Improved detection of inundation below the forest canopy using normalized lidar intensity data

Authors: LANG, M.W. - Us Fish And Wildlife Service; KIM, V. - National Oceanic & Atmospheric Administration (NOAA); McCarty, Gregory; LI, X. - US Department Of Agriculture (USDA); YEO, I.Y. - Newcastle University; HUANG, C. - University Of Maryland; DU, L. - US Department Of Agriculture (USDA)

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., McCarty, G.W., Li, X., Yeo, I., Huang, C., Du, L. 2020. Improved detection of inundation below the forest canopy using normalized lidar intensity data. Remote Sensing. 12:707. https://doi.org/doi: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 variation, 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 are difficult to map under forest vegetation. Commonly used lidar (Light Detection and Ranging) instrumentation uses reflections from a scanning laser to map topography and, as a byproduct, also collects maps of the intensity of laser light reflection. Work has shown that these intensity data provide accurate information on forested wetland inundation when trees have lost their leaves, but that the presence of evergreen vegetation can interfere with collection of inundation information. This study demonstrated a data processing approach (normalization) to correct for the influence of evergreens on inundation mapping. Improved inundation maps will permit more accurate mapping of forested wetlands and allow better training of artificial intelligence (AI) procedures for assessing wetland ecosystem services provision in agricultural landscapes.

Technical Abstract: To best conserve wetlands and manage associated ecosystem services in the face of climate and land-use variation, 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 are difficult to map under forest vegetation. Commonly used lidar (Light Detection and Ranging) instrumentation uses reflections from a scanning laser to map topography and, as a byproduct, also collects maps of the intensity of laser light reflection. Work has shown that these intensity data provide accurate information on forested wetland inundation when trees have lost their leaves, but that the presence of evergreen vegetation can interfere with collection of inundation information. This study demonstrated a data processing approach (normalization) to correct for the influence of evergreens on inundation mapping. Improved inundation maps will permit more accurate mapping of forested wetlands and allow better training of artificial intelligence (AI) procedures for assessing wetland ecosystem services provision in agricultural landscapes.Since the 1990s, sensor technologies for precision agriculture applications include the use of soil electrical conductivity (ECa) sensors carried by tractors or all-terrain vehicles for mapping soil textural properties used in management zone delineation. Unoccupied aerial system (UAS) imagery may serve as an enhanced tool for management zone delineation. This is because UAS data collection, unlike previous approaches, is relatively flexible when data can be collected, can cover relatively large areas in a short amount of time and provide remote sensing information that maps soil and plant conditions relevant for precision agriculture management. The purpose of this study was to evaluate UAS imagery relevant to ECa data over an irrigated cotton field in terms of their ability to: 1) predict cotton plant height and seed cotton yield, and 2) define cotton management zones based on these traits. The results suggest that UAS imagery of vegetation indices and land surface temperature can offer valuable information for cotton management zone delineation that other precision agricultural techniques such as ECa data are unable to provide.