Mapping Boreal Forest Species and Canopy Height using Airborne SAR and Lidar Data in Interior Alaska

Accurate vegetation information is essential for analyzing above-ground biomass and understanding subsurface characteristics, such as root biomasss, soil or ganic ma tter and soil moisture profiles. This paper investigates novel mappings of forest species and canopy height in interior Alaska. We emp...

Full description

Saved in:
Bibliographic Details
Main Authors: Zhao, Yuhuan, Chen, Richard H., Bakian-Dogaheh, Kazem, Whitcomb, Jane, Yi, Yonghong, Kimball, John S., Moghaddam, Mahta
Format: Conference Proceeding
Language:English
Subjects:
Biological system modeling
FIA
forest canopy height
forest species
Forestry
G-LiHT
Laser radar
Radio frequency
Random Forests
Scattering
Soil moisture
TVSF timber inventory
UAVSAR
Vegetation mapping
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Accurate vegetation information is essential for analyzing above-ground biomass and understanding subsurface characteristics, such as root biomasss, soil or ganic ma tter 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.
ISSN:2153-7003
DOI:10.1109/IGARSS46834.2022.9883311