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

TL;DR

This review comprehensively analyzes UAV-based tree detection methods using point cloud and aerial imagery, highlighting the rise of deep learning techniques and comparing LiDAR and DAP data applications for forestry and agriculture.

Contribution

It provides a systematic classification and comparison of UAV-based tree detection methods, emphasizing the increasing role of deep learning and offering guidance for future research and practical applications.

Findings

Deep learning methods now account for 45% of tree detection studies by 2022.

LiDAR and DAP data have distinct advantages and applications in tree detection.

The review highlights trends and suggests future directions for UAV-based forestry monitoring.

Abstract

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.