A Land Cover Variation Model of Water Level for the Floodplain of Tonle Sap, Cambodia, Derived From ALOS PALSAR and MODIS Data

The floodplain around Tonle Sap, Cambodia is strongly influenced by seasonal variations in water level. In the wet season, lacustrine landforms and vegetated areas are partly inundated due to increases in the water level. Conversely, they are gradually emerged when the flooding recedes during the dry season. Because floods in Tonle Sap are an annual event, a land cover variation model that takes into account water level is necessary to predict areal changes in each land cover class at the floodplain. To establish this model, we used the Phased Array L-band Synthetic Aperture Radar (PALSAR) backscattering coefficients, normalized difference vegetation index (NDVI) values, and tasseled cap (TC) transformations of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2007 to 2010 to estimate the areal variation of six land cover classes during the annual flood pulse. The radar backscattering coefficients correlated well with NDVI values during the dry season, but the relationship vanished during the wet season. According to our model, a backscattering coefficient change from -8.4 dB to -20.6 dB for lowland shrubs in the flood developing stage corresponded to an areal percentage change of un-flooded lowland shrubs from 16.3% to 0.5% of the total study area. Once the water level increased to the peak of flooding, 46.2% of the lowland shrub area was immersed. Our model also predicted that approximately 41.8% of the total study area was replaced with a water surface at the peak of flooding. When we compared the two results obtained using our model at 6 m above mean sea level (amsl) and using a digital terrain model (DTM) and the land use map, we observed a large difference between the two models in the areal percentage of the corresponding land cover of un-flooded lowland shrubs (-10.3%). Our land cover variation model can be used to predict areal changes in land cover classes during flood development and recession stages, and can also provide insight into flood dynamics, thereby enabling flood management in this region.