The existence of humans and the preservation of the natural ecological equilibrium depend greatly on trees. The semantic segmentation of trees is very important. It is crucial to learn how to properly and automatically extract a tree’s elements from photographic images. Problems with traditional tree image segmentation include low accuracy, a sluggish learning rate, and a large amount of manual intervention. This research suggests the use of a well-known network segmentation technique based on deep learning called Yolo v7 to successfully accomplish the accurate segmentation of tree images. Due to class imbalance in the dataset, we use the weighted loss function and apply various types of weights to each class to enhance the segmentation of the trees. Additionally, we use an attention method to efficiently gather feature data while reducing the production of irrelevant feature data. According to the experimental findings, the revised model algorithm’s evaluation index outperforms other widely used semantic segmentation techniques. In addition, the detection speed of the Yolo v7 model is much faster than other algorithms and performs well in tree segmentation in a variety of environments, demonstrating the effectiveness of this method in improving the segmentation performance of the model for trees in complex environments and providing a more effective solution to the tree segmentation issue.

Traditional field surveys are expensive, time-consuming, laborious, and difficult to perform, especially in mountainous and dense forests, which imposes a burden on forest management personnel and researchers. This study focuses on predicting forest growing stock, one of the most significant parameters of a forest resource assessment. First, three schemes were designed—Scheme 1, based on the study samples with mixed tree species; Scheme 2, based on the study samples divided into dominant tree species groups; and Scheme 3, based on the study samples divided by dominant tree species groups—the evaluation factors are fitted by least-squares equations, and the non-significant fitted-factors are removed. Second, an overall evaluation indicator system with 17 factors was established. Third, remote sensing images of Landsat Thematic Mapper, digital elevation model, and the inventory for forest management planning and design were integrated in the same database. Lastly, a backpropagation neural network based on the Levenberg–Marquardt algorithm was used to predict the forest growing stock. The results showed that the group estimation precision exceeded 90%, which is the highest standard of total sampling precision of inventory for forest management planning and design in China. The prediction results for distinguishing dominant tree species were better than for mixed dominant tree species. The results also showed that the performance metrics for prediction could be improved by least-squares equation fitting and significance filtering of the evaluation factors.

The forest growing stock is one of the key indicators in monitoring forest resources, and its quantitative estimation is of great significance. Based on multi-source data, including Sentinel-1 radar remote sensing data, Sentinel-2 optical remote sensing data, digital elevation model (DEM), and inventory data for forest management planning and design, the Lasso feature selection method was used to remove the non-significant indicators, and three machine learning algorithms, GBDT, XGBoost, and CatBoost, were used to estimate forest growing stock. In addition, four category features, forest population, dominant tree species, humus thickness, and slope direction, were involved in estimating forest growing stock. The results showed that the addition of category features significantly improved the performance of the models. To a certain extent, radar remote sensing data also could improve estimating accuracy. Among the three models, the CatBoost model (R² = 0.78, MSE = 0.62, MAE = 0.59, MAPE = 16.20%) had the highest estimating accuracy, followed by XGBoost (R² = 0.75, MSE = 0.71, MAE = 0.62, MAPE = 18.28%) and GBDT (R² = 0.72, MSE = 0.78, MAE = 0.68, MAPE = 20.28%).

The diameter at breast height (DBH) is an important factor used to estimate important forestry indices like forest growing stock, basal area, biomass, and carbon stock. The traditional DBH ground surveys are time-consuming, labor-intensive, and expensive. To reduce the traditional ground surveys, this study focused on the prediction of unknown DBH in forest stands using existing measured data. As a comparison, the tree age was first used as the only independent variable in establishing 13 kinds of empirical models to fit the relationship between the age and DBH of the forest subcompartments and predict DBH growth. Second, the initial independent variables were extended to 19 parameters, including 8 ecological and biological factors and 11 remote sensing factors. By introducing the Spearman correlation analysis, the independent variable parameters were dimension-reduced to satisfy very significant conditions (p ≤ 0.01) and a relatively large correlation coefficient (r ≥ 0.1). Finally, the remaining independent variables were involved in the modeling and prediction of DBH using a multivariate linear regression (MLR) model and generalized regression neural network (GRNN) model. The (root-mean-squared errors) RMSEs of MLR and GRNN were 1.9976 cm and 1.9655 cm, respectively, and the R² were 0.6459 and 0.6574 respectively, which were much better than the values for the 13 traditional empirical age–DBH models. The use of comprehensive factors is beneficial to improving the prediction accuracy of both the MLR and GRNN models. Regardless of whether remote sensing image factors were included, the experimental results produced by GRNN were better than MLR. By synthetically introducing ecological, biological, and remote sensing factors, GRNN produced the best results with 1.4688 cm in mean absolute error (MAE), 13.78% in MAPE, 1.9655 cm for the RMSE, 0.6574 for the R², and 0.0810 for the Theil’s inequality coefficient (TIC), respectively. For modeling and prediction based on more complex tree species and a wider range of samples, GRNN is a desirable model with strong generalizability.

Diameter at breast height (DBH) is the main metric for standing tree growth measurements. Developing fast and efficient measurement devices for measuring DBH is of great values in forest resource surveys. In this paper, we propose a new tree diameter measurement instrument developed using self-reset displacement sensor, which is equipped with a Personal Computer (PC) terminal to enable the measurement, transmission, storage, and analysis of tree diameters in one. The measurements showed an overall bias of 1.44% and an accuracy of 97.35% compared to the caliper, a conventional diameter measuring instrument. The overall bias was −0.52% compared to the diameter tape, and the measurement accuracy was 98.43%. Compared to the conventional devices, the proposed device is small and easy to carry, the measurement efficiency is significantly improved, and the measurement data can be directly derived for analysis. Compared with related electronic measuring instruments, this proposed device has the advantages of small volume, easy operation, large measurement range, and wider applicability.

The forest stock volume (FSV) is one of the crucial indicators to reflect the quality of forest resources. Variable selection methods are usually used for FSV estimated models. However, few studies have explored which variable selection methods can make the selected data set have better explanatory and robustness for the same dominant tree species in different regions after the feature variables were filtered by the feature selection methods. In this study, we chose six dominant tree species from Lin’an District, Anji County, and a part of Longquan City. The tree species include broad-leaved, coniferous, Masson pine, Chinese fir, coniferous and broad-leaved mixed forest, and all tree species which include the above five groups of tree species. The last two tree species were represented by mixed and all, respectively. Then, the satellite images, terrain factors, and forest inventory data were selected by six variable selection methods (least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), stepwise regression (Step-Reg), permutation importance (PI), mean decrease impurity (MDI), and SelectFromModel based on LightGBM (SFM)), according to different dominant tree types in different regions. The selected variables were formed into a new dataset divided by different dominant trees. Besides, extreme gradient boosting (XGBoost) was used, combined with variable selection methods to estimate the FSV. The performed results are as follows: In the feature selection of coniferous, RFE performed better both in the average and in the separate regions. In the feature selection of Chinese fir and all, PI performed better both in the average and in the separate regions. In the feature selection of Masson pine, MDI performed better both in the average and in the separate regions. In the feature selection of mixed, MDI performed better in the average while RFE performed better in the separate regions comprehensively. The results showed that not only in separate regions, but the average result two factors, RFE, MDI, and PI all performed well to select variables to estimate the FSV. Furthermore, we selected the top five high feature-importance factors of different tree types, and the results showed that tree age and canopy density were both of great importance to the estimation of FSV. Besides, in the exhibited results of feature selection methods, compared with no variable selection, the research also found that variable selection can improve the performance of the model. Additionally, from the results of different tree types in different regions, we also found that small-scale and diversity of dominant tree types may lead to the instability and unreliability of experimental results. The study provides some insight into the application the optimal variable selection methods of the same dominant tree type in different regions. This study will help the development of variable selection methods to estimate FSV.