Commenced in January 2007
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Mapping Paddy Rice Agriculture using Multi-temporal FORMOSAT-2 Images

Authors: Yi-Shiang Shiu, Meng-Lung Lin, Kang-Tsung Chang, Tzu-How Chu

Abstract:

Most paddy rice fields in East Asia are small parcels, and the weather conditions during the growing season are usually cloudy. FORMOSAT-2 multi-spectral images have an 8-meter resolution and one-day recurrence, ideal for mapping paddy rice fields in East Asia. To map rice fields, this study first determined the transplanting and the most active tillering stages of paddy rice and then used multi-temporal images to distinguish different growing characteristics between paddy rice and other ground covers. The unsupervised ISODATA (iterative self-organizing data analysis techniques) and supervised maximum likelihood were both used to discriminate paddy rice fields, with training areas automatically derived from ten-year cultivation parcels in Taiwan. Besides original bands in multi-spectral images, we also generated normalized difference vegetation index and experimented with object-based pre-classification and post-classification. This paper discusses results of different image classification methods in an attempt to find a precise and automatic solution to mapping paddy rice in Taiwan.

Keywords: paddy rice fields; multi-temporal; FORMOSAT-2images, normalized difference vegetation index, object-basedclassification.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1055447

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References:


[1] J. L. Maclean, D. C. Dawe, B. Hardy and G. P. Hettel, Rice almanac: Source book for the most important economic activity on earth CABI Publishing, 2002.
[2] X. Xiao, S. Boles, S. Frolking, C. Li, J. Y. Babu, W. Salas and B. Moore Iii, "Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images", Remote Sensing of Environment, vol. 100, pp. 95-113, 2006.
[3] FAOSTAT, Statistical database of the food and agricultural organization of the United Nations, 2001.
[4] M. D. Turner and R. G. Congalton, "Classification of multi-temporal SPOT-XS satellite data for mapping rice fields on a West African floodplain", International Journal of Remote Sensing, vol. 19, pp. 21-41, 1998.
[5] X. Xiao, S. Boles, S. Frolking, W. Salas, B. Moore, C. Li, L. He and R. Zhao, "Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data", International Journal of Remote Sensing, vol. 23, pp. 3009-3022, 2002.
[6] X. Xiao, L. He, W. Salas, C. Li, B. Moore, R. Zhao, S. Frolking and S. Boles, "Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields", International Journal of Remote Sensing, vol. 23, pp. 3595-3604, 2002.
[7] A. E. Daniels, "Incorporating domain knowledge and spatial relationships into land cover classifications: a rule-based approach", International Journal of Remote Sensing, vol. 27, pp. 2949 - 2975, 2006.
[8] A. S. Laliberte, A. Rango, J. E. Herrick, E. L. Fredrickson and L. Burkett, "An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography", Journal of Arid Environments, vol. 69, pp. 1-14, 2007.
[9] D. Stow, Y. Hamada, L. Coulter and Z. Anguelova, "Monitoring shrubland habitat changes through object-based change identification with airborne multispectral imagery", Remote Sensing of Environment, vol. 112, pp. 1051-1061, 2008.
[10] F. M. B. Van Coillie, L. P. C. Verbeke and R. R. De Wulf, "Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium", Remote Sensing of Environment, vol. 110, pp. 476-487, 2007.