Accurate individual tree detection by unmanned aerial vehicles (UAVs) is a critical technique for smart forest management and serves as the foundation for evaluating ecological functions. Existing object detection and segmentation methods, on the other hand, have reduced accuracy when detecting and segmenting individual trees in complicated urban forest landscapes, as well as poor mask segmentation quality. This study proposes a novel Mask-CSP-attention-coupled network (MCAN) based on the Mask R-CNN algorithm. MCAN uses the Cross Stage Partial Net (CSPNet) framework with the Sigmoid Linear Unit (SiLU) activation function in the backbone network to form a new Cross Stage Partial Residual Net (CSPResNet) and employs a convolutional block attention module (CBAM) mechanism to the feature pyramid network (FPN) for feature fusion and multiscale segmentation to further improve the feature extraction ability of the model, enhance its detail information detection ability, and improve its individual tree detection accuracy. In this study, aerial photography of the study area was conducted by UAVs, and the acquired images were used to produce a dataset for training and validation. The method was compared with the Mask Region-based Convolutional Neural Network (Mask R-CNN), Faster Region-based Convolutional Neural Network (Faster R-CNN), and You Only Look Once v5 (YOLOv5) on the test set. In addition, four scenes—namely, a dense forest distribution, building forest intersection, street trees, and active plaza vegetation—were set up, and the improved segmentation network was used to perform individual tree segmentation on these scenes to test the large-scale segmentation ability of the model. MCAN’s average precision (AP) value for individual tree identification is 92.40%, which is 3.7%, 3.84%, and 12.53% better than that of Mask R-CNN, Faster R-CNN, and YOLOv5, respectively. In comparison to Mask R-CNN, the segmentation AP value is 97.70%, an increase of 8.9%. The segmentation network’s precision for the four scenes in multi-scene segmentation ranges from 95.55% to 92.33%, showing that the proposed network performs high-precision segmentation in many contexts.

Moso bamboo forests, recognized as a distinctive and significant forest resource in subtropical China, contribute substantially to efficient carbon sequestration. The accurate assessment of the aboveground biomass (AGB) in Moso bamboo forests is crucial for evaluating their impact on the carbon balance within forest ecosystems at a regional scale. In this study, we focused on the Moso bamboo forest located in Shanchuan Township, Zhejiang Province, China. The primary objective was to utilize various data sources, namely UAV-LiDAR (UL), Sentinel-2 (ST), and a combination of UAV-LiDAR with Sentinel-2 (UL + ST). Employing the Boruta algorithm, we carefully selected characterization variables for analysis. Our investigation delved into establishing correlations between UAV-LiDAR characterization parameters, Sentinel-2 feature parameters, and the aboveground biomass (AGB) of the Moso bamboo forest. Ground survey data on Moso bamboo forest biomass served as the basis for our analysis. To enhance the accuracy of AGB estimation in the Moso bamboo forest, we employed three distinct modeling techniques: multivariate linear regression (MLR), support vector regression (SVR), and random forest (RF). Through this approach, we aimed to compare the impact of different data sources and modeling methods on the precision of AGB estimation in the studied bamboo forest. This study revealed that (1) the point cloud intensity of UL, the variables of canopy cover (CC), gap fraction (GF), and leaf area index (LAI) reflect the structure of Moso bamboo forests, and the variables indicating the height of the forest stand (AIH1, AIHiq, and Hiq) had a significant effect on the AGB of Moso bamboo forests, significantly impact Moso bamboo forest AGB. Vegetation indices such as DVI and SAVI in ST also exert a considerable effect on Moso bamboo forest AGB. (2) AGB estimation models constructed based on UL consistently demonstrated higher accuracy compared with ST, achieving R² values exceeding 0.7. Regardless of the model used, UL consistently delivered superior accuracy in Moso bamboo forest AGB estimation, with RF achieving the highest precision at R² = 0.88. (3) Integration of ST with UL substantially improved the accuracy of AGB estimation for Moso bamboo forests across all three models. Specifically, using RF, the accuracy of AGB estimation increased by 97.7%, with R² reaching 0.89 and RMSE reduced by 124.4%. As a result, the incorporation of LiDAR data, which reflects the stand structure, has proven to enhance the accuracy of aboveground biomass (AGB) estimation in Moso bamboo forests when combined with multispectral remote sensing data. This integration serves as an effective solution to address the limitations of single optical remote sensing methods, which often suffer from signal saturation, leading to lower accuracy in estimating Moso bamboo forest biomass. This approach offers a novel perspective and opens up new possibilities for improving the precision of Moso bamboo forest biomass estimation through the utilization of multiple remote sensing sources.

Parameters such as the leaf area index (LAI), canopy chlorophyll content (CCH), and canopy carotenoid content (CCA) are important indicators for evaluating the ecological functions of forests. Currently, rapidly developing unmanned aerial vehicles (UAV) equipped with hyperspectral technology provide advanced technical means for the real-time dynamic acquisition of regional vegetation canopy parameters. In this study, a hyperspectral sensor mounted on a UAV was used to acquire the data in the study area, and the canopy parameter estimation model of moso bamboo forests (MBF) was developed by combining the PROSAIL radiative transfer model and the machine learning regression algorithm (MLRA), inverted the canopy parameters such as LAI, CCH, and CCA. The method first utilized the extended Fourier amplitude sensitivity test (EFAST) method to optimize the global sensitivity analysis and parameters of the PROSAIL model, and the successive projections algorithm (SPA) was used to screen the characteristic wavebands for the inversion of MBF canopy parameter inversion. Then, the optimized PROSAIL model was used to construct the ‘LAI-CCH-CCA-canopy reflectance’ simulation dataset for the MBF; multilayer perceptron regressor (MLPR), extra tree regressor (ETR), and extreme gradient boosting regressor (XGBR) employed used to construct PROSAIL_MLPR, PROSAIL_ETR, and PROSAIL_XGBR, respectively, as the three hybrid models. Finally, the best hybrid model was selected and used to invert the spatial distribution of the MBF canopy parameters. The following results were obtained: Waveband sensitivity analysis reveals 400–490 and 710–1000 nm as critical for LAI, 540–650 nm for chlorophyll, and 490–540 nm for carotenoids. SPA narrows down the feature bands to 43 for LAI, 19 for CCH, and 9 for CCA. The three constructed hybrid models were able to achieve high-precision inversion of the three parameters of the MBF, the model fitting accuracy of PROSAIL_MLRA reached more than 95%, with lower RMSE values, and the PROSAIL_XGBR model yielded the best fitting results. Our study provides a novel method for the inversion of forest canopy parameters based on UAV hyperspectral data.

Optimizing the spatial structure of forests is important for improving the quality of forest ecosystems. Light detection and ranging (LiDAR) could accurately extract forest spatial structural parameters, which has significant advantages in spatial optimization and resource monitoring. In this study, we used unmanned aerial vehicle LiDAR (UAV-LiDAR) and backpack-LiDAR to acquire point cloud data of Metasequoia plantation forests from different perspectives. Then the parameters, such as diameter at breast height and tree height, were extracted based on the point cloud data, while the accuracy was verified using ground-truth data. Finally, a single-tree-level thinning tool was developed to optimize the spatial structure of the stand based on multi-objective planning and the Monte Carlo algorithm. The results of the study showed that the accuracy of LiDAR-based extraction was (R² = 0.96, RMSE = 3.09 cm) for diameter at breast height, and the accuracy of R² and RMSE for tree height extraction were 0.85 and 0.92 m, respectively. Thinning improved stand objective function value Q by 25.40%, with the most significant improvement in competition index CI and openness K of 17.65% and 22.22%, respectively, compared to the pre-optimization period. The direct effects of each spatial structure parameter on the objective function values were ranked as follows: openness K (1.18) > aggregation index R (0.67) > competition index CI (0.42) > diameter at breast height size ratio U (0.06). Additionally, the indirect effects were ranked as follows: aggregation index R (0.86) > diameter at breast height size ratio U (0.48) > competition index CI (0.33). The study realized the optimization of stand spatial structure based on double LiDAR data, providing a new reference for forest management and structure optimization.

(1) Background: A three-dimensional (3D) real scene is a digital representation of the multidimensional dynamic real-world structure that enables the realistic and stereoscopic expression of actual scenarios, and is an important technological tool for urban refinement management. The above-ground biomass (AGB) of urban forests is an important indicator that reflects the urban ecological environment; therefore, the accurate estimation of AGB is of great significance for evaluating urban ecological functions. (2) Methods: In this study, multiangle aerial photographs of urban street trees were obtained via an unmanned aerial vehicle (UAV) single-lens five-way flight, from 0°, 0°, 90°, 180°, 270°, and five other directions. The multiple view stereo (MVS) algorithm was used to construct three-dimensional realistic models of two tree species: ginkgo and camphor. Then, structural parameters such as tree height, crown diameter, and crown volume were estimated from the 3D real-scene models. Lastly, single-tree AGB models were developed based on structural parameters. (3) Results: The results of this study indicated the following: (A) The UAV visible-light realistic 3D model had clear texture and truly reflected the structural characteristics of two tree species, ginkgo and camphor. (B) There was a significant correlation between the reference tree height, crown diameter and crown volume obtained from the realistic 3D model and the measured values; the R² for ginkgo height was 0.90, the R² for camphor crown diameter was 0.87, and the R² for ginkgo crown volume was 0.89. (C) The accuracy of the AGB estimation models constructed with tree height and canopy volume as variables was generally higher than that of models with tree height and canopy diameter; the model with the highest accuracy of AGB estimation for ginkgo was the linear model with a validation accuracy R² of 0.96 and RMSE of 8.21 kg, while the model with the highest accuracy of AGB estimation for camphor was the quadratic polynomial model with a validation accuracy R² of 0.92 and RMSE of 27.74 kg. (4) Conclusions: This study demonstrated that the UAV 3D real-scene model can achieve high accuracy in estimating single-wood biomass in urban forests. In addition, for both tree species, there was no significant difference between the AGB estimates based on the UAV 3D real scene and LiDAR and the measured AGB. These results of urban single-wood AGB estimation based on the UAV 3D real-scene model were consistent with those of LiDAR and even with the measured AGB. Therefore, based on the UAV 3D real-scene model, the single-wood biomass can be estimated with high accuracy. This represents a new technical approach to urban forest resource monitoring and ecological environment function evaluation.

Forest spatial structure is of significant importance for studying forest biomass accumulation and management. However, above-ground biomass (AGB) estimation based on satellite remote sensing struggles to capture forest spatial structure information, which to some extent affects the accuracy of AGB estimation. To address this issue, this study focused on Chinese fir (Cunninghamia lanceolata) plantations in Zhejiang Province. Using UAV-LiDAR (unmanned aerial vehicle light detection and ranging) data and a seed-point-based individual tree segmentation algorithm, information on individual fir trees was obtained. Building on this foundation, structural parameters such as neighborhood comparison (U), crowding degree (C), uniform angle index (W), competition index (CI), and canopy openness (K) were calculated, and their distribution characteristics analyzed. Finally, these parameters were integrated with UAV-LiDAR point cloud features to build machine learning models, and a geographical detector was used to quantify their contribution to AGB estimation. The research findings indicate the following: (1) The studied stands exhibited a random spatial pattern, moderate competition, and sufficient growing space. (2) A significant correlation existed between the U and AGB (r > 0.6), followed by CI. The optimal stand structure for AGB accumulation was C = 0.25, U < 0.5, CI in (0, 0.8], and K > 0.3. (3) The four machine learning models constructed by coupling spatial structure with point cloud features all improved the accuracy of AGB estimation for the fir forest to some extent. Among them, the XGBoost model performed best, achieving a model accuracy (R²) of 0.92 and a relatively low error (RMSE = 14.02 kg). (4) Geographical detector analysis indicated that U and CI contributed most to AGB estimation, with q-values of 0.44 and 0.37, respectively.