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Improvement and Validation of NASA/MODIS NRT Global Flood Mapping

Li Lin,L. Di,Junmei Tang,E. Yu,Chen Zhang,Md. Shahinoor Rahman,R. Shrestha,L. Kang

Published 2019 in Remote Sensing

ABSTRACT

The remote-sensing based Flood Crop Loss Assessment Service System (RF-CLASS) is a web service based system developed and managed by the Center for Spatial Information Science and Systems (CSISS). The system uses Moderate Resolution Imaging Spectroradiometer (MODIS)-based flood data, which was implemented by the Dartmouth Flood Observatory (DFO), to provide an estimation of crop loss from floods. However, due to the spectral similarity between water and shadow, a noticeable amount of false classification of shadow can be found in the DFO flood products. Traditional methods can be utilized to remove cloud shadow and part of mountain shadow. This paper aims to develop an algorithm to filter out noise from permanent mountain shadow in the flood layer. The result indicates that mountain shadow was significantly removed by using the proposed approach. In addition, the gold standard test indicated a small number of actual water surfaces were misidentified by the proposed algorithm. Furthermore, experiments also suggest that increasing the spatial resolution of the slope helped reduce more noise in mountains. The proposed algorithm achieved acceptable overall accuracy (>80%) in all different filters and higher overall accuracies were observed when using lower slope filters. This research is one of the very first discussions on identifying false flood classification from terrain shadow by using the highly efficient method.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    Remote Sensing

  • Publication date

    2019-01-21

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/RS11020205
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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