Pine wilt disease (PWD) poses a significant threat to global pine resources because of its rapid spread and management challenges. This study uses high-resolution helicopter imagery and the deep learning model You Only Look Once version 7 (YOLO v7) to detect symptomatic trees in forests. Attention mechanism technology from artificial intelligence is integrated into the model to enhance accuracy. Comparative analysis indicates that the YOLO v7-SE model exhibited the best performance, with a precision rate of 0.9281, a recall rate of 0.8958, and an F1 score of 0.9117. This study demonstrates efficient and precise automatic detection of symptomatic trees in forest areas, providing reliable support for prevention and control efforts, and emphasizes the importance of attention mechanisms in improving detection performance.

The rapid and accurate estimation of forest carbon stock is important for analyzing the carbon cycle. In order to obtain forest carbon stock efficiently, this paper utilizes airborne LiDAR data to research the applicability of different feature screening methods in combination with machine learning in the carbon stock estimation model. First, Spearman’s Correlation Coefficient (SCC) and Extreme Gradient Boosting tree (XGBoost) were used to screen out the variables that were extracted via Airborne LiDAR with a higher correlation with carbon stock. Then, Bagging, K-nearest neighbor (KNN), and Random Forest (RF) were used to construct the carbon stock estimation model. The results show that the height statistical variable is more strongly correlated with carbon stocks than the density statistical variables are. RF is more suitable for the construction of the carbon stock estimation model compared to the instance-based KNN algorithm. Furthermore, the combination of the XGBoost algorithm and the RF algorithm performs best, with an R² of 0.85 and an MSE of 10.74 on the training set and an R² of 0.53 and an MSE of 21.81 on the testing set. This study demonstrates the effectiveness of statistical feature screening methods and Random Forest for carbon stock estimation model construction. The XGBoost algorithm has a wider applicability for feature screening.