Forests have been the most important components of terrestrial ecosystems to maintain the ecological balance and national ecological security. Unmanned aerial vehicles (UAVs) have emerged as the representatives of intelligent forestry equipment in recent years, with the ever-increasing demand for forestry informatization, precision, and intelligence. UAV technology can also play a key role in the precise investigation, design, planning, and management of forests. The concept of precision forestry has gained much recognition in the industry, leading to the high demand for UAV technology. A hot issue is to effectively synergize the UAV technology with the development needs of modern forestry, in order to achieve the precise quality and sustainable forest. This review was focused on the background and current status of UAV technology in the field of precision forestry. A combination of bibliometric analysis and case study was employed to provide an in-depth summary of the application of UAV technology in various areas of precision forestry, including forest resources investigation and monitoring, early warning of forest pests and diseases, forest fire prevention and control, forestry plant protection, and forestry management and law enforcement. The advantages and limitations of UAV technology were objectively analyzed in these areas. The study results indicate that the UAV technology made significant progress in the field of precision forestry, primarily driven by the demands of forestry practitioners. Traditional operations can be expected to update and improve the key UAV technologies. The UAV was used to provide high-resolution image data support for the forest resources survey and monitoring, enabling the efficient extraction of forest stand factors. In terms of early warning of forest pests and diseases, UAV was utilized to accurately identify the various pests and diseases, and then delineate the infected forest areas. In forest fire prevention and control, the UAV was used to predict and monitor the forest fires, providing for the latest news for firefighting. In forestry plant protection, the UAV applications in pesticide application, irrigation, and seeding effectively reduced resource wastage. In forestry management and law enforcement, the UAV was used to offer a safer way to monitor the forest ecosystems, prevent wildlife, and combat illegal activities. Some challenges were also given to the UAV technology in the implementation of precision forestry. These challenges mainly included the inadequate battery capacity of UAVs, high prices of UAV sensors, difficulties in fusing multi-source data, limited clustering and intelligent capabilities of UAVs, as well as the urgent improvement in the UAV-related laws and regulations. In conclusion, this finding can provide valuable insights into the application of UAV technology in precision forestry. Deep integration of UAV technology can also be further strengthened for the entry needs in the future, in order to promote the rapid and sustainable development in precision forestry.

Northern foothill of the Qinling Mountains region has been one of the unique ecological treasures in China, due to the rich and diverse ecological environment, as well as the complex terrain and variable climate. However, it is ever increasing impact of human activities on the local natural environment and ecosystems, as economic development and population growth. Severe challenges have posed on the protection of the ecological environment. Consequently, it is urgent to investigate the ecological environment quality in the stability and sustainable development of the ecosystem. This study aims to assess the ecological sensitivity in the northern foothills of the Qinling Mountains using the driving-force-pressure-state-impact-response-management (DPSIRM) and patch-generating land use simulation (PLUS) models. Initially, 17 indicators were selected to evaluate the ecological sensitivity in 2010, 2015 and 2020, according to the DPSIRM framework. Furthermore, the PLUS model was employed to simulate the spatial distribution of land use from 2020 to 2030. Various scenarios were also considered to integrate the present ecological situation. Subsequently, the simulated and predicted data of land use was served as the key influencing factors to assess the ecological environment. The spatial distribution of ecological sensitivity was predicted in the study area in the future. The findings reveal that the ecologically sensitive regions within the northern foothills were primarily centered around the urban core, indicating the greater resilience to disturbance, as the distance increased from urban centers. Furthermore, the proportion of non-sensitive areas gradually increased from 9% to 26% between 2010 and 2020, with the highest share of low-sensitive zones in 2010 (38%), medium-sensitive zones peaking in 2015 (28%), and the highest proportion of low-sensitive regions in 2020 (30%). Notably, the proportion of extremely sensitive zones was remained relatively low and consistent (5%-10%), indicating an overall favorable trend of the gradual improvement in the ecological environment over time. Furthermore, the patterns of land use were primarily comprised the farmland, woodland, and grassland, according to some projections under three distinct scenarios in the period from 2020 to 2030. Therefore, the ecological preservation was aligned with development and prioritizing sustainable strategies, which was most closely with current ecological objectives. The spatial distribution of ecological sensitivity was obtained within the context of ecological protection. Pertinent measures can be concurrently implemented in distinct sensitive areas. A robust foundation can be offered for the whole development and sustainable utilization of the ecosystem. The findings can also provide the solid refence to preserve the stability and sustainable ecosystem, particularly for the rational allocation and utilization of production, living, and ecological zones within the Qinling region.

Unmanned aerial vehicle (UAV) and ground-based photogrammetry have been essential to the forest resource surveys, due to the low cost, high efficiency and scalability. However, the complex and heterogeneous nature of forest environments can often lead to the UAV platforms with the less understory information. While the ground platforms can frequently miss the canopy details. The information gaps can be caused by different observation perspectives from UAVs and ground platforms. Some challenges are also remained to efficiently register photogrammetric point clouds. In this study, a point cloud registration was proposed for forest images, according to three-dimensional (3D) point cloud from UAV and ground photogrammetry. Key points of feature were then extracted using coarse-to-fine registration, including the tree top and the ground-level tree center. The specific procedures were as follows: 1) Image Point Cloud Acquisition. In UAV platforms, DJI Phantom 4 RTK UAV were used to capture plot images via oblique photogrammetry. In ground platforms, a "simulated flight path" was employed to obtain the plot images. 3D reconstruction was implemented to generate 3D point cloud data. 2) Feature Point Extraction. Single tree segmentation was combined with AABB bounding boxes and Euclidean clustering. The highest points of individual UAV tree canopies were extracted as feature points. Euclidean clustering was used on ground image point clouds. Single trees were then segmented to extract the ground-level tree center as ground feature points. 3) Coarse Registration. Feature points were mapped from 3D to 2D. Transformation relationships were calculated using improved simulated annealing, partial/full point pair transformation and scale adjustment. The coarse registration was achieved in the original point cloud. 4) Fine Registration. Precise registration was achieved to reduced further error using iterative closest point. A series of tests were performed on six sample plots with different tree species. The results demonstrated the following: 1) The high accuracy and reliability were validated experimentally in the single tree segmentation with AABB bounding boxes and Euclidean clustering. 2) The better performance and stability were achieved in the forest image point cloud registration with improved simulated annealing and iterative closest point. The average errors of coarse and fine registration were 0.09 and 0.06 m, respectively, in the six sample plots. The coarse registration was closely matched the fine registration, indicating the refined registration of image point clouds from UAV and ground platforms. 3) Poplar trees shared a greater impact on coarse registration, compared with ginkgo and catalpa trees. While there was the minimal impact of different tree species on the fine registration. 4) Scale differences were emerged as the influencing factors on forest image point cloud registration. It was necessary to consider and correct during registration. Therefore, cross-platform registration was successfully implemented on the forest image point clouds using UAV and ground-based photogrammetry. A promising approach was presented to tackle the challenges of forest image point cloud registration. This finding can also provide the sound support to forest resource surveys, and 3D forest reconstruction in the wide application of image point clouds. Furthermore, the practical insights can be offered for the precision and intelligent forestry production.