Timely and accurate information on the spatial distribution of urban trees is critical for sustainable urban development, management and planning. Compared with satellite-based remote sensing, Unmanned Aerial Vehicle (UAV) remote sensing has a higher spatial and temporal resolution, which provides a new method for the accurate identification of urban trees. In this study, we aim to establish an efficient and practical method for urban tree identification by combining an object-oriented approach and a random forest algorithm using UAV multispectral images. Firstly, the image was segmented by a multi-scale segmentation algorithm based on the scale determined by the Estimation of Scale Parameter 2 (ESP2) tool and visual discrimination. Secondly, spectral features, index features, texture features and geometric features were combined to form schemes S1–S8, and S9, consisting of features selected by the recursive feature elimination (RFE) method. Finally, the classification of urban trees was performed based on the nine schemes using the random forest (RF), support vector machine (SVM) and k-nearest neighbor (KNN) classifiers, respectively. The results show that the RF classifier performs better than SVM and KNN, and the RF achieves the highest accuracy in S9, with an overall accuracy (OA) of 91.89% and a Kappa coefficient (Kappa) of 0.91. This study reveals that geometric features have a negative impact on classification, and the other three types have a positive impact. The feature importance ranking map shows that spectral features are the most important type of features, followed by index features, texture features and geometric features. Most tree species have a high classification accuracy, but the accuracy of Camphor and Cinnamomum Japonicum is much lower than that of other tree species, suggesting that the features selected in this study cannot accurately distinguish these two tree species, so it is necessary to add features such as height in the future to improve the accuracy. This study illustrates that the combination of an object-oriented approach and the RF classifier based on UAV multispectral images provides an efficient and powerful method for urban tree classification.

Olive trees, which are planted widely in China, are economically significant. Timely and accurate acquisition of olive tree crown information is vital in monitoring olive tree growth and accurately predicting its fruit yield. The advent of unmanned aerial vehicles (UAVs) and deep learning (DL) provides an opportunity for rapid monitoring parameters of the olive tree crown. In this study, we propose a method of automatically extracting olive crown information (crown number and area of olive tree), combining visible-light images captured by consumer UAV and a new deep learning model, U²-Net, with a deeply nested structure. Firstly, a data set of an olive tree crown (OTC) images was constructed, which was further processed by the ESRGAN model to enhance the image resolution and was augmented (geometric transformation and spectral transformation) to enlarge the data set to increase the generalization ability of the model. Secondly, four typical subareas (A–D) in the study area were selected to evaluate the performance of the U²-Net model in olive crown extraction in different scenarios, and the U²-Net model was compared with three current mainstream deep learning models (i.e., HRNet, U-Net, and DeepLabv3+) in remote sensing image segmentation effect. The results showed that the U²-Net model achieved high accuracy in the extraction of tree crown numbers in the four subareas with a mean of intersection over union (IoU), overall accuracy (OA), and F1-Score of 92.27%, 95.19%, and 95.95%, respectively. Compared with the other three models, the IoU, OA, and F1-Score of the U²-Net model increased by 14.03–23.97 percentage points, 7.57–12.85 percentage points, and 8.15–14.78 percentage points, respectively. In addition, the U²-Net model had a high consistency between the predicted and measured area of the olive crown, and compared with the other three deep learning models, it had a lower error rate with a root mean squared error (RMSE) of 4.78, magnitude of relative error (MRE) of 14.27%, and a coefficient of determination (R²) higher than 0.93 in all four subareas, suggesting that the U²-Net model extracted the best crown profile integrity and was most consistent with the actual situation. This study indicates that the method combining UVA RGB images with the U²-Net model can provide a highly accurate and robust extraction result for olive tree crowns and is helpful in the dynamic monitoring and management of orchard trees.

Accurate estimation of forest aboveground biomass (AGB) is vital for carbon accounting and sustainable forest management, yet it remains constrained by the scale mismatch between field plots and satellite imagery. To address this challenge, we developed and validated a UAV-driven hierarchical framework integrating a Stacking Ensemble Learning (SEL) strategy for high-precision regional AGB estimation in structurally complex Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) plantations. First, by exploiting the synergistic interactions among height, intensity, density, and vegetation metrics, the plot-scale SEL model achieved high precision estimates (R2 = 0.88, RMSE = 25.50 t/ha). These fine scale AGB maps were then aggregated using a polygon-to polygon upscaling strategy, generating robust training samples at the satellite pixel scale. Subsequently, these samples were used to train a regional SEL model that fused multi-source datasets— including Sentinel-1 SAR textures, Sentinel-2 spectral indices, and SRTM-derived terrain variables—to produce a regional AGB map. The proposed Ground-UAV-Satellite hierarchical framework achieved superior regional-scale accuracy (R2 = 0.65, RMSE = 35.83 t/ha), significantly outperforming the conventional Ground-Satellite method (R2 = 0.46) by increasing the explained variance by approximately 40% and reducing the RMSE by 21%. This research establishes a scalable and transferable methodology that mitigates sampling bias and scale-related uncertainties, offering a robust tool for large-area, operational forest-carbon monitoring.