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Remote Sens. 2017, 9(2), 105; doi:10.3390/rs9020105

Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to Maximize Detection of Forested Inundation Extent in the Delmarva Peninsula, USA

1
U.S. Geological Survey, Geosciences and Environmental Change Science Center, P.O. Box 25046, DFC, MS980, Denver, CO 80225, USA
2
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Current address: U.S. Fish and Wildlife Service National Wetland Inventory, Falls Church, VA 22041, USA.
*
Author to whom correspondence should be addressed.
Academic Editors: Qiusheng Wu, Charles Lane, Chunqiao Song, Deepak R. Mishra, Xiaofeng Li and Prasad S. Thenkabail
Received: 30 September 2016 / Revised: 9 January 2017 / Accepted: 20 January 2017 / Published: 25 January 2017
View Full-Text   |   Download PDF [4880 KB, uploaded 25 January 2017]   |  

Abstract

Natural variability in surface-water extent and associated characteristics presents a challenge to gathering timely, accurate information, particularly in environments that are dominated by small and/or forested wetlands. This study mapped inundation extent across the Upper Choptank River Watershed on the Delmarva Peninsula, occurring within both Maryland and Delaware. We integrated six quad-polarized Radarsat-2 images, Worldview-3 imagery, and an enhanced topographic wetness index in a random forest model. Output maps were filtered using light detection and ranging (lidar)-derived depressions to maximize the accuracy of forested inundation extent. Overall accuracy within the integrated and filtered model was 94.3%, with 5.5% and 6.0% errors of omission and commission for inundation, respectively. Accuracy of inundation maps obtained using Radarsat-2 alone were likely detrimentally affected by less than ideal angles of incidence and recent precipitation, but were likely improved by targeting the period between snowmelt and leaf-out for imagery collection. Across the six Radarsat-2 dates, filtering inundation outputs by lidar-derived depressions slightly elevated errors of omission for water (+1.0%), but decreased errors of commission (−7.8%), resulting in an average increase of 5.4% in overall accuracy. Depressions were derived from lidar datasets collected under both dry and average wetness conditions. Although antecedent wetness conditions influenced the abundance and total area mapped as depression, the two versions of the depression datasets showed a similar ability to reduce error in the inundation maps. Accurate mapping of surface water is critical to predicting and monitoring the effect of human-induced change and interannual variability on water quantity and quality. View Full-Text
Keywords: Radarsat-2; Worldview-3; inundation; forested wetlands; lidar; depressions; topographic wetness index Radarsat-2; Worldview-3; inundation; forested wetlands; lidar; depressions; topographic wetness index
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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

Vanderhoof, M.K.; Distler, H.E.; Mendiola, D.A.T.G.; Lang, M. Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to Maximize Detection of Forested Inundation Extent in the Delmarva Peninsula, USA. Remote Sens. 2017, 9, 105.

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