GLAS星载激光雷达和Landsat/ETM+数据的森林生物量估算

基于大脚印激光雷达数据和野外观测数据,该文提出一种获取脚印点内森林生物量的新思路,并结合陆地卫星数据应用于长白山地区森林地上生物量估算。首先,基于3种森林类型(针叶林、阔叶林和针阔混交林),采用多元逐步回归方法建立激光雷达波形指数与脚印点内实测平均树高的回归模型,估算全部脚印点内的平均树高;然后根据脚印点内样方的野外观测数据(平均树高和平均胸径)以及它们与样方生物量的拟合方程估算没有野外调查数据对应的脚印点的生物量;最后对3种森林类型的脚印点森林生物量在各森林覆盖度条件下进行分层分区统计得到生物量等级图。验证比较遥感估算的生物量与野外调查数据推算的生物量,总体误差在0~30(t·hm~(-2))之间,均方根误差为14.66(t·hm~(-2))。     

无人机激光雷达人工林林分高估测模型分析EI北大核心CSCD

中国人工林面积居世界第一,精确地对人工林结构进行监测具有重要意义。本研究以内蒙古自治区赤峰市旺业甸林场内的落叶松和油松人工林为研究对象,利用无人机激光雷达LiDAR(Light Detection And Ranging)离散点云数据和地面样地调查数据对人工林林分高进行建模,通过点云特征变量与地面测量的6种林分高(包括:Lorey’s高、算术平均高、最大高、优势树高、中位数高和树冠面积加权高)间的Pearson’s相关性筛选自变量,然后利用全子集回归构建不同林分高估测模型,并采用交叉检验法进行精度评价。结果表明:激光雷达点云高度百分位数与不同林分高相关性均较高,通过一元线性回归构建的不同林分高结果最优,且估测模型的自变量均为高度特征变量。Lorey’s高(R^(2)=0.91—0.97,rRMSE=2.75%—3.96%)、优势树高(R^(2)=0.86—0.97,rRMSE=3.72%—3.83%)和树冠面积加权高(R^(2)=0.86—0.96,rRMSE=3.81%—4.73%)估测精度最高,算术平均高(R^(2)=0.85—0.94,rRMSE=4.52%—6.07%)和中位数高(R^(2)=0.80—0.95,rRMSE=5.37%—7.34%)次之,最大高(R^(2)=0.69—0.87,rRMSE=6.19%—8.09%)最低。针对不同森林类型,落叶松人工林林分高估测精度最优,优于不区分森林类型模型的估测精度(ΔR^(2)=0—0.05,ΔrRMSE=-0.69%—1.97%),优于油松林林分高模型的估测精度(ΔR^(2)=0.06—0.18,ΔrRMSE=-1.90%—1.13%)。无人机激光雷达可以用于估测北方温带针叶林的林分高,能够满足人工林资源调查快速、精确的要求。     

基于遥感的区域尺度森林地上生物量估算研究

森林是陆地生态系统最大的碳库,精确估算森林生物量是陆地碳循环研究的关键。首先从机载LiDAR数据中提取高度和密度统计量,采用逐步回归模型进行典型样区生物量估算;然后利用机载LiDAR数据估算的生物量作为样本数据,与多光谱遥感数据Landsat8 OLI的波段反射率及植被指数建立回归模型,实现区域尺度森林地上生物量估算。实验结果显示,机载LiDAR数据估算的鼎湖山样区生物量与地面实测生物量的相关性R2达0.81,生物量RMSE为40.85 t/ha,说明机载LiDAR点云数据的高度和密度统计量与生物量存在较高的相关性。以机载LiDAR数据估算的生物量为样本数据,结合多光谱遥感数据Landsat8 OLI估算粤西北地区的森林地上生物量,精度验证结果为:R2为0.58,RMSE为36.9 t/ha;针叶林、阔叶林和针阔叶混交林等3种不同森林类型生物量的估算结果为:R2分别为0.51(n=251)、0.58(n=235)和0.56(n=241),生物量RMSE分别为24.1 t/ha、31.3 t/ha和29.9 t/ha,估算精度相差不大。总体上看,利用遥感数据可以开展区域尺度的森林地上生物量估算,为森林固碳监测提供有力的参考数据。     

机器学习算法在森林地上生物量估算中的应用

森林地上生物量是森林生产力的重要评价指标,对其进行高效监测对维持全球碳平衡和保护生态系统具有重要意义。本文首先基于冠层高度模型数据,通过分水岭分割算法得到单木冠幅边界;然后在单木冠幅范围内提取23个LiDAR变量,结合佩诺布斯科特试验森林的87组实测数据,利用随机森林和支持向量机建立森林地上生物量估算模型;最后对样地模型估算的结果进行了比较,讨论了预测结果及其精度。结果表明:本文选用的随机森林模型和支持向量机模型在估算森林地上生物量的应用中获得了较高的精度;并且,随机森林模型在基于机载雷达数据估测森林地上生物量中的估算精度更高,模型泛化能力更强,制图精度也更好,具有更好的适用性。     

中国南方森林冠顶高度Lidar反演—以江西省为例

激光雷达(Lidar)与光学遥感的有效结合对中国南方区域森林冠顶高度反演意义重大,而国产卫星将为中国森林生态研究提供新的数据源。本文联合利用大脚印激光雷达GLA和国产MERSI数据,在实现GLAS波形数据处理和不同地形条件下森林冠顶高度反演算法基础上,建立了区域尺度不同森林类型林分冠顶高度GLAS+MERSI联合反演关系模型,进行了江西地区森林冠顶高度反演。总体上,GLAS激光雷达森林冠顶高度估算精度较高;且在与MERSI 250 m数据的联合反演模型中,针叶林模型精度较好(R²=0.7325);阔叶林次之(R²=0.6095);混交林较差(R²=0.4068)。分析发现,考虑了光学遥感生物物理参数的GLAS+MERSI联合关系模型在区域森林冠顶高度估算中有较高精度,且在空间分布上与土地覆盖数据分布特征非常一致。     

基于GLAS激光雷达反演森林生物量

森林生物量是森林生态系统的重要指标。GLAS大光斑回波信息与森林结构参数存在较强的相关性,适用于森林生物量的反演。本文简要介绍了GLAS激光雷达系统及其特点,利用GLAS的9波形参数对小兴安岭部分地区进行针叶林与阔叶林的生物量估算,结果显示,引入纠正参数后生物量估测模型的决定系数R²由0.657提高到0806,均方根误差（RMSE）减小为35 Mg/ha,表明利用GLAS进行森林地上生物量估测时,需要考虑地形因素对反演精度的影响。     

基于GLAS激光雷达反演森林生物量北大核心CSCD

森林生物量是森林生态系统监测的重要指标。GLAS大光斑回波信息与森林结构参数存在较强的相关性,适用于森林生物量的反演。本文简要介绍了GLAS激光雷达系统及其特点,利用GLAS的9波形参数对小兴安岭部分地区进行针叶林与阔叶林的生物量估算,结果显示,引入纠正参数后生物量估测模型的决定系数R2由0.657提高到0.806,均方根误差(RMSE)减小为35 Mg/ha,表明利用GLAS进行森林地上生物量估测时,需要考虑地形因素对反演精度的影响。     

低矮植被的无人机激光雷达测高精度分析

针对无人机激光雷达估测低矮植被高度的精度大小,本文以3m以下低矮树木为研究对象,通过数值模拟方法得到不同航高、不同扫描角情况下激光脚点坐标和点云估测单一树木高度的最大测量误差值;对比实测树高,分析了激光点云估测树高的精度。结果表明,在航高为30m、扫描范围为(-50°,5°)的情况下,无人机激光雷达获取的激光脚点坐标误差和由激光点云估测低矮树高(3m以下)的误差均可以达到cm级;激光点云估测单一树木高度与实测高度的决定系数为0.977,均方根差为5cm,标准均方根差为4%。因此,应用无人机激光雷达数据可以快速、精确获取低矮植被高度信息,进而为反演植被生物量和植被长势信息监测提供重要依据。     

利用激光雷达和多角度频谱成像仪数据估测森林垂直参数

植被的结构参数如植被高度、生物量、水平和垂直分布等，是影响陆地与大气能量交换乃至生物圈多样性的重要因素。多数遥感系统虽然可以提供植被水平结构的图像，但是不能提供植被成分垂直分布的信息。大尺度激光雷达仪器如LVIS产生的激光雷达信号，已成功地用于估计树高和森林生物量，然而大多数激光雷达仪器不具备图像能力，只能提供一个区域内的采样数据。其他的遥感数据如多角度高光谱、多频率多时相辐射计或雷达数据，可根据GLAS（Geoscience Laser Altimeter System）采样的测量用来推断出连续的森林结构区域覆盖参数。 MISR（Multi-angle Imaging Spectrometer）对陆表多角度的成像能力，可以通过BRDF的各向异性提供植被的结构信息。结合激光雷达的垂直采样和MISR的图像，区域内乃至全球性的森林空间参数的成像是可能的。ICESat卫星上的GLAS数据、Terra卫星上的MISR数据为区域或全球性森林结构参数提供了可能。本文的研究目的是评估GLAS数据，分析类似于MISR的数据对森林结构参数的估计能力。本文中使用了LVIS、AirMISR和GLAS数据。通过对GLAS树高的测量与GLAS像元内来自LVIS的平均树高对比，发现它们是高度相关的。同时还探讨了多角度频谱成像仪数据预测树高信息的能力，这将在今后区域内森林结构参数映射加以研究。     

三峡库区森林叶面积指数多模型遥感估算

叶面积指数(leaf area index,LAI)是定量研究森林生态系统能量交换的一个重要结构参数。本文利用野外观测LAI,以及Landsat TM计算的7种常用植被指数和5个自定义植被指数,通过筛选建立了不同森林类型的LAI估算模型,其中,针叶林采用多元逐步回归模型,阔叶林与混交林采用主成分分析模型,最终通过多个模型估算三峡库区区域尺度森林LAI。利用样地实测LAI数据进行精度验证,针叶林、阔叶林和混交林的均方根误差分别为0. 829 4,1. 111 5和1. 790 9,判定系数R2均达到了0. 77以上。研究结果将为森林生态系统和碳循环研究提供基础数据。     

Airborne LiDAR and Terrestrial Laser Scanning Derived Vegetation Obstruction Factors for Visibility Models

Research presented here explores the feasibility of leveraging vegetation data derived from airborne light detection and ranging (LiDAR) and terrestrial laser scanning (TLS) for visibility modeling. Using LiDAR and TLS datasets of a lodgepole pine (Pinus contorta) dominant ecosystem, tree canopy and trunk obstructions were isolated relevant to a discrete visibility beam in a short‐range line‐of‐sight model. Cumulative obstruction factors from vegetation were compared with reference visibility values from digital photographs along sightline paths. LiDAR‐derived tree factors were augmented with single‐scan TLS data for obstruction prediction. Good correlation between datasets was found up to 10 m from the terrestrial scanner, but fine scale visibility modeling was problematic at longer distances. Analysis of correlation and regression results reveal the influence of obstruction shadowing inherent to discrete LiDAR and TLS, potentially limiting the feasibility of modeling visibility over large areas with similar technology. However, the results support the potential for TLS‐derived subcanopy metrics for augmenting large amounts of aerial LiDAR data to significantly improve models of forest structure. Subtle LiDAR processing improvements, including more accurate tree delineation through higher point density aerial data, combined with better vegetation quantification processes for TLS data, will advance the feasibility and accuracy of data integration.     

Deriving airborne laser scanning based computational canopy volume for forest biomass and allometry studies

A computational canopy volume (CCV) based on airborne laser scanning (ALS) data is proposed to improve predictions of forest biomass and other related attributes like stem volume and basal area. An approach to derive the CCV based on computational geometry, topological connectivity and numerical optimization was tested with sparse-density, plot-level ALS data acquired from 40 field sample plots of 500–1000 m² located in a boreal forest in Norway. The CCV had a high correspondence with the biomass attributes considered when derived from optimized filtrations, i.e. ordered sets of simplices belonging to the triangulations based on the point data. Coefficients of determination (R²) between the CCV and total above-ground biomass, canopy biomass, stem volume, and basal area were 0.88–0.89, 0.89, 0.83–0.97, and 0.88–0.92, respectively, depending on the applied filtration. The magnitude of the required filtration was found to increase according to an increasing basal area, which indicated a possibility to predict this magnitude by means of ALS-based height and density metrics. A simple prediction model provided CCVs which had R² of 0.77–0.90 with the aforementioned forest attributes. The derived CCVs always produced complementary information and were mainly able to improve the predictions of forest biomass relative to models based on the height and density metrics, yet only by 0–1.9 percentage points in terms of relative root mean squared error. Possibilities to improve the CCVs by a further analysis of topological persistence are discussed.     

Tree species identification in mixed coniferous forest using airborne laser scanning

This study tests the capacity of relatively low density (<1 return/m²) airborne laser scanner data for discriminating between Douglas-fir, western larch, ponderosa pine, and lodgepole pine in a western North American montane forest and it evaluates the relative importance of intensity, height, and return type metrics for classifying tree species. Collectively, Exploratory Data Analysis, Pearson Correlation, ANOVA, and Linear Discriminant Analysis show that structural and intensity characteristics generated from LIDAR data are useful for classifying species at dominant and individual tree levels in multi-aged, mixed conifer forests. Proportions of return types and mean intensities are significantly different between species ($p$-value < 0.001) for plot-level dominant species and individual trees. Classification accuracies based on single variables range from 49%–61% at the dominant species level and 37%–52% for individual trees. The accuracy can be improved to 95% and 68% respectively by using multiple variables. The inclusion of proportion of return type greatly improves the classification accuracy at the dominant species level, but not for individual trees, while canopy height improves the accuracy at both levels. Overall differences in intensity and return type between species largely reflect variations in the physical structure of trees and stands. These results are consistent with the findings of others and point to airborne laser scanning as a useful source of data for species classification. However, there are still many knowledge gaps that prevent accurate mapping of species using ALS data alone, particularly with relatively sparse datasets like the one used in this study. Further investigations using other datasets in different forest types will likely result in improvements to species identification and mapping for some time to come.     

Mapping individual trees with airborne laser scanning data in an European lowland forest using a self-calibration algorithm

Traditional field-based forest inventories tend to be expensive, time-consuming, and cover only a limited area of a forested region. Remote sensing (RS), especially airborne laser scanning (ALS) has opened new possibilities for operational forest inventories, particularly at the single-tree level, and in the prediction of single-tree characteristics. Throughout the world, forests have varying characteristics that necessitate the development of modern, effective, and versatile tools for ALS data processing. To address this need, we aimed to develop a tool for individual tree detection (ITD) utilising a self-calibrating algorithm procedure and to verify its accuracy using the complicated forest structure of near natural forests in the temperate zone.This study was carried out in the Polish part of the Białowieża Forest (BF). The airborne laser scanner (ALS) and color-infrared (CIR) datasets were acquired for more than 60 000 ha. Field-based measurements were performed to provide reference data at the single tree level. We introduced a novel ITD method that is self-calibrated and uses a hierarchical analyses of the canopy height model.There were more than 20 000 000 of trees in first layer in BF above 7 m height. Trees visible from above were divided into coniferous, deciduous and mixed trees that were then matched with an accuracy of 85 %, 85 % and 75 %, respectively. Compared to existing methods, the proposed method is more flexible and achieves better results, especially for deciduous species. Before application of the presented method to other regions, the calibration based on the developed optimisation procedure is needed.     

The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity

Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence.     

Single tree biomass modelling using airborne laser scanning

Accurate forest biomass mapping methods would provide the means for e.g. detecting bioenergy potential, biofuel and forest-bound carbon. The demand for practical biomass mapping methods at all forest levels is growing worldwide, and viable options are being developed. Airborne laser scanning (ALS) is a promising forest biomass mapping technique, due to its capability of measuring the three-dimensional forest vegetation structure. The objective of the study was to develop new methods for tree-level biomass estimation using metrics derived from ALS point clouds and to compare the results with field references collected using destructive sampling and with existing biomass models. The study area was located in Evo, southern Finland. ALS data was collected in 2009 with pulse density equalling approximately 10 pulses/m². Linear models were developed for the following tree biomass components: total, stem wood, living branch and total canopy biomass. ALS-derived geometric and statistical point metrics were used as explanatory variables when creating the models. The total and stem biomass root mean square error per cents equalled 26.3% and 28.4% for Scots pine (Pinus sylvestris L.), and 36.8% and 27.6% for Norway spruce (Picea abies (L.) H. Karst.), respectively. The results showed that higher estimation accuracy for all biomass components can be achieved with models created in this study compared to existing allometric biomass models when ALS-derived height and diameter were used as input parameters. Best results were achieved when adding field-measured diameter and height as inputs in the existing biomass models. The only exceptions to this were the canopy and living branch biomass estimations for spruce. The achieved results are encouraging for the use of ALS-derived metrics in biomass mapping and for further development of the models.     

机载激光雷达平均树高提取研究

为了研究机载激光雷达(LiDAR)树高提取技术,以山东省泰安市徂徕山林场为实验区,于2005年5月进行了机载LiDAR数据获取和外业测量.通过对LiDAR点云数据的分类处理,分别得到了试验区的地面点云子集、植被点云子集和高程归一化的植被点云子集.基于高程归一化的植被点云子集计算了上四分位数处的高度,与实地测量的数据进行了比较,并结合中国森林调查规程进行了实用性分析.结果表明:对于较低密度的点云数据,使用分位数法可以较好地进行林分平均高的估计;机载激光雷达技术对树高估计是可行的,精度都高于87%,总体平均精度为90.59%,其中阔叶树的精度高于针叶树.该试验精度可以满足中国二类森林调查规程中平均树高因子的一般商品林和生态公益林的精度要求,对国有商品林小班的调查精度要求(5%)存在一点差距,需要在国有商品林区进一步开展验证工作.对本试验区而言,已经可以满足其作为森林公园生态公益林的调查要求.     

Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds

Forest plantations are an important source of terrestrial carbon sequestration. The forest of Robinia pseudoacacia in the Yellow River Delta (YRD) is the largest artificial ecological protection forest in China. However, more than half of the forest has appeared different degrees of dieback and even death since the 1990s. Timely and accurate estimation of the forest aboveground biomass (AGB) is a basis for studying the carbon cycle of forests. Light Detecting and Ranging (LiDAR) has been proved to be one of the most powerful methods for forest biomass estimation. However, because of an irregular and overlapping shape of the broadleaved forest canopy in a growing season, it is difficult to segment individual trees and estimate the tree biomass from airborne LiDAR data. In this study, a new method was proposed to solve this problem of individual tree detection in the Robinia pseudoacacia forest based on a combination of the Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) with the Backpack-LiDAR. The proposed method mainly consists of following steps: (i) at a plot level, trees in the UAV-LiDAR data were detected by seed points obtained by an individual tree segmentation (ITS) method from the Backpack-LiDAR data; (ii) height and diameter at breast height (DBH) of an individual tree would be extracted from UAV and Backpack LiDAR data, respectively; (iii) the individual tree AGB would be calculated through an allometric equation and the forest AGB at the plot level was accumulated; and (iv) the plot-level forest AGB was taken as a dependent variable, and various metrics extracted from UAV-LiDAR point cloud data as independent variables to estimate forest AGB distribution in the study area by using both multiple linear regression (MLR) and random forest (RF) models. The results demonstrate that: (1) the seed points extracted from Backpack-LiDAR could significantly improve the overall accuracy of individual tree detection (F = 0.99), and thus increase the forest AGB estimation accuracy; (2) compared with MLR model, the RF model led to a higher estimation accuracy (p < 0.05); and (3) LiDAR intensity information selected by both MLR and RF models and laser penetration rate (LP) played an important role in estimating healthy forest AGB.