As an important component of the urban ecosystem, street trees have made an outstanding contribution to alleviating urban environmental pollution. Accurately extracting tree characteristics and species information can facilitate the monitoring and management of street trees, as well as aiding landscaping and studies of urban ecology. In this study, we selected the suburban areas of Beijing and Zhangjiakou and investigated six representative street tree species using unmanned aerial vehicle (UAV) tilt photogrammetry. We extracted five tree attributes and four combined attribute parameters and used four types of commonly-used machine learning classification algorithms as classifiers for tree species classification. The results show that random forest (RF), support vector machine (SVM), and back propagation (BP) neural network provide better classification results when using combined parameters for tree species classification, compared with those using individual tree attributes alone; however, the K-nearest neighbor (KNN) algorithm produced the opposite results. The best combination for classification is the BP neural network using combined attributes, with a classification precision of 89.1% and F-measure of 0.872, and we conclude that this approach best meets the requirements of street tree surveys. The results also demonstrate that optical UAV tilt photogrammetry combined with a machine learning classification algorithm is a low-cost, high-efficiency, and high-precision method for tree species classification.

With the implementation of human activities, such as logging, reclamation, and construction, the increasing fragmentation of ecological space and the increasing blockage of biological migration corridors cause many threats to biodiversity conservation. In this study, we used the Northwest Beijing Ecological Containment Area as the research area. Based on an integrated circuit theoretical model, we identified functional connectivity networks and analyzed the spatial and temporal changes of ecological blockage patterns in the region from 1998–2018 in terms of the landscape connectivity, ecological breakpoints, pinch points, and barriers, respectively. The results show that the average remote sensing ecological index had a trend of decreasing and then increasing, and a total of 33, 34, and 63 habitat core areas and 70, 74, and 152 ecological corridors were identified in 1998, 2010, and 2018, respectively. The regions with high ecological blockage were mainly in the central part of Yanqing District, the southwest corner of the study area, and the eastern urban area. Although the number of potential ecological corridors gradually increases with the probability of migration in the study area, the blockage status and vulnerability of the ecological corridors continue to increase due to the conflict between land uses. The ecological status of the study area reflects the comprehensive effectiveness of the capital’s high-quality development under the strategic deployment of ecological civilization. In the context of habitat fragmentation, the effective protection and restoration of the ecological conditions in the ecological function areas is of great importance in guaranteeing the ecological quality and sustainable development of the country.

Accurate modeling of windbreaks is essential for the precise assessment of wind protection performance. However, in most windbreak studies, the models used the approximate shape of the simulated trees, resulting in significant differences between the simulated results and the actual situation. In this study, terrestrial laser scanning (TLS) was used to extract tree parameters, which were used in a quantitative structural model (AdQSM) to recreate the tree structure and restore the wind field environment using the computational fluid dynamics software PHOENICS. In addition, we compared the bias, precision, and accuracy of porosity of Ginkgo biloba (with elliptical crown) and Populus alba (with conical crown), which have been commonly used in previous windbreak studies. The results showed that AdQSM has a high reduction rate and ability to reproduce the field conditions of the study area. After wind field simulation, the wind speed root mean square errors of the point cloud model at three heights (3, 6, and 9 m) were 0.272, 0.377, and 0.437 m/s, respectively, and the wind speed correlation coefficients r were 0.967, 0.965, and 0.937, respectively, which were significantly more accurate than those of the remaining two structures. Finally, the porosity of the windbreak forest obtained using the modeled sample plot showed a higher correlation with the wind permeability coefficient than that obtained using the existing approach. Windbreak models with three different porosities under the same conditions had different effects on the wind environment, particularly the location of the maximum wind speed reduction, variation of wind speed with porosity, and recovery rate of leeward wind speed. TLS can accurately extract windbreak factors and calculate the porosity, thus greatly improving the reliability of windbreak effect research in windbreak forests. This study provides a promising direction for future research related to the simulation of windbreak effects in windbreak forests.