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Assessment of Multi-temporal Radar Imagery in Mapping Land System for Rainfed Lowland Rice in Northeast Thailand S. Kaojarern Asian Institute of Technology, P.O. Box 4, Khlong Luang Pathumthani 12120, Thailand. skaoja@ait.ac.th J.P. Delsol Groupement pour le Developpement de la Teledetection Aerospatiale 8-10 rue Hermes, F 31526 Ramonville Saint-Agne Cedex, France Thuy Le Toan Centre d’Etudes Spatiales de la BIOshpere, 18 Avenue Edouard Belin 31401 Toulouse Cedex 4, France S.P. Kam International Rice Research Institute, DAPO Box. 7777 Metro Manila, Philippines Thailand is the largest exporter of rice in the world market with a reputation of high grain quality. Eighty five percent of the rice area in Thailand is rainfed lowland rice ecosystem and one-half of the rice land is located in the Northeast region. This region is a heterogeneous rainfed lowland rice environment covering a broad area of complex natural system and therefore exhibits diverse conditions of local landscape, sparse tree stands in the rice fields, and light soil with low moisture retention. Mapping land system into various classes will lead to better management and specific strategy applied to each class. This subject has been little studied using remote sensing. The approach chosen have brought together elements that have been already studied elsewhere (in Indonesia by Le Toan et al., 1997; in Vietnam by Liew et al., 1997; in Thailand by Aschbacher et al., 1995; in Japan by Kurosu et al., 1993) but in terms of thematic results there is a new development for mapping land system for rice. Test Site and Land System Units The test site is situated in Ubon Ratchathani province, Northeastern Thailand between 14o 30' and 15o 30' north latitudes and 104o 30' and 105o 30' east longitudes, covering an area of 75x75 km2. Mountain ranges lie in the south as the border between Cambodia and in the east as the border between Loas. Land system classification for rainfed lowland rice environments according to local conditions in the Northeast Thailand (Junthotai et al., 1990), are using a system approach based on a combination of biophysical land components, i.e. vegetation cover conditions, terrain types, relief, surface materials and soils (Table 1). Materials Six ERS-2 SAR images [C-band, l=5.6 cm, 23o incidence angle and VV (vertical transmit and vertical receive) polarization, 35 days revisit capacity, and 100 km swath width] representing the rice growing season of 1997 were acquired from June to January of the years 1996 and 1997. The temporal series of these images, due to limited funding, was put on a monthly sequence for the growing season of 1997, starting by June and July 1997 and followed by September, October, and November 1996, and January 1997 images. All images were acquired at the Center of Remote Imaging, Sensing and Processing (CRISP) in Singapore. Landsat-5 Thematic Mapper (TM) imagery acquired on January 21, 1991, was visually interpreted for identification of general terrain types and land use classes. The 1:50,000 topographic maps (1984) from the Royal Thai Survey department, the 1:100,000 soil map (1991) and the 1:100,000 land use map (1988) from the Land Development Department were used to identify and map land system units with GIS techniques. Methodology Two main steps for SAR image processing were pre-processing and classification. The pre-processing step included registration, calibration, conversion to 8 bit data, multitemporal and spatial filtering. Mutitemporal and spatial filtering, considered as the most critical step, involves reducing speckle to a level where the error in classification is acceptable. After pre-processing, the resulting filtered images have in principle 294 equivalent number of looks {(6*3)(7* 7/3) ENL}. According to Le Toan et al., 1997, the ENL ~100 looks, from two multidate images, are allowed to detect changes in radar intensity less than 1 dB with a confidence interval of 80%. The classification procedure comprises selection of training areas, signature analysis, interpretation, and classification. Training samples were selected based on the desired classes of land system units for rice. The resulting 16 signature profiles corresponding to non-rice (9 signature profiles) and rice (7 signature profiles) were delineated. Classification was done using supervised maximum likelihood classifier as this classifier is considered to be the most accurate, compared with others if a set of criteria is met. These criteria include: temporal changes of signatures allowing discrimination of classes, the appropriate numbers and dates of selected images, normal distribution of training samples, and a large number for each training sample. The classification was applied and the results are discussed in Section 6.
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