Unmanned Aerial Vehicles (UAVs) are considered cutting-edge technology with
highly cost-effective and flexible usage scenarios. Although many papers have
reviewed the application of UAVs in agriculture, the review of the application
for tree detection is still insufficient. This paper focuses on tree detection
methods applied to UAV data collected by UAVs. There are two kinds of data, the
point cloud and the images, which are acquired by the Light Detection and
Ranging (LiDAR) sensor and camera, respectively. Among the detection methods
using point-cloud data, this paper mainly classifies these methods according to
LiDAR and Digital Aerial Photography (DAP). For the detection methods using
images directly, this paper reviews these methods by whether or not to use the
Deep Learning (DL) method. Our review concludes and analyses the comparison and
combination between the application of LiDAR-based and DAP-based point cloud
data. The performance, relative merits, and application fields of the methods
are also introduced. Meanwhile, this review counts the number of tree detection
studies using different methods in recent years. From our statics, the
detection task using DL methods on the image has become a mainstream trend as
the number of DL-based detection researches increases to 45% of the total
number of tree detection studies up to 2022. As a result, this review could
help and guide researchers who want to carry out tree detection on specific
forests and for farmers to use UAVs in managing agriculture production.

无人机在树木检测中的应用方法进行了回顾和分析，包括利用点云数据的 LiDAR 和数字航空摄影 (DAP) 方法以及直接使用图像的深度学习 (DL) 方法，并统计了近年来使用不同方法进行树木检测的研究数量，指出基于图像的 DL 方法在树木检测研究中已成为主流趋势。该回顾可为希望在特定森林进行树木检测研究的科研人员提供帮助和指导，同时也可帮助农民利用无人机管理农业生产。

无人机点云和航空图像的树木检测方法综述

A Comprehensive Review on Tree Detection Methods Using Point Cloud and  Aerial Imagery from Unmanned Aerial Vehicles

Climate crisis and correlating prolonged, more intense periods of drought
threaten tree health in cities and forests. In consequence, arborists and
foresters suffer from increasing workloads and, in the best case, a consistent
but often declining workforce. To optimise workflows and increase productivity,
we propose a novel open-source end-to-end approach that generates helpful
information and improves task planning of those who care for trees in and
around cities. Our approach is based on RGB and multispectral UAV data, which
is used to create tree inventories of city parks and forests and to deduce tree
vitality assessments through statistical indices and Deep Learning. Due to EU
restrictions regarding flying drones in urban areas, we will also use
multispectral satellite data and fifteen soil moisture sensors to extend our
tree vitality-related basis of data. Furthermore, Bamberg already has a
georeferenced tree cadastre of around 15,000 solitary trees in the city area,
which is also used to generate helpful information. All mentioned data is then
joined and visualised in an interactive web application allowing arborists and
foresters to generate individual and flexible evaluations, thereby improving
daily task planning.

研究利用 RGB 和多光谱无人机数据，欧盟规定下还加入多光谱卫星数据和土壤湿度传感器，生成城市公园和森林的树木清单并通过统计指数和深度学习推导出树木生命状态，提出了一个开源的端到端方法来优化城市和森林中树木的管理，提高工作效率和任务规划。

支持树匠和林业工人的任务规划：基于无人机数据的树木清查和生长情况评估的深度学习方法比较

Task Planning Support for Arborists and Foresters: Comparing Deep  Learning Approaches for Tree Inventory and Tree Vitality Assessment Based on  UAV-Data