面向森林植被的星载大光斑激光雷达回波仿真

利用星载激光雷达的大光斑全波形数据估测植被结构参数、监测森林生态已受到广泛关注。为了更准确地理解森林植被的结构参数和光学特性对激光雷达回波波形的影响,利用实测森林植被数据提取植被空间分布的统计规律,考虑地形坡度变化和植被冠层反射特性的影响,生成参数化的森林植被空间轮廓反射模型,结合星载激光雷达的回波理论,建立了面向植被的星载激光雷达波形仿真器。由大兴安岭地区的实测植被数据提取的统计规律生成的森林目标仿真波形与地球科学激光测高仪系统（Geoscience Laser Altimeter System,GLAS）真实回波波形具有较好的一致性,平均相关系数R2达到0.91。通过波形仿真分析发现,光斑尺寸减小有利于大坡度地形的森林信息反演,研究成果对中国未来研制星载激光雷达载荷的系统参数设计具有参考意义。     

吉林长白山森林冠顶高度激光雷达与MERSI联合反演

将激光雷达与光学遥感相结合进行区域林分冠顶高度联合反演,提出了大脚印激光雷达GLAS脚点波形数据处理和不同地形条件下的森林冠顶高度反演算法,并建立了区域尺度不同森林类型林分冠顶高度GLAS＋MERSI联合反演模型,制作了长白山地区森林冠顶高度图。     

多源遥感数据支持下的区域性森林冠层高度估测

结合机载LiDAR数据,提出了一种改进的GLAS光斑点冠层高度地形校正模型,以校正后的GLAS光斑点作为输入样本,结合MODIS遥感影像,利用支持向量回归（SVR）的方法对研究区森林冠层高度进行分生态区估测,并利用野外调查数据和机载LiDAR冠层高度结果对估测结果进行验证。结果显示:研究区的坡度等级直接影响GLAS光斑点森林冠层高度估测精度,改进的地形校正模型可以较好的减小坡度对GLAS光斑点森林冠层高度估测的影响,模型精度RMSE稳定在3.25~3.48 m;不同生态分区的SVR模型估测精度较为稳定,其RMSE=6.41~7.56 m;与算数平均高相比,样地的Lorey's高与制图结果拟合最好,不同生态分区平均估测精度为80.3%。机载LiDAR冠层高度结果的验证平均精度为79.5%,和Lorey's高验证结果呈现较好的一致性。     

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。     

广西-东盟多源遥感数据的森林高度反演及应用

森林生态系统在调节生态气候与碳循环方面发挥着重要作用,森林高度是衡量森林生态系统功能的重要参数。利用单一遥感数据获取森林冠层高度会受到多种制约。因此,本文使用星载激光雷达ICESat-2提供的高质量离散森林冠层高度点,结合Sentinel-1、Landsat 8及地形数据,采用随机森林方法建立不同影像特征组合森林冠层高度的回归模型,并分析各特征对森林高度反演的影响,最后将模型应用于广西森林冠层高度制图。试验结果表明,多源遥感数据可有效提高森林冠层高度反演精度,在所利用遥感数据中,特征重要性从大到小依次为光学特征、地形特征、SAR特征,“L8+SRTM+Sentinel-1+邻域均值”特征组合的反演精度最高,加入邻域均值特征进行森林冠层高度反演效果最佳,随机森林模型能精确绘制森林冠层高度。     

激光雷达森林参数反演研究进展

激光雷达通过发射激光能量和接收返回信号的方式,来获取高精度的森林空间结构和林下地形信息。全波形激光雷达通过记录返回信号的全部能量,得到亚米级植被垂直剖面;离散回波激光雷达记录的单个或多个回波,表示来自不同冠层的回波信号。星载激光雷达一般采用全波形或光子计数激光剖面系统,仅能获取卫星轨道下方的单波束或多波束数据,用于区域/全球范围的森林垂直结构及变化观测。机载激光雷达多采用离散回波或全波形激光扫描系统,能够获取飞行轨迹下方特定视场范围内的扫描数据,用于林分/区域范围的森林结构观测。地基激光雷达多采用离散回波激光扫描系统,获取以测站为中心的球形空间内扫描数据,用于单木/样地范围的森林结构观测。激光雷达单木因子估测方法可分为CHM单木法、NPC单木法和体元单木法3类。CHM单木法通过局部最大值识别树冠顶点,采用区域生长或图像分割算法识别树冠边界或树冠主方向,NPC单木法一般通过空间聚类或形态学算法识别单木,体元单木法在3维体元空间采用区域生长或空间聚类算法识别树冠。根据激光雷达冠层高度分布可以估测林分因子,冠层高度分布特征来自于离散点云或全波形。多时相激光雷达可用于森林生长量、生物量变化等监测,以及森林采伐、灾害等引起的结构变化监测。随着激光雷达技术的发展,它将在森林调查、生态环境建模等生产与科学研究领域中得到更为广泛的应用。     

高斯混合模型在星载激光全波形数据反演树高中的应用

全波形激光雷达后向散射回波,通过分解返回波形获取多种地物属性信息,在森林结构参数反演方面具有显著的优势,但是,当波形变形或者存在饱和度和前向散射时,高斯分量参数确定不准确以及有效波形起始位置不准确,降低波形分解精度。本文采用高斯混合模型对波形进行拟合,利用期望最大算法估计混合模型参数,抑制高斯分量初值敏感问题,特别是在大范围树高估算且要求一定精度的时候,以确定波形分解并且反演树高。本算法基于C++编程实现,实验结果表明,高斯混合模型能较好地拟合GLAS波形数据且对树高提取精度提升明显,该方法有着很好的有效性、稳定性和精确性。     

利用波形去噪算法的森林最大冠顶高估测研究

针对大光斑激光雷达回波信号噪声影响森林冠顶高估测精度,且回波分析法判定回波位置受限于平坦地区的问题,利用高斯低通滤波和小波去噪两种方法对GLAS波形进行去噪处理,提出了结合均方根倍差法和回波分析法来判定回波位置的有效算法。经小波去噪后信号的信噪比23.360 704,均方根误差为0.000 233 3,经均方根倍差法和回波分析法相结合来判定回波位置估测的冠顶高结果与实测结果相关性系数r值为0.864,效果均优于高斯低通滤波去噪。基于GLAS回波数据实验结果表明:小波去噪较好地实现了对回波信号的去噪处理,均方根倍差法和回波分析法相结合,实现了对坡度相对较大地区的GLAS波形的回波开始位置和地面回波位置的准确判定,对森林冠顶高的精确估算具有重要意义。     

卫星激光测高植被目标回波的半解析仿真模型

目前激光星载激光测高仪已被广泛应用于植被目标特征的提取,证明了激光雷达在林业行业的巨大应用潜力。植被目标的回波波形复杂,本文提出了一种基于半解析法植被目标的回波仿真模型,可以较好地仿真特定输入参数产生的波形。使用GLAS测高系统经过大兴安岭区域的激光波形和实地林木样地参数为依据,仿真波形与GLAS回波的相关系数R²均达到0.91以上。利用回波仿真模型定量控制如冠层几何形状、区域坡度、地面粗糙度和林下植被的变化并快速获取大量波形的优势,独立地分析每个因素对回波波形的影响,为植被目标反演的数据源选取及对回波波形展宽的分析提供指导意见。     

ICESat-2植被冠层高度和地表高程数据产品用于森林高度提取的效果评价

由美国宇航局（NASA）研制的新一代冰、云和陆地高程卫星（ICESat-2）于2018-09-15发射成功,其搭载的先进地形激光测高系统（ATLAS）采用微脉冲多波束光子计数激光雷达技术,可用于全球高程线采样数据的获取。目前公开发布了9种数据产品,其中包括植被冠层高度和地表高程数据产品（ATL08）,为全球森林结构参数的估算提供了新的契机。本文以美国宾夕法尼亚州斯奈德县和印度尼西亚西加里曼丹吉打邦为研究区,在温带森林和热带雨林两种不同的生态系统立地条件下,对 ICESat-2的ATL08数据产品用于森林高度估算的效果进行评价。首先建立了地形高程数据产品（ATL03）与ATL08数据产品的关联规则,以获取分别记录在这两种产品中的光子空间分布信息和分类信息;进而以机载小光斑激光雷达数据为参考,对ATL08数据产品的光子分类可靠性及其用于森林高度估算的准确性进行了分析评价。结果表明：（1）在温带森林情况下,ATL08数据产品提供的平均冠层高度和最大冠层高度与参考数据的相关系数（R2）分别为0.54和0.61,相对误差分别为16.78%和10.71%,表明ATL08数据产品的光子分类结果能够用于刻画森林冠层结构和林下地形;（2）在热带雨林情况下,到达地面的光子数量相较于温带森林明显减少,地面光子类型识别的可靠性低,ATL08数据产品提供的平均冠层高度和最大冠层高度与参考数据的相关系数（R2）分别为0.21和0.19;（3）森林覆盖度的增大会导致ATL08计算的冠层高度误差增大,热带雨林平均冠层高度的误差随着坡度增大有增大趋势,在坡度为0°—10°、10°—20°和20°—30°共3组情况下,误差分别为5.7 m、6.6 m和9.3 m。因此,在高森林覆盖度情况下,现有的ATL08数据产品难以直接用于森林高度的提取。     

A novel approach to estimate canopy height using ICESat/GLAS data: A case study in the New Forest National Park,UK

The Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and land Elevation Satellite (ICESat) is a spaceborne LiDAR sensor. It is the first LiDAR instrument which can digitize the backscattered waveform and offer near global coverage. Among others, scientific objectives of the mission include precise measurement of vegetation canopy heights. Existing approaches of waveform processing for canopy height estimation suggest Gaussian decomposition of the waveform which has the limitation to properly characterize significant peaks and results in discrepant information. Moreover, in most cases, Digital Terrain Models (DTMs) are required for canopy height estimation. This paper presents a new automated method of GLAS waveform processing for extracting vegetation canopy height in the absence of a DTM. Canopy heights retrieved from GLAS waveforms were validated with field measured heights. The newly proposed method was able to explain 79% of variation in canopy heights with an RMSE of 3.18 m, in the study area. The unexplained variation in canopy heights retrieved from GLAS data can be due to errors introduced by footprint eccentricity, decay of energy between emitted and received signals, uncertainty in the field measurements and limited number of sampled footprints.Results achieved with the newly proposed method were encouraging and demonstrated its potential of processing full-waveform LiDAR data for estimating forest canopy height. The study also had implications on future full-waveform spaceborne missions and their utility in vegetation studies.     

An improved method for estimating forest canopy height using ICESat-GLAS full waveform data over sloping terrain: A case study in Changbai mountains,China

Light Detection And Ranging (LiDAR) has a unique capability for estimating forest canopy height, which has a direct relationship with, and can provide better understanding of the aboveground forest carbon storage. The full waveform data of the large-footprint LiDAR Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat), combined with field measurements of forest canopy height, were employed to achieve improved estimates of forest canopy height over sloping terrain in the Changbai mountains region, China. With analyzing ground-truth experiments, the study proposed an improved model over Lefsky's model to predict maximum canopy height using the logarithmic transformation of waveform extent and elevation change as independent variables. While Lefsky's model explained 8–89% of maximum canopy height variation in the study area, the improved model explained 56–92% of variation within the 0–30° terrain slope category. The results reveal that the improved model can reduce the mixed effects caused by both sloping terrain and rough land surface, and make a significant improvement for accurately estimating maximum canopy height over sloping terrain.     

高分二号异轨立体数据的森林高度提取

森林植被碳储量的空间分布格局及其动态变化是陆地生态系统碳收支核算的基础。作为森林地上生物量的重要指示因子,森林高度的精确估算是提高森林植被碳储量估算精度的关键。现有研究已证明,由专业星载摄影测量系统获取的立体观测数据可用于森林高度提取,但光学遥感数据最大的问题是受云雨等天气因素的影响严重。区域森林地上生物量产品的生产需要充分挖掘潜在数据源。国产高分二号卫星(GF-2)虽然不是为获取立体观测数据而设计的专业星载摄影测量系统,但其获取的图像空间分辨率可达0.8 m,且具备±35°的的侧摆能力,在重复观测区域可构成异轨立体观测。本文以分别获取于2015年6月20日和2016年7月19的GF-2数据作为立体像对,其标称轨道侧摆角分别为0.00118°和20.4984°,以激光雷达数据获取的林下地形(DEM)和森林高度(CHM)为参考,对利用GF-2立体观测数据进行森林高度提取进行了研究。通过对立体处理得到的摄影测量点云的栅格化得到DSM,以激光雷达数据提供的DEM作为林下地形,得到了GF-2的CHM。结果表明GF-2提取的CHM与激光雷达CHM空间分布格局较为一致,两者之间存在明显的相关性,像素对像素的线性相关性(R2)达到0.51,均方根误差(RMSE)为3.6 m。研究结果表明,在林下地形已知的情况下,GF-2立体观测数据可用于森林高度估算。     

Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data

A new individual tree-based algorithm for determining forest biomass using small footprint LiDAR data was developed and tested. This algorithm combines computer vision and optimization techniques to become the first training data-based algorithm specifically designed for processing forest LiDAR data. The computer vision portion of the algorithm uses generic properties of trees in small footprint LiDAR canopy height models (CHMs) to locate trees and find their crown boundaries and heights. The ways in which these generic properties are used for a specific scene and image type is dependent on 11 parameters, nine of which are set using training data and the Nelder–Mead simplex optimization procedure. Training data consist of small sections of the LiDAR data and corresponding ground data. After training, the biomass present in areas without ground measurements is determined by developing a regression equation between properties derived from the LiDAR data of the training stands and biomass, and then applying the equation to the new areas. A first test of this technique was performed using 25 plots (radius = 15 m) in a loblolly pine plantation in central Virginia, USA (37.42N, 78.68W) that was not intensively managed, together with corresponding data from a LiDAR canopy height model (resolution = 0.5 m). Results show correlations (r) between actual and predicted aboveground biomass ranging between 0.59 and 0.82, and RMSEs between 13.6 and 140.4 t/ha depending on the selection of training and testing plots, and the minimum diameter at breast height (7 or 10 cm) of trees included in the biomass estimate. Correlations between LiDAR-derived plot density estimates were low (0.22 ≤ r ≤ 0.56) but generally significant (at a 95% confidence level in most cases, based on a one tailed test), suggesting that the program is able to properly identify trees. Based on the results it is concluded that the validation of the first training data-based algorithm for determining forest biomass using small footprint LiDAR data was a success, and future refinement and testing are merited.     

Ground peak identification in dense shrub areas using large footprint waveform LiDAR and Landsat images

Large footprint waveform LiDAR data have been widely used to extract tree heights. These heights are typically estimated by subtracting the top height from the ground. Compared to the top height detection, the identification of the ground peak in a waveform is more challenging. This is particularly evident in ground detection in shrub areas, where the reflection of the shrub canopy may significantly overlap with the ground reflection. To tackle this problem, a novel method based on Partial Curve-Fitting (PCF) of the shrub peak was developed to detect the ground peak. Results indicated that the PCF method improves ground identification by 32–42%, compared to existing methods. To offer further improvement, a Multi-Algorithm Integration Classifier (MAIC) was built to fuse multiple ground peak algorithms and selectively apply the best method for each waveform plot. The PCF ground peak identification method along with the MAIC-based fusion is expected to significantly improve ground detection and shrub height estimation, thus assisting biodiversity, forest succession, and carbon sequestration studies, while offering an early example of future multiple algorithm integration.     

机载LiDAR点云密度对DEM精度的影响

机载激光雷达技术(LiDAR)作为一项先进的遥感技术,是植被覆盖区DEM获取的重要手段之一,而不同地形坡度条件及点云密度对DEM产品质量有重要影响。本文以辽宁省某市的机载LiDAR数据为基础,选取5种不同地形坡度的点云数据,通过随机、等间距及基于曲率3种不同的点云抽稀方法,按照点云保留率为80%、60%、40%、20%和10%共5个不同梯度的抽稀倍数对原始点云进行抽稀简化处理,生成与之对应的DEM并对其进行精度评价,以此研究地形坡度、点云抽稀方法、抽稀倍数对DEM精度的影响。结果表明,DEM精度与地形坡度呈负相关关系,即RMSE随地形坡度升高不断增加;基于曲率的抽稀方法在地形坡度＞30°时,相较于其他两种方法RMSE较小,具有明显优势;40%的点云保留率是平衡DEM精度与数据存储效率的一个节点,当点云保留率<40%时,DEM的高程RMSE会迅速增大。该研究对于利用机载LiDAR进行大范围DEM生产具有一定的指导和借鉴意义。     

Forest canopy height estimation using ICESat/GLAS data and error factor analysis in Hokkaido,Japan

Spaceborne light detection and ranging (LiDAR) enables us to obtain information about vertical forest structure directly, and it has often been used to measure forest canopy height or above-ground biomass. However, little attention has been given to comparisons of the accuracy of the different estimation methods of canopy height or to the evaluation of the error factors in canopy height estimation. In this study, we tested three methods of estimating canopy height using the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and land Elevation Satellite (ICESat), and evaluated several factors that affected accuracy. Our study areas were Tomakomai and Kushiro, two forested areas on Hokkaido in Japan. The accuracy of the canopy height estimates was verified by ground-based measurements. We also conducted a multivariate analysis using quantification theory type I (multiple-regression analysis of qualitative data) and identified the observation conditions that had a large influence on estimation accuracy. The method using the digital elevation model was the most accurate, with a root-mean-square error (RMSE) of 3.2 m. However, GLAS data with a low signal-to-noise ratio (⩽10.0) and that taken from September to October 2009 had to be excluded from the analysis because the estimation accuracy of canopy height was remarkably low. After these data were excluded, the multivariate analysis showed that surface slope had the greatest effect on estimation accuracy, and the accuracy dropped the most in steeply sloped areas. We developed a second model with two equations to estimate canopy height depending on the surface slope, which improved estimation accuracy (RMSE = 2.8 m). These results should prove useful and provide practical suggestions for estimating forest canopy height using spaceborne LiDAR.     

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。