Enhanced detection of inundation below the forest canopy using multi-return LiDAR intensity data

Authors: LANG, M. W. - Department Of Fish And Wildlife; Kim, Vincent; McCarty, Gregory; XIA, LI - Former ARS Employee; YEO, I.Y. - University Of Newcastle; HUANG, C - University Of Maryland; DU, L - Former ARS Employee

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/19/2020
Publication Date: 2/21/2020
Citation: Lang, M., Kim, V.P., McCarty, G.W., Xia, L., Yeo, I., Huang, C., Du, L. 2020. Enhanced detection of inundation below the forest canopy using multi-return LiDAR intensity data. Remote Sensing. 12(4):707. https://doi.org/10.3390/rs12040707.
DOI: https://doi.org/10.3390/rs12040707

Interpretive Summary: 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 extent and function. Since in situ monitoring of wetlands at the landscape scale is typically cost and time prohibitive, remotely sensed data are commonly used to assess wetlands at this spatial scale. Traditionally optical images, such as aerial photography, in conjunction with field data were used to map wetlands. But this method is subject to considerable error. With the rapid development of lidar technology and increased availability of lidar data, many applications of this dataset have not been fully developed. This is particularly true for lidar intensity data. In an earlier study, we found that lidar intensity data were well suited for the identification of inundation below the forest canopy due to the strong absorption of incident near-infrared energy by water and the ability to isolate bare earth returns thus reducing the influence of the canopy. The goal of this subsequent study 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-coniferous forests and forested wetlands, and to develop an inundation mapping approach that is robust in areas of varying levels of evergreen influence. Results demonstrate the confounding influence of forest canopy gap fraction and inundation, and the effectiveness of a simple normalization process based on first and bare earth lidar intensity returns. Prior to normalization, signatures from inundated deciduous forest and non-inundated evergreen forest were not significantly different. After normalization inundated deciduous forest could be distinguished from evergreen forest and inundation was mapped with an accuracy between 97 and 99 %. 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. Results of this study will help watershed managers improve mapping of wetlands, particularly in forested areas containing significant evergreen populations.

Technical Abstract: 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 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 within forested wetlands. This study investigated the different but often interacting effects of evergreen vegetation and inundation on leaf-off bare earth return lidar intensity within mixed deciduous-coniferous forests on the Coastal Plain of Maryland, and developed an inundation mapping approach that is robust in areas of varying levels of evergreen influence. Results demonstrate the confounding influence of forest canopy gap fraction and inundation, and the effectiveness of a simple normalization process based on first and bare earth lidar intensity returns. Prior to normalization, signatures from inundated deciduous forest and non-inundated evergreen forest were not significantly different. After normalization inundated deciduous forest could be distinguished from evergreen forest and inundation was mapped with an accuracy between 97 and 99 %. 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.