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

摘要/Abstract

摘要：

目的: 融合机载与手持激光雷达（LiDAR）数据，探讨其在单木参数提取中的优势，分析机载和手持LiDAR数据在不同林分类型和林下植被条件下的适用性，为多源LiDAR数据在精细化森林调查中的应用提供科学依据。方法: 选取亚热带福建省3个典型区，基于采集的机载和手持LiDAR数据，采用自动分割与目视解译相结合的方式准确分割样地单木，提取相应的单木参数并评估2种数据估算单木材积的性能，分析林分类型和林分复杂性对单木参数提取精度的影响。结果: 1）融合机载和手持LiDAR数据提取的单木胸径的相对均方根误差（rRMSE）为5.72%~5.84%，树高的rRMSE为7.36%~7.83%，与单独使用手持扫描数据相比，树高误差（rRMSE）减小3.29%~4.19%。2）基于融合LiDAR数据，杉木在不同林下植被条件下均可获得高精度的单木胸径，rRMSE为7.05%，马尾松在林下植被简单条件下可获得较高精度的单木胸径，而阔叶林的单木胸径存在较大不确定性。3）基于提取的单木胸径和树高计算单木材积的rRMSE为16.11%~17.18%，与单独使用手持扫描数据相比，单木材积误差减小4.21%~4.30%。结论: 融合机载与手持LiDAR数据估测单木参数的精度受林分类型和林下植被条件的影响，不同场景单木参数提取精度的评估可为后续激光雷达技术优化地面调查工作提供理论支撑。

关键词: 单木参数, 伐倒木, 手持扫描仪, 机载激光雷达, 林业遥感

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

中图分类号:

S771.8

李云鹤,王思荣,李逸洒,陆灯盛. 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[J]. 林业科学, 2026, 62(6): 82-95.

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.

图/表 12

图1

表1

研究使用的数据情况"

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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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%）

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