Advancing tree detection in forest environments: A deep learning object detector approach with UAV LiDAR data

IF 6 2区 环境科学与生态学 Q1 ENVIRONMENTAL STUDIES Urban Forestry & Urban Greening Pub Date : 2025-01-26 DOI:10.1016/j.ufug.2025.128695
Sina Jarahizadeh, Bahram Salehi
{"title":"Advancing tree detection in forest environments: A deep learning object detector approach with UAV LiDAR data","authors":"Sina Jarahizadeh,&nbsp;Bahram Salehi","doi":"10.1016/j.ufug.2025.128695","DOIUrl":null,"url":null,"abstract":"<div><div>The initial phase in determining tree parameters within urban and forest environments is Individual Tree Detection (ITD), which includes tree count, spatial distribution, height, volume, crown dimensions, and species identification. This process holds significance in applications like urban forest inventory, planning, and tree carbon accounting. Traditional airborne and spaceborne remote sensing data lack the precision required for ITD due to their coarse spatial resolution. High-resolution multispectral and Light Detection and Ranging (LiDAR) data collected by Unmanned Aerial Vehicle (UAV) sensors have enabled detailed ITD and tree parameter estimation. However, the processing of such very high-resolution data presents challenges. While existing algorithms for processing 2-dimensional (2D) and 3-dimensional (3D) data from airborne sensors exist, they prove impractical for UAV data, primarily due to its extremely high spatial resolution. Recent strides in deep-learning algorithms offer promising solutions for ITD using UAV data. This paper introduces a novel ITD method using a modified You Only Look Once V7 (YOLO V7) deep learning object detection framework, employing UAV LiDAR data. The approach involves rasterizing point clouds in various channels, including Vertical Density (VD), Canopy Height Model (CHM), Gradient of the CHM (G-CHM), and Local Binary Pattern of the CHM (LBP-CHM). Subsequently, the YOLO V7 object detector is employed to identify the bounding box of each tree. The modified YOLO7 algorithm is trained and tested on UAV LiDAR data collected over diverse regions of interest, encompassing pine, deciduous, and mixed tree types with varying tree densities. The results exhibit a substantial enhancement over the previously developed YOLO3 on airborne LiDAR data, showcasing heightened accuracy, precision, and recall within the ranges of 0.7–0.94, 0.76–0.99, and 0.8–0.97, respectively. From a practical standpoint, our automated method holds potential for urban tree inventory updates and serves as a valuable tool for ground-truthing large-scale satellite-based forest structure and biomass estimation, among various other applications.</div></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":"105 ","pages":"Article 128695"},"PeriodicalIF":6.0000,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Urban Forestry & Urban Greening","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1618866725000299","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0

Abstract

The initial phase in determining tree parameters within urban and forest environments is Individual Tree Detection (ITD), which includes tree count, spatial distribution, height, volume, crown dimensions, and species identification. This process holds significance in applications like urban forest inventory, planning, and tree carbon accounting. Traditional airborne and spaceborne remote sensing data lack the precision required for ITD due to their coarse spatial resolution. High-resolution multispectral and Light Detection and Ranging (LiDAR) data collected by Unmanned Aerial Vehicle (UAV) sensors have enabled detailed ITD and tree parameter estimation. However, the processing of such very high-resolution data presents challenges. While existing algorithms for processing 2-dimensional (2D) and 3-dimensional (3D) data from airborne sensors exist, they prove impractical for UAV data, primarily due to its extremely high spatial resolution. Recent strides in deep-learning algorithms offer promising solutions for ITD using UAV data. This paper introduces a novel ITD method using a modified You Only Look Once V7 (YOLO V7) deep learning object detection framework, employing UAV LiDAR data. The approach involves rasterizing point clouds in various channels, including Vertical Density (VD), Canopy Height Model (CHM), Gradient of the CHM (G-CHM), and Local Binary Pattern of the CHM (LBP-CHM). Subsequently, the YOLO V7 object detector is employed to identify the bounding box of each tree. The modified YOLO7 algorithm is trained and tested on UAV LiDAR data collected over diverse regions of interest, encompassing pine, deciduous, and mixed tree types with varying tree densities. The results exhibit a substantial enhancement over the previously developed YOLO3 on airborne LiDAR data, showcasing heightened accuracy, precision, and recall within the ranges of 0.7–0.94, 0.76–0.99, and 0.8–0.97, respectively. From a practical standpoint, our automated method holds potential for urban tree inventory updates and serves as a valuable tool for ground-truthing large-scale satellite-based forest structure and biomass estimation, among various other applications.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
11.70
自引率
12.50%
发文量
289
审稿时长
70 days
期刊介绍: Urban Forestry and Urban Greening is a refereed, international journal aimed at presenting high-quality research with urban and peri-urban woody and non-woody vegetation and its use, planning, design, establishment and management as its main topics. Urban Forestry and Urban Greening concentrates on all tree-dominated (as joint together in the urban forest) as well as other green resources in and around urban areas, such as woodlands, public and private urban parks and gardens, urban nature areas, street tree and square plantations, botanical gardens and cemeteries. The journal welcomes basic and applied research papers, as well as review papers and short communications. Contributions should focus on one or more of the following aspects: -Form and functions of urban forests and other vegetation, including aspects of urban ecology. -Policy-making, planning and design related to urban forests and other vegetation. -Selection and establishment of tree resources and other vegetation for urban environments. -Management of urban forests and other vegetation. Original contributions of a high academic standard are invited from a wide range of disciplines and fields, including forestry, biology, horticulture, arboriculture, landscape ecology, pathology, soil science, hydrology, landscape architecture, landscape planning, urban planning and design, economics, sociology, environmental psychology, public health, and education.
期刊最新文献
Editorial Board Understanding street tree inequities: The interrelation of urban layout and socio-economics How to quantify multidimensional perception of urban parks? Integrating deep learning-based social media data analysis with questionnaire survey methods The contribution of geolocated data to the diagnosis of urban green infrastructure. Tenerife insularity as a benchmark Plant smellscape: A key avenue to connect nature and human well-being
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1