Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?

Kamoske, Aaron G.; Dahlin, Kyla M.; Read, Quentin D.; Record, Sydne; Stark, Scott C.; Serbin, Shawn P.; Zarnetske, Phoebe L.

doi:10.1111/geb.13516

Title: Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?

Journal Article · Fri May 13 00:00:00 EDT 2022 · Global Ecology and Biogeography

DOI:https://doi.org/10.1111/geb.13516· OSTI ID:1867756

^[1]; Dahlin, Kyla M. ^[2];

^[3]; Record, Sydne ^[4]; Stark, Scott C. ^[5]; Serbin, Shawn P. ^[6];

^[7]

Department of Geography, Environment, &, Spatial Sciences Michigan State University East Lansing Michigan USA, USDA Forest Service Geospatial Technology and Applications Center Salt Lake City Utah USA
Department of Geography, Environment, &, Spatial Sciences Michigan State University East Lansing Michigan USA, Program in Ecology, Evolution, and Behavior Michigan State University East Lansing Michigan USA
National Socio‐Environmental Synthesis Center Annapolis Maryland USA
Department of Biology Bryn Mawr College Bryn Mawr Pennsylvania USA
Department of Forestry Michigan State University East Lansing Michigan USA
Environmental and Climate Sciences Department Upton Brookhaven National Laboratory New York New York USA
Program in Ecology, Evolution, and Behavior Michigan State University East Lansing Michigan USA, Department of Integrative Biology Michigan State University East Lansing Michigan USA

Abstract Aim Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m). Location Eastern USA. Time period 2017–2018. Taxa studied Trees. Methods With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics. Results Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity. Main conclusions We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE; National Science Foundation (NSF); Bryn Mawr College K.G. Research Fund; National Socio-Environmental Synthesis Center (SESYNC); Michigan State University; USDA

Grant/Contract Number:: DE‐SC0012704; SC0012704; 1702379; 1926567; 1926568; DBI-1639145

OSTI ID:: 1867756

Alternate ID(s):: OSTI ID: 1869267; OSTI ID: 1872822

Report Number(s):: BNL-223008-2022-JAAM

Journal Information:: Global Ecology and Biogeography, Journal Name: Global Ecology and Biogeography Vol. 31 Journal Issue: 7; ISSN 1466-822X

Publisher:: Wiley-BlackwellCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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54 ENVIRONMENTAL SCIENCES
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