Object-based flood mapping and affected rice field estimation with landsat 8 OLI and MODIS data

Phuong D. Dao, Yuei An Liou

Research output: Contribution to journalArticlepeer-review

77 Scopus citations


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.

Original languageEnglish
Pages (from-to)5077-5097
Number of pages21
JournalRemote Sensing
Issue number5
StatePublished - 2015


  • Cambodia
  • Flood mapping
  • Landsat
  • Moderate Resolution Imaging Spectroradiometer (MODIS)
  • Object-based image analysis
  • Paddy rice
  • Scale parameter estimation
  • Segmentation


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