叶方向3维空间分布的地面激光雷达反演与分析

叶方向(包括叶倾角和方位角)是决定林冠内部及其下层太阳辐射在3维空间分布的重要因素,并进而影响植被的光合作用效率和林冠的二向性反射特性。本文利用地面激光雷达扫描系统(TLS)对一棵人工阔叶树在水平圆形轨道设置等间距6站进行扫描,获取其完整覆盖的3维点云数据。在手动选取78片点云完整叶片数据的基础上,通过重建单点在其邻域内的法向量和所在叶片主轴方向分别得到了叶的倾角和方位角分布,将构成每片叶子的所有点的倾角和方位角各自均值作为其倾角和方位角。通过与利用角度尺和罗盘进行逐叶片手动测量结果对比发现:基于TLS计算的叶倾角和方位角与手动测量结果均具有较强的相关性,R2分别为0.88(N=209,p0.001)和0.97(N=78,p0.001)。本文方法能准确获取林冠元素的3维空间分布,为估算森林冠层在任意给定光照条件下的消光系数提供了理论基础,为促进地面激光雷达在植被冠层3维结构参数,特别是叶面积指数的反演起到了积极作用。     

地面激光雷达的单木真实叶面积指数提取

针对已有的测量叶面积指数(LAI)的方法中,LAI测量结果受其定义、采样方法、数据分析和仪器误差等影响产生极大差异的问题,该文使用地面激光雷达(TLS)提取LAI,对北京林业大学校园内具有代表性的单株树木进行了扫描,通过对数据预处理提取出树冠点云,将其模拟为半球图像后运用球极平面投影和Lambert方位角等面积投影两种投影方法,通过统计面积的方法分别计算不同投影方法和图像划分方法下的孔隙率,进而计算出真实叶面积指数。同时与利用叶面积指数仪LAI-2000所测得的数据进行对比。研究结果表明,地面激光雷达提取单木真实叶面积指数与实测值对比,两种投影下18个环的图像划分方法均更接近真实值,其中在Lambert方位角等面积投影下计算结果更准确。     

地基激光雷达的玉兰林冠层叶面积密度反演

叶面积密度LAD(Leaf Area Density)是表征冠层内部叶面积垂直分布的重要参数,其分布廓线的准确反演对研究植被碳氮循环、初级生产力和生物量估算等具有重要意义。本文在电子科技大学校内建立实验样区,利用地基激光雷达Leica Scan Station C10和数码相机获取玉兰林高分辨率3维激光点云数据和真彩色影像。利用监督分类将真彩色影像中枝干等非光合组织与叶片分离,再将像素分类信息映射给点云数据,从而提取叶片点云。通过点云数据体元化,并引入2维凸包算法确定垂直方向分层树冠边界,获取激光接触冠层的频率;随机选择不同高度的多个叶片,利用特征值法进行叶片平面拟合,估算出叶倾角,并结合天顶角估算叶倾角校正因子;最后基于体元的冠层分析VCP(Voxel-based Canopy Profiling)方法实现树林冠层LAD反演。结果表明体元化的叶片点云数据能准确确定树林冠层边界和统计接触频率实现LAD反演;反演的LAD变化走势与区域林木冠层叶片垂直分布相吻合,在冠层中下部随着高度的增加叶面积密度也随之增加,在4 m高度处达到最大值1 m2/m3,之后随着高度的增加叶面积密度逐渐降低。根据LAD计算得到的累积叶面积指数LAI为3.20 m2/m2,与LAI-2200实测的叶面积指数相比,相对误差为1.26%。     

多回波点云数据解算单株木叶面积指数

以行道树无患子为研究目标,采用地面激光扫描(TLS)技术提取单木分回波点云数据。获取全波形数据、单目标数据、首次回波数据、其余次回波数据,建立基于多回波点云的算法,利用消光系数法提取不同投影分辨率0.01 m、0.02 m和0.03 m的的单株树叶面积指数(LAI)。利用2维影像数据数字半球影像(DHP)和LAI2200提取对应单株树的叶面积指数,进行比较分析,以检验其精度。结果表明:点云投影的分辨率与激光回波都对LAI有极显著影响,其中分辨率为0.02 m和0.03 m的估算结果与LAI2200所得估算结果相近,且差异不显著;单目标回波数据用于LAI的解算,可以同LAI2200的2维影像数据结果进行相互验证。使用单目标回波数据,0.02 m投影分辨率可以最大程度的保证单株LAI的精度,其与LAI2200测定的数据进行截距为0的线性回归,斜率达到0.827。本研究所做多回波地面激光数据计算叶面积指数的算法拓展了地面激光扫描的应用领域,为立木生长量信息准确提取和树木精确建模提供了重要的技术参考。     

激光雷达的立体体元模型在森林冠层间隙率提取中的应用

针对光学森林冠层间隙率提取方法受天气影响较大的不足,本文基于激光雷达点云数据,提出一种立体体元模型方法进行森林冠层提取,并采用点云投影、半球成像两种常用方法与其进行对比。结果表明：不同体元大小提取的间隙率结果在变化趋势基本一致,当体元大小越大时,提取的间隙率结果越小;当与点云投影、DHP两种方法进行对比时,随着体元增大,本文方法提取结果与两种方法提取结果相关性也逐渐增高,当天顶角为0°～80°、10°～65°时,相关系数在0.91以上,表明本文方法可用于森林冠层间隙率提取。     

ICESat-2机载实验光子云数据自适应去噪及分类算法

第二代星载激光雷达冰、云和陆地测高卫星ICESat-2（The Ice， Cloud， and Land Elevation Satellite-2）搭载了先进光子计数式激光雷达，使用了全新的微脉冲多波束光子计数式激光雷达。由于光子计数式激光雷达的自身特点，其光子云数据具有受噪声光子影响大、信噪比与扫描时间相关、光子分布密度不均匀等问题，目前开发的去噪算法并不能很好的应用于不同的光子云数据。基于以上问题，本文提出一种改进的去噪算法，首先分析光子云内部特征并自适应选择最优参数进行粗去噪，然后进行两次精去噪，最后对光子云进行分类并拟合出地面线及冠层顶线，为提取森林冠层高度提供基础。使用该算法对MABEL数据进行去噪实验，实验结果表明：该去噪算法的一次去噪对不同环境下MABEL数据在夜间的去噪平均精确度为94.5%，F1-score为96.3%，日间平均精确度为86.7%，F1-score为91.7%，且三次去噪算法完成后能够显著提升光子云去噪精度。实验证明该算法对MABEL光子云数据具有较好去噪效果和稳定性，可为ICESat-2数据处理提供参考。本文的光子分类算法能够从光子数据中提取冠层顶点、地面点及林内光子并在此算法中进一步精确去除剩余噪点，最终光子分类结果显示该算法能够从复杂光子云数据中提取森林剖面结构。     

一种自适应的坡度阈值地面点云分割方法

针对城市道路斜坡地形场景中地面欠分割或过分割的问题,提出了一种自适应的激光雷达地面分割算法。首先将激光点云按照水平角度分辨率进行有序组织,然后求取同一水平角度下前后扫描圈间激光点云的距离和局部坡度,最后采用自适应水平距离、局部高度和全局高度阈值区分地面点和非地面点。结合40线激光雷达进行多场景实例分析,结果表明本文算法分割的准确率更高,处理每帧数据均用时约1ms,满足无人驾驶汽车的实时性需求。提出了一种自适应的激光雷达地面分割算法,实现了对激光雷达地面点云的准确分割。     

双通道三维成像激光雷达技术研究

激光雷达作为一种三维测量系统,通过采集的激光测量点(点云)对测量区域实现三维测量覆盖。由于三维测量覆盖的网格密度受测距范围、激光发射频率、扫描角度和扫描频率等因素限制,单通道激光扫描难以完成高网格密度三维点云数据采集。为探索提高激光雷达扫描点云网格密度的有效途径,本文建立了激光雷达双通道扫描系统模型,分析了该模型的双通道三维测量的实现机理,设计了双通道机载激光雷达扫描演示系统。通过对比目标地物的实际扫描试验结果,验证了双通道激光雷达扫描仪的系统可行性和获得更高分辨率、更完整测量区域表面三维点云的能力。     

利用无人机激光雷达提取玉米叶面积密度

叶面积密度可以表征冠层内部叶面积的垂直分布,是作物生长发育、营养诊断和育种研究的重要结构参数。激光雷达通过发射多脉冲和接收多回波信号可以探测到作物冠层内部信息。首先基于无人机激光雷达获取60个小区多航线的玉米点云数据,采用基于接触频率的体素法对叶面积密度进行估算,再对多个体素大小进行分析得到最优体素大小(0.2 m);其次对各航线以及航线叠加效果进行对比,得到无人机激光雷达获取点云数据的最优激光脉冲入射角(-30°～52°);然后结合玉米叶倾角和激光脉冲入射角对叶面积密度估算模型进行校正,从而提高叶面积密度估算精度;最后通过对不同种植密度和不同品种的玉米叶面积密度分布进行分析,得到不同品种玉米的发育快慢、株型特点以及最合理的种植密度。以上结果可为基于无人机激光雷达数据估算叶面积密度提供指导,并为玉米育种和科学管理提供参考。     

激光雷达数据在森林垂直结构参数反演中的应用综述

激光雷达能够获得高精度的森林垂直结构信息和林下地形信息,在林业研究领域具有其他遥感技术无法代替的优势。本文详细介绍了星载激光雷达系统和机载激光雷达系统工作原理基础上,阐述了两种激光雷达数据在森林单木因子估算、森林冠层高度估算、森林生物量和蓄积量估算、森林叶面积指数估算方面的应用情况,并与地面激光雷达和多源数据融合进行了展望。     

Retrieval of boreal forest LAI using a forest reflectance model and empirical regressions

Spectral invariants provide a novel approach for characterizing canopy structure in forest reflectance models and for mapping biophysical variables using satellite images. We applied a photon recollision probability (p) based forest reflectance model (PARAS) to retrieve leaf area index (LAI) from fine resolution SPOT HRVIR and Landsat ETM+ satellite data. First, PARAS was parameterized using an extensive database of LAI-2000 measurements from five conifer-dominated boreal forest sites in Finland, and mixtures of field-measured forest understory spectra. The selected vegetation indices (e.g. reduced simple ratio, RSR), neural networks and kNN method were used to retrieve effective LAI (L_e) based on reflectance model simulations. For comparison, we established empirical vegetation index-LAI regression models for our study sites. The empirical RSR–L_e regression performed best when applied to an independent test site in southern Finland [RMSE 0.57 (24.2%)]. However, the difference to the best reflectance model based retrievals produced by neural networks was only marginal [RMSE 0.59 (25.1%)]. According to this study, the PARAS model provides a simple and flexible modelling tool for calibrating algorithms for LAI retrieval in conifer-dominated boreal forests. The advantage of PARAS is that it directly uses field measurements to parameterize canopy structure (LAI-2000, hemispherical photographs) and optical properties of foliage and understory.     

Retrieval of remotely sensed LAI using Landsat ETM+ data and ground measurements of solar radiation and vegetation structure: Implication of leaf inclination angle

A time series of leaf area index (LAI) of a managed birch forest in Germany (near Dresden) has been developed based on 16-day normalized difference vegetation index (NDVI) data from the Landsat ETM+ sensor at 30 m resolution. The Landsat ETM+ LAI was retrieved using a modified physical radiative transfer (RTM) model which establishes a relationship between LAI, fractional vegetation cover (fC), and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. In situ measurements of photosynthetically active radiation (PAR) and vegetation structure parameters using hemispherical photography (HSP) served for calibration of model parameters, while data from litter collection at the study site provided the ground-based estimates of LAI for validation of modelling results. Influence of view-illumination conditions on optical properties of canopy was simulated by a view angle geometry model incorporating the solar zenith angle and the sensor viewing angle. Effects of intra-annual and inter-annual variability of structural properties of the canopy on the light extinction coefficient were simulated by implementing variability of the leaf inclination angle (LIA), which was confirmed in the study site. The results revealed good compatibility of the produced Landsat ETM+ LAI data set with the litter-estimated LAI. The results also showed high sensitivity of the LAI retrieval algorithm to variability of structural properties of the canopy: the implementation of LIA dynamics into the LAI retrieval algorithm significantly improved the model accuracy.     

Retrieval of effective leaf area index (LAIe) and leaf area density (LAD) profile at individual tree level using high density multi-return airborne LiDAR

As an important canopy structure indicator, leaf area index (LAI) proved to be of considerable implications for forest ecosystem and ecological studies, and efficient techniques for accurate LAI acquisitions have long been highlighted. Airborne light detection and ranging (LiDAR), often termed as airborne laser scanning (ALS), once was extensively investigated for this task but showed limited performance due to its low sampling density. Now, ALS systems exhibit more competing capacities such as high density and multi-return sampling, and hence, people began to ask the questions like—“can ALS now work better on the task of LAI prediction?” As a re-examination, this study investigated the feasibility of LAI retrievals at the individual tree level based on high density and multi-return ALS, by directly considering the vertical distributions of laser points lying within each tree crown instead of by proposing feature variables such as quantiles involving laser point distribution modes at the plot level. The examination was operated in the case of four tree species (i.e. Picea abies, Pinus sylvestris, Populus tremula and Quercus robur) in a mixed forest, with their LAI-related reference data collected by using static terrestrial laser scanning (TLS). In light of the differences between ALS- and TLS-based LAI characterizations, the methods of voxelization of 3D scattered laser points, effective LAI (LAIe) that does not distinguish branches from canopies and unified cumulative LAI (ucLAI) that is often used to characterize the vertical profiles of crown leaf area densities (LADs) was used; then, the relationships between the ALS- and TLS-derived LAIes were determined, and so did ucLAIs. Tests indicated that the tree-level LAIes for the four tree species can be estimated based on the used airborne LiDAR (R² = 0.07, 0.26, 0.43 and 0.21, respectively) and their ucLAIs can also be derived. Overall, this study has validated the usage of the contemporary high density multi-return airborne LiDARs for LAIe and LAD profile retrievals at the individual tree level, and the contribution are of high potential for advancing forest ecosystem modeling and ecological understanding.     

激光雷达回波模型辅助的坡地森林冠层高度反演

大光斑激光雷达数据已广泛应用于森林冠层高度提取,但通常仅限于地形坡度小于20°的平缓地区。在地形坡度大于20°的陡峭山区,地形引起的波形展宽使得地面回波和植被回波信息混合在一起,给森林冠层高度提取带来巨大挑战。本文利用激光雷达回波模型和地形信息,提出了一种模型辅助的坡地森林冠层高度反演算法。该方法以激光雷达回波信号截止点为参考,定义了波形高度指数H50和H75,使用激光雷达回波模型与已知地形信息模拟裸地的激光雷达回波,将裸地回波信号截止点与森林激光雷达回波信号截止点对齐,利用裸地回波计算常用的波形相对高度指数RH50和RH75,对森林冠层高度进行反演。并与高斯波形分解法和波形参数法的反演结果进行了比较。研究结果表明:(1)利用所提取的波形指数RH50和RH75对胸高断面积加权平均高(Lorey’s height)进行了估算,在坡度小于20°时,高斯波形分解法、波形参数法和模型辅助法的估算结果与实测值线性拟合的相关系数(R2)分别为0.70,0.78和0.98,对应的均方根误差(RMSE)分别为2.90 m,2.48 m和0.60 m,模型辅助法略优于其他两种方法;(2)在坡度大于20°时,高斯波形分解法、波形参数法和模型辅助法的R2分别为0.14,0.28和0.97,相应的RMSE分别为4.93 m,4.53 m和0.81 m,模型辅助法明显优于其他两种方法;(3)在0°—40°时,模型辅助法对Lorey’s height估算结果与实测值的R2为0.97,RMSE为0.80 m。本研究提出的模型辅助法具有更好的地形适应性,在0°—40°的坡度范围内具备对坡地森林冠层高度反演的潜力。     

Local variability in the timing and intensity of tropical dry forest deciduousness is explained by differences in forest stand age

Tropical Dry Forest deciduousness is a behavioral response to climate conditions that determines ecosystem-level carbon uptake, energy flux, and habitat conditions. It is regulated by factors related to stand age, and landscape scale variability in deciduous phenology may affect ecosystem functioning in forests throughout the tropics. This study determines whether observed phenological differences are explainable by forest age in the southern Yucatán Peninsula in Mexico, where forest clearing for shifting cultivation has created a mosaic of forest stands of varying age. Matched-pair statistical tests compare neighboring forest pixels of different age class (12–22 years versus 22+ years) and detect significant differences in Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI)-derived metrics related to the timing and intensity of deciduousness during three dry seasons (2008–2011). In all seasons, young forests exhibit significantly more intense deciduousness, measured as total seasonal change of EVI normalized by annual maximum EVI (p < 0.001), and larger normalized EVI change during successive dry season months relative to start-of-dry-season EVI (p < 0.001), than neighboring older forests subject to similar environmental conditions.     

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))。     

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。     

植被冠层立体结构与叶片倾角的偏振光效应

植被偏振特性研究对于植被监测与组分定量反演具有极其重要的作用。植被冠层的反射辐射具有偏振特性,这种特性与入射辐射和植被冠层结构相关。本文分析了偏振对光子—叶片—冠层之间细微相互作用及其变化的有效探测能力,并利用研究型扫描式偏振辐射仪RSP(Research Scanning Polarimeter)数据系统对比分析了偏振对不同叶倾角分布的估测。通过上述研究得出以下结论:(1)偏振观测能够对光线在冠层立体结构中的透射反射再出射过程给出精细刻画,若不用偏振手段对这一过程进行甄别并去除,则直接测算的植被散射系数会产生高达140%的误差;(2)利用偏振手段可以为高精度大倾角、多时相遥感观测提供可能,以此可改变目前光学遥感小角度、垂直观测的较严格约束;(3)偏振辐射呈现出随波长的稳定特性(相关系数0.96),使得利用偏振手段可以更好地研究冠层结构;(4)不同叶倾角分布对入射辐射存在不同的偏振反射,为利用多角度偏振信息进行遥感植被精细分类提供了新的途径。本文详细描述冠层结构和植被偏振特性的相互作用,通过对冠层立体结构与叶倾角的研究,刻画了植被定量遥感的方向性信息与高精度实现,为高分辨率遥感定量化的有效信息挖掘提供了新手段。