A deep-learning-based tree species classification for natural secondary forests using unmanned aerial vehicle hyperspectral images and LiDAR

高光谱成像 遥感 激光雷达 天然林 环境科学 树(集合论) 航测 航空影像 地理 农林复合经营 数学分析 数学
作者
Ye Ma,Yuting Zhao,Jungho Im,Yinghui Zhao,Zhen Zhen
出处
期刊:Ecological Indicators [Elsevier BV]
卷期号:159: 111608-111608 被引量:19
标识
DOI:10.1016/j.ecolind.2024.111608
摘要

Accurate tree species classification is essential for forest resource management and biodiversity assessment. However, classifying tree species becomes challenging in natural secondary forests due to the difficulties in outlining the tree crown boundary. In this study, an object-based framework for tree species classification in the Experimental Forestry Farm of Northeast Forestry University, located in Heilongjiang Province, China, was developed based on unmanned aerial vehicle (UAV) hyperspectral images (HSIs) and UAV light detection and ranging (LiDAR) data using convolutional neural networks (CNNs). The study area was characterized by representative natural secondary forests that encompass diverse tree species, such as Korean pine (Pinus koraiensis Sieb. et Zucc.), White birch (Betula platyphylla Suk.), Siberian elm (Ulmus pumila L.), and Manchurian ash (Fraxinus mandshurica Rupr.). This study included two key processes: (1) the u-shaped network (U-net) algorithm was employed with the simple linear iterative clustering (SLIC) algorithm, that is, the U-SLIC algorithm, for individual tree crown delineation (ITCD), and (2) the performances of one-dimensional CNN (1D-CNN), two-dimensional CNN (2D-CNN), and three-dimensional CNN (3D-CNN) models for tree species classification were compared while investigating the role of an attention mechanism (convolutional block attention module, CBAM) added to CNN models (1D-/2D-/3D-CNN + CBAM). The results showed that the U-SLIC algorithm obtained a satisfactory accuracy for the ITCD procedure, with a recall of 0.92, precision of 0.79, and F-score of 0.85. The feature selection effectively enhanced the CNN models' performances for tree species classification. Furthermore, adding the CBAM resulted in overall accuracy (OA) improvements of 0.08, 0.11, and 0.09 for the 1D-CNN, 2D-CNN, and 3D-CNN, respectively. The 1D-CNN + CBAM model performed best with an OA of 0.83 when utilizing the selected HSI and LiDAR features. This framework highlighted the utilization and integration of multiple deep-learning algorithms in complex natural forests, serving as prerequisites for forest management decisions, biodiversity conservation, and carbon stock estimation.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
睡觉了完成签到 ,获得积分10
1秒前
量子星尘发布了新的文献求助10
2秒前
乖猫要努力应助精明寻梅采纳,获得10
2秒前
AI完成签到,获得积分10
5秒前
xiaosu发布了新的文献求助30
6秒前
6秒前
谢琉圭完成签到,获得积分10
8秒前
领导范儿应助稳重的若雁采纳,获得10
11秒前
11秒前
12秒前
wu完成签到,获得积分20
12秒前
momo发布了新的文献求助10
13秒前
14秒前
田様应助123采纳,获得10
14秒前
18秒前
zying完成签到,获得积分10
21秒前
22秒前
双楠应助wangjue采纳,获得10
22秒前
27秒前
雪白尔岚发布了新的文献求助10
31秒前
31秒前
31秒前
慕青应助momo采纳,获得10
32秒前
从容冰夏完成签到,获得积分10
34秒前
34秒前
桐桐应助Candy采纳,获得10
35秒前
YR完成签到,获得积分10
37秒前
欧阳月空发布了新的文献求助10
37秒前
周em12_发布了新的文献求助10
38秒前
糊涂涂完成签到 ,获得积分10
38秒前
39秒前
41秒前
Zhang完成签到,获得积分10
42秒前
43秒前
43秒前
44秒前
45秒前
圆锥香蕉给ZEcholy的求助进行了留言
46秒前
jisujun发布了新的文献求助10
46秒前
李小宁发布了新的文献求助10
46秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989334
求助须知:如何正确求助?哪些是违规求助? 3531428
关于积分的说明 11253936
捐赠科研通 3270119
什么是DOI,文献DOI怎么找? 1804887
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809173