Abstract
Accurate vegetation information is essential for analyzing above-ground biomass and understanding subsurface characteristics, such as root biomasss, soil <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">or</sup> ganic <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ma</sup> 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.
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