Detection of Rice Fields in Rasht Township Using Multi-Temporal Landsat-8 Images

Document Type : Original Article


1 M.Sc. Student of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran

2 Assistant Prof., Dep. of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran


Rice has become one of the most important food security items in many countries, especially Iran. In this study, a model was proposed to select Landsat-8 satellite time-series images in order to prepare a map of paddy lands. The method is based on the phenological characteristics of rice plants and annual surface temperature data from the MODIS sensor. After preprocessing satellite images, they were classified using an object-based approach and fuzzy functions. Various data such as a digital elevation model, land surface temperature, and spectral indices including NDVI, EVI, NDBI and LSWI are used to improve the classification process. In addition, information about the segmentation of the image is employed during the process of classification. Because of the different traits of paddy fields, a digital elevation model with a resolution of 12.5 meters was used to help differentiate paddy lands from other vegetation. In addition, a comparison was made between the results of classification based on object-based and pixel-based methods. The results showed that the object-based classification yields better results than the pixel-based method with special considerations. The classification result following validation using ENVI software pixel-based classification indicated an overall accuracy of 92 percent and a kappa value of 0.89. This is in contrast to the object-based classification technique in the eCognition software, which yielded an overall accuracy of 94 percent and a kappa coefficient of 0.92.


Dai, S.P., Luo, H.X., Fang, J.H., Cao, J.H., Li, H.L., Li, M.F., Wang, L.L. & Luo, W., 2014, Object-Oriented Classification of Rubber Plantations from Landsat Satellite Imagery, The Third International Conference on Agro-Geoinformatics, Beijing, 2014, PP. 1-4.
Dong, J., Xiao, X., Menarguez, M.A., Zhang, G., Qin, Y., Thau, D., Biradar, C. & Moore, B., 2016, Mapping Paddy Rice Planting Area in Northeastern Asia with Landsat 8 Images, Phenology-Based Algorithm and Google Earth Engine, Remote Sensing of Environment, 185, PP. 142-154.
Gupta, N. & Bhadauria, H.S., 2014, Object-Oriented Approach of Information Extraction from Panchromatic Satellite Images Based on Fuzzy Logic, 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence), Noida, PP. 651-656.
[1]. Zerrouki & Bouchaffra
Johansen, K. & Johansen, K., 2011, Time-Series Analysis of Rainforest Clearing in Sabah, Borneo Using Landsat Imagery, 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images (Multi-Temp), Trento, 2011, PP. 277-280.
Jozdani, S.E., Johnson, B.A. & Chen, D., 2019, Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification, Remote Sensing, 11, P. 1713.
Kong, C., Fei, H., Peng, W. & Jing, L., 2008, Effect of Allelopathic Rice Varieties Combined with Cultural Management Options on Paddy Field Weeds, Journal of Pest Management Science, 64, PP. 276-282.
Lambin, E., Geist, H., Reynolds, J. & Stafford, M., 2007, Integrated Human-Environment Approaches of Land Degradation in Drylands, In: Costanza. Cambridge, Mass. MIT, PP. 331-340.

Le Toan, T., Ribbes, F., Wang, L.-F., Floury, N., Ding, K.-H., Kong, J.A., Fujita, M. & Kurosu, T., 1997, Rice Crop Mapping and Monitoring Using ERS-1 Data Based on Experiment and Modeling Results, IEEE Transactions on Geoscience and Remote Sensing, 35, PP. 41-56.

Liu, E., Zhou, W., Zhou, J., Shao, H. & Yang, X., 2013, Combining Spectral with Texture Features Into Object-Oriented Classification in Mountainous Terrain Using Advanced Land Observing Satellite Image, Journal of Mountain Science, 10, PP. 768-776.

Mosleh, M.K. & Hassan, Q.K., 2014, Development of a Remote Sensing-Based “Boro” Rice Mapping System, Remote Sensing, 6, PP. 1938-1953.
Mosleh, M.K., Hassan, Q.K. & Chowdhury, E.H., 2015, Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production, a Review, Sensors, 15, PP. 769-791.
Newman, M., Mc Laren, K. & Wilson, B., 2014, Comparing the Effects of Classification Techniques on Landscape-Level Assess-ments: Pixel-Based Versus Objectbased Classification, International Journal of Remote Sensing, 32, PP. 4055-4073.
Pal, M. & Mather, P.M., 2005, Support Vector Machines for Classification in Remote Sensing, International Journal of Remote Sensing, 26, PP. 1007-1011.

Prabaharan, S., Srinivasa, R., Lakshumanan, C. & Ramalingam, M., 2010, Remote Sensing and GIS Applications on Change Detection Study in Coastal Zone Using Multi Temporal Satellite Data, International Journal of Geomatics and Geosciences, 1, PP. 159-166.

Rawat, J. & Kumar, M., 2015, Monitoring land Use/Cover Change Using Remote Sensing and GIS Techniques: A Case Study of Hawalbagh Block, District Almora, Uttarakhand, India, The Egyptian Journal of Remote Sensing and Space Science, 18, PP. 77-84.

Salehi, B., Jefferies, W., Adlakha, P., Chen, Z. & Bobby, P., 2014, A Pixel- and Object-Based Image Analysis Framework for Automatic Well Site Extraction at Regional Scales Using Landsat Data, IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, PP. 1741-1744.

Sheikhzadeh, G., Shirkhodaei, M. & Tabibi, M., 2016, The Effect of Consumer Environmental Concerns on the Intention to Buy Iranian and Foreign Rice, Third National Conference on Environmental Science and Management, Ardabil, PP. 45-57.
Wardlow, B., Stephen, D., Egbertb, L. & Kastens, J., 2007, Analysis of Time-Series MODIS 250 M Vegetation Index Data for Crop Classification in the U.S. Central Great Plains, Remote Sensing of Environment, 108, PP. 290-310.
Xiao, X., Boles, S., Frolking, S., Li, C., Babu, J.Y., Salas, W. & Moore, B., 2006, Mapping Paddy Rice Agriculture in South and Southeast Asia Using Multi-Temporal MODIS Images, Remote Sensing of Environment, 100, PP. 95-113.
Xiao, X., He, L., Salas, W., Li, C., Moore III B., Zhao, R., Frolking S. & Boles, S., 2002, Quantitative Relationships between Field-Measured Leaf Area Index and Vegetation Index Derived from VEGETATION Images for Paddy Rice Fields, International Journal of Remote Sensing, 23, PP. 3595-3604.
Xing, X., & Shen, J., 2018, Offshore Oil Slicks Extraction by Landsat Data Based on eCognition Software in South China Sea, International Conference on Audio, Language and Image Processing (ICALIP), PP. 144-147.
Zerrouki, N. & Bouchaffra, D., 2014, Pixel-Based or Object-Based: Which Approach is More Appropriate for Remote Sensing Image Classification?, IEEE International Conference on Systems, Man, and Cybernetics October 5-8, San Diego, CA, PP. 864-869.
Zhang, J. & Jia, L., 2014, A Comparison of Pixel-Based and Object-Based Land Cover Classification Methods in an Arid/Semi-Arid Environment of Northwestern China, Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA), Changsha, PP. 403-407.
Zhang, Y., Lu, K., He, N. & Zhang, P., 2006, Research on Land Use/Cover Classification Based on RS and GIS, Second International Symposium on Plant Growth Modeling and Applications, Beijing, PP. 244-250.
Zhang, X., Zhang, Q., Zhang, G. & Gui, Z., 2017, A Comparison Study of Normalized Difference Water Index and Object-Oriented Classification Method in River Network Extraction from Landsat-tm Imagery, 2nd International Conference on Frontiers of Sensors Technologies (ICFST), Shenzhen, PP. 198-203.