Object-based Flood Mapping and Affected Paddy Rice Estimation in Cambodia using Landsat 8 OLI and MODIS Data

• Main Authors: DAO DUC PHUONG, 陶德方
• Other Authors: Liou, Yuei-An
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/22008800748434820968

碩士 === 國立中央大學 === 遙測科技碩士學位學程 === 103 === Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains. It usually experiences devastating floods induced by an overwhelming concentration of rainfall water over the Tonle Sap Lake’s and Mekong River’s banks during monsoon seasons. Flood damage assessment in the rice ecosystem plays an important role in this region as local residents rely heavily on agricultural production. This study introduced an object-based approach to flood mapping and affected rice field estimation in central Cambodia after the flood event induced by a severse typhoon occurred in October 2013 using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data. For flood mapping, the Landsat 8 OLI Level 1 products were used to detect inundation regions. However, the images over northwestern region was covered by cloud; therefore, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was appied to generate cloud-free Landsat-scale synthetic data for flood detection in this area. For rice identification, Terra (MOD13Q1) and Aqua (MYD13Q1) MODIS vegetation index products were utilized to identify the paddy rice field based on seasonal inter-variation between vegetation and water index during the transplanting stage. In this approach, image segmentation process was conducted with optimal scale parameter estimation based on the variation of objects’ local variances. The inundated area was identified with an overall accuracy of higher than 95% compared to those derived from finer spatial resolution images. The rice classification result was well correlated with the statistical data at a commune level (R2 = 0.675). The inundation and paddy rice maps were overlaid and further analyzed to estimate rice area impacted by the disaster. The flood mapping and affected rice estimation result is really useful as it provides local governments with valuable information for flooding mitigation and post-flooding compensation and restoration. The success and findings of this procedure could be promisingly applied in other areas to timely observe and assess the impacts of flood disasters at a large scale and in the areas where in situ flooding observation is inoperable or radar remotely sensed data is unavailable.