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Remote Sens. 2017, 9(9), 890; doi:10.3390/rs9090890

Fusion of SAR, Optical Imagery and Airborne LiDAR for Surface Water Detection

1
Department of Geological Sciences and Geological Engineering, Queen’s University, Kingston K7L 3N6, ON, Canada
2
Department of Biology, Queen’s University, Kingston K7L 3N6, ON, Canada
*
Author to whom correspondence should be addressed.
Received: 10 July 2017 / Revised: 21 August 2017 / Accepted: 22 August 2017 / Published: 28 August 2017
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
View Full-Text   |   Download PDF [21189 KB, uploaded 28 August 2017]   |  

Abstract

The detection and monitoring of surface water and its extent are critical for understanding floodwater hazards. Flooding and undermining caused by surface water flow can result in damage to critical infrastructure and changes in ecosystems. Along major transportation corridors, such as railways, even small bodies of water can pose significant hazards resulting in eroded or washed out tracks. In this study, heterogeneous data from synthetic aperture radar (SAR) satellite missions, optical satellite-based imagery and airborne light detection and ranging (LiDAR) were fused for surface water detection. Each dataset was independently classified for surface water and then fused classification models of the three datasets were created. A multi-level decision tree was developed to create an optimal water mask by minimizing the differences between models originating from single datasets. Results show a water classification uncertainty of 4–9% using the final fused models compared to 17–23% uncertainty using single polarization SAR. Of note is the use of a high resolution LiDAR digital elevation model (DEM) to remove shadow and layover effects in the SAR observations, which reduces overestimation of surface water with growing vegetation. Overall, the results highlight the advantages of fusing multiple heterogeneous remote sensing techniques to detect surface water in a predominantly natural landscape. View Full-Text
Keywords: TerraSAR-X; SAR; LiDAR; WorldView-2; surface water; NDVI; NDWI; decision tree TerraSAR-X; SAR; LiDAR; WorldView-2; surface water; NDVI; NDWI; decision tree
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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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MDPI and ACS Style

Irwin, K.; Beaulne, D.; Braun, A.; Fotopoulos, G. Fusion of SAR, Optical Imagery and Airborne LiDAR for Surface Water Detection. Remote Sens. 2017, 9, 890.

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