联合地基激光雷达与无人机影像的树种识别

doi:10.12302/j.issn.1000-2006.202205041

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南京林业大学学报（自然科学版） ›› 2024, Vol. 48 ›› Issue (4): 104-112.doi: 10.12302/j.issn.1000-2006.202205041

所属专题：专题报道Ⅲ：智慧林业之森林可视化研究

• 专题报道Ⅲ：智慧林业之森林可视化研究（执行主编李凤日、张怀清、曹林） • 上一篇下一篇

联合地基激光雷达与无人机影像的树种识别

钟浩(), 王楚虹, 林文树^*()

东北林业大学机电工程学院,黑龙江哈尔滨 150040

收稿日期:2022-05-26 修回日期:2022-10-05 出版日期:2024-07-30 发布日期:2024-08-05
通讯作者: *林文树(linwenshu@nefu.edu.cn),教授。
作者简介:
钟浩(260919837@qq.com),博士生。
基金资助:
国家自然科学基金项目(31971574);中央高校基本科研业务费专项(2572021AW49);东北林业大学林业工程一流学科博士创新基金项目(LYGC202114);黑龙江省自然科学基金联合引导项目(LH2020C049)

Tree species identification of combined TLS date and UAV images

ZHONG Hao(), WANG Chuhong, LIN Wenshu^*()

College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040,China

Received:2022-05-26 Revised:2022-10-05 Online:2024-07-30 Published:2024-08-05

摘要/Abstract

摘要：

【目的】应用遥感技术进行树种识别已成为森林调查的重要手段之一,但利用近地面遥感手段进行树种识别时存在地基激光雷达(LiDAR)点云数据树冠上层信息不足和无人机影像树冠下层的信息缺失的问题,联合多源遥感数据快速准确识别树种,对研究和保护森林资源具有重要意义。【方法】以哈尔滨市东北林业大学城市林业示范基地中樟子松(Pinus sylvestris var. mongolica)、黑皮油松(P. tabuliformis var. mukdensis)、水曲柳(Fraxinus mandshurica)、胡桃楸(Juglans mandshurica)为研究对象进行树种识别研究,获取地基LiDAR与无人机影像数据,通过对高重叠度无人机影像进行处理得到无人机影像点云,将无人机影像点云与地基LiDAR点云数据进行配准融合,对融合后数据进行单木分割得到单木点云,基于单木点云提取形状特征、结构特征、树干颜色特征和树冠颜色特征,借助支持向量机分类算法进行树种识别并结合随机森林算法对不同特征的识别能力进行了分析。【结果】利用所有特征进行树种识别取得试验最优结果,其总精度和Kappa 系数分别为93.48%和0.91。相较于其他对比方案,其总精度和Kappa 系数分别提升4.35~16.31个百分点和0.06~0.22。【结论】提出了一种地基LiDAR数据与无人机影像点云数据进行融合的树种识别方法,该方法能够在一定程度上弥补树种识别中特征提取时地基LiDAR点云数据树冠上层信息不足,以及无人机影像树冠下层信息缺失等问题。充分利用多源数据所包含的丰富信息进行树种识别,可有效提高树种识别精度。

关键词: 树种识别, 多源遥感, 激光雷达, 无人机影像

Abstract:

【Objective】Rapid and accurate identification of tree species is crucial for the research and protection of forest resources. Identification of tree species by remote sensing technology has become an important method of forest investigation. However, there are some problems in tree species identification by remote sensing, such as a lack of information in the upper canopy of terrestrial laser scanning (TLS) data and the lower canopy of unmanned aerial vehicle (UAV) images. Therefore, identifying tree species requires multi-source remote sensing data.【Method】In this study, Pinus sylvestris var. mongolica, P. tabuliformis var. mukdensis, Fraxinus mandshurica and Juglans mandshurica in the urban forestry demonstration base of Northeast Forestry University at Harbin were used as the research objects to identify tree species. TLS point cloud data and UAV image data were acquired. Through the processing of UAV images, the photogrammetric point cloud and orthophoto image were obtained. The UAV image point cloud and TLS point cloud data were registered and fused, then divided into single tree points. Based on the single tree point cloud, shape features, structure features, tree trunk color features, and crown color features were extracted, and tree species identification was performed by a support vector machine classification algorithm. Subsequently, the ability of the method to identify tree species using different characteristics was analyzed by random forest algorithm.【Result】Optimal results were obtained when all the features were used to identify tree species. The total accuracy and Kappa coefficient of tree species identification results were 93.48% and 0.91, respectively, which were improved by 4.35-16.31percentage points and 0.06-0.22, respectively, compared with other comparison schemes.【Conclusion】The tree species recognition method based on the fusion of TLS data and UAV image point cloud data proposed in this study can compensate for the lack of information in the upper canopy of TLS point cloud data and the lower canopy of UAV images to a certain extent, and make full use of the rich information contained in multi-source data for tree species recognition. The method can effectively improve the accuracy of tree species identification.

Key words: tree species identification, multi-source remote sensing, LiDAR, unmanned aerial rehicle (UAV) images

中图分类号:

S771.8

钟浩,王楚虹,林文树. 联合地基激光雷达与无人机影像的树种识别[J]. 南京林业大学学报（自然科学版）, 2024, 48(4): 104-112.

ZHONG Hao, WANG Chuhong, LIN Wenshu. Tree species identification of combined TLS date and UAV images[J].Journal of Nanjing Forestry University (Natural Science Edition), 2024, 48(4): 104-112.DOI: 10.12302/j.issn.1000-2006.202205041.

图/表 8

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方案 scheme	点云数据源 point cloud data source	特征组合 features combination	数据融合方式 data fusion type
1	地基LiDAR TLS	形状+结构	单一数据 single data
2		树干颜色
3		形状+结构+树干颜色
4	无人机影像UAV image	树冠颜色
5	地基LiDAR+无人机影像 TLS + UAV image	形状+结构	融合数据 fusion data
6		形状+结构+树干颜色
7		形状+结构+树干颜色+树冠颜色

树种 tree species	融合类型 fusion type	D_BH/ cm	H_T/ m	L_C/ m	A_C/ m²	V_C/m³
樟子松 P. sylvestnis var. mongolica	融合	26.1	16.68	4.03	13.28	59.44
	未融合		15.28	3.65	11.32	42.58
黑皮油松 P. tabuliformis var. mukdensis	融合	23.6	13.52	5.26	23.58	103.35
	未融合		13.01	4.90	20.79	79.77
水曲柳 F. mandshurica	融合	29.5	20.71	6.35	33.93	172.48
	未融合		20.13	6.06	31.01	147.01
胡桃楸 J. mandshurica	融合	33.4	17.37	7.16	42.96	206.27
	未融合		16.37	6.64	37.46	174.57

方案 scheme	参数 parameter	各树种识别精度/% accuracy				总精度/% OA	Kappa系数 Kappa coefficient
方案 scheme	参数 parameter	樟子松 P. sylvestris var. mogolica	黑皮油松 P. tabuliformis var. mukdensis	水曲柳 F. mandshurica	胡桃楸 J. mandshurica	总精度/% OA	Kappa系数 Kappa coefficient
1	生产者精度PA	83.33	50.00	73.91	92.59	77.17	0.69
	用户精度UA	71.43	69.23	73.91	89.29
2	生产者精度PA	91.67	83.33	78.26	77.78	82.61	0.77
	用户精度UA	81.48	78.95	81.82	87.50
3	生产者精度PA	91.67	72.22	82.61	92.59	85.87	0.81
	用户精度UA	91.67	76.47	90.48	83.33
4	生产者精度PA	95.83	88.89	82.61	85.19	88.04	0.84
	用户精度UA	88.46	94.12	82.61	88.46
5	生产者精度PA	83.33	55.56	86.96	92.59	81.52	0.75
	用户精度UA	83.33	71.43	76.92	89.29
6	生产者精度PA	83.33	94.44	86.96	92.59	89.13	0.85
	用户精度UA	83.33	85.00	90.91	96.15
7	生产者精度PA	95.83	94.44	86.96	96.30	93.48	0.91
	用户精度UA	92.00	94.44	95.24	92.86

联合地基激光雷达与无人机影像的树种识别

Tree species identification of combined TLS date and UAV images

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