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1.
亚热带森林参数的机载激光雷达估测 总被引：5，自引：2，他引：3
通过应用机载激光雷达数据,在分析云南省中部的78块样地的基础上提出2个预测森林不同生物特性的统计模型(加权平均高度的预测模型和生物量的预测模型),并讨论了预测结果及其精确性。从激光雷达数据中提取了2组变量(树冠高度变量组和植被密度变量组)作为自变量,采用逐步回归方法进行自变量选择。结果表明,激光雷达数据与森林的平均树高和地上各部分生物量有很强的相关性。对于3种不同森林类型(针叶林,阔叶林和混交林),平均树高估测均能达到比较高的精度;生物量的估测结果是针叶林优于阔叶林,混交林的生物量与激光雷达数据则没有明显相关性。最后,对回归分析的结果和影响预测精度的因素进行讨论,认为预测结果的精度可能与森林类型、激光雷达采样时间和采样密度以及坐标误差等因素有关。 相似文献
2.
森林冠层高度和生物量估算对估算森林碳收支方面起到重要作用。本文以广东省森林为研究对象,以全球生态系统动态调查(GEDI)激光雷达卫星为数据源,分别采用回归树和克里金插值算法,对广东省的森林冠层高度和生物量进行反演。研究结果表明,广东省的树木高度普遍在10～20 m,占比超过50%。树高高值出现在粤北的韶关、肇庆等市,树高普遍在15～20 m;而湛江市的平均树高最低,普遍不足10 m。广东省森林生物量最大值为335.85 t/hm2,最小值为5.25 t/hm2,平均值为98.27 t/hm2。森林生物量高值区域主要分布在粤东山区和粤西山区,而广东省平原和城市化地区森林生物量则较低。本文结果为估算广东省森林生态系统碳吸收提供科学依据。 相似文献
3.
基于遥感的区域尺度森林地上生物量估算研究 总被引：1，自引：0，他引：1
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。 相似文献
4.
探讨了可见光立体像对遥感数据在森林平均树高估算研究方向的可行性,为解决大区域快速提取森林平均树高参数的科学问题提供技术支撑。利用GeoEye-1卫星立体像对中提供的有理多项式系数(RPC)参数和数字表面模型(DSM)与数字高程模型(DEM)的理论原理,建立了基于DSM和DEM空间相差模型建立林分冠层高度估算方法流程。结果表明:基于湖南攸县黄丰桥国有林场GeoEye-1立体像对影像数据,按照估算流程,最终得到试验区小班尺度的样地平均树高遥感提取结果。结合样地地面实测控制点和地面小班数据调查数据,该方法提取的研究区平均树高总体误差率在83.1%,其中最大误差为3.773 m,最小误差为0.025 m。因此,本研究是一种可以快速获得研究区大范围森林平均树高参数的创新、可行的方法。 相似文献
5.
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
6.
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。 相似文献
7.
材积是森林清查工作的一个重要参数，基于地基激光雷达点云的树木定量结构模型（QSM）重建方法能够实现林木材积的非破坏性获取，解决了传统森林原位调查方式耗时耗力的问题。但由于伐木材积真值的获取较难实现，使得量化结构模型方法的材积获取能力在树干及各级树枝水平上尚未开展研究，且仅应用于单木尺度地基激光雷达点云中，缺乏基于样方尺度扫描点云进行材积获取的探究。因此本文分别在单木及样方尺度完成QSM重建方法在树干及各级枝材积估算结果评估。实验结果表明，基于单木及样方尺度地基激光雷达点云均能有效地获取树干和一级枝的材积，而次级枝的材积估算存在明显的偏差：样方扫描尺度点云的树干及全树材积估算精度与单木尺度相当，估算偏差均为5%及10%左右，而一级枝材积估算偏差略大，其中单木尺度一级枝估算偏差在10%左右，样方尺度一级枝材积估算偏差在15%左右；此外，林分密度与样方尺度枝干材积估算精度呈负相关关系，在较低林分密度（425株/ha、625株/ha和925株/ha）的样方中树干材积估算误差均在5%以内，一级枝材积估算误差在15%左右，另外受树干及一级枝材积低估与各次级枝材积高估的部分中和效应影响，样方内总蓄积... 相似文献
8.
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 总被引：3，自引：0，他引：3
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。 相似文献
9.
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
10.
Pi-SAR极化数据与K分布指数估算森林生物量与实验验证 总被引：1，自引：0，他引：1
用2002年和2003年日本Pi-SAR全极化数据,研究日本北海道苫小牧森林地区的森林生物量.雷达后向散射系数随森林生物量的增大而增大并迅速达到饱和,L波段雷达数据饱和点约为40t/hm2,X波段仅约为20t/hm2.在SAR数据统计分布中,K分布的指数参数在饱和点以上仍随生物量的增大而增大,并且HV极化方式时相关性最高.根据交叉极化数据K分布的指数参数与森林生物量的关系,本文估算了23个观测点的森林生物量,结果表明平均准确率为85%.因此该算法可以作为一种新的估算森林生物量的手段. 相似文献
11.
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
12.
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。 相似文献
13.
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. 相似文献
14.
Quantitative estimates of forest vertical and spatial distribution using remote sensing technology play an important role in better understanding forest ecosystem function, forest carbon storage and the global carbon cycle. Although most remote sensing systems can provide horizontal distribution of canopies, information concerning the vertical distribution of canopies cannot be detected. Fortunately, laser radars have become available, such as GLAS (Geoscience laser altimeter system). Because laser radar can penetrate foliage, it is superior to other remote sensing technologies for detecting vertical forest structure and has higher accuracy. GLAS waveform data were used in this study to retrieve average tree height and biomass in a GLAS footprint area in Heilongjiang Province. However, GLAS data are not spatially continuous. To fill the gaps, MISR (multi- angle imaging spectrometer) spectral radiance was chosen to predict the regional continuous tree height by developing a multivariate linear regression model. We compared tree height estimated by the regression model and GLAS data. The results confirmed that estimates of tree height and biomass based on GLAS data are considerably more accurate than estimates based on traditional methods. The accuracy is approximately 90%. MISR can be used to estimate tree height in continuous areas with a robust regression model. The R2, precision and root mean square error of the regression model were 0.8, 83% and 1 m, respectively. This study provides an important reference for mapping forest vertical parameters. 相似文献
15.
Field surveys are often a primary source of aboveground biomass (AGB) data, but plot-based estimates of parameters related to AGB are often not sufficiently precise, particularly not in tropical countries. Remotely sensed data may complement field data and thus help to increase the precision of estimates and circumvent some of the problems with missing sample observations in inaccessible areas. Here, we report the results of a study conducted in a 15,867 km² area in the dry miombo woodlands of Tanzania, to quantify the contribution of existing canopy height and biomass maps to improving the precision of canopy height and AGB estimates locally. A local and a global height map and three global biomass maps, and a probability sample of 513 inventory plots were subject to analysis. Model-assisted sampling estimators were used to estimate mean height and AGB across the study area using the original maps and then with the maps calibrated with local inventory plots. Large systematic map errors – positive or negative – were found for all the maps, with systematic errors as great as 60–70 %. After being calibrated locally, the maps contributed substantially to increasing the precision of both mean height and mean AGB estimates, with relative efficiencies (variance of the field-based estimates relative to the variance of the map-assisted estimates) of 1.3–2.7 for the overall estimates. The study, although focused on a relatively small area of dry tropical forests, illustrates the potential strengths and weaknesses of existing global forest height and biomass maps based on remotely sensed data and universal prediction models. Our results suggest that the use of regional or local inventory data for calibration can substantially increase the precision of map-based estimates and their applications in assessing forest carbon stocks for emission reduction programs and policy and financial decisions. 相似文献
16.
Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (>150 Mg/ha, and >300 Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean >300 Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter- and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R2 = 0.54, RMSE = 48.3 Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain “wall-to-wall” AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ∼50 Mg/ha and R2 = 0.66 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values. 相似文献
17.
Field surveys are often a primary source of aboveground biomass (AGB) data, but plot-based estimates of parameters related to AGB are often not sufficiently precise, particularly not in tropical countries. Remotely sensed data may complement field data and thus help to increase the precision of estimates and circumvent some of the problems with missing sample observations in inaccessible areas. Here, we report the results of a study conducted in a 15,867 km² area in the dry miombo woodlands of Tanzania, to quantify the contribution of existing canopy height and biomass maps to improving the precision of canopy height and AGB estimates locally. A local and a global height map and three global biomass maps, and a probability sample of 513 inventory plots were subject to analysis. Model-assisted sampling estimators were used to estimate mean height and AGB across the study area using the original maps and then with the maps calibrated with local inventory plots. Large systematic map errors – positive or negative – were found for all the maps, with systematic errors as great as 60–70 %. The maps contributed nothing or even negatively to the precision of mean height and mean AGB estimates. However, after being calibrated locally, the maps contributed substantially to increasing the precision of both mean height and mean AGB estimates, with relative efficiencies (variance of the field-based estimates relative to the variance of the map-assisted estimates) of 1.3–2.7 for the overall estimates. The study, although focused on a relatively small area of dry tropical forests, illustrates the potential strengths and weaknesses of existing global forest height and biomass maps based on remotely sensed data and universal prediction models. Our results suggest that the use of regional or local inventory data for calibration can substantially increase the precision of map-based estimates and their applications in assessing forest carbon stocks for emission reduction programs and policy and financial decisions. 相似文献
18.
Kiran Chand Thumaty Rakesh Fararoda Suresh Middinti Rajashekar Gopalakrishnan C. S. Jha V. K. Dadhwal 《Journal of the Indian Society of Remote Sensing》2016,44(1):31-39
Reliable and accurate estimates of tropical forest above ground biomass (AGB) are important to reduce uncertainties in carbon budgeting. In the present study we estimated AGB of central Indian deciduous forests of Madhya Pradesh (M.P.) state, India, using Advanced Land Observing Satellite – Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) L-band data of year 2010 in conjunction with field based AGB estimates using empirical models. Digital numbers of gridded 1?×?1° dual polarization (HH & HV) PALSAR mosaics for the study area were converted to normalized radar cross section (sigma naught - σ0). A total of 415 sampling plots (0.1 ha) data collected over the study area during 2009–10 was used in the present study. Plot-level AGB estimates using volume equations representative to the study area were computed using field inventory data. The plot-level AGB estimates were empirically modeled with the PALSAR backscatter information in HH, HV and their ratios from different forest types of the study area. The HV backscatter information showed better relation with field based AGB estimates with a coefficient of determination (R2) of 0.509 which was used to estimate spatial AGB of the study area. Results suggested a total AGB of 367.4 Mt for forests of M.P. state. Further, validation of the model was carried out using observed vs. predicted AGB estimates, which suggested a root mean square error (RMSE) of ±19.32 t/ha. The model reported robust and defensible relation for observed vs. predicted AGB values of the study area. 相似文献
19.
中国南方森林冠顶高度Lidar反演—以江西省为例 总被引：1，自引：0，他引：1
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。 相似文献