融合机载与手持激光雷达数据的亚热带典型森林单木参数提取精度比较分析

doi:10.11707/j.1001-7488.LYKX20250578

Abstract

Abstract:

Objective: This study aims to explore the advantages of integrating data collected by handheld laser scanning (HLS) and airborne laser scanning (ALS) for individual tree parameter extraction and to assess their applicability under different forest types and understory conditions, so as to provide a scientific basis for the application of multi-source LiDAR data in precise forest surveys. Method: Three typical areas in the subtropical Fujian Province were targeted, and the HLS and ALS data were used to accurately segment individual trees in plots through a combination of automatic segmentation and visual interpretation. Individual tree parameters were extracted, and the performance of both data in estimating individual tree volume was evaluated. The influence of forest types and stand complexity on the results of individual tree parameter extraction was examined. Result: 1) The relative root mean square errors (rRMSE) for extracting diameter at breast height (DBH) and tree height from the integrated HLS and ALS data were 5.72%–5.84% and 7.36%–7.83%, respectively, and the tree-height rRMSE was reduced by 3.29%–4.19% compared with HLS alone. 2) Based on the integrated LiDAR data, Cunninghamia lanceolata was able to obtain high-precision individual tree diameter at breast height under different understory vegetation conditions, with an rRMSE of 7.05%, but Pinus massoniana was able to was able to only have good results under simple understory conditions, while broadleaf forests had high uncertainty in different understory conditions. 3) The rRMSE of single wood volume calculated based on the extracted DBH and tree height was 16.11%–17.18%. Comparing the results from HLS data alone, rRMSE of single wood volume with the integrated HLS and ALS data was reduced by 4.21%–4.30%. Conclusion: The accuracy of estimating individual tree parameters by using fused ALS and HLS data is influenced by forest types and understory conditions. The evaluation of the accuracy of individual tree parameter extraction in different scenarios provides theoretical support for optimizing future field surveys with LiDAR.

Key words: individual tree parameters, felled tree, handheld laser scanner, airborne LiDAR, forestry remote sensing

CLC Number:

S771.8

Yunhe Li,Sirong Wang,Yisa Li,Dengsheng Lu. Comparative Analysis of Individual Tree Parameter Extraction Accuracy Using Integrated Data Collected by Airborne and Handheld LiDAR in Subtropical Typical Forests[J]. Scientia Silvae Sinicae, 2026, 62(6): 82-95.

Figures/Tables 12

Fig.1

Table 1

Datasets used in research"

数据类型 Data type	内容描述 Descriptions						获取时间 Acquisition time	用途 Purpose
数据类型 Data type	研究区域 Study area	林分类型（样地数量） Forest types (number of plots)	样地总株数Total number of stems	实测株数Number of measured stems	平均胸径 Average DBH/cm	平均树高 Average tree height/m	获取时间 Acquisition time	用途 Purpose
采伐样地内单木量测 Individual tree measurement in harvested plots	白砂国有林场 Baisha State-Owned Forest Farm	马尾松（1）Masson pine	72	72	14.4	13.5	2024?02	基于融合LiDAR提取的单木参数的精度验证 Accuracy validation of individual tree parameters extracted from fused LiDAR data
采伐样地内单木量测 Individual tree measurement in harvested plots	白砂国有林场 Baisha State-Owned Forest Farm	杉木（1） Chinese fir	45	45	12.2	9.5	2024?02
手持扫描样地内单木量测 Individual tree measurement in HLS plots	白砂国有林场 Baisha State-Owned Forest Farm	马尾松（4） Masson pine	352	28	15.5	\	2022?10?11	基于融合LiDAR数据，对手持扫描样地中不同林分类型和林下植被状况的单木参数提取结果进行精度验证和比较分析 Accuracy validation and comparative analysis of individual tree parameter extraction results for different forest types and understory conditions in HLS plots based on fused LiDAR data
		杉木（1）Chinese fir	75	75	14.0	\
		阔叶（2）Broadleaf	70	52	16.1	\
	水西国有林场 Shuixi State-Owned Forest Farm	杉木（4）Chinese fir	156	156	23.5	\	2023?02
	武夷山国家公园 Wuyishan National Forest Park	马尾松（2）Masson pine	135	105	21.9	\	2023?08
		杉木（5）Chinese fir	335	335	16.8	\
		阔叶（4）Broadleaf	306	74	12.3	\
手持激光扫描数据 HLS data	白砂国有林场手持扫描样地 HLS plots in Baisha State-Owned Forest Farm	HLS数据使用LiGripH120传感器采集，扫描视场角为280°× 360°，LiDAR测距精度可达到±3 cm，点频320 000 pts·s^?1，测量范围为120 m HLS data are acquired using LiGripH120 laser sensor. The device has a field of view of 280°×360°， LiDAR ranging accuracy reached up to ±3 cm， measuring up to 320 000 pts·s^?1 in a single return mode within a maximum range of 120 m					2022?10?11	单独基于手持扫描数据以及融合机载LiDAR数据提取采伐林与手持扫描样地单木参数 Individual tree parameters extracted separately from HLS data and fused LiDAR data in the harvested and HLS plots
	白砂国有林场采伐林 Harvested stands in Baisha State-Owned Forest Farm						2024?02
	水西国有林场 Shuixi State-Owned Forest Farm						2023?02
	武夷山国家公园 Wuyishan National Forest Park						2023?08
机载激光扫描数据 ALS data	白砂国有林场 Baisha State-Owned Forest Farm	利用AEROS-912动力三角翼飞机搭载Riegl VUX-240机载激光雷达系统采集LiDAR数据，视场角75°，LiDAR测距精度可达到 0.2 cm，点云密度高于40 pt·m^?2 LiDAR data are acquired using AEROS-912 aircraft equipped with a Riegl VUX-240 LiDAR system. The LiDAR system has a field of view of 75° and a ranging precision of up to 0.2 cm. The average point density is over 40 pt·m^?2					2022?10?11	融合手持扫描数据提取采伐林与手持扫描样地单木参数 Extraction of individual tree parameters in harvested and HLS plots using fused LiDAR data
	水西国有林场 Shuixi State-Owned Forest Farm						2023?10
	武夷山国家公园 Wuyishan National Forest Park						2022?12

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References 0

	邓云程, 王金亮, 刘钱威, 等. 提取林木胸径的F-LS算法. 遥感信息, 2022, 37 (5): 77- 84. doi: 10.3969/j.issn.1000-3177.2022.05.012
	Deng Y C, Wang J L, Liu Q W, et al. Tree DBH extraction based on F-LS algorithm. Remote Sensing Information, 2022, 37 (5): 77- 84. doi: 10.3969/j.issn.1000-3177.2022.05.012
	范光鹏, 许亮亮, 蔡会德, 等. 基于地基激光雷达点云的桉树单木参数提取与地上碳储量测定. 林业科学, 2026, 62 (3): 88- 99. doi: 10.11707/j.1001-7488.LYKX20240701
	Fan G P, Xu L L, Cai H D, et al. Extraction of individual Eucalyptus tree parameters and determination of aboveground carbon stock based on terrestrial LiDAR point clouds. Scientia Silvae Sinicae, 2026, 62 (3): 88- 99. doi: 10.11707/j.1001-7488.LYKX20240701
	国家林业局. 2014. 立木生物量模型及碳计量参数: 杉木（LY/T 2264—2014）. 北京: 中国标准出版社.
	State Forestry Administration. 2014. Tree biomass models and related parameters to carbon accounting for Cunninghamia lanceolata (LY/T 2264—2014). Beijing: Standards Press of China. ［in Chinese］
	胡中洋, 陕　亮, 陈翔宇, 等. CHM与DSM相结合的无人机激光雷达单木分割. 林业科学, 2024, 60 (8): 14- 24. doi: 10.11707/j.1001-7488.LYKX20230079
	Hu Z Y, Shan L, Chen X Y, et al. Individual tree segmentation of UAV-LiDAR based on the combination of CHM and DSM. Scientia Silvae Sinicae, 2024, 60 (8): 14- 24. doi: 10.11707/j.1001-7488.LYKX20230079
	陆灯盛, 蒋先蝶, 李云鹤, 等. 基于激光雷达数据的森林生物量估测研究. 遥感学报, 2025, 29 (6): 2035- 2064. doi: 10.11834/jrs.20255022
	Lu D S, Jiang X D, Li Y H, et al. Forest biomass estimation with LiDAR data. National Remote Sensing Bulletin, 2025, 29 (6): 2035- 2064. doi: 10.11834/jrs.20255022
	王伟伟, 庞　勇, 杜黎明, 等. 超体素约简和谱聚类结合的机载LiDAR点云单木分割. 遥感学报, 2022, 26 (8): 1650- 1661.
	Wang W W, Pang Y, Du L M, et al. Individual tree segmentation for airborne LiDAR point cloud data using spectral clustering and supervoxel-based algorithm. Journal of Remote Sensing, 2022, 26 (8): 1650- 1661.
	杨晨辉, 程寿民, 高谢雨, 等. 基于干形拟合的TLS长白落叶松树干参数提取. 林业科学, 2025, 61 (8): 154- 163. doi: 10.11707/j.1001-7488.LYKX20240596
	Yang C H, Cheng S M, Gao X Y, et al. Stem parameter estimation method for Larix olgensis based on TLS data of stem shape fitting. Scientia Silvae Sinicae, 2025, 61 (8): 154- 163. doi: 10.11707/j.1001-7488.LYKX20240596
	曾伟生. 杉木相容性立木材积表系列模型研建. 林业科学研究, 2014, 27 (1): 6- 10. doi: 10.13275/j.cnki.lykxyj.2014.01.002
	Zeng W S. Establishment of compatible tree volume equation system of Chinese fir. Forest Science Research, 2014, 27 (1): 6- 10. doi: 10.13275/j.cnki.lykxyj.2014.01.002
	Bauwens S, Bartholomeus H, Calders K, et al. Forest inventory with terrestrial LiDAR: A comparison of static and handheld mobile laser scanning. Forests, 2016, 7 (6): 127. doi: 10.3390/f7060127
	Brede B, Terryn L, Barbier N, et al. Non-destructive estimation of individual tree biomass: allometric models, terrestrial and UAV laser scanning. Remote Sensing of Environment, 2022, 280, 113180. doi: 10.1016/j.rse.2022.113180
	Cabo C, Del Pozo S, Rodríguez-Gonzálvez P, et al. Comparing terrestrial laser scanning (TLS) and wearable laser scanning (WLS) for individual tree modeling at plot level. Remote Sensing, 2018, 10 (4): 540. doi: 10.3390/rs10040540
	Dai W X, Yang B S, Liang X L, et al. Automated fusion of forest airborne and terrestrial point clouds through canopy density analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 156, 94- 107. doi: 10.1016/j.isprsjprs.2019.08.008
	Dai W X, Kan H Y, Tan R C, et al. Multisource forest point cloud registration with semantic-guided keypoints and robust RANSAC mechanisms. International Journal of Applied Earth Observation and Geoinformation, 2022, 115, 103105. doi: 10.1016/j.jag.2022.103105
	de Conto T, Olofsson K, Görgens E B, et al. Performance of stem denoising and stem modelling algorithms on single tree point clouds from terrestrial laser scanning. Computers and Electronics in Agriculture, 2017, 143, 165- 176. doi: 10.1016/j.compag.2017.10.019
	Du L M, Pang Y, Wang Q, et al. A LiDAR biomass index-based approach for tree- and plot-level biomass mapping over forest farms using 3D point clouds. Remote Sensing of Environment, 2023, 290, 113543. doi: 10.1016/j.rse.2023.113543
	Duanmu J L, Xing Y Q. Annular neighboring points distribution analysis: a novel PLS stem point cloud preprocessing algorithm for DBH estimation. Remote Sensing, 2020, 12 (5): 808. doi: 10.3390/rs12050808
	Fekry R, Yao W, Cao L, et al. Ground-based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest. Forest Ecosystems, 2022, 9, 100065. doi: 10.1016/j.fecs.2022.100065
	Hu Y, Xu X L, Wu F Y, et al. Estimating forest stocks volume in Hunan Province, China, by integrating in situ plot data, Sentinel-2 images, and linear and machine learning regression models. Remote Sensing, 2020, 12 (1): 186. doi: 10.3390/rs12010186
	Hyyppä E, Yu X W, Kaartinen H, et al. Comparison of backpack, handheld, under-canopy UAV, and above-canopy UAV laser scanning for field reference data collection in boreal forests. Remote Sensing, 2020, 12 (20): 3327. doi: 10.3390/rs12203327
	Jin S N, Zhang W M, Shao J, et al. Estimation of larch growth at the stem, crown, and branch levels using ground-based LiDAR point cloud. Journal of Remote Sensing, 2022, 2022, 9836979. doi: 10.34133/2022/9836979
	Jurjević L, Liang X, Gašparović M, et al. Is field-measured tree height as reliable as believed – Part Ⅱ. A comparison study of tree height estimates from conventional field measurement and low-cost close-range remote sensing in a deciduous forest. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 169, 227- 241. doi: 10.1016/j.isprsjprs.2020.09.014
	Li Y, Li G, Wang S, et al. Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne LiDAR data. Geo-spatial Information Science, 2025, 28 (5): 2183- 2204. doi: 10.1080/10095020.2024.2439399
	Liang X L, Hyyppä J, Kaartinen H, et al. International benchmarking of terrestrial laser scanning approaches for forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 144, 137- 179. doi: 10.1016/j.isprsjprs.2018.06.021
	Liang X L, Kankare V, Hyyppä J, et al. Terrestrial laser scanning in forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 115, 63- 77. doi: 10.1016/j.isprsjprs.2016.01.006
	Liang X L, Wang Y S, Pyörälä J, et al. Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements. Forest Ecosystems, 2019, 6 (1): 1- 16. doi: 10.1186/s40663-019-0173-3
	Liao K, Li Y H, Zou B Z, et al. Examining the role of UAV LidAR data in improving tree volume calculation accuracy. Remote Sensing, 2022, 14 (17): 4410. doi: 10.3390/rs14174410
	Lin W, Lu Y G, Li G Y, et al. A comparative analysis of modeling approaches and canopy height-based data sources for mapping forest growing stock volume in a northern subtropical ecosystem of China. GIScience & Remote Sensing, 2022, 59 (1): 568- 589. doi: 10.1080/15481603.2022.2044139
	Liu J C, Feng Z K, Mannan A, et al. Comparing non-destructive methods to estimate volume of three tree taxa in Beijing, China. Forests, 2019, 10 (2): 92. doi: 10.3390/f10020092
	Lu D S, Chen Q, Wang G X, et al. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth, 2016, 9 (1): 63- 105. doi: 10.1080/17538947.2014.990526
	Lu D S, Jiang X D. A brief overview and perspective of using airborne LiDAR data for forest biomass estimation. International Journal of Image and Data Fusion, 2024, 15 (1): 1- 24. doi: 10.1080/19479832.2024.2309615
	Moe K, Owari T, Furuya N, et al. Comparing individual tree height information derived from field surveys, LiDAR and UAV-DAP for high-value timber species in Northern Japan. Forests, 2020, 11 (2): 223. doi: 10.3390/f11020223
	Oveland I, Hauglin M, Giannetti F, et al. Comparing three different ground based laser scanning methods for tree stem detection. Remote Sensing, 2018, 10 (4): 538. doi: 10.3390/rs10040538
	Qi Y, Coops N C, Daniels L D, et al. Comparing tree attributes derived from quantitative structure models based on drone and mobile laser scanning point clouds across varying canopy cover conditions. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 192, 49- 65. doi: 10.1016/j.isprsjprs.2022.07.021
	Seidel D, Annighöfer P, Ammer C, et al. Quantifying understory complexity in unmanaged forests using TLS and identifying some of its major drivers. Remote Sensing, 2021, 13 (8): 1513. doi: 10.3390/rs13081513
	Tao S L, Wu F F, Guo Q H, et al. Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 110, 66- 76. doi: 10.1016/j.isprsjprs.2015.10.007
	Vorster A G, Evangelista P H, Stovall A E L, et al. Variability and uncertainty in forest biomass estimates from the tree to landscape scale: the role of allometric equations. Carbon Balance and Management, 2020, 15 (1): 8. doi: 10.1186/s13021-020-00143-6
	Wan P, Wang T J, Zhang W M, et al. Quantification of occlusions influencing the tree stem curve retrieving from single-scan terrestrial laser scanning data. Forest Ecosystems, 2019, 6 (1): 43. doi: 10.1186/s40663-019-0203-1
	Wang D, Liang X L, Mofack G I, et al. Individual tree extraction from terrestrial laser scanning data via graph pathing. Forest Ecosystems, 2021, 8, 1- 11. doi: 10.1186/s40663-021-00340-w
	Wang Y S, Lehtomäki M, Liang X L, et al. Is field-measured tree height as reliable as believed: a comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forest. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 147, 132- 145. doi: 10.1016/j.isprsjprs.2018.11.008
	Xie Y, Yang T, Wang X, et al. Applying a portable backpack LiDAR to measure and locate trees in a nature forest plot: Accuracy and error analyses. Remote Sensing, 2022, 14 (8): 1806. doi: 10.3390/rs14081806
	Yang J M, Yuan W W, Lu H C, et al. Assessing the performance of handheld laser scanning for individual tree mapping in an urban area. Forests, 2024, 15 (4): 575. doi: 10.3390/f15040575
	Zhao Y T, Im J, Zhen Z. Towards accurate individual tree parameters estimation in dense forest: optimized coarse-to-fine algorithms for registering UAV and terrestrial LiDAR data. GIScience & Remote Sensing, 2023, 60 (1): 2197281. doi: 10.1080/15481603.2023.2197281

参数 Parameters	马尾松Masson pine		杉木Chinese fir
参数 Parameters	RMSE （rRMSE）	Bias （Bias%）	RMSE （rRMSE）	Bias （Bias%）
手持扫描数据提取的树高和胸径 Tree height and DBH extracted from HLS data	0.027 （20.32%）	–0.019 （–15.01%）	0.014 （21.48%）	–0.010 （–14.64%）
融合数据提取的树高和胸径 Tree height and DBH extracted from fused LiDAR data	0.021 （16.11%）	–0.014 （–10.91%）	0.015 （17.18%）	–0.006 （–9.25%）
手持扫描数据提取的胸径 DBH extracted from HLS data	0.027 （20.40%）	–0.017 （–12.64%）	0.014 （20.25%）	–0.004 （–6.25%）

林分类型 Forest types	林下植被状况 Understory conditions	样地个数 Number of Plots	实际株数 Number of measured trees	自动分割Automated segmentation				手动分割Manual segmentation
林分类型 Forest types	林下植被状况 Understory conditions	样地个数 Number of Plots	实际株数 Number of measured trees	株数 No. of trees	R （%）	P （%）	F₁ （%）	株数 No. of trees	R （%）	P （%）	F₁ （%）
马尾松 Masson pine	简单 Simple	2	189	192	98.41	96.88	97.64	187	98.94	100	99.47
	中等 Medium	2	129	135	91.47	87.41	89.39	127	95.35	96.85	96.09
	复杂 Complex	2	169	212	88.76	70.75	78.74	175	94.67	91.43	93.02
杉木 Chinese fir	简单 Simple	3	143	147	97.9	95.24	96.55	146	99.3	97.26	98.27
	中等 Medium	4	225	252	92.00	82.14	86.79	220	95.56	97.73	96.63
	复杂 Complex	3	198	296	90.40	60.47	72.47	197	94.95	95.43	95.19
阔叶 Broadleaf	简单 Simple	2	107	99	88.79	95.96	92.23	103	93.46	97.09	95.24
	中等 Medium	2	156	138	79.49	89.86	84.35	168	92.95	86.31	89.51
	复杂 Complex	2	113	204	75.22	41.67	53.63	111	84.96	86.49	85.71

林下植被状况 Understory conditions	马尾松Masson pine		杉木Chinese fir		阔叶Broadleaf
林下植被状况 Understory conditions	RMSE （rRMSE）	Bias （Bias%）	RMSE （rRMSE）	Bias （Bias%）	RMSE （rRMSE）	Bias （Bias%）
简单 Simple	1.37 （8.78%）	–0.62 （–3.99%）	1.31 （7.38%）	–0.76 （–4.28%）	2.06 （12.48%）	–1.02 （–6.21%）
中等 Medium	2.06 （11.27%）	0.37 （2.00%）	1.38 （7.18%）	–0.59 （–3.0%）	1.86 （21.75%）	–0.35 （–5.32%）
复杂 Complex	1.55 （8.24%）	–0.90 （–4.78%）	1.35 （6.55%）	–0.51 （–2.49%）	4.43 （27.12%）	1.40 （8.59%）
总体 Total	1.63 （9.43%）	–0.44 （–2.54%）	1.35 （7.05%）	–0.63 （–3.25%）	3.15 （19.85%）	0.18 （1.12%）

林下植被状况 Understory conditions	马尾松Masson pine		杉木Chinese fir		阔叶Broadleaf
林下植被状况 Understory conditions	RMSE （rRMSE）	Bias （Bias%）	RMSE （rRMSE）	Bias （Bias%）	RMSE （rRMSE）	Bias （Bias%）
简单 Simple	0.21 （1.60%）	–0.13 （–0.96%）	0.95 （8.32%）	–0.06 （–0.55%）	1.77 （19.02%）	–0.10 （–1.10%）
中等 Medium	2.69 （17.03%）	–1.89 （–11.96%）	2.83 （17.05%）	–2.07 （–12.50%）	2.50 （21.59%）	–0.48 （–4.13%）
复杂 Complex	4.15 （21.92%）	–3.17 （–16.70%）	3.65 （19.13%）	–2.96 （–15.54%）	3.74 （23.64%）	–3.01 （–19.01%）
总体 Total	2.66 （16.92%）	–1.45 （–9.22%）	2.66 （17.13%）	–1.64 （–10.54%）	2.82 （22.95%）	–1.29 （–10.49%）

[1]	Yining Lian,Hao Lu,Yongjian Huai,Haifeng Xu,Langning Huo,Zhichao Wang. Point Cloud Semantic-Guided Individual Tree Segmentation and Parameter Estimation Using UAV Laser Scanning [J]. Scientia Silvae Sinicae, 2026, 62(4): 106-117.
[2]	Caihong Duan,Hui Lin,Jiangping Long,Peisong Yang,Zilin Ye,Tingchen Zhang,Xunwei Li,Lixin Zhu. Remote Sensing Estimation of Eucalyptus Age and Stem Volume Combining Improved Simulating Continuous Change Detection with Classification Algorithm [J]. Scientia Silvae Sinicae, 2025, 61(4): 46-55.
[3]	Dan Kong,Yong Pang,Xiaojun Liang,Liming Du,Yu Bai. Individual Tree Segmentation from ALS Point Clouds Based on Layers Stacking Algorithm [J]. Scientia Silvae Sinicae, 2024, 60(3): 87-99.
[4]	Zhou Xiangbei, Li Chungan, Dai Huabing, Yu Zhu, Li Zhen, Su Kai. Effects of Point Cloud Density on the Estimation Accuracy of Large-Area Subtropical Forest Inventory Attributes Using Airborne LiDAR Data [J]. Scientia Silvae Sinicae, 2023, 59(9): 23-33.
[5]	Chungan Li,Zhen Li. Generalizing Predictive Models of Sub-Tropical Forest Inventory Attributes Using an Area-Based Approach with Airborne LiDAR Data [J]. Scientia Silvae Sinicae, 2021, 57(10): 23-35.
[6]	Langning Huo,Xiaoli Zhang. Individual Tree Information Extraction and Accuracy Evaluation Based on Airborne LiDAR Point Cloud by Multilayer Clustering Method [J]. Scientia Silvae Sinicae, 2021, 57(1): 85-94.
[7]	You Haotian, Xing Yanqiu, Peng Tao, Ding Jianhua. Research on the Effect of Side-Overlap between Airborne LiDAR Adjacent Swaths on the Coniferous Forest Structural Parameters Estimation [J]. Scientia Silvae Sinicae, 2018, 54(6): 109-118.
[8]	Yan Enping, Zhao Yunlin, Lin Hui, Mo Dengkui, Wang Guangxing. Estimation of Forest Carbon Density Based on Geostatistics and Multi-Resource Remote Sensing Data [J]. Scientia Silvae Sinicae, 2017, 53(7): 72-84.
[9]	Cao Lin, Dai Jinsong, Pang Yong, Zhao Bing, Xu Jianxin, Li Zengyuan. Estimation of Urban Building Rooftop-Received Solar Energy by LiDAR and Irradiation Model in the Urban Vegetation Shading Environment [J]. Scientia Silvae Sinicae, 2014, 50(2): 99-110.
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Comparative Analysis of Individual Tree Parameter Extraction Accuracy Using Integrated Data Collected by Airborne and Handheld LiDAR in Subtropical Typical Forests

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