat acreage by 26.2% in season; while increase in mustard Table 4 Mustard and Wheat acreage (in thousand hectares) estimates for Kota and Baran districts (Rajasthan) Season BOR estimates Mustard Wheat BOR: Board of Revenue, Rajasthan Figure 4 Cropping pattern change using two-year two-date remote sensing data ( vs ). acreage by 65.9%, and decrease in wheat acreage by 11.5% in season. To monitor the dynamic phenomenon like cropping pattern changes demanding clear discrimination among the competing crops, huge amount of spatial and temporal cloud free satellite data is required. WiFS data available from IRS-1C, -1D and -P3 satellites has been extremely useful for such spatio-temporal studies because of its large swath and viewing geometry. Conclusion The study illustrates the use of multi-year two-date IRS WiFS data for detecting, mapping and monitoring the large changes in cropping pattern in Kota-Baran districts of Rajasthan. Depending upon the availability of remote sensing data, two approaches have been developed. To arrive at final and accurate cropping pattern changes, the remote sensing data over 57

Acknowledgement The authors are thankful to Dr. R. R. Navalgund, former Dy. Director (RESA) and Shri J. S. Parihar, Group Director (ARG) for their encouragement.

6 Acknowledgement The authors are thankful to Dr. R. R. Navalgund, former Dy. Director (RESA) and Shri J. S. Parihar, Group Director (ARG) for their encouragement. References Figure 5 Cropping pattern change using two-year two-date remote sensing data ( vs ) whole of the crop cycle have been used; although, to forecast the cropping pattern changes well before harvesting, remote sensing data for two dates (Jan-Feb months) for each of the seasons have been used. In the first approach, the areas under cloud cover on one or more dates had been analyzed using remaining cloud free data set; while in the second approach, using two date data the areas under cloud cover could not be analyzed. The change forecasts made by the second approach, using two-date remote sensing data are 11% higher compared to those made by the first approach using complete remote sensing data set for and seasons. Changes in cropping pattern from mustard to wheat in were picked up very well; while in season the reversal of cropping pattern change has been observed. Crop shift from mustard to wheat in was 22.5% of previous year s mustard acreage. The change from wheat to mustard in season was of the order of 32.25% of previous year s wheat acreage. The Bureau of Revenue (Rajasthan ) estimates show drastic decrease in mustard acreage and increase in wheat acreage in Similarly, in mustard acreage increased drastically while wheat acreage decreased considerably from their previous year s values. De Roover, B., De Mudker, S.C. and Goossens. R The regional inventory (MARS) in Belgium. Proc. Int. Symp. Operationalization of Remote Sensing April, ITC, Enschede, The Netherlands. Kar, G. and Chakravarty N.V.K Predicting crop growth and aphid incidence in Brassica under semi-arid environment. The Indian Journal of Agricultural Sciences, 70(1) Joseph, G., Iyenger, V. S.. Ratan, Ram, Nagachenchaiah, K., Kiran Kumar. A. S., Aradhye, B. V., Gupta, K. K. and Samudraiah, D.R.M Cameras for Indian remote sensing satellites IRS 1C. Current Science, 70: Mc-Donald, R.B. and Hall, F.G Global crop forecasting. Science, 280,670p. Navalgund, R.R., Parihar. J.S., Ajai and Rao, P.P.N Crop inventory using remotely sensed data. Current Science, 61, NDC IRS-1D data users handbook (Hyderabad, India: National Remote Sensing Agency). Oza, M.P. Bhagia, N. Dutta, S., Patel, J. H. and Dadhwal, V.K National wheat acreage estimation for using multi-date IRS-1C WiFS data. J. Indian Society of Remote Sensing, 24(4), Oza. M.P., Vyas, S.P., Rajak, D.R., Bhagia, N., Singh, A.K. and Dadhwal, V.K National Wheat Production Forecast ( ) Using Multi-Date WiFS and Meteorological Data. Space Applications Centre (ISRO), Ahmedabad, India. Scientific Note, RSAM/SAC/FASAL- TD/SN/01/99). Oza, M.P., Rajak, D.R., Bhagia, N., Nain, A.S. and Dadhwal, V.K National Wheat Production Forecast Using Multi-Date WiFS and Meteorologica Data for season. Space Applications Centre (ISRO), Ahmedabad, India. Scientific Note, RSAM/SAC/FASAL-TD/SN/09/ Palaniappan, S.P Cropping Systems in the Crops: Principles and Management. Wiley Eastern Limited, New Delhi and Tamilnadu Agricultural University, Coimbtore. Rajak, D.R., Oza, M.P., Bhagia. N., Vyas, S.P., Patel, J.H. and Dadhwal, V.K Regional wheat production forecasting using multi-date WiFS data: An Indian experience. Submitted to ISPRS journal of Photogrammetry and Remote Sensing, June Sharman, M.J The agriculture project of the Joint Research Centre: Operational use of Remote Sensing for agricultural statistics. Proc. Int. Symp. Operationalization of Remote Sensing, April, ITC, Enschede, The Netherlands. Singh S Dynamics of cropping pattern in northern India. Perspectives in Agricultural Geography, Vol-3. Concept Publishing Company, New Delhi, Ed- Noor Mohammed,

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