Abstract
The subtropical country like Bangladesh has perennial cloud cover in the monsoon season which creates difficulties in mapping floodplain land use/cover using optical remote sensing data. For its all weather imaging capabilities, RADARSAT SAR is a potential image data source to map the seasonal dynamics in the floodplain. Bangladesh occupies the lower portion of two of the largest rivers of the world, the Ganges and the Barahmaputra- Jamuna, and large amount of water and sediments flow through them in monsoon season. This flow causes recurrent flooding in 25%to35% of the country in an average year and it can nesting site as high as about 60%-65% of the total area of the country.

To support a reliable floodplain land use/cover mapping tool, image processing techniques including filtering an classification algorithms - were evaluated for extraction of information from RADARSAR SAR data. "Two adaptive filter, Gamma-map and Frost were tested on the data. Nine RADARSAT Fine Beam(F3)images was used to cover temporal variability of land use/cover during the monsoon of 1998.This molti-temporal data set was classified using a maximum likelihood classifier. The two filtering techniques influenced the accuracy level for land use/cover dynamics classification. The accuracy of frost filtered dataset (i.e.69%)was better than that of gamma-map filtered dataset(i.e. 64%)n . The advantages and disadvantages of the filtering techniques for floodplain land use/cover mapping are discussed and potential of satellite SAR as a floodplain land use/cover mapping tool in Bangladesh is demonstrated.

1. Introduction
Satellite radar remote sensing is being explored and increasingly utilized for land use/cover mapping during monsoon seasons of subtropical countries. To investigate the potential of RADARSAT SAR for study of floodplain dynamics, this project was launched in collaboration with RADARSAT SAR International under the Advanced Data Research Opportunity (ADRO Project 418). This work builds upon that reported in earlier studies (Hasan et al. 1998, Martin et. al. 1998). The main focus of this study was to review image processing techniques, including filtering and its impact on digital classification, as well as the utility of multi-temporal image dataset.

Filtering is essential to reduce the speckle of SAR data and different filtering techniques have been developed by researchers. Commonly used filters are adaptive filters, such as Lee (Lee 1980), Frost (Frost et. al.1982), Kuan (Kuan et al., 1985) Gamma- MAP (kuan et al., 1987). Gamma -Map filter has been reported suitable for flood monitoring using SAR imagery in Bangladesh (Fap 19,1995;EGIS 1997). On the other hand the Frost has been reported better for crop type and land cover discrimination (Connery et al.,1996) using SAR imagery . In this study, Gamma- Map and Forest filter were evaluated to determine their effectiveness in land use/cover discrimination .

The simple method of radar data classification is density slicing (Richards 1986). FAP 19 (1995) used this method to classify the SAR images into four classes:

(1) urban/homestead areas;(2) non- flooded cropland; (3) folded crop; and (4) river, fold water and bells. FAP 19 found overall accuracy of more than 80% Dlorio et al. (1995) evaluated RADARSAT performance in identifying land use using an unsupervised classification. Minimum distance and parallelepiped classification algorithms, which required multiple images of the same area Wu (1984) used a maximum likelihood classifier to classify the land cover into eight classes and have found overall accuracy of 59% using SAR imagery. Nieuwenhuis and Schotten (1992) used maximum like hood classifier for land cover monitoring in the Netherlands. They reported that classification accuracy varied from 60 to 90% .

Many researchers (Hoogeboom1983, Bush and Ulbay 1978, Brisco et al., 1984 Foody et al. 1989) have classified crop types from multi-temporal SAR data and obtained significant results, whereas single data SAR can map the extent of open water flooding (FAP 19, 1995;EGIS 1997). Pope et al. (1994) concluded that multi-temporal data dramatically improved detection and delineation of tropical vegetation in flooded and non-flooded environments.

2. Study Area and Data used
The study area is a floodplain formed by three distributaries of the Jamuna river: the Elanjane the Pungle and the Lohojong. The central part of the floodplain is protected by embankments and has controlled flooding during the monsoon season. The protected area is locally called the Lohojang floodplain. The entire study area lies within 240 06/ 56//N to 240 25/02//N altitude and 890 46/27// E to 900 00/ 50// longitude types of flooding are common in this area: controlled and natural.

RADARAST F3 images were the primary imagery used in this research. IRS-ID PAN image was used as base map. In addition, aerial photos of 1990 were used for identifying the field monitoring plots. In addition SPOT XSS hardcopy at 1:50,000 scale were used to georeferenced the the RADARSAT SAR and IRS-ID PAN images . On the acquisition dates of the SAR images (Table1) extensive field information described conditions of land cover degree of flooding, crop or vegetation canopy , crop height etc. Field data were arranged in a GIS and the main categories included settlements, permanent water bodies, and seasonal flooding , inundated or non-inundated crops.

Table 1. Description of the satellite data used in this study

Satellite, Sensor and Beam Mode	Acquisition date	Season
IRS ID PAN	Feb.12,1998	Dry
RADARSAT SAR F3	MAY 28, 1998	Pre-mansoon
RADARSAT SAR F3	June 21,1998	Pre-mansoon
RADARSAT SAR F3	July 15,1998	mansoon
RADARSAT SAR F3	Aug 08,1998	mansoon
RADARSAT SAR F3	Sep01,1998	mansoon
RADARSAT SAR F3	Sep 25,1998	mansoon
RADARSAT SAR F3	Oct 19,1998	Post-mansoon
RADARSAT SAR F3	Dec 06,1998	Post-mansoon
RADARSAT SAR F3	Dec 30,1998	Post-mansoon