Subtropical “Yambaru” forest, situated in the northern part of Okinawa Island of Japan, has a precious ecosystem inhabited by many endemic species. However, this region is also the center for forestry on Okinawa. Th...Subtropical “Yambaru” forest, situated in the northern part of Okinawa Island of Japan, has a precious ecosystem inhabited by many endemic species. However, this region is also the center for forestry on Okinawa. Therefore, sustainable forestry activities should take into consideration the natural environment. To contribute to sustainable forest management in the region, we conducted prediction of the site index at fine-scale resolution by using multiple regression analysis with easily calculated topographic factors. For the multiple regression analysis with site index as a dependent variable, three topographic factors (the effective relief, openness, and elevation) were adopted as independent variables. Approximately 68% of the variance was explained, and the effective relief was the variable with the greatest influence. This means that it is possible to predict forest productivity at a finer scale of resolution than ever before. For sustainable forest management of sites where environmental conservation and forestry are conflicting, it is useful to estimate the site index at the finest scale of resolution practically available in the field. It might be possible to improve estimation accuracy by examining further environmental factors in the future.展开更多
文摘Subtropical “Yambaru” forest, situated in the northern part of Okinawa Island of Japan, has a precious ecosystem inhabited by many endemic species. However, this region is also the center for forestry on Okinawa. Therefore, sustainable forestry activities should take into consideration the natural environment. To contribute to sustainable forest management in the region, we conducted prediction of the site index at fine-scale resolution by using multiple regression analysis with easily calculated topographic factors. For the multiple regression analysis with site index as a dependent variable, three topographic factors (the effective relief, openness, and elevation) were adopted as independent variables. Approximately 68% of the variance was explained, and the effective relief was the variable with the greatest influence. This means that it is possible to predict forest productivity at a finer scale of resolution than ever before. For sustainable forest management of sites where environmental conservation and forestry are conflicting, it is useful to estimate the site index at the finest scale of resolution practically available in the field. It might be possible to improve estimation accuracy by examining further environmental factors in the future.
