Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Remote Sensing

Phuong D. Dao¹  Yuei-An Liou¹  Clement Atzberger² 

[1] Center for Space and Remote Sensing Research, National Central University, Jhongli District, Taoyuan City 320, Taiwan; E-Mail:;Center for Space and Remote Sensing Research, National Central University, Jhongli District, Taoyuan City 320, Taiwan; E-Mail

关键词: object-based image analysis;    segmentation;    scale parameter estimation;    flood mapping;    paddy rice;    Landsat;    Moderate Resolution Imaging Spectroradiometer (MODIS);    Cambodia;   

DOI  :  10.3390/rs70505077

来源: mdpi

【 摘 要 】

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. In this approach, image segmentation processing was conducted with optimal scale parameter estimation based on the variation of objects’ local variances. The inundated area was identified by using Landsat 8 images with an overall accuracy of higher than 95% compared to those derived from finer spatial resolution images. Moderate Resolution Imaging Spectroradiometer (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. The rice classification result was well correlated with the statistical data at a commune level (R² = 0.675). The flood mapping and affected rice estimation results are useful to provide local governments with valuable information for flooding mitigation and post-flooding compensation and restoration.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.