Temperate forests in Japan are generally divided into cool-temperate and warm-temperate zones, but the “intermediate temperate forests” located between them have not yet been clearly defined in terms of classification criteria or vegetation characteristics. Abies firma, an evergreen conifer adapted to cool-temperate climates, is regarded as an important indicator species for these transitional zones. The objective of this study was to identify the individual distribution of Abies firma within natural forests by combining UAV aerial imagery with LiDAR-derived canopy height data, applying machine learning and crown-based classification methods. UAV surveys were conducted in three different seasons, and automatic detection of Abies individuals was performed. The highest detection accuracy was obtained from imagery captured in April, when forest canopy colors showed clear seasonal variation and shadows were minimal. In this case, recall reached 71% and precision 86%. Furthermore, analysis of the relationship between detected Abies distribution and topographic factors revealed that shorter individuals tended to occur more frequently in gently sloping flat areas. These findings suggest that intermediate temperate forests dominated by Abies provide favorable conditions for accurate detection, and they demonstrate the effectiveness of remote sensing approaches for automated tree species identification.
TAKEI et al. (Thu,) studied this question.