Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?
Abstract
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 themore »
- Authors:
-
[1];
[3];
[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
- Publication Date:
- Research Org.:
- Brookhaven National Laboratory (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE; National Science Foundation (NSF); Bryn Mawr College K.G. Research Fund; National Socio-Environmental Synthesis Center (SESYNC); Michigan State University; USDA
- OSTI Identifier:
- 1867756
- Alternate Identifier(s):
- OSTI ID: 1869267; OSTI ID: 1872822
- Report Number(s):
- BNL-223008-2022-JAAM
Journal ID: ISSN 1466-822X
- Grant/Contract Number:
- DE‐SC0012704; SC0012704; 1702379; 1926567; 1926568; DBI-1639145
- Resource Type:
- Published Article
- Journal Name:
- Global Ecology and Biogeography
- Additional Journal Information:
- Journal Name: Global Ecology and Biogeography Journal Volume: 31 Journal Issue: 7; Journal ID: ISSN 1466-822X
- Publisher:
- Wiley-Blackwell
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; forest diversity; hyperspectral imagery; airborne lidar; forest canopies; landscape ecology; biodiversity; remote sensing
Citation Formats
Kamoske, Aaron G., Dahlin, Kyla M., Read, Quentin D., Record, Sydne, Stark, Scott C., Serbin, Shawn P., and Zarnetske, Phoebe L. Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?. United Kingdom: N. p., 2022.
Web. doi:10.1111/geb.13516.
Kamoske, Aaron G., Dahlin, Kyla M., Read, Quentin D., Record, Sydne, Stark, Scott C., Serbin, Shawn P., & Zarnetske, Phoebe L. Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?. United Kingdom. https://doi.org/10.1111/geb.13516
Kamoske, Aaron G., Dahlin, Kyla M., Read, Quentin D., Record, Sydne, Stark, Scott C., Serbin, Shawn P., and Zarnetske, Phoebe L. Fri .
"Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?". United Kingdom. https://doi.org/10.1111/geb.13516.
@article{osti_1867756,
title = {Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?},
author = {Kamoske, Aaron G. and Dahlin, Kyla M. and Read, Quentin D. and Record, Sydne and Stark, Scott C. and Serbin, Shawn P. and Zarnetske, Phoebe L.},
abstractNote = {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.},
doi = {10.1111/geb.13516},
journal = {Global Ecology and Biogeography},
number = 7,
volume = 31,
place = {United Kingdom},
year = {Fri May 13 00:00:00 EDT 2022},
month = {Fri May 13 00:00:00 EDT 2022}
}
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