Mapping tree canopy cover and canopy height with L-band SAR using LiDAR data and Random Forests
Light detection and ranging (LiDAR) data can provide direct measurements of vegetation structures but are limited by the sparse spatial coverage. Polarimetric synthetic aperture radar (SAR) can perform large-scale high-resolution mapping without weather constraints but the information about vegetati...
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ftnasajpl:oai:trs.jpl.nasa.gov:2014/53067 2023-05-15T15:02:39+02:00 Mapping tree canopy cover and canopy height with L-band SAR using LiDAR data and Random Forests Chen, Richard H Pinto, Naiara Duan, Xueyang Tabatabaeenejad, Alireza Moghaddam, Mahta 2022-01-10T19:47:25Z application/pdf http://hdl.handle.net/2014/53067 en_US eng Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2020 VIRTUAL IGARSS 2020 - IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, Hawaii, September 26 - October 2, 2020 CL#20-2930 http://hdl.handle.net/2014/53067 Preprint 2022 ftnasajpl 2022-01-16T18:00:57Z Light detection and ranging (LiDAR) data can provide direct measurements of vegetation structures but are limited by the sparse spatial coverage. Polarimetric synthetic aperture radar (SAR) can perform large-scale high-resolution mapping without weather constraints but the information about vegetation and ground subsurface are mixed in the backscatter data. In this paper, we adopted the Random Forests algorithm to train an upscaling function using tree canopy cover (TCC) and canopy height model (CHM) derived from Goddard’s LiDAR, Hyperspectral and Thermal Imager (G-LiHT) data. The regression model is then applied to the L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign to map the TCC and CHM over the Delta Junction area in interior Alaska. NASA/JPL Report Arctic Alaska JPL Technical Report Server Arctic |
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Open Polar |
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JPL Technical Report Server |
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ftnasajpl |
language |
English |
description |
Light detection and ranging (LiDAR) data can provide direct measurements of vegetation structures but are limited by the sparse spatial coverage. Polarimetric synthetic aperture radar (SAR) can perform large-scale high-resolution mapping without weather constraints but the information about vegetation and ground subsurface are mixed in the backscatter data. In this paper, we adopted the Random Forests algorithm to train an upscaling function using tree canopy cover (TCC) and canopy height model (CHM) derived from Goddard’s LiDAR, Hyperspectral and Thermal Imager (G-LiHT) data. The regression model is then applied to the L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign to map the TCC and CHM over the Delta Junction area in interior Alaska. NASA/JPL |
format |
Report |
author |
Chen, Richard H Pinto, Naiara Duan, Xueyang Tabatabaeenejad, Alireza Moghaddam, Mahta |
spellingShingle |
Chen, Richard H Pinto, Naiara Duan, Xueyang Tabatabaeenejad, Alireza Moghaddam, Mahta Mapping tree canopy cover and canopy height with L-band SAR using LiDAR data and Random Forests |
author_facet |
Chen, Richard H Pinto, Naiara Duan, Xueyang Tabatabaeenejad, Alireza Moghaddam, Mahta |
author_sort |
Chen, Richard H |
title |
Mapping tree canopy cover and canopy height with L-band SAR using LiDAR data and Random Forests |
title_short |
Mapping tree canopy cover and canopy height with L-band SAR using LiDAR data and Random Forests |
title_full |
Mapping tree canopy cover and canopy height with L-band SAR using LiDAR data and Random Forests |
title_fullStr |
Mapping tree canopy cover and canopy height with L-band SAR using LiDAR data and Random Forests |
title_full_unstemmed |
Mapping tree canopy cover and canopy height with L-band SAR using LiDAR data and Random Forests |
title_sort |
mapping tree canopy cover and canopy height with l-band sar using lidar data and random forests |
publisher |
Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2020 |
publishDate |
2022 |
url |
http://hdl.handle.net/2014/53067 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Alaska |
genre_facet |
Arctic Alaska |
op_relation |
VIRTUAL IGARSS 2020 - IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, Hawaii, September 26 - October 2, 2020 CL#20-2930 http://hdl.handle.net/2014/53067 |
_version_ |
1766334576752328704 |