In the recent decade, people’s interest for forests has increased tremendously throughout the globe. So, providing good forest landscape space to visitors has become an important issue for the landscape designers. Because, good forest resource planning and development can not only drive the sustainable development of the forest industry, but also its ripple effect can induce improvement in agricultural industry and economy in the region. The present study identified the evaluative behaviors among visitors walking on the trails of Akasawa national recreational forest, the birthplace of forest bathing in Japan. The study determined the factors that influenced the satisfaction rates of the visitors. The significance of this study is not only to improve the planning and design of the forest space itself, but also to provide theoretical guidance for the planning of the regional landscape of the rural areas attached to it. The study results revealed, visitors’ subjects for evaluation changed with the surrounding environment. While visitors appreciated large-scale landscape elements, such as “large-size trees” and “buttress roots” within coniferous forests. They valued small-scale landscape elements such as “flowering plants” within broad-leaved or mixed forests. Another important issue came out as a result of the present study is that the light and dark changes in the forest walking trail can continuously awaken the evaluation behavior of tourists. In this study visitors displayed evaluative behaviors most frequently on the Mukaiyama Course. It is rich in different types of forest stands and has variance illumination levels. Factors that influence the satisfaction rates of visitors were “landscape”, “vegetation”, “trail condition” and “ease of walking”. Factors that influence the frequency of evaluative behaviors included “landscapes” surrounding the trails and “ease of walking”. Through this study we can see that when we need to plan the forest spatial walking trails, it is very necessary to consider small-scale landscape elements, rich in different types of forest stands with varying illumination levels.

The accurate and scalable estimation of carbon stocks in living biomass remains challenging in structurally heterogeneous forests subjected to different silvicultural treatments. This study presents a multi-sensor machine learning framework that integrates unmanned aerial vehicle (UAV)-derived multispectral imagery with UAV- and airborne light detection and ranging (LiDAR) data for spatially explicit carbon stock estimation in managed forests of central and eastern Hokkaido, northern Japan. Field measurements from 38 plots were used for model development and validation. Spectral features derived from UAV multispectral imagery and structural metrics derived from UAV and airborne LiDAR data were integrated within a multi-sensor framework and evaluated using Multiple Linear Regression (MLR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), with MLR serving as a baseline model. A key objective was to quantify the relative contributions of spectral and structural sensing information for carbon stock estimation in silviculturally treated forests through the systematic comparison of canopy height model (CHM)-only, RGB + CHM, and multispectral + CHM datasets. The machine learning models consistently outperformed the baseline MLR model, with XGBoost generally outperforming RF and achieving a maximum validation R² of 0.88 and root mean squared error (RMSE) of 27.41 Mg C ha⁻¹. Although the improvement in plot-level prediction accuracy over the CHM-only configuration was modest, integrating multispectral imagery with LiDAR-derived structural metrics reduced prediction errors and systematic bias in wall-to-wall carbon stock mapping. These findings highlight the complementary roles of structural and spectral remote sensing information for spatially explicit carbon stock estimation in silviculturally treated forests.