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Crop Pattern Mapping of Tumkur Taluk Using NDVI Technique: A Remote Sensing and GIS Approach
Available online at www.sciencedirect.com
ScienceDirect Aquatic Procedia 4 (2015) 1397 – 1404
INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE 2015)
Crop Pattern Mapping of Tumkur Taluk using NDVI Technique: A Remote Sensing and GIS Approach Bharathkumar L*., M.A. Mohammed-Aslam Dept. of Geology, Central University of Karnataka, Kadaganchi, Gulbarga, Karnataka-585311, India
Abstract Agriculture is the backbone of our country. Most of the Land in Tumkur taluk is occupied by agriculture. Due to lack of awareness, farmers grow different variety of crops in different localities. Ultimately they face the problems in crop production due to low yield. Crop suitability map provide solutions to all these problems. The study area Tumkur Taluk is located in southern part of Karnataka and belongs to semi-arid climatic condition. The cropping pattern includes majorly Coconut plantation, Arecanut plantation, Banana plantation, Ragi, Wheat, Maize, Jowar and other crops. Crop pattern study was carried out using the NDVI processing of Landsat 8 data. NDVI result is recoded by taking the training sets in field visit. Finally the crop pattern map is prepared using ArcGIS tools. Among current cropping patterns Coconut plantation, Ragi, Rice, Maize, Wheat were consume less water and they are currently suitable for the area. But Arecanut plantation, Rice and Banana plantation consume huge amount of water and were not at all suitable for the regional climatic condition. The major result deducing from this study is, Mines and Geology department accounted 34500 bore wells in Tumkur district. Every day minimum 10 crore litre of water is extracted from the ground water reservoirs. Our suitability crop modelling map will minimize the maintenance of ground water. Suitability crop pattern map includes high income and yield to farmers within less maintenance and less water usage with respect to the climatic conditions. © Published by Elsevier B.V.B.V. This is an open access article under the CC BY-NC-ND license © 2015 2015The TheAuthors. Authors. Published by Elsevier (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of ICWRCOE 2015. Peer-review under responsibility of organizing committee of ICWRCOE 2015 Keywords: Remote Sensing and GIS, NDVI, Training set, Crop Pattern Map, Crop Suitability Map.
* Corresponding author. E-mail address: [email protected]
2214-241X © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of ICWRCOE 2015 doi:10.1016/j.aqpro.2015.02.181
1398
L. Bharathkumar and M.A. Mohammed-Aslam / Aquatic Procedia 4 (2015) 1397 – 1404
1. Introduction Cropping pattern is a basic element of cropping system. Cropping pattern is the proportion of an area undergoing various crops at a point with respect to space and time. Majorly the cropping patterns were closely influenced by geo-climatic, socio-economic, political and historical factors (Wardlow and Egbert, 2002). Tumkur taluk belongs to central dry zone. Most of the Indian economy depends on agriculture. Indian agriculture mostly depends on Monsoons. As the monsoon is seasonal, agriculture production requires external Water resource for crop yields. According to global consumption of water resource, agriculture accounts for 70% of water resource. Remaining 20% is accounting for industrial use and 10% for domestic purpose. It is possible to sustain the water resource completely by adopting the scientific method of agriculture practices. Remote Sensing and GIS plays an important role in cost effective agricultural applications and implementations (Zhang and Hoffman., 2011). NDVI image is often useful because of its crop biomass; vigour and canopy cover (Son and Cru, 2012). 2. Study area The study area Tumkur taluk falls under Tumkur district, Karnataka, India as shown in Fig. 1. As the area is under semi arid climatic condition it is necessary to sustain the water resource. The average rainfall of the Tumkur taluk is 687.9 mm. Major proportion of the study area is covered by sandy soil. Cropping patterns play an important role in groundwater and surface water exploration. In Tumkur Taluk majorly we can found coconut plantation, Arecanut plantation, mango plantation, Ragi, Wheat, Maize, Jowar, and other Cereals crop varieties. The study covers an area of 1031.34 sq. kms.
Fig. 1. Study area map
3. Methodology Landsat 8 data is downloaded from the USGS (United State Geological Survey) Earth explorer website (earthexplorer.usgs.gov) and processed in Erdas imagine 9.1 and Arcmap 9.3 softwares. The data is processed under NDVI to extract the vegetation information. NDVI Stands for Normal Difference Vegetative Index, which gives the vegetative proportions in an area. The formula to calculate NDVI is shown below. NDVI= (NIR-Red) / (NIR+Red) The value of NDVI ranges from -1 to +1. The data is recoded in Erdas Imagine software by taking training sets using GPS control points. The training set selections were taken according to major crop production in the study area. As a result the major crops like Coconut plantation, Arecanut plantation, Ragi, Paddy and other mixed crops were taken into considerations. Later the crop type classifications were successfully achieved by recoding the NDVI data from the training sets. Suitability crop map is achieved by integrating the knowledge of spatial decisions as shown in Fig. 2.
L. Bharathkumar and M.A. Mohammed-Aslam / Aquatic Procedia 4 (2015) 1397 – 1404
1399
Landsat 8 TIRS Data
NDVI analysis
Training set collection
Recode of NDVI Dataset
Crop pattern mapping
Accuracy assessment analysis
Crop suitability mapping
Fig. 2. Flow of Methodology Chart
4. Results and discussions The total extent of the study area is 1031.34 sq. kms. As per our crop pattern mapping 329.33 sq. kms is covered by Coconut and Arecanut plantation, 174.01 sq. kms is covered by Non-vegetative class, 277.36 sq. kms is covered by Paddy cultivation, 2.89 sq. kms is covered by dense forest and 247.74 sq. kms is covered by Ragi cultivation. The water required for crops depend mainly on climatic conditions. Crops which are growing in Semi arid regions consume the following quantity of water for 1 KG of crop yield. Table 1. Table showing Water requirements for Crop varieties Sl. No.
Crop variety
Water requirement in Litres for 1 KG crop production
1
Coconut
1425
2
Arecanut
3542
3
Paddy
2497
4
Ragi
627
5
Potato
287
6
banana
790
7
Cabbage
237
8
tomato
214
(Source: IME, http://www.theguardian.com/news/datablog/2013/jan/10/how-much-water-food-production-waste)
1400
L. Bharathkumar and M.A. Mohammed-Aslam / Aquatic Procedia 4 (2015) 1397 – 1404 Table 2. Taluk wise details of water utilised for Agriculture budget in Tumkur Taluk Purpose Irrigation
Dug wells
Bore wells
4396
17068
(Source: Tumkur District Brochure, http://cgwb.gov.in/District_Profile/karnataka/Tumkur_Brochure.pdf)
Table 3. Taluk wise details of water budget in Tumkur Taluk as per Tumkur District Brochure Purpose
Domestic
No.of Habitations
752
No. Of Bore wells
2314
No.of Piped
No. of Mini
Water supply
water supply
Schemes
schemes
100
501
Accuracy assessment Firstly the Landsat 8 data is processed in NDVI to get vegetative and non-vegetative cover maps. Crop pattern distribution maps of Tumkur Taluk were prepared using Supervised Classification and Training site selections. Based on the training site selection NDVI map is recoded into Cocunut and Arecanut plantation map, Ragi distribution map, Paddy distribution map, Non vegetative cover and Forest distribution maps. The accuracy assessment is done by using the accuracy assessment tool in ERDAS Imagine 9.1 software. The accuracy of the classification is found to be 76% as the crop classification is very sensitive and is associated with the mixed crops like cereals, fruits, and vegetables.
Fig. 3. NDVI map
Fig. 4. Training set Collection
L. Bharathkumar and M.A. Mohammed-Aslam / Aquatic Procedia 4 (2015) 1397 – 1404
Fig. 5. Crop pattern map
Fig. 7. Ragi Crop Distribution Map
Fig. 6. Cocunut and Arecanut Plantation map
Fig. 8. Paddy Crop Distribution map
1401
1402
L. Bharathkumar and M.A. Mohammed-Aslam / Aquatic Procedia 4 (2015) 1397 – 1404
Fig. 9. Non-Vegetative Cover map
Fig. 10. Forest Distribution map
5. Crop Suitability Map Crop phenology mainly depend on the climatic conditions, Soil moisture content, Composition of soil, water availability and daily insolation. As the soil variety is sandy soil, it is very acidic requires plenty of organic matters to improve moisture contents. As the area is purely semi arid climatic it is necessary to avoid high water feeding plants like Arecanut, Paddy and Banana plantations. The priority must be given to such plants, which feeds less water. Crop suitability map is prepared by integrating Drainage, Slope and Existing crop information. Weightages were given according to table shown below Table 4. Weightage assessment for different layer to propose Crop Suitability map Drainage distance
Slope in percentage
Exhisting crop
Suitable Crop
1 KM radius
0 to 10
Paddy
Paddy
1.1 to 2.5 KM radius
11 to 25
Ragi, Cocunut
Ragi
2.6 to 5 KM radius
26 to 40
Ragi, Cocunut
Cocunut
More than 5 KM radius
41 to 55
Ragi, Cocunut
Cocunut
L. Bharathkumar and M.A. Mohammed-Aslam / Aquatic Procedia 4 (2015) 1397 – 1404
Fig. 11. Draianage network map
Fig. 13. Slope map
Fig. 12. Drainage Network and Training set map
Fig. 14. Integrated Crop Suitability Map
1403
1404
L. Bharathkumar and M.A. Mohammed-Aslam / Aquatic Procedia 4 (2015) 1397 – 1404
Fig. 15. Crop Suitability map
6. Conclusion The study illustrates the effective use of Remote Sensing and GIS techniques in Crop Pattern mapping and Crop suitability mappings. Landsat 8 data is analysed under NDVI and supervised classification processes to carry out the cropping information. The crop growth of an area depends upon climatic conditions, Terrain factors and water resource of an area. Thematic layers like Drainage, Slope and Current crop activity map were taken into consideration to perform the analysis. The crop suitability map aims at sustainability of water resource in the region. Crop suitability map clearly highlights less water consuming crops. Crop suitability map highlights the crop production of Ragi, Coconut plantation and paddy fields in order to potentiality of water resource of an area. Crop suitability map can provide a guideline to farmers to achieve high yield in spatial patterns. References Abou EL-Magd, I., Tanton, T. W., 2003. Improvements in land use mapping for irrigated agriculture from satellite sensor data using a multi-stage maximum likelihood classification. International Journal of Remote Sensing 24(21), 4197-4206. Chen, Y., Xu, X., Zhang, D., et al., 2006. Correlation of vegetation distribution and terrain factors in northwestern of Sichuan Longmen mountain. Chinese Journal of Ecology 25(9), 1052-1055. Hatfield, J.L., Prueger, J.H., 2010. Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sensing 2(2), 562-578. Kogan, F.N., 1995. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research 15(11), 91100. Kuenzer, C., Knauer, K., 2013. Remote sensing of rice crop areas. International Journal of Remote Sensing 34(6), 2101-2139. Motohka, T., Nasahara, K.N., Miyata, A., Mano, M., Tsuchida, S., 2009. Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset. International Journal of Remote Sensing 30(17), 4343-4357. Singh, N.J., Kudrat, M., Jain, K., Pandey, K., 2011. Cropping pattern of Uttar Pradesh using IRS-P6 (AWiFS) data. International Journal of Remote Sensing 32(16), 4511-4526. Son, N.T., Chen, C.F., Cru, C.R. 2012. Mapping Major Cropping Patterns in Southeast Asia from Modis Data Using Wavelet Transform and Artificial Neural Networks. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1, 421425. Walsh, S.J., Crawford, T.W., Welsh, W.F., Crews-Meyer, K.A. 2001. A multiscale analysis of LULC and NDVI variation in Nang Rong district, northeast Thailand. Agriculture, Ecosystems & Environment 85(1), 47-64. Wardlow, B.D., Egbert, S.L. 2002. Discriminating cropping patterns in the US Central Great Plains region using time-series MODIS 250-meter NDVI data–Preliminary Results. In Proceedings, Pecora 15 and Land Satellite Information IV Conference, 10-15. Zhang, H., Lan, Y., Lacey, R., Hoffmann, W.C., Westbrook, J.K., 2011. Spatial analysis of NDVI readings with different sampling densities. Transactions of the ASABE 54(1), 349-354.
ScienceDirect Aquatic Procedia 4 (2015) 1397 – 1404
INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE 2015)
Crop Pattern Mapping of Tumkur Taluk using NDVI Technique: A Remote Sensing and GIS Approach Bharathkumar L*., M.A. Mohammed-Aslam Dept. of Geology, Central University of Karnataka, Kadaganchi, Gulbarga, Karnataka-585311, India
Abstract Agriculture is the backbone of our country. Most of the Land in Tumkur taluk is occupied by agriculture. Due to lack of awareness, farmers grow different variety of crops in different localities. Ultimately they face the problems in crop production due to low yield. Crop suitability map provide solutions to all these problems. The study area Tumkur Taluk is located in southern part of Karnataka and belongs to semi-arid climatic condition. The cropping pattern includes majorly Coconut plantation, Arecanut plantation, Banana plantation, Ragi, Wheat, Maize, Jowar and other crops. Crop pattern study was carried out using the NDVI processing of Landsat 8 data. NDVI result is recoded by taking the training sets in field visit. Finally the crop pattern map is prepared using ArcGIS tools. Among current cropping patterns Coconut plantation, Ragi, Rice, Maize, Wheat were consume less water and they are currently suitable for the area. But Arecanut plantation, Rice and Banana plantation consume huge amount of water and were not at all suitable for the regional climatic condition. The major result deducing from this study is, Mines and Geology department accounted 34500 bore wells in Tumkur district. Every day minimum 10 crore litre of water is extracted from the ground water reservoirs. Our suitability crop modelling map will minimize the maintenance of ground water. Suitability crop pattern map includes high income and yield to farmers within less maintenance and less water usage with respect to the climatic conditions. © Published by Elsevier B.V.B.V. This is an open access article under the CC BY-NC-ND license © 2015 2015The TheAuthors. Authors. Published by Elsevier (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of ICWRCOE 2015. Peer-review under responsibility of organizing committee of ICWRCOE 2015 Keywords: Remote Sensing and GIS, NDVI, Training set, Crop Pattern Map, Crop Suitability Map.
* Corresponding author. E-mail address: [email protected]
2214-241X © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of ICWRCOE 2015 doi:10.1016/j.aqpro.2015.02.181
1398
L. Bharathkumar and M.A. Mohammed-Aslam / Aquatic Procedia 4 (2015) 1397 – 1404
1. Introduction Cropping pattern is a basic element of cropping system. Cropping pattern is the proportion of an area undergoing various crops at a point with respect to space and time. Majorly the cropping patterns were closely influenced by geo-climatic, socio-economic, political and historical factors (Wardlow and Egbert, 2002). Tumkur taluk belongs to central dry zone. Most of the Indian economy depends on agriculture. Indian agriculture mostly depends on Monsoons. As the monsoon is seasonal, agriculture production requires external Water resource for crop yields. According to global consumption of water resource, agriculture accounts for 70% of water resource. Remaining 20% is accounting for industrial use and 10% for domestic purpose. It is possible to sustain the water resource completely by adopting the scientific method of agriculture practices. Remote Sensing and GIS plays an important role in cost effective agricultural applications and implementations (Zhang and Hoffman., 2011). NDVI image is often useful because of its crop biomass; vigour and canopy cover (Son and Cru, 2012). 2. Study area The study area Tumkur taluk falls under Tumkur district, Karnataka, India as shown in Fig. 1. As the area is under semi arid climatic condition it is necessary to sustain the water resource. The average rainfall of the Tumkur taluk is 687.9 mm. Major proportion of the study area is covered by sandy soil. Cropping patterns play an important role in groundwater and surface water exploration. In Tumkur Taluk majorly we can found coconut plantation, Arecanut plantation, mango plantation, Ragi, Wheat, Maize, Jowar, and other Cereals crop varieties. The study covers an area of 1031.34 sq. kms.
Fig. 1. Study area map
3. Methodology Landsat 8 data is downloaded from the USGS (United State Geological Survey) Earth explorer website (earthexplorer.usgs.gov) and processed in Erdas imagine 9.1 and Arcmap 9.3 softwares. The data is processed under NDVI to extract the vegetation information. NDVI Stands for Normal Difference Vegetative Index, which gives the vegetative proportions in an area. The formula to calculate NDVI is shown below. NDVI= (NIR-Red) / (NIR+Red) The value of NDVI ranges from -1 to +1. The data is recoded in Erdas Imagine software by taking training sets using GPS control points. The training set selections were taken according to major crop production in the study area. As a result the major crops like Coconut plantation, Arecanut plantation, Ragi, Paddy and other mixed crops were taken into considerations. Later the crop type classifications were successfully achieved by recoding the NDVI data from the training sets. Suitability crop map is achieved by integrating the knowledge of spatial decisions as shown in Fig. 2.
L. Bharathkumar and M.A. Mohammed-Aslam / Aquatic Procedia 4 (2015) 1397 – 1404
1399
Landsat 8 TIRS Data
NDVI analysis
Training set collection
Recode of NDVI Dataset
Crop pattern mapping
Accuracy assessment analysis
Crop suitability mapping
Fig. 2. Flow of Methodology Chart
4. Results and discussions The total extent of the study area is 1031.34 sq. kms. As per our crop pattern mapping 329.33 sq. kms is covered by Coconut and Arecanut plantation, 174.01 sq. kms is covered by Non-vegetative class, 277.36 sq. kms is covered by Paddy cultivation, 2.89 sq. kms is covered by dense forest and 247.74 sq. kms is covered by Ragi cultivation. The water required for crops depend mainly on climatic conditions. Crops which are growing in Semi arid regions consume the following quantity of water for 1 KG of crop yield. Table 1. Table showing Water requirements for Crop varieties Sl. No.
Crop variety
Water requirement in Litres for 1 KG crop production
1
Coconut
1425
2
Arecanut
3542
3
Paddy
2497
4
Ragi
627
5
Potato
287
6
banana
790
7
Cabbage
237
8
tomato
214
(Source: IME, http://www.theguardian.com/news/datablog/2013/jan/10/how-much-water-food-production-waste)
1400
L. Bharathkumar and M.A. Mohammed-Aslam / Aquatic Procedia 4 (2015) 1397 – 1404 Table 2. Taluk wise details of water utilised for Agriculture budget in Tumkur Taluk Purpose Irrigation
Dug wells
Bore wells
4396
17068
(Source: Tumkur District Brochure, http://cgwb.gov.in/District_Profile/karnataka/Tumkur_Brochure.pdf)
Table 3. Taluk wise details of water budget in Tumkur Taluk as per Tumkur District Brochure Purpose
Domestic
No.of Habitations
752
No. Of Bore wells
2314
No.of Piped
No. of Mini
Water supply
water supply
Schemes
schemes
100
501
Accuracy assessment Firstly the Landsat 8 data is processed in NDVI to get vegetative and non-vegetative cover maps. Crop pattern distribution maps of Tumkur Taluk were prepared using Supervised Classification and Training site selections. Based on the training site selection NDVI map is recoded into Cocunut and Arecanut plantation map, Ragi distribution map, Paddy distribution map, Non vegetative cover and Forest distribution maps. The accuracy assessment is done by using the accuracy assessment tool in ERDAS Imagine 9.1 software. The accuracy of the classification is found to be 76% as the crop classification is very sensitive and is associated with the mixed crops like cereals, fruits, and vegetables.
Fig. 3. NDVI map
Fig. 4. Training set Collection
L. Bharathkumar and M.A. Mohammed-Aslam / Aquatic Procedia 4 (2015) 1397 – 1404
Fig. 5. Crop pattern map
Fig. 7. Ragi Crop Distribution Map
Fig. 6. Cocunut and Arecanut Plantation map
Fig. 8. Paddy Crop Distribution map
1401
1402
L. Bharathkumar and M.A. Mohammed-Aslam / Aquatic Procedia 4 (2015) 1397 – 1404
Fig. 9. Non-Vegetative Cover map
Fig. 10. Forest Distribution map
5. Crop Suitability Map Crop phenology mainly depend on the climatic conditions, Soil moisture content, Composition of soil, water availability and daily insolation. As the soil variety is sandy soil, it is very acidic requires plenty of organic matters to improve moisture contents. As the area is purely semi arid climatic it is necessary to avoid high water feeding plants like Arecanut, Paddy and Banana plantations. The priority must be given to such plants, which feeds less water. Crop suitability map is prepared by integrating Drainage, Slope and Existing crop information. Weightages were given according to table shown below Table 4. Weightage assessment for different layer to propose Crop Suitability map Drainage distance
Slope in percentage
Exhisting crop
Suitable Crop
1 KM radius
0 to 10
Paddy
Paddy
1.1 to 2.5 KM radius
11 to 25
Ragi, Cocunut
Ragi
2.6 to 5 KM radius
26 to 40
Ragi, Cocunut
Cocunut
More than 5 KM radius
41 to 55
Ragi, Cocunut
Cocunut
L. Bharathkumar and M.A. Mohammed-Aslam / Aquatic Procedia 4 (2015) 1397 – 1404
Fig. 11. Draianage network map
Fig. 13. Slope map
Fig. 12. Drainage Network and Training set map
Fig. 14. Integrated Crop Suitability Map
1403
1404
L. Bharathkumar and M.A. Mohammed-Aslam / Aquatic Procedia 4 (2015) 1397 – 1404
Fig. 15. Crop Suitability map
6. Conclusion The study illustrates the effective use of Remote Sensing and GIS techniques in Crop Pattern mapping and Crop suitability mappings. Landsat 8 data is analysed under NDVI and supervised classification processes to carry out the cropping information. The crop growth of an area depends upon climatic conditions, Terrain factors and water resource of an area. Thematic layers like Drainage, Slope and Current crop activity map were taken into consideration to perform the analysis. The crop suitability map aims at sustainability of water resource in the region. Crop suitability map clearly highlights less water consuming crops. Crop suitability map highlights the crop production of Ragi, Coconut plantation and paddy fields in order to potentiality of water resource of an area. Crop suitability map can provide a guideline to farmers to achieve high yield in spatial patterns. References Abou EL-Magd, I., Tanton, T. W., 2003. Improvements in land use mapping for irrigated agriculture from satellite sensor data using a multi-stage maximum likelihood classification. International Journal of Remote Sensing 24(21), 4197-4206. Chen, Y., Xu, X., Zhang, D., et al., 2006. Correlation of vegetation distribution and terrain factors in northwestern of Sichuan Longmen mountain. Chinese Journal of Ecology 25(9), 1052-1055. Hatfield, J.L., Prueger, J.H., 2010. Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sensing 2(2), 562-578. Kogan, F.N., 1995. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research 15(11), 91100. Kuenzer, C., Knauer, K., 2013. Remote sensing of rice crop areas. International Journal of Remote Sensing 34(6), 2101-2139. Motohka, T., Nasahara, K.N., Miyata, A., Mano, M., Tsuchida, S., 2009. Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset. International Journal of Remote Sensing 30(17), 4343-4357. Singh, N.J., Kudrat, M., Jain, K., Pandey, K., 2011. Cropping pattern of Uttar Pradesh using IRS-P6 (AWiFS) data. International Journal of Remote Sensing 32(16), 4511-4526. Son, N.T., Chen, C.F., Cru, C.R. 2012. Mapping Major Cropping Patterns in Southeast Asia from Modis Data Using Wavelet Transform and Artificial Neural Networks. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1, 421425. Walsh, S.J., Crawford, T.W., Welsh, W.F., Crews-Meyer, K.A. 2001. A multiscale analysis of LULC and NDVI variation in Nang Rong district, northeast Thailand. Agriculture, Ecosystems & Environment 85(1), 47-64. Wardlow, B.D., Egbert, S.L. 2002. Discriminating cropping patterns in the US Central Great Plains region using time-series MODIS 250-meter NDVI data–Preliminary Results. In Proceedings, Pecora 15 and Land Satellite Information IV Conference, 10-15. Zhang, H., Lan, Y., Lacey, R., Hoffmann, W.C., Westbrook, J.K., 2011. Spatial analysis of NDVI readings with different sampling densities. Transactions of the ASABE 54(1), 349-354.