Mapping Paddy Rice Agriculture using Multi-temporal FORMOSAT-2 Images
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.
[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.
[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.
@article{"International Journal of Earth, Energy and Environmental Sciences:50710", author = "Yi-Shiang Shiu and Meng-Lung Lin and Kang-Tsung Chang and Tzu-How Chu", title = "Mapping Paddy Rice Agriculture using Multi-temporal FORMOSAT-2 Images", 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.", volume = "4", number = "7", pages = "256-7", }