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Mapping Forested Wetland Inundation in the Delmarva Peninsula, USA Using Deep Convolutional Neural Networks
Open AccessArticle

Improved Detection of Inundation below the Forest Canopy using Normalized LiDAR Intensity Data

1
U.S. Fish and Wildlife Service, 5275 Leesburg Pike, Falls Church, VA 22041, USA
2
National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service, 5830 University Research Ct, College Park, MD 20740, USA
3
Hydrology and Remote Sensing Laboratory, USDA-ARS, Beltsville, MD 20705, USA
4
School of Engineering, The University of Newcastle, Callaghan, NSW 2308, Australia
5
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 707; https://doi.org/10.3390/rs12040707
Received: 31 December 2019 / Revised: 10 February 2020 / Accepted: 19 February 2020 / Published: 21 February 2020
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
To best conserve wetlands and manage associated ecosystem services in the face of climate and land-use change, wetlands must be routinely monitored to assess their extent and function. Wetland extent and function are largely driven by spatial and temporal patterns in inundation and soil moisture, which to date have been challenging to map, especially within forested wetlands. The objective of this paper is to investigate the different, but often interacting effects, of evergreen vegetation and inundation on leaf-off bare earth return lidar intensity within mixed deciduous-evergreen forests in the Coastal Plain of Maryland, and to develop an inundation mapping approach that is robust in areas of varying levels of evergreen influence. This was achieved through statistical comparison of field derived metrics, and development of a simple yet robust normalization process, based on first of many, and bare earth lidar intensity returns. Results demonstrate the confounding influence of forest canopy gap fraction and inundation, and the effectiveness of the normalization process. After normalization, inundated deciduous forest could be distinguished from non-inundated evergreen forest. Inundation was mapped with an overall accuracy between 99.4% and 100%. Inundation maps created using this approach provide insights into physical processes in support of environmental decision-making, and a vital link between fine-scale physical conditions and moderate resolution satellite imagery through enhanced calibration and validation. View Full-Text
Keywords: canopy gap fraction; hydroperiod; inundation; lidar intensity; swamp; wetland mapping canopy gap fraction; hydroperiod; inundation; lidar intensity; swamp; wetland mapping
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MDPI and ACS Style

Lang, M.W.; Kim, V.; McCarty, G.W.; Li, X.; Yeo, I.-Y.; Huang, C.; Du, L. Improved Detection of Inundation below the Forest Canopy using Normalized LiDAR Intensity Data. Remote Sens. 2020, 12, 707. https://doi.org/10.3390/rs12040707

AMA Style

Lang MW, Kim V, McCarty GW, Li X, Yeo I-Y, Huang C, Du L. Improved Detection of Inundation below the Forest Canopy using Normalized LiDAR Intensity Data. Remote Sensing. 2020; 12(4):707. https://doi.org/10.3390/rs12040707

Chicago/Turabian Style

Lang, Megan W.; Kim, Vincent; McCarty, Gregory W.; Li, Xia; Yeo, In-Young; Huang, Chengquan; Du, Ling. 2020. "Improved Detection of Inundation below the Forest Canopy using Normalized LiDAR Intensity Data" Remote Sens. 12, no. 4: 707. https://doi.org/10.3390/rs12040707

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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