• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.461-467
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• 2020

The aim of this study was to utilize Kozak's stem taper model to develop both a stem taper equation and a stem volume table for Criptomeria japonica, a tree species distributed across Korea. A total of 1,000 sample trees were cut and collected across the country to measure their diameters by stem height. The equation was then used to estimate examine their stem shapes. Our results show that the Fitness Index for the equation was 98.7%, the Mean Absolute Deviation (MAD) was -0.0142, and the MAD was 1.1640, thus indicating a high level of fitness. A statistically significant difference (p < 0.05) was also found from the analysis of discrepancies between a current table and the new table used in this study. It is therefore suggested that the new table-with data from actual stands-will contribute to enhancing the accuracy of national and municipal forest statistics and reducing losses caused by imprecise data on available forest resources.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.245-256
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• 2021

This study was conducted to identify the applicability of a LiDAR sensor to forest resources inventories by comparing data on a tree's position, height, and DBH obtained by the sensor with those by existing forest inventory methods, for the tree species of Criptomeria japonica in Jeolmul forest in Jeju, South Korea. To this end, a backpack personal LiDAR (Greenvalley International, Model D50) was employed. To facilitate the process of the data collection, patterns of collecting the data by the sensor were divided into seven ones, considering the density of sample plots and the work efficiency. Then, the accuracy of estimating the variables of each tree was assessed. The amount of time spent on acquiring and processing the data by each method was compared to evaluate the efficiency. The findings showed that the rate of detecting standing trees by the LiDAR was 100%. Also, the high statistical accuracy was observed in both Pattern 5 (DBH: RMSE 1.07 cm, Bias -0.79 cm, Height: RMSE 0.95 m, Bias -3.2 m), and Pattern 7 (DBH: RMSE 1.18 cm, Bias -0.82 cm, Height: RMSE 1.13 m, Bias -2.62 m), compared to the results drawn in the typical inventory manner. Concerning the time issue, 115 to 135 minutes per 1ha were taken to process the data by utilizing the LiDAR, while 375 to 1,115 spent in the existing way, proving the higher efficiency of the device. It can thus be concluded that using a backpack personal LiDAR helps increase efficiency in conducting a forest resources inventory in an planted coniferous forest with understory vegetation, implying a need for further research in a variety of forests.