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1.
结合机载、星载激光雷达对GLAS(地球科学激光测高系统)光斑范围内的森林地上生物量进行估测,并利用MODIS植被产品以及MERIS土地覆盖产品进行了云南省森林地上生物量的连续制图。机载LiDAR扫描的260个训练样本用于构建星载GLAS的森林地上生物量估测模型,模型的决定系数(R2)为0.52,均方根误差(RMSE)为31Mg/ha。研究结果显示,云南省总森林地上生物量为12.72亿t,平均森林地上生物量为94Mg/ha。估测的森林地上生物量空间分布情况与实际情况相符,森林地上生物量总量与基于森林资源清查数据的估测结果相符,表明了利用机载LiDAR与星载ICESatGLAS结合进行大区域森林地上生物量估测的可靠性。 相似文献
2.
机载LiDAR数据估算样地和单木尺度森林地上生物量 总被引：2，自引：0，他引：2
利用机载激光雷达点云数据,结合大量实测单木结构信息,分别从样地和单木尺度估算了森林地上生物量AGB。首先,利用局部最大值单木提取算法提取了每个样地内的单木结构参数,并针对样地和单木尺度分别计算了一组激光雷达变量。然后,利用激光雷达变量和地上生物量及其两者的对数形式,从样地和单木尺度分别构建了估算模型。最后,针对两种尺度估算过程中存在的不确定性进行了详细讨论。结果表明:(1)样地和单木尺度模型估算的森林地上生物量与地面实测值都具有明显的相关性,且对数模型估算效果要优于非对数模型;(2)样地尺度模型估算效果(R2=0.84,rRMSE=0.23)明显优于单木尺度模型(R2=0.61,rRMSE=0.46);(3)按树木类型分别进行估算可以提高单木地上生物量的估算精度;(4)不论是样地还是单木尺度地上生物量估算都存在一定的不确定性,与样地尺度相比,单木尺度估算过程的不确定性更大,这种不确定性主要来自单木识别过程。 相似文献
3.
The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha−1. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R2 = 0.62 > 0.52, RMSE = 50.36 Mg ha−1<56.63 Mg ha−1). Through a trend extrapolation method, this study inferred that the G∼LiDAR∼S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G∼S2 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R2 = 0.62, RMSE = 51.03 Mg ha−1). Consequently, this study presents the first investigation of AGB for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests. 相似文献
4.
估算森林地上生物量(AGB)对于全球实现碳中和目标至关重要。本文以美国缅因州Howland森林为研究区域,借助地面实测样地数据,对比分析协同不同数据源(高光谱和LiDAR)和机器学习算法(随机森林、支持向量机、梯度提升决策树和K最邻近回归)的研究,以改善Howland森林的生物量估计精度。结果表明,采用LiDAR和高光谱植被指数变量模型的最佳精度分别为0.874和0.868,协同高光谱和LiDAR变量并采用梯度提升决策树回归模型的精度为0.927,即多源遥感数据要优于单一数据源。高光谱和LiDAR数据的协同使用对于提高类似于Howland地区或更广泛区域的生物量估计的准确性,具有普遍的适用性与一定的应用前景。 相似文献
5.
机器学习算法在森林地上生物量估算中的应用 总被引：1，自引：0，他引：1
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。 相似文献
6.
Forest canopy cover (CC) and above-ground biomass (AGB) are important ecological indicators for forest monitoring and geoscience applications. This study aimed to estimate temperate forest CC and AGB by integrating airborne LiDAR data with wall-to-wall space-borne SPOT-6 data through geostatistical modeling. Our study involved the following approach: (1) reference maps of CC and AGB were derived from wall-to-wall LiDAR data and calibrated by field measurements; (2) twelve discrete LiDAR flights were simulated by assuming that LiDAR data were only available beneath these flights; (3) training/testing samples of CC and AGB were extracted from the reference maps inside and outside the simulated flights using stratified random sampling; (4) The simple linear regression, ordinary kriging and regression kriging model were used to extend the sparsely sampled CC/AGB data to the entire study area by incorporating a selection of SPOT-6 variables, including vegetation indices and texture variables. The regression kriging model was superior at estimating and mapping the spatial distribution of CC and AGB, as it featured the lowest mean absolute error (MAE; 11.295% and 18.929 t/ha for CC and AGB, respectively) and root mean squared error (RMSE; 17.361% and 21.351 t/ha for CC and AGB, respectively). The predicted and reference values of both CC and AGB were highly correlated for the entire study area based on the estimation histograms and error maps. Finally, we concluded that the regression kriging model was superior and more effective at estimating LiDAR-derived CC and AGB values using the spatially-reduced samples and the SPOT-6 variables. The presented modeling workflow will greatly facilitate future forest growth monitoring and carbon stock assessments for large areas of temperate forest in northeast China. It also provides guidance on how to take full advantage of future sparsely collected LiDAR data in cases where wall-to-wall LiDAR coverage is not available from the perspective of geostatistics. 相似文献
7.
森林地上生物量遥感反演方法综述 总被引：9，自引：0，他引：9
森林地上生物量反演对理解和监测生态系统及评估人类生产生活的影响有着重要作用,日益发展的遥感技术使全球及大区域的生物量估算成为可能。近年来,不同的遥感技术和反演方法被广泛用于估算森林生物量。本文首先总结了现有的全球及区域生物量产品及其不确定性,然后综述了3类方法在森林地上生物量遥感反演中的应用,即基于单源数据的参数化方法、基于多源数据的非参数化方法和基于机理模型的反演方法,阐述了各类反演方法的特点、优势及局限性。最后从机理模型研究、多源遥感数据协同、生物量季节变化研究和遥感数据源不断丰富4个方面对今后的生物量遥感反演研究进行了展望。 相似文献
8.
Phil Wilkes Mathias Disney Matheus Boni Vicari Kim Calders Andrew Burt 《Carbon balance and management》2018,13(1):10
Background
Urban trees have long been valued for providing ecosystem services (mitigation of the “heat island” effect, suppression of air pollution, etc.); more recently the potential of urban forests to store significant above ground biomass (AGB) has also be recognised. However, urban areas pose particular challenges when assessing AGB due to plasticity of tree form, high species diversity as well as heterogeneous and complex land cover. Remote sensing, in particular light detection and ranging (LiDAR), provide a unique opportunity to assess urban AGB by directly measuring tree structure. In this study, terrestrial LiDAR measurements were used to derive new allometry for the London Borough of Camden, that incorporates the wide range of tree structures typical of an urban setting. Using a wall-to-wall airborne LiDAR dataset, individual trees were then identified across the Borough with a new individual tree detection (ITD) method. The new allometry was subsequently applied to the identified trees, generating a Borough-wide estimate of AGB.Results
Camden has an estimated median AGB density of 51.6 Mg ha–1 where maximum AGB density is found in pockets of woodland; terrestrial LiDAR-derived AGB estimates suggest these areas are comparable to temperate and tropical forest. Multiple linear regression of terrestrial LiDAR-derived maximum height and projected crown area explained 93% of variance in tree volume, highlighting the utility of these metrics to characterise diverse tree structure. Locally derived allometry provided accurate estimates of tree volume whereas a Borough-wide allometry tended to overestimate AGB in woodland areas. The new ITD method successfully identified individual trees; however, AGB was underestimated by ≤?25% when compared to terrestrial LiDAR, owing to the inability of ITD to resolve crown overlap. A Monte Carlo uncertainty analysis identified assigning wood density values as the largest source of uncertainty when estimating AGB.Conclusion
Over the coming century global populations are predicted to become increasingly urbanised, leading to an unprecedented expansion of urban land cover. Urban areas will become more important as carbon sinks and effective tools to assess carbon densities in these areas are therefore required. Using multi-scale LiDAR presents an opportunity to achieve this, providing a spatially explicit map of urban forest structure and AGB.9.
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems 总被引：7，自引：0，他引：7
Dengsheng Lu Qi Chen Guangxing Wang Lijuan Liu Guiying Li Emilio Moran 《International Journal of Digital Earth》2016,9(1):63-105
Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates over large areas. More research is needed to extract a vertical vegetation structure (e.g. canopy height) from interferometry synthetic aperture radar (InSAR) or optical stereo images to incorporate it into horizontal structures (e.g. canopy cover) in biomass estimation modeling. 相似文献
10.
集成Landsat OLI和机载LiDAR条带数据的亚热带森林生物量制图 总被引：1，自引：0，他引：1
以亚热带天然次生林为研究对象,借助一个条带的少量LiDAR点云数据和覆盖整个研究区的免费Landsat OLI多光谱数据,并结合地面实测数据,探索森林生物量低成本高精度制图方法。首先,提取了OLI和LiDAR特征变量,并与地上和地下生物量进行相关分析以筛选变量;然后,借助LiDAR数据覆盖区的样地和条带LiDAR数据构建"LiDAR生物量模型";再从LiDAR反演生物量的结果中进行采样,结合OLI特征变量构建"LiDAR-OLI模型";最后,与单独使用OLI多光谱数据建立的"OLI估算模型"结果进行比较,分析精度并验证新方法的效果。结果表明,"LiDAR-OLI模型"对地上和地下生物量的模型拟合效果较好且均优于"OLI模型",且其交叉验证的精度也较高并优于"OLI模型",从而证明了新方法的可靠性及有效性。本研究为主、被动遥感技术在中小尺度上协同反演森林参数提供了实验基础,也为基于全覆盖免费OLI多光谱数据及条带LiDAR数据的低成本森林生物量制图探索了技术路线。 相似文献
11.
Purity Rima Mbaabu Yousif Ali Hussin Michael Weir Hammad Gilani 《Journal of the Indian Society of Remote Sensing》2014,42(4):745-754
The impact of forest management activities on the ability of forest ecosystems to sequester and store atmospheric carbon is of increasing scientific and social concern. This is because a quantitative understanding of how forest management enhances carbon storage is lacking in most forest management regimes. In this paper two forest regimes, government and community-managed, in Kayar Khola watershed, Chitwan, Nepal were evaluated based on field data, very high resolution (VHR) GeoEye-1 satellite image and airborne LiDAR data. Individual tree crowns were generated using multi-resolution segmentation, which was followed by two tree species classification (Shorea robusta and Other species). Species allometric equations were used in both forest regimes for above ground biomass (AGB) estimation, mapping and comparison. The image objects generated were classified per species and resulted in 70 and 82 % accuracy for community and government forests, respectively. Development of the relationship between crown projection area (CPA), height, and AGB resulted in accuracies of R2 range from 0.62 to 0.81, and RMSE range from 10 to 25 % for Shorea robusta and other species respectively. The average carbon stock was found to be 244 and 140 tC/ha for community and government forests respectively. The synergistic use of optical and LiDAR data has been successful in this study in understanding the forest management systems. 相似文献
12.
Obtaining reliable measures of tree canopy height across large areas is a central element of forest inventory and carbon accounting. Recent years have seen an increased emphasis on the use of active sensors like Radar and airborne LiDAR (light detection and scanning) systems to estimate various 3D characteristics of canopy and crown structure that can be used as predictors of biomass. However, airborne LiDAR data are expensive to acquire, and not often readily available across large remote landscapes. In this study, we evaluated the potential of stereo imagery from commercially available Very High Resolution (VHR) satellites as an alternative for estimating canopy height variables in Australian tropical savannas, using a semi-global dense matching (SGM) image-based technique. We assessed and compared the completeness and vertical accuracy of extracted canopy height models (CHMs) from GeoEye 1 and WorldView 1 VHR satellite stereo pairs and summarised the factors influencing image matching effectiveness and quality.Our results showed that stereo dense matching using the SGM technique severely underestimates tree presence and canopy height. The highest tree detection rates were achieved by using the near-infrared (NIR) band of GE1 (8–9%). WV1-GE1 cross-satellite (mixed) models did not improve the quality of extracted canopy heights. We consider these poor detection rates and height retrievals to result from: i) the clumping crown structure of the dominant Eucalyptus spp.; ii) their vertically oriented leaves (affecting the bidirectional reflectance distribution function); iii) image band radiometry and iv) wind induced crown movement affecting stereo-pair point matching. Our detailed analyses suggest that current commercially available VHR satellite data (0.5 m resolution) are not well suited to estimating canopy height variables, and therefore above ground biomass (AGB), in Eucalyptus dominated north Australian tropical savanna woodlands. 相似文献
13.
激光雷达在森林参数反演中的应用 总被引：1，自引：0，他引：1
激光雷达是近年来国际上发展十分迅速的主动遥感技术,在森林参数的定量测量和反演上取得了成功的应用。在林业上,高采样密度激光雷达能够获取单株木3维结构特征,采用不同的数据处理方法,可以得到不同精度的单株木参数。利用激光雷达测量森林参数不仅节省了人力,还提高了工作效率,现在已经成为快速获取树木几何参数的一种有效方法。文中主要介绍了LiDAR工作原理、类型及特点、影响LiDAR数据质量的因素、国内外LiDAR的发展状况及应用领域,重点介绍了国内外利用LiDAR数据反演森林参数(树高、郁闭度、冠幅、林分密度、断面积和蓄积量等)的方法和研究进展,同时对今后LiDAR在森林参数反演方面的研究作了展望。 相似文献
14.
地基激光雷达的玉兰林冠层叶面积密度反演 总被引：1，自引：0，他引：1
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%。 相似文献
15.
This study scrutinises the use of terrestrial laser scanning (TLS) to measure diameter at breast height (DBH) and tree height at individual tree species level. LiDAR point cloud scans are collected from uniformly defined control points. The result of processed TLS data demonstrates the precise measurements of tree height and DBH by comparing it with field data (DBH, tree height, tree species and location). The average tree height and DBH obtained through TLS measurements were 9.44?m and 43.30?cm, respectively. A linear equation between TLS derived parameters and field measured values were established, which gave the coefficient of determination (r2) of 0.79 and 0.96 for tree height and DBH, respectively. Further, these parameters were used to calculate above ground biomass (AGB) for individual tree species by considering a non-destructive approach. The total AGB and carbon stock from 80 different trees are computed to be 49.601 and 22.320?tonnes, respectively. 相似文献
16.
GLAS星载激光雷达和Landsat/ETM+数据的森林生物量估算 总被引：1，自引：0，他引：1
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))。 相似文献
17.
激光雷达森林参数反演研究进展 总被引：6，自引：0，他引：6
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。 相似文献
18.
Support vector machines for tree species identification using LiDAR-derived structure and intensity variables 总被引：1，自引：0，他引：1
Zhenyu Zhang 《国际地球制图》2013,28(4):364-378
Tree species identification and forest type classification are critical for sustainable forest management and native forest conservation. Recent success in forest classification and tree species identification using LiDAR (light detection and ranging)-derived variables has been reported in many studies. However, there is still considerable scope for further improvement in classification accuracy. It has driven research into more efficient classifiers such as support vector machines (SVMs) to take maximum advantage of the information extracted from LiDAR data for potential increases in the accuracy of tree species classification. This study demonstrated the success of the SVMs for the identification of the Myrtle Beech (the dominant species of the Australian cool temperate rainforest in the study area) and adjacent tree species – notably, the Silver Wattle at individual tree level using LiDAR-derived structure and intensity variables. An overall accuracy of 92.8% was achieved from the SVM approach, showing significant advantages of the SVMs over the traditional classification methods such as linear discriminant analysis in terms of classification accuracy. 相似文献
19.
森林地上生物量的极化相干层析估计方法 总被引：2，自引：1，他引：1
基于微波的后向散射系数估计森林地上生物量(AGB)易受后向散射系数饱和的影响,而利用森林高度,根据生长方程估计AGB,却没有考虑和AGB密切相关的林分密度、树种组成、林层垂直分布等空间结构特征的作用,针对这些问题,提出一种基于极化相干层析(Polarization Coherence Tomography,PCT)技术的AGB估计方法。基于德国宇航局(DLR)机载SAR系统(ESAR)获取的特劳斯坦(Traunstein)试验区L-波段极化干涉SAR(PolInSAR)数据,通过对具有不同AGB水平的典型林分的相对反射率函数曲线的分析,定义了9个与AGB具有相关性的特征参数。然后基于20个林分的实测AGB数据,以林分尺度上这9个特征参数的平均值为自变量,以实测林分平均AGB为因变量,采用逐步回归分析法构建了AGB估测模型,并对该模型进行评价,对影响模型估计精度的因素进行分析,结果表明,由PCT提取的相对反射率函数特征参数对AGB很敏感,充分利用相对反射率函数信息可提高AGB估计精度。 相似文献
20.
Fabio Maselli Marta ChiesiAlessandro Montaghi Enzo Pranzini 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(5):662-671
Airborne LiDAR techniques can provide accurate measurements of tree height, from which estimates of stem volume and forest woody biomass can be obtained. These techniques, however, are still expensive to apply repeatedly over large areas. The current paper presents a methodology which first transforms mean stand heights obtained from LiDAR over small strips into relevant stem volume estimates. These are then extended over an entire forest by applying two estimation methods (k-NN and locally calibrated regression) to Landsat ETM+ images. The methodology is tested over a coastal area covered by pine forest in the Regional Park of San Rossore (Central Italy). The results are evaluated by comparison with the ground stem volumes of a recent forest inventory, taking into consideration the effect of stand size. In general, the accuracies of two estimation methods are dependent on the size of the forest stands and are satisfactory only when considering stands larger than 5-10 ha. The outputs of the parametric regression procedure are slightly more stable than those of k-NN and more faithfully reproduce the spatial patterns of the ground data. 相似文献