共查询到20条相似文献，搜索用时 31 毫秒
1.
《International Journal of Digital Earth》2013,6(3):307-323
ABSTRACTForests of the Sierra Nevada (SN) mountain range are valuable natural heritages for the region and the country, and tree height is an important forest structure parameter for understanding the SN forest ecosystem. There is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial resolution. In this study, we presented a method to map wall-to-wall forest tree height (defined as Lorey’s height) across the SN at 70-m resolution by fusing multi-source datasets, including over 1600 in situ tree height measurements and over 1600?km2 airborne light detection and ranging (LiDAR) data. Accurate tree height estimates within these airborne LiDAR boundaries were first computed based on in situ measurements, and then these airborne LiDAR-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System (GLAS) footprints. Finally, the random forest algorithm was used to model the SN tree height from these GLAS tree heights, optical imagery, topographic data, and climate data. The results show that our fine-resolution SN tree height product has a good correspondence with field measurements. The coefficient of determination between them is 0.60, and the root-mean-squared error is 5.45?m. 相似文献
2.
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. 相似文献
3.
Wei Yao Stefan Hinz Uwe Stilla 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(3):260-271
Airborne LiDAR data are characterized by involving not only rich spatial but also temporal information. It is possible to extract vehicles with motion artifacts from single-pass airborne LiDAR data, based on which the motion state and velocity of vehicles can be identified and derived. In this paper, a complete strategy for urban traffic analysis using airborne LiDAR data is presented. An adaptive 3D segmentation method is presented to facilitate the task of vehicle extraction. The method features an ability to detect local arbitrary modes at multi scales, thereby making it particularly appropriate for partitioning complex point cloud data. Vehicle objects are then extracted by a binary classification using object-based features. Furthermore, the motion analysis for extracted vehicles is performed to distinguish between moving and stationary ones. Finally, the velocity is estimated for moving vehicles. The applicability and efficiency of the presented strategy is demonstrated and evaluated on three ALS datasets acquired for the propose of city mapping, where up to 87% of vehicles have been extracted and up to 83% of moving traffic can be recovered together with reasonable velocity estimates. It can be concluded that airborne LiDAR data can provide value-added products for traffic monitoring applications, including vehicle counts, location and velocity, along with traditional products such as building models, DEMs and vegetation models. 相似文献
4.
Automatic Registration Between Low-Altitude LiDAR Point Clouds and Aerial Images Using Road Features
Peipei He Xinjing Wang Youchuan Wan Jingzhong Xu Wei Yang 《Journal of the Indian Society of Remote Sensing》2018,46(12):1963-1973
Among the many means of acquiring surface information, low-altitude light detection and ranging (LiDAR) systems (e.g., unmanned aerial vehicle LiDAR, UAV-LiDAR) have become an important approach to accessing geospatial information. Considering the lower level of hardware technology in low-altitude LiDAR systems compared to that in airborne LiDAR, and the greater flexibility in-flight, registration procedures must be first performed to facilitate the fusion of laser point data and aerial images. The corner points and edges of buildings are frequently used for the automatic registration of aerial imagery with LiDAR data. Although aerial images and LiDAR data provide powerful support for building detection, adaptive edge detection for all types of building shapes is difficult. To deal with the weakness of building edge detection and reduce matching-related computation, the study presents a novel automatic registration method for aerial images, with LiDAR data, on the basis of main-road information in urban areas. Firstly, vector road centerlines are extracted from raw LiDAR data and then projected onto related aerial images with the use of coarse exterior orientation parameters (EOPs). Secondly, the corresponding image road features of each LiDAR vector road are determined using an improved total rectangle-matching approach. Finally, the endpoints of the conjugate road features obtained from the LiDAR data and aerial images are used as ground control points in space resection adjustment to refine the EOPs; an iterative strategy is used to obtain optimal matching results. Experimental results using road features verify the feasibility, robustness and accuracy of the proposed approach. 相似文献
5.
Inland water bodies are globally threatened by environmental degradation and climate change. On the other hand, new water bodies can be designed during landscape restoration (e.g. after coal mining). Effective management of new water resources requires continuous monitoring; in situ surveys are, however, extremely time-demanding. Remote sensing has been widely used for identifying water bodies. However, the use of optical imagery is constrained by accuracy problems related to the difficulty in distinguishing water features from other surfaces with low albedo, such as tree shadows. This is especially true when mapping water bodies of different sizes. To address these problems, we evaluated the potential of integrating hyperspectral data with LiDAR (hereinafter “integrative approach”). The study area consisted of several spoil heaps containing heterogeneous water bodies with a high variability of shape and size. We utilized object-based classification (Support Vector Machine) based on: (i) hyperspectral data; (ii) LiDAR variables; (iii) integration of both datasets. Besides, we classified hyperspectral data using pixel-based approaches (K-mean, spectral angle mapper). Individual approaches (hyperspectral data, LiDAR data and integrative approach) resulted in 2–22.4 % underestimation of the water surface area (i.e, omission error) and 0.4–1.5 % overestimation (i.e., commission error).The integrative approach yielded an improved discrimination of open water surface compared to other approaches (omission error of 2 % and commission error of 0.4 %). We also evaluated the success of detecting individual ponds; the integrative approach was the only one capable of detecting the water bodies with both omission and commission errors below 10 %. Finally, the assessment of misclassification reasons showed a successful elimination of shadows in the integrative approach. Our findings demonstrate that the integration of hyperspectral and LiDAR data can greatly improve the identification of small water bodies and can be applied in practice to support mapping of restoration process. 相似文献
6.
使用LiDAR单一数据进行点云分割工作时,基于斜率的严格分割LiDAR点云的方法不能很好的适应复杂地物
的分类工作。本文将LiDAR粗分割后的点云转换为高度图像和反射强度图像,并求取高度图像GLCM高度纹理。将4
种GLCM高度纹理、地面粗糙系数、平均高度和平均反射强度共7种纹理作为识别地面覆盖物的特征,并利用后向传播
神经网络(BP-ANN)方法对LiDAR数据进行地物识别。实验表明,这种方法能够从LiDAR独立数据源中有效的实现地
物分类,实验获得的精度大于90%。与传统的最大似然法进行对比,BP-ANN的分类精度高于最大似然法。当预设地
面类型能同时满足被光学影像和LiDAR数据识别的条件时,LiDAR高度纹理分类与光学影像分类结果的一致性达到
76.5%。 相似文献
7.
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。 相似文献
8.
In light of climate and land use change, stakeholders around the world are interested in assessing historic and likely future flood dynamics and flood extents for decision-making in watersheds with dams as well as limited availability of stream gages and costly technical resources. This research evaluates an assessment and communication approach of combining GIS, hydraulic modeling based on latest remote sensing and topographic imagery by comparing the results to an actual flood event and available stream gages. On August 28th 2011, floods caused by Hurricane Irene swept through a large rural area in New York State, leaving thousands of people homeless, devastating towns and cities. Damage was widespread though the estimated and actual floods inundation and associated return period were still unclear since the flooding was artificially increased by flood water release due to fear of a dam break. This research uses the stream section right below the dam between two stream gages North Blenheim and Breakabeen along Schoharie Creek as a case study site to validate the approach. The data fusion approach uses a GIS, commonly available data sources, the hydraulic model HEC-RAS as well as airborne LiDAR data that were collected two days after the flood event (Aug 30, 2011). The aerial imagery of the airborne survey depicts a low flow event as well as the evidence of the record flood such as debris and other signs of damage to validate the hydrologic simulation results with the available stream gauges. Model results were also compared to the official Federal Emergency Management Agency (FEMA) flood scenarios to determine the actual flood return period of the event. The dynamic of the flood levels was then used to visualize the flood and the actual loss of the Old Blenheim Bridge using Google Sketchup. Integration of multi-source data, cross-validation and visualization provides new ways to utilize pre- and post-event remote sensing imagery and hydrologic models to better understand and communicate the complex spatial-temporal dynamics, return periods and potential/actual consequences to decision-makers and the local population. 相似文献
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尝试应用机载LiDAR技术测绘1：10 000比例尺地形图3D（DLG、DEM、DOM）产品，给出了机载LiDAR测绘3D产品的技术流程，并选择荒漠地区作为试验区，验证了此种技术方法在荒漠地区测绘3D产品的可行性，分析了成果精度。试验证明，该方法可以满足荒漠区域的1：10 000比例尺3D基础数据生产要求，且具有外业工作量小、自动化程度高、成图快、高程精度高、受外界环境影响小等优点，同时也总结了该方法中有待完善之处。该方法为荒漠地区3D基础测绘数据获取提供了有益借鉴。 相似文献
12.
机载多光谱LiDAR系统能够快速、准确地获取地物的空间几何和光谱信息,为地物覆盖分类和目标识别提供新的数据源。近年来,基于三维点云的深度学习算法取得了一系列突破性进展,然而直接将不规则的原始点云数据输入深度学习模型进行基于点的分类存在一定的困难。本文提出了一种基于FPS-KNN的样本生成方法,用于基于深度学习的机载多光谱LiDAR数据分类。该方法首先对输入数据进行归一化处理;然后利用最远点采样方法(FPS)和K近邻法(KNN)在输入数据中生成一系列规则大小的训练样本数据集。通过机载多光谱LiDAR数据的试验表明,该方法所生成的样本不仅符合卷积神经网络所要求的输入数据形式,而且能够确保对输入场景的完整覆盖。 相似文献
13.
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. 相似文献
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Jose A. Manzanera Antonio García-Abril Cristina Pascual Rosario Tejera Susana Martín-Fernández Timo Tokola 《地理信息系统科学与遥感》2016,53(6):723-738
Forest stand structure is an important concept for ecology and planning in sustainable forest management. In this article, we consider that the incorporation of complementary multispectral information from optical sensors to Light Detection and Ranging (LiDAR) may be advantageous, especially through data fusion by back-projecting the LiDAR points onto the multispectral image. A multivariate data set of both LiDAR and multispectral metrics was related with a multivariate data set of stand structural variables measured in a Scots pine forest through canonical correlation analysis (CCA). Four statistically significant pairs of canonical variables were found, which explained 83.0% accumulated variance. The first pair of canonical variables related indicators of stand development, i.e. height and volume, with LiDAR height metrics. CCA also found attributes describing stand density to be related to LiDAR and spectral variables determining canopy coverage. Other canonical variables pertained to Lorenz curve-derived attributes, which are measures of within-stand tree size variability and heterogeneity, able to discriminate even-sized from uneven-sized stands. The most relevant result was to find that metrics derived from the multispectral sensor showed significant explanatory potential for the prediction of these structural attributes. Therefore, we concluded that metrics derived from the optical sensor have potential for complementing the information from the LiDAR sensor in describing structural properties of forest stands. We recommend the use of back-projecting for jointly exploiting the synergies of both sensors using similar types of metrics as they are customary in forestry applications of LiDAR. 相似文献
16.
城区机载LiDAR数据与航空影像的自动配准 总被引：2，自引：0，他引：2
为解决机载LiDAR数据与航空影像集成应用中二者的配准问题,提出了一种机载LiDAR数据与航空影像配准的方法。首先,直接在LiDAR点云中提取建筑物3维轮廓线,通过将轮廓线规则化得到由两条相互垂直的直线段组成的建筑物角特征,并在航空影像上提取直线特征;然后,根据影像初始外方位元素将建筑物角特征投影到航空影像上,并采用一定的相似性测度在影像上寻找同名的影像角特征;最后,将角特征的角点当作控制点,利用传统的摄影测量光束法区域网平差解求影像新的外方位元素。解算过程中采用循环迭代策略。本方法的主要特点是,直接从LiDAR点云中提取线特征,避免了常规方法从距离图(或强度图)中提取线特征所产生的内插误差。通过与现有基于点云强度图的配准方法的对比实验表明,在低精度初始外方位元素的辅助下,本文方法能够达到较高的配准精度。 相似文献
17.
机载激光雷达及高光谱的森林乔木物种多样性遥感监测 总被引：1，自引：0，他引：1
利用机载LiDAR和高光谱数据并结合37个地面调查样本数据,基于结构差异与光谱变异理论,通过相关分析法分别筛选了3个最优林冠结构参数和6个最优光谱指数,在单木尺度上利用自适应C均值模糊聚类算法,在神农架国家自然保护区开展森林乔木物种多样性监测,实现了森林乔木物种多样性的区域成图。研究结果表明,(1)基于结合形态学冠层控制的分水岭算法可以获得较高精度的单木分割结果(R~2=0.88,RMSE=13.17,P0.001);(2)基于LiDAR数据提取的9个结构参数中,95%百分位高度、冠层盖度和植被穿透率为最优结构参数,与Shannon-Wiener指数的相关性达到R~2=0.39—0.42(P0.01);(3)基于机载高光谱数据筛选的16个常用的植被指数中,CRI、OSAVI、Narrow band NDVI、SR、Vogelmann index1、PRI与Shannon-Wiener指数的相关性最高(R~2=0.37—0.45,P0.01);(4)在研究区,利用以30 m×30 m为窗口的自适应模糊C均值聚类算法可预测的最大森林乔木物种数为20,物种丰富度的预测精度为R~2=0.69,RMSE=3.11,Shannon-Wiener指数的预测精度为R~2=0.70,RMSE=0.32。该研究在亚热带森林开展乔木物种多样性监测,是在区域尺度上进行物种多样性成图的重要实践,可有效补充森林生物多样性本底数据的调查手段,有助于实现生物多样性的长期动态监测及科学分析森林物种多样性的现状和变化趋势。 相似文献
18.
Individual tree crown delineation is of great importance for forest inventory and management. The increasing availability of high-resolution airborne light detection and ranging (LiDAR) data makes it possible to delineate the crown structure of individual trees and deduce their geometric properties with high accuracy. In this study, we developed an automated segmentation method that is able to fully utilize high-resolution LiDAR data for detecting, extracting, and characterizing individual tree crowns with a multitude of geometric and topological properties. The proposed approach captures topological structure of forest and quantifies topological relationships of tree crowns by using a graph theory-based localized contour tree method, and finally segments individual tree crowns by analogy of recognizing hills from a topographic map. This approach consists of five key technical components: (1) derivation of canopy height model from airborne LiDAR data; (2) generation of contours based on the canopy height model; (3) extraction of hierarchical structures of tree crowns using the localized contour tree method; (4) delineation of individual tree crowns by segmenting hierarchical crown structure; and (5) calculation of geometric and topological properties of individual trees. We applied our new method to the Medicine Bow National Forest in the southwest of Laramie, Wyoming and the HJ Andrews Experimental Forest in the central portion of the Cascade Range of Oregon, U.S. The results reveal that the overall accuracy of individual tree crown delineation for the two study areas achieved 94.21% and 75.07%, respectively. Our method holds great potential for segmenting individual tree crowns under various forest conditions. Furthermore, the geometric and topological attributes derived from our method provide comprehensive and essential information for forest management. 相似文献
19.
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
20.
Up‐to‐date and accurate digital elevation models (DEMs) are essential for many applications such as numerical modeling of mass movements or mapping of terrain changes. Today the Federal Department of Topography, swisstopo, provides Digital Terrain Models (DTMs) and Digital Surface Models (DSMs) derived from airborne LiDAR data with a high spatial resolution of 2 m covering the entire area of Switzerland below an elevation of 2000 m a.s.l.. However, above an elevation of 2000 m a.s.l., which is typical for high‐alpine terrain, the best product available is the a DTM with a spatial resolution of 25 m. This spatial resolution is insufficient for many applications in complex terrain. In this study, we investigate the quality of DSMs derived from opto‐electronic scanner data (ADS80; acquired in autumn 2010) using photogrammetric image correlation techniques based on the multispectral nadir and backward looking sensor data. As reference, we take a high precision airborne LiDAR data set with a spatial resolution of ca. 0.5 m, acquired in late summer 2010, covering the Grabengufer/Dorfbach catchment near Randa, VS. We find the deviations between the two datasets are surprisingly low. In terrain with inclination angles of less than 30° the RMSE is below 0.5 m. In extremely steep terrain of more than 50° the RMSE goes up to 2 m and outliers increase significantly. We also find dependencies of the deviations on illumination conditions and ground cover classes. Finally we discuss advantages and disadvantages of the different data acquisition methods. 相似文献