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Remote Sens. 2015, 7(5), 5077-5097; doi:10.3390/rs70505077

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

1
Center for Space and Remote Sensing Research, National Central University, Jhongli District, Taoyuan City 320, Taiwan
2
Taiwan Group on Earth Observations, Zhubei City, Hsinchu County 30274, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad Thenkabail
Received: 4 February 2015 / Revised: 30 March 2015 / Accepted: 13 April 2015 / Published: 24 April 2015
(This article belongs to the Special Issue Earth Observations for the Sustainable Development)
View Full-Text   |   Download PDF [32806 KB, uploaded 24 April 2015]   |  

Abstract

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. View Full-Text
Keywords: object-based image analysis; segmentation; scale parameter estimation; flood mapping; paddy rice; Landsat; Moderate Resolution Imaging Spectroradiometer (MODIS); Cambodia object-based image analysis; segmentation; scale parameter estimation; flood mapping; paddy rice; Landsat; Moderate Resolution Imaging Spectroradiometer (MODIS); Cambodia
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Dao, P.D.; Liou, Y.-A. Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data. Remote Sens. 2015, 7, 5077-5097.

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