With the launch of the ICESat-2 satellite, global-scale forest parameter monitoring has entered a new phase. However, the background noise in ICESat-2 lidar data significantly impairs the accuracy of signal photon extraction. This study introduces a direction-adaptive DBSCAN method for denoising ICESat-2 photon point clouds, integrating elevation histogram-based coarse denoising with adaptive clustering for fine denoising. The method is applied to data from the Gongbella River Nature Reserve. An innovative aspect of this approach is the introduction of elliptical tilt angle adaptation, which dynamically adjusts the elliptical orientation of the photon point cloud to determine the optimal tilt angle, thus optimizing the denoising effect and reducing computational and memory demands. The direction-adaptive DBSCAN algorithm improves denoising accuracy by dynamically adjusting the neighborhood radius based on the elliptic tilt angle and the distribution of the point cloud. Additionally, the density threshold selection is optimized using the Otsu method, enhancing the accuracy of distinguishing noise photons from signal photons. The method was validated using data from the Gongbella River Nature Reserve, showing significant improvements in denoising accuracy. Compared to existing methods, recall (R) increased by 6.91%, precision (P) improved by 8.82%, and both the F1-score and accuracy rose by 9.52%. The photon point cloud denoising algorithm demonstrated substantial accuracy improvements across multiple data strips, making it particularly effective for processing complex data from ICESat-2, with broad application potential. Both quantitative and qualitative analyses confirm that the algorithm outperforms traditional methods in signal-to-noise ratio and denoising performance, providing reliable technical support for extracting photon point cloud elevation data from forest surfaces and canopies.

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.

The precise prevention and control of forest pests and diseases has always been a research hotspot in ecological environmental protection. With the continuous advancement of sensor technology, the fine-grained identification of discolored tree crowns based on UAV technology has become increasingly important in forest monitoring. Existing deep learning models face challenges such as prolonged training time and low recognition accuracy when identifying discolored tree crowns caused by pests or diseases from airborne images. To address these issues, this study improves the Faster-RCNN model by using Inception-ResNet-V2 as the feature extractor, replacing the traditional VGG16 feature extractor, aiming to enhance the accuracy of discolored tree crown recognition. Experiments and analyses were conducted using UAV aerial imagery data from Jilin Changbai Mountain. The improved model effectively identified discolored tree crowns caused by pine wood nematodes, achieving a precision of 90.22%, a mean average precision (mAP) of 83.63%, and a recall rate of 92.33%. Compared to the original RCNN model, the mAP of the improved model increased by 4.68%, precision improved by 10.11%, and recall improved by 5.23%, significantly enhancing the recognition performance of discolored tree crowns. This method provides crucial technical support and scientific basis for the prevention and control of forest pests and diseases, facilitating early detection and precise management of forest pest outbreaks.