Yasushi Minowa and Tsukumo Nakanishi: Relationship between sample size and classification accuracy for tree species identification based on leaf shape. Jpn. J. For. Plann. 55: 61~75, 2022　The aim of this study was to verify the relationship between sample size and classification accuracy for tree species identification based on leaf shape. In this study, 6,516 leaves （20 species, 250-350 leaves of each tree species） served as samples. The following leaf-shape parameters were measured: circularity, ratio of minor axis to major axis for fitting optimal elliptical, ratio of perimeter to optimal elliptical orbit, and convexity. Two decision-tree algorithms （J48, random forest） and a neural network （multilayer perceptron） were used for machine-learning classification. A performance evaluation of the proposed model was performed using the Matthews correlation coefficient （MCC）. The estimation method of the necessary sample size for measuring each leaf-shape parameter used the design method of sample size with power analysis. The MCC of the training data ranged from 0.755 to 1.000, and that of the test data ranged from 0.737 to 0.744. Every machine learning model resulted in high classification accuracy for training and test data, respectively. Almost all models showed a relatively high classification accuracy when the number of tree species was small; however, the classification accuracy tended to decrease with increasing number of tree species. When the sample sizes were small, the classification accuracy of each machine learning model was low. Even if the sample sizes were larger, the classification accuracy did not necessarily improve for any machine learning model. The results of the estimation for sample size for each leaf-shape parameter suggested that the sample size in this study was insufficient to identify tree species from leaf shape.

Tomohiro Nishizono, Eiji Kodani, Hidesato Kanomata, Kazuo Hosoda, Keiko Fukumoto, Yosuke Yamada and Tomomasa Amano: Optimal harvest age based on timber production profitability of Japanese cedar-planted forest: analyzing data from long-term experimental monitoring plots in the Japan Sea side of the Tohoku region, Northern Japan. Jpn. J. For. Plann. 55: 77~93, 2022　We analyzed data from 29 long-term experimental monitoring plots installed in Japanese cedar （Cryptomeria japonica D. Don）-planted forest in Northern Japan to examine the optimal harvest age based on timber production profitability. We investigated the change in forest rent （FR） and internal rate of return （IRR） with changing harvest age. Under current economic conditions, the indexes in half of the plots were typically negative for all harvest ages. We could not determine the optimal harvest age using the indexes; however under current economic conditions, with subsidies, the indexes in all plots were positive above a certain harvest age. Under this condition, FR and IRR reached their maximums in 72-102 years （mean, 87.3 years） and 58-97 years （mean, 80 years）, respectively. The relationship of harvest age on timber production profitability for FR indicated that older harvest ages are suitable for the examined forests. Also, the relationship for IRR indicated that for forests with low productivity older harvest age is suitable, and that for forests with high productivity, thinning practice can change and control the optimum harvest age （earlier and older）.

Tatsuki Yoshii and Naoto Matsumura: Validation of tree height measurement with UAV-SfM technique combining ALS data using ICP algorithm. Jpn. J. For. Plann. 55: 95~101, 2022　The aim of this research is to derive tree height with UAV-SfM technique combining ALS data without a GCPs setting. A 3D point cloud acquired by the UAV-SfM was georeferenced with an ICP algorithm using an ALS point cloud. A CHM was calculated by subtraction between a DSM with the georeferenced point cloud acquired by the UAV and a DTM acquired by the ALS. The result of the accuracy assessment showed that RMSE of tree height was 0.30m. The result suggests that a combination of an UAV-SfM technique and ALS is valid for tree height measurement.