Diameter-height curve is a function that defines the relationship between diameter at breast height and tree height. It is the mean trend on a scatter plot that shows the relationship between tree diameter at breast height and tree height. This curve shifts to the upper right corner as the tree ages in a uniformly aged plantation forest. Hence, it is commonly used to estimate the parameters of a model that describes the diameter-height curve by tree age. The transition in diameter-height curve with age can be interpreted as a change in the parameters of the model over time. However, the behavior of the parameters estimated independently from age-specific data may not accurately capture the features in the transition of the diameter-height curves, due to the variabilities of observations within each age of a forest stand. Against this background, in this paper, we introduce varying coefficients into the regression model for diameter-height curve to describe the transition of diameter-height curves as a function of time. The proposed method is then applied to a forest growth data of sugi (Cryptomeria japonica) stand in a village in Hoshino, Japan.

This paper focuses on the selection of height-diameter curve (HDC) which characterizes the relationship between tree height and diameter at breast height (DBH) for growth prediction. Tree height and DBH are commonly used variables in monitoring forest growth and predicting its stock. To select the appropriate HDC among multiple candidates, empirical rules or mathematical approaches based on the residual sum of squares have been applied in previous research. In this paper we apply cross-validation (CV) criterion to select an appropriate HDC for the purpose of forecasting. The CV criterion is easy to use because it is based on simple iterative process without any assumptions required on the candidate models for HDC. Not only is CV easy to use, it also evaluates forecast accuracy, which is consistent with the objectives for HDC use. In this paper, we demonstrate the results for analyzing real life data of sugi (Cryptomeria japonica) stands in Japan by preparing five candidates of HDC. We also show the results of numerical experiments for verifying the ability of the method introduced in this paper.

In this work we discuss possibilities and challenges in utilization of several statistical methods for assessment of forest resources related to forest inventories, especially question of dataset size where the time and resources required for data collection are often in contrast to sample size and analysis of all potential parameters of potential models. The combination of a priori knowledge of the phenomena being studied (tree number, wood volume…) and understanding of behavior of individual variables provided by remote sensing instruments (different predictor variables) is crucial for production of reliable models for forest resource assessment. Using our dataset we compared two regression techniques and one machine learning for predictor analysis for wood volume estimation. All techniques in general provided similar results in terms of variable importance and accuracy, but in more detailed analysis differences appeared, indicating that if possible biological knowledge and understanding of variables should not be neglected.

Growth and yield projections and current inventory assessment of a forest stand are essential tasks for successfully conducting sustainable forest management. However, much is still unknow about the amount and distribution of forest resources in subtropical island of Okinawa, Japan. Therefore, there is an urgent need to develop an effective and efficient measurement system to evaluate various forest inventory parameters such as tree diameter, tree height, and tree volume for an intricately shaped tree species common in subtropical areas. In recent years there has been rising interest in the so-called Unmanned Aerial Vehicle (UAV) - Structure-from-Motion (SfM) - Multi-View Stereo (MVS) (UAV-SfM-MVS) survey approach, which processes image data captured by UAV to develop 3D models and allows the efficient estimation of various forest inventory parameters of standing trees, without the alteration of the surrounding environment. However, the application of UAV-SfM-MVS survey approach in subtropical areas is scarce. With the aim of establishing a relatively simple but accurate forest measurement method that allows for efficient data collection of broad-leaved tree species common in a subtropical forest, we were able to conduct field experiments and acquire video data from a drone flown under tree canopy. In this paper, we developed a 3D models using SfM-MVS technique to estimate tree diameter and volume. To validate our approach, the proposed method was compared with ground truth measurements. We concluded that our approach was able to estimate tree diameters and stem volumes in our surveyed plots with a high degree of accuracy at root mean square error (RMSE) of 0.4 ~ 0.7 cm for DBH and RMSE of 0.0045 ~ 0.0147 m³ for stem volume within a shorter survey time. Although modeling of tree crown and tree parts where understory vegetation impeded camera view remains a topic for future research, we were able to demonstrate that under canopy UAV-SfM-MVS survey approach is relatively simple but provide highly accurate measurements of standing trees in subtropical forests without altering the surrounding environment.

The trend of men’s out-migration has been increasing in recent years, resulting in an additional workload on women in the rural communities of Nepal. The consequent impact of such out-migration on community-based forest management is not well known. In this context, this study attempts to determine the effect of men’s out-migration on women’s participation in forestry activities and the factors that affect the participation level. The study results are drawn from household surveys, focus group discussions, key informant surveys, and village meetings in the Dhodsing community forest user group of Sundarbazar municipality-8, Lamjung District of Nepal. The finding shows that despite having less technical knowledge, women’s involvement in forest conservation increased after men’s out-migration. The main reasons behind the increase in women’s participation was the exposure of women to social and conservation works. Household income, presence of mother-in-law in the house, level of women’s education, the primary occupation of households, self-employment status, having children, and livestock status are the factors that influence women’s participation in forestry activities. However, age of the of the household head, family size, household type, migration duration, and place of migration does not affect the participation level. Men’s out-migration has increased women’s workload but has contributed to the household economy in the study area. We suggest that community forest user groups embrace women’s participation as an opportunity to empower by providing them with appropriate practical forestry skills and building women’s ownership in the community forestry decision-making process.