激光雷达森林结构指标在森林植物多样性评估中的研究进展

doi:10.11707/j.1001-7488.LYKX20230476

Abstract

Abstract:

Protecting forest ecosystems necessitates timely, accurate, and dynamic information on the status and changes in forest plant diversity, including an analysis and evaluation of the contributing factors. The development of remote sensing technology enables multi-scale monitoring, with optical remote sensing data based on the spectral variation hypothesis facilitating the monitoring of forest plant diversity. However, spectral diversity, while reflective of horizontal forest diversity, has a limited ability to monitor the vertical structure of forests. Despite the great potential of forest structural complexity for understanding biodiversity, comprehensive tools for measuring forest structure have been limited. The recent development of active remote sensing technologies, such as LiDAR, has expanded the detection of forest plant diversity from the spectral to the structural dimension, offering an opportunity to clarify the structure-function relationship of forests. The main objective of this review is to summarize the use of LiDAR data in biodiversity-related assessments of forests. We highlight topics related to forest structural parameters, structural diversity, species diversity, dead wood, understorey, forest type and successional stage. We found that different LiDAR structural metrics significantly explain various forest biodiversity indicators and are influenced by factors such as data acquisition, sensor type, sampling scale, forest type, and geographic location. The fusion of spectral information from optical images with structural information from LiDAR is highly advantageous in studies considering tree species and diversity. LiDAR remote sensing technology enables the monitoring and assessment of forest structure and diversity at different scales. The integration of spaceborne LiDAR and satellite images helps to understand the impacts of changes in the vertical structure of the canopy on ecosystem composition and function globally, offering broad applications in the conservation and management of biodiversity.

Key words: LiDAR remote sensing, forest vegetation structure, structure diversity, biodiversity, multi-scale monitoring

CLC Number:

Luxia Liu,Bo Hu,Guoqing Sang,Yuyu Liu. Assessing Forest Vegetation Diversity Using Forest Structure Indicators Based on LiDAR Remote Sensing: A Review[J]. Scientia Silvae Sinicae, 2025, 61(1): 176-196.

Figures/Tables 3

Table 1

Description of the LiDAR-derived metrics"

类别 Category	分组 Subcategory	变量常用名称 Name	描述 Description
高度变量 Height related metrics	点云高度分位数 Percentile of point cloud height	HP5，HP10…， HP75，HP95	冠层点云高度分布的百分位数（5%、10%、... 、75%、95%） Percentile of canopy point cloud height distribution (5%，10%，... ，75%，95%)
	点云高度分布范围 Range of point cloud height	H_min，H_max，H_range	冠层点云高度分布的最小、最大高度以及高度差 The minimal，maximum and range of canopy point cloud height distribution
	集中趋势 Concentration trend of point cloud height	H_mean，H_median，H_mode	冠层点云高度分布的平均、中位数、众数值 The mean，median and mode of canopy point cloud height distribution
		MAD_median	中值以上所有点云高度绝对偏差的中值 Median of the absolute deviations in height above the median point cloud
		MAD_mode	中值以上所有点云高度绝对偏差的众数 Mode of the absolute deviations in height of point cloud above the median
	离散趋势 Dispersion trend of point cloud height	H_var，H_stdev，H_cv	冠层点云高度分布的方差、标准差、变异系数 The variance，standard deviation and coefficient of variation of canopy point cloud height
	离散趋势 Dispersion trend of point cloud height	AAD	冠层点云高度分布的平均绝对偏差 The average absolute deviation of canopy point cloud height
	其他统计参数 Other statistical parameters	H_skewness，H_kurtosis	冠层点云高度分布的偏度和峰度 The skewness and kurtosis of canopy point cloud height
		L-moments	冠层点云高度分布的线性矩，包括 L1、L2、L3、L4 The L-moments of canopy point cloud height(L1，L2，L3，L4)
		L-moment skewness	冠层点云高度分布的线性矩偏度值 The L-moment skewness of canopy point cloud height
		L-moment kurtosis	冠层点云高度分布的线性矩峰度值 The L-moment kurtosis of canopy point cloud height
	冠层高度模型统计参数 Indices derived from canopy height models	CHM_mean，CHM_stdev	CHM的平均值与方差 The mean and standard deviation of canopy height models
冠层覆盖变量 Canopy cover related metrics	按回波区分 Return-related	rCCH	指定高度（如均值、众数、地面）以上第一、二、三次回波百分比 Percentage of first，second，and third returns above a given height (such as mean，mode，ground level height)
	按密度区分 Density-related	DP5，DP 10…，DP 75，DP 95	大于5%、10%、... 、75%、95%分位数高度的所有植被点占全部点的比例 Percentage of vegetation point cloud returns above the percentile heights (5%，10%，...，75%，95%)
	冠层覆盖度 Canopy cover	CC	根据冠层点云比例或者形态学滤波方法获取 Derived from canopy point cloud percentage or morphological filtering
冠层垂直分布变量 Canopy vertical related metrics	冠层高度分布剖面 Canopy height distribution	CHD	一般由冠层点云垂直分布频率获取或者进行Weibull分布拟合 Derived from canopy point cloud vertical frequency distribution or fitted with Weibull distribution
	叶面积密度剖面 Leaf area density profile	LAD	通过地面以上的某冠层高度的间隙率与消光系数之间的关系进行计算 Calculated from the relationship between gap and extinction coefficient for a given canopy height
	叶面积密度剖面 Leaf area density profile	LAD_cv，LAD_mean，LAD_stdev	叶面积密度剖面的变异系数、平均值、标准差 The coefficient of variation，mean and standard deviation of Leaf area density profile
	叶高多样性 Foliage height diversity	FHD	通过点云高度分布异质性计算 Calculated by point cloud height distribution heterogeneity
地形参数 Topography	海拔、坡度、坡向 Elevation，slope，aspect		通过LiDAR数据生成的数字高程模型计算 Calculated from LiDAR digital elevation model (DEM)
	地形粗糙度 Terrain roughness
	地形湿润指数 Topographic wetness index	TWI

Table 1

Table 2

Description of the LiDAR estimated forest structural attributes"

尺度 Scale	森林结构属性 Forest structural attributes	主要数据来源 LiDAR data	方法与LiDAR特征变量 Methods and LiDAR-derived metrics	参考文献 Reference
林分尺度Stand scale	覆盖度、树冠开阔度 Coverage&Canopy openness	星载和机载激光雷达 Satellite and airborne LiDAR	使用点云、波形特征建立统计模型 Building statistical models with point cloud and waveform features	Szostak et al.，2022; Narine et al.，2022; Geremew et al.，2024
	冠层高度 Canopy height			Wang et al.，2022
	生物量、蓄积量 Biomass&Volume			Rodda et al.，2024
	林冠分层 Canopy stratification		使用波形特征或点云拟合的波形特征 Derived from waveform features or fitted waveforms features from point cloud	Yun et al.，2022; Zhou et al.，2023
	林冠垂直分布及其复杂性 Canopy vertical distribution and its complexity		使用点云、波形特征进行描述 Derived from waveform and point cloud features	Almeida et al.，2019b; Wang et al.，2020; Grotti et al.，2020
	林下植被分布及生物量 Plant distribution and biomass under canopy	星载、机载和地基激光雷达 Satellite，airborne and terrestrial LiDAR	使用点云、波形特征建立统计模型 Building statistical models with point cloud and waveform features	Crespo-Peremarch et al.， 2018; 2020
	单木分割 Tree segmentation	机载和地基激光雷达 Airborne and terrestrial LiDAR	使用CHM的图像特征、点云几何和辐射特征进行分割 Segmentation based on CHM or geometry and radiometric features of point cloud	Zhao et al.，2014; Windrim et al.，2020; Shen et al.，2022
	树种分类 Tree species classification	机载和地基激光雷达 Airborne and terrestrial LiDAR	使用树冠几何、辐射特征进行树种分类 Tree species classification based on geometry and radiometric features from tree canopy	Liu et al.，2017a; Micha?owska et al.，2021
单木尺度 Tree scale	树干结构参数 Stem structure parameters	地基激光雷达 Terrestrial LiDAR	使用点云几何特征构建 Derived from geometry features of point cloud	Hyypp? et al.，2020
	树冠结构参数 Crown structure parameters	机载和地基激光雷达 Airborne and terrestrial LiDAR	使用点云统计、几何特征构建 Derived from statistical and geometry features of point cloud	Rocha et al.，2023
	叶分布 Foliage distribution	地基激光雷达 Terrestrial LiDAR	基于孔隙、接触频率、生物物理回归等方法统计点云分布 Statistics of point cloud distribution based on gap，contact frequency or biophysical regression method	Ma et al.，2018
	枝叶分离 Branch and leaf separation	地基激光雷达 Terrestrial LiDAR	使用点云辐射和几何特征进行区分 Distinguishing branches and leaves using point cloud radiation and geometric features	Ferrara et al.，2018
	单木骨架提取 Tree skeleton extraction	地基激光雷达 Terrestrial LiDAR		Li et al.，2023

Table 2

Table 3

Studies of LiDAR for forest species diversity modeling"

研究区 Location	森林类型 Forest type	物种调查目标 Survey trees	物种多样性指标 Species diversity indices	取样面积Sample area/m²	遥感数据源 Remote sensing data	模型 Modeling	模型结果 Model performance （R²）	参考文献 References
尤卡坦半岛 Yucatan Peninsula	热带干旱森林 Tropical dry forests	DBH≥7.5 cm	SR	400~2200	ALS	MLR	0.32~0.67	Hernández-Stefanoni et al.，2014
智利 Chile	混交次生林 Mixed secondary forests	ALL	SR	225	ALS&EO1-Hyperion	MLR	0.57	Ceballos et al.，2015
加纳 Ghana	热带森林 Tropical forests	DBH≥20 cm	SR	1600	ALS	MARS	0.64	Vaglio Laurin et al.，2016
智利 Chile	混交次生林 Mixed secondary forests	ALL	SR	225	ALS	GLM	0.66	Lopatin et al.，2016
美国北卡罗来纳州 NC，USA	次生松林&硬木林 Successional pine & Mature hardwood forests	ALL	SR	0.01~900	ALS& Hyperspectral	RF	0.19~0.70	Hakkenberg et al.，2018
尤卡坦半岛 Yucatan Peninsula	热带干旱森林 Tropical dry forests	DBH≥2.5 cm	SR，H	1000	ALS&RapidEye	MLR	0.89，0.81	George-Chacon et al.，2019
加蓬 Gabon	热带森林 Tropical forests	DBH≥10 cm	SR，H	400~10 000	ALS	LR	0.71	Marselis et al.，2019
泛热带地区 Pan-tropical region	热带森林 Tropical forests	DBH≥10 cm	SR	625~10 000	ALS	PR	0.39	Marselis et al.，2020
意大利&德国 Italy&Germany	温带森林 Temperate forests	DBH≥5 cm	SR，H，D	10000	ALS	LR	意大利Italy:0.18，0.63，0.57 德国Germany:0.48，0.56、0.56	Torresani et al.，2020
美国全国 USA	NA	DBH≥10 cm	SR，H	400	ALS	SGLMM	0.53，0.5	Hakkenberg et al.，2021
坦桑尼亚 Tanzania	热带森林 Tropical forests	DBH≥1 cm	SR，H	700	ALS	LMM	0.45，0.38	Mauya et al.，2021
中国普洱 Puer，China	亚热带森林 Subtropical forests	DBH≥5 cm	H	700	ALS& Hyperspectral	RF	LiDAR:0.5 Hyperspectral:0.48 LiDAR+ Hyperspectral:0.69	刘鲁霞等，2022
中国 China	亚热带森林 Subtropical forests	DBH≥1 cm	SR，H，J	400	UAV LiDAR	SSAEM	SR:0.27~0.62 H:0.37~0.67 J:0.32~0.61	Zhang et al.，2022
中国昆明 Kunming，China	亚热带森林 Subtropical forests	TH≥2 m	SR，H，D	400	ICESat-2&GF-1	GBRT	ICESat-2:0.62 GF-1:0.58 ICESat-2+GF-1:0.64	Yang et al.，2023
意大利&德国 Italy&Germany	温带森林 Temperate forests	DBH≥5 cm	SR，H	10 000	GEDI&Sentinel-2&Landsat8	MLR	意大利Italy:0.58，0.39 德国Germany:0.31，0.41	Torresani et al.，2023
中国东北 Northeast China	温带混交林 Temperate mixed forests	DBH≥10 cm	H，D，J	2500	GEDI&Sentinel-2	RF	0.72，0.78，0.86	Ren et al.，2023

Table 3

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Assessing Forest Vegetation Diversity Using Forest Structure Indicators Based on LiDAR Remote Sensing: A Review

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