Research Papers

Wavelet filtering of time-series moderate resolution imaging spectroradiometer data for rice crop mapping using support vector machines and maximum likelihood classifier

[+] Author Affiliations
Chi-Farn Chen, Ly-Yu Chang

National Central University, Centre for Space and Remote Sensing Research, Taoyuan County 32001, Taiwan

National Central University, Department of Civil Engineering, Taoyuan County 32001, Taiwan

Nguyen-Thanh Son, Cheng-Ru Chen

National Central University, Department of Civil Engineering, Taoyuan County 32001, Taiwan

J. Appl. Remote Sens. 5(1), 053525 (May 26, 2011). doi:10.1117/1.3595272
History: Received October 04, 2010; Revised February 04, 2010; Accepted May 03, 2011; Published May 26, 2011; Online May 26, 2011
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Rice is the most important economic crop in Vietnam's Mekong Delta (MD). It is the main source of employment and income for rural people in this region. Yearly estimates of rice growing areas and delineation of spatial distribution of rice crops are needed to devise agricultural economic plans and to ensure security of the food supply. The main objective of this study is to map rice cropping systems with respect to monitoring agricultural practices in the MD using time-series moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 250-m data. These time-series NDVI data were derived from the 8-day MODIS 250-m data acquired in 2008. Various spatial and nonspatial data were also used for accuracy verification. The method used in this study consists of the following three main steps: 1. filtering noise from the time-series NDVI data using wavelet transformation (Coiflet 4); 2. classification of rice cropping systems using parametric and nonparametric classification algorithms: the maximum likelihood classifier (MLC) and support vector machines (SVMs); and 3. verification of classification results using ground truth data and government rice statistics. Good results can be found using wavelet transformation for cleaning rice signatures. The results of classification accuracy assessment showed that the SVMs outperformed the MLC. The overall accuracy and Kappa coefficient achieved by the SVMs were 89.7% and 0.86, respectively, while those achieved by the MLC were 76.2% and 0.68, respectively. Comparison of the MODIS-derived areas obtained by the SVMs with the government rice statistics at the provincial level also demonstrated that the results achieved by the SVMs (R2 = 0.95) were better than the MLC (R2 = 0.91). This study demonstrates the effectiveness of using a nonparametric classification algorithm (SVMs) and time-series MODIS NVDI data for rice crop mapping in the Vietnamese MD.

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© 2011 Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation

Chi-Farn Chen ; Nguyen-Thanh Son ; Cheng-Ru Chen and Ly-Yu Chang
"Wavelet filtering of time-series moderate resolution imaging spectroradiometer data for rice crop mapping using support vector machines and maximum likelihood classifier", J. Appl. Remote Sens. 5(1), 053525 (May 26, 2011). ; http://dx.doi.org/10.1117/1.3595272


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