Improvement and Validation of NASA/MODIS NRT Global Flood Mapping

Li Lin,Md Rahman,Lingjun Kang,Chen Zhang,Junmei Tang,Eugene Yu,Ranjay Shrestha,Liping Di

doi:10.3390/rs11020205

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.

Highlights

Flood, the most widespread and frequent natural disaster in the world, has profound impacts on populations worldwide with significant loss of life and property each year [1,2]
Applications focused on the flooded area delineation using optical remote sensing such as SPOT XS [13], the Landsat Thermatic Mapper (TM)/Multispectral Scanner (MSS) [14,15], and the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) [16]
Comparison experiments in this study suggested that the proposed algorithm could correctly identify false classifications from terrain shadow in mountain regions

Summary

Introduction

The most widespread and frequent natural disaster in the world, has profound impacts on populations worldwide with significant loss of life and property each year [1,2]. Applications focused on the flooded area delineation using optical remote sensing such as SPOT XS [13], the Landsat Thermatic Mapper (TM)/Multispectral Scanner (MSS) [14,15], and the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) [16] Much of these pioneering works developed detection methods based on the spectral characteristics of studying objectives during floods. A microwave sensor, such as the synthetic aperture radar (SAR) light detecting and ranging (LIDAR), has been used as an excellent tool to monitor flood in bad weather condition due to its capability in penetrating clouds as an active sensor [17,18] This data still faces obstacles in accurately delineating the flood extension, particular for the inundated with the wind-induced ripples or tree-mixed surface problem [18]. The incident angle and consequent variation in back scatter as well as its discontinuous acquisition pose difficulties in the quick response from flooding using active sensors [17]

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Discussion

Conclusion