When it comes to forest management and protection, knowledge is key. Therefore, forest mapping is crucial to obtain the required knowledge towards profitable resource exploitation and increased resilience against wildfires. Within this context, this paper presents a literature review on tree classification and segmentation using data acquired by unmanned aerial vehicles, with special focus on the last decade (2013–2023). The latest research trends in this field are presented and analyzed in two main vectors, namely: (1) data, where used sensors and data structures are resumed; and (2) methods, where remote sensing and data analysis methods are described, with particular focus on machine learning approaches. The study and review methodology filtered 979 papers, which were then screened, resulting in the 144 works included in this paper. These are systematically analyzed and organized by year, keywords, purpose, sensors, and methods used, easily allowing the readers to have a wide, but at the same time detailed, view of the latest trends in automatic tree classification and segmentation using unmanned aerial vehicles. This review shows that image processing and machine learning techniques applied to forestry and segmentation and classification tasks are focused on improving the accuracy and interpretability of the results by using multi-modal data, 3D information, and AI methods. Most works use RGB or multispectral cameras, or LiDAR scanners, individually. Classification is mostly carried out using supervised methods, while segmentation mostly uses unsupervised machine learning techniques.
Forestry is the science and craft of creating, managing, using, conserving and repairing forests, woodlands, and associated resources for human and environmental benefits. Forest management as one of the categories of forestry, is essential to exploit forest’s full economic and environmental value while ensuring the safety and resilience of the territory against natural or anthropogenic threats such as wildfires. UAV-based remote sensing is a powerful tool for forestry related tasks and measurements. Various studies and experiments have been conducted by different teams all around the world; proving the effectiveness and efficiency of this remote sensing platform and the machine learning techniques used for the analysis of the acquired data. In this study a multirotor UAV equipped with a LiDAR sensor payload is used to produce high density point cloud of a forested area in northern Portugal. The acquired data is then used to produce point cloud-driven digital models for various forestry tasks including individual tree detection, calculation of diameter at breast height, total height and tree crown diameter. The calculated results are then validated by comparing them to the field data. The proposed methodology has potential applications for the detailed mapping of forest and wildland urban interface environments using autonomous, time and cost-effective means, towards proper forest land management for profitability and wildfire risk assessment.
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