低密度LiDARデータによる人工針葉樹林の林分パラメータの推定

摘要

Forest stand variables (mean diameter, mean height, stand volume, stand density, and stand carbon stock) were estimated in manmade coniferous forest stands comprising Hinoki {Chamaecyparis obtuse) and Sugi (Cryptomeria japonica), using low-density light detection and ranging (LiDAR). LiDAR data were obtained on a transect in western. Shikoku Island using 2 pulses per square meter. We chose young to old plots along the transect and measured forest stand variables. LiDAR indices were derived for the first and last pulse of a digital canopy height model: average; maximum; coefficient of variation; 10, 20, …, 90 percentiles; and 10, 20, …, 90% canopy density. A logarithmic multiple regression analysis, a linear single regression analysis with variable selection, and a vegetation profile method were employed to compare LiDAR indices and forest stand variables with Root Mean Square Error (RMSE). Among those methods, the linear single regression was the most precise in terms of average height (RMSE: 1.5 meter), and the logarithmic multiple regression was most precise for mean diameter (RMSE: 3.1 centimeter), stand density (RMSE: 837 trees per hectare), stand volume (RMSE: 68.2 cubic meter per hectare), and stand carbon stock (RMSE: 18.0 ton per hectare). Although the vegetation profile method was less precise for stand volume (RMSE: 105.7 cubic meter per hectare), the slope of the vegetation profile equation was stable in the results.%本論では，低密度LiDARを用いて，代表的な林分パラメータである平均直径，平均樹高，本数密度，林分材積，炭素蓄積量を広域推定する手法を検討した。広域での林分パラメータ推定をする際に，航空機LiDARは，観測費用がかかるという欠点があるが,推定精度が高く，広域の推定処理が容易な優れた手法である。より精度の高い情報に基づく森林の管理と評価のために，基礎的あるいは応用的の両面から航空機LiDARによる林分パラメータの広域把握手法について，今後もさらに研究を進める必要がある。