TY - GEN
T1 - Mapping Boreal Forest Species and Canopy Height using Airborne SAR and Lidar Data in Interior Alaska
AU - Zhao, Yuhuan
AU - Chen, Richard H.
AU - Bakian-Dogaheh, Kazem
AU - Whitcomb, Jane
AU - Yi, Yonghong
AU - Kimball, John S.
AU - Moghaddam, Mahta
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Accurate vegetation information is essential for analyzing above-ground biomass and understanding subsurface characteristics, such as root biomasss, soil organic matter and soil moisture profiles. This paper investigates novel mappings of forest species and canopy height in interior Alaska. We employ Random Forests to train a regression model for canopy height mapping and a classification model for forest species mapping utilizing L-band and P-band Uninhabited Aerial Vehicle Synthetic Aperture Radar(UAVSAR). For canopy height, canopy height model (CHM) data derived from Goddard's LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) are treated as ground truth. For forest species prediction, Tanana Valley State Forest (TVSF) Timber Inventory and Forest Inventory and Analysis (FIA) data are used as reference. The experimental results show the proposed method yields a root-mean-square error of 1.90 m for forest height estimation and overall accuracy of 79.54% for forest species classification. They also demonstrate the feasibility of obtaining precise vegetation information by data-driven methods, which can be further used to enhance forest radar scattering forward models.
AB - Accurate vegetation information is essential for analyzing above-ground biomass and understanding subsurface characteristics, such as root biomasss, soil organic matter and soil moisture profiles. This paper investigates novel mappings of forest species and canopy height in interior Alaska. We employ Random Forests to train a regression model for canopy height mapping and a classification model for forest species mapping utilizing L-band and P-band Uninhabited Aerial Vehicle Synthetic Aperture Radar(UAVSAR). For canopy height, canopy height model (CHM) data derived from Goddard's LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) are treated as ground truth. For forest species prediction, Tanana Valley State Forest (TVSF) Timber Inventory and Forest Inventory and Analysis (FIA) data are used as reference. The experimental results show the proposed method yields a root-mean-square error of 1.90 m for forest height estimation and overall accuracy of 79.54% for forest species classification. They also demonstrate the feasibility of obtaining precise vegetation information by data-driven methods, which can be further used to enhance forest radar scattering forward models.
KW - FIA
KW - G-LiHT
KW - Random Forests
KW - TVSF timber inventory
KW - UAVSAR
KW - forest canopy height
KW - forest species
UR - http://www.scopus.com/inward/record.url?scp=85140383256&partnerID=8YFLogxK
U2 - 10.1109/IGARSS46834.2022.9883311
DO - 10.1109/IGARSS46834.2022.9883311
M3 - Conference contribution
AN - SCOPUS:85140383256
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4955
EP - 4958
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Y2 - 17 July 2022 through 22 July 2022
ER -