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Three-dimensional building models are important for various applications, such as disaster management and urban planning. The development of laser scanning sensor technologies has resulted in many different approaches for efficient building model generation using LiDAR data. Despite this effort, generation of these models lacks economical and reliable techniques that fully exploit the advantage of LiDAR data. Therefore, this research aims to develop a framework for fully-automated building model generation by integrating data-driven and model-driven methods using LiDAR datasets.The building model generation starts by employing LiDAR data for building detection and approximate boundary determination. The generated building boundaries are then integrated into a model-based processing strategy because LiDAR derived planes show irregular boundaries due to the nature of LiDAR point acquisition. The focus of the research is generating models for the buildings with right-angled-corners, which can be described with a collection of rectangles under the assumption that the majority of the buildings in urban areas belong to this category. Therefore, by applying the Minimum Bounding Rectangle (MBR) algorithm recursively, the LiDAR boundaries are decomposed into sets of rectangles for further processing. At the same time, the quality of the MBRs is examined to verify that the buildings, from which the boundaries are generated, are buildings with right-angled-corners. The parameters that define the model primitives are adjusted through a model-based boundary fitting procedure using LiDAR boundaries. The level of details in the final Digital Building Model is based on the number of recursions during the MBR processing, which in turn are determined by the LiDAR point density. The model-based boundary fitting improves the quality of the generated boundaries and as seen in experimental results, the quality depends on the average LiDAR point spacing. This research thus develops an approach which not only automates the building model generation, but also achieves the best accuracy of the model while utilizing only LiDAR data. 相似文献
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Nicholas Coops Andrew Loughhead Philip Ryan Ron Hutton 《International journal of geographical information science》2013,27(4):345-361
In absence of other limitations, the growth rate of a plant is dependent upon the amount of heat it receives. Each species, whether a crop, weed or disease organism, is adapted to grow at its optimum rate within a specific temperature range. Within this range, the growing degree days (GDD) is the heat accumulation above a given base temperature for a specific time period, such as a crop's growing season or phenological stage. In this paper we detail a methodology to predict GDD for synthetically generated average growing seasons derived from long term average climate data over the Australian continent. An application of these techniques has been made using the GEODATA 9 second DEM, with temperature threshold values estimated to characterize optimum growth in citrus (Citrus sinensis (L.) Osbeck). Three major determinants of the annual growth cycle of Citrus sp. were established and predicted on a spatial basis including the starting day of the growing season, the GDD for a growing season, and the time required to accumulate an arbitrarily selected 2000 GDD from the estimated starting day. When these critical environmental factors are expressed on a spatial basis, covering the Australian continent, the combination can be used to identify locations where new crop varieties can most effectively be grown to maximize fruit quality and productivity, or to extend the harvest season. Likewise, new germplasm introduced to Australia from overseas can be horticulturally assessed at sites climatically matched to the source location. 相似文献
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??????LIDAR??????????????????????????????????????????????????????????????????LIDAR????????????????δ????????????????????????????????????÷?????????Ч????????????LIDAR???????????е???????????????????????????Ч???? 相似文献
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Controls on coastal dune morphology, shoreline erosion and barrier island response to extreme storms 总被引：2，自引：0，他引：2
The response of a barrier island to an extreme storm depends in part on the surge elevation relative to the height and extent of the foredunes which can exhibit considerable variability alongshore. While it is recognized that alongshore variations in dune height and width direct barrier island response to storm surge, the underlying causes of the alongshore variation remain poorly understood. This study examines the alongshore variation in dune morphology along a 11 km stretch of Santa Rosa Island in northwest Florida and relates the variation in morphology to the response of the island during Hurricane Ivan and historic and storm-related rates of shoreline erosion. The morphology of the foredune and backbarrier dunes was characterized before and after Hurricane Ivan using Empirical Orthogonal Function (EOF) analysis and related through Canonical Correlation Analysis (CCA). The height and extent of the foredune, and the presence and relative location of the backbarrier dunes, varied alongshore at discrete length scales (of ~ 750, 1450 and 4550 m) that are statistically significant at the 95% confidence level. Cospectral analysis suggests that the variation in dune morphology is correlated with transverse ridges on the inner-shelf, the backbarrier cuspate headlands, and the historical and storm-related trends in shoreline change. Sections of the coast with little to no dune development before Hurricane Ivan were observed in the narrowest portions of the island (between headlands), west of the transverse ridges. Overwash penetration tended to be larger in these areas and island breaching was common, leaving the surface close to the watertable and covered by a lag of shell and gravel. In contrast, large foredunes and the backbarrier dunes were observed at the widest sections of the island (the cuspate headlands) and at crest of the transverse ridges. Due to the large dunes and the presence of the backbarrier dunes, these areas experienced less overwash penetration and most of the sediment from the beachface and dunes was deposited within the upper-shoreface. It is argued that this sediment is returned to the beachface through nearshore bar migration following the storm and that the areas with larger foredunes and backbarrier dunes have smaller rates of historical shoreline erosion compared to areas with smaller dunes and greater transfer of sediment to the washover terrace. Since the recovery of the dunes will vary depending on the availability of sediment from the washover and beachface, it is further argued that the alongshore pattern of dune morphology and the response of the island to the next extreme storm is forced by the transverse ridges and island width through alongshore variations in storm surge and overwash gradients respectively. These findings may be particularly important for coastal managers involved in the repair and rebuilding of coastal infrastructure that was damaged or destroyed during Hurricane Ivan. 相似文献
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机载LIDAR数据的树高识别算法与应用分析 总被引：1，自引：0，他引：1
利用机载激光雷达数据提取天然次生林的树高,旨在探索影响树高提取精度的主要因素。首先,采用高精度曲面建模平差算法(Adjustment Computation of High-accuracy Surface Modeling,HASM-AD)生成研究区不同空间分辨率的数字高程模型(Digital Elevation Model,DEM)、数字地表模型(Digital Surface Model,DSM)和冠层高度模型(Canopy Height Model,CHM);其次,用树顶点识别算法提取林木树高,设置不同树高识别范围,对比分析不同CHM分辨率和不同树高识别范围对树高提取精度的影响;最后,以天涝池流域30个实测样地数据为样本,对提取精度进行检验。结果显示：提取的样地平均树高与实测值具有明显线性相关关系,线性回归系数为0.694;树高识别范围是影响树高提取精度的重要因素,CHM分辨率对其影响较小。研究表明,采用高采样密度的雷达点云数据、正确选择CHM生成方法和改进树顶点识别算法是提高天然次生林树高提取精度的有效途径。 相似文献
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以车载激光扫描点云数据为研究对象,提出一种由粗到细且快速获取点云中建筑物3维位置边界的方法。首先,通过分析格网内部点云的空间分布特征(平面距离、高程差异和点密集程度等)确定激光扫描点的权值,采用距离加权倒数IDW(Inverse Distance Weighted)内插方法生成车载激光扫描点云的特征图像。然后,采用阈值分割、轮廓提取与跟踪等手段提取特征图像中的建筑物目标的粗糙边界。最后,对粗糙边界内部的建筑物目标点云进行平面分割,提取建筑物的立面特征并构建立面不规则三角网TIN(Triangulated Irregular Network),并在建筑物先验框架知识条件下自动提取建筑物的精确3维位置边界。 相似文献
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基于LIDAR数据的森林参数反演方法研究 总被引：5，自引：0，他引：5
森林结构参数诸如林分平均高、平均冠幅、平均胸径、林分密度、地上生物量等的空间分布对于森林可持续经营管理具有重要意义。以黑河流域祁连山大野口典型森林区为研究区,采用高密度LIDAR小脚印点云数据,在进行单木结构参数提取的基础上,按20 m×20 m大小的网格进行了小区域森林参数反演研究。首先由LIDAR点云数据生成冠层高度模型(Canopy Height Model,CHM),从CHM中估测单株木结构参数树的位置、树高、冠幅。然后采用多元逐步回归分析法建立样地(20 m×20 m)尺度上LIDAR估测的平均树高、冠幅等与实测森林参数(林分平均高、林分算数平均高、平均冠幅、平均胸径、林分密度、地上生物量)之间的关系。结果表明,林分平均高、林分算术平均高、地上生物量的估测方程精度较高,R2均大于0.7,平均冠幅、平均胸径、林分密度的估测方程R2均大于0.5,根据建立的方程得到了森林参数的空间分布图。高密度LIDAR数据可以得到较高精度的森林参数空间分布图,对于森林可持续经营管理以及林相图的更新等具有重要意义,同时对小流域森林水文科学的研究具有重要的应用价值。 相似文献
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一种针对激光雷达强度图像的滤波算法研究 总被引：5，自引：1，他引：5
基于激光雷达数据的特点．提出了一种融合的中值滤波算法。运用此算法和传统的中值滤波算法以及均值算法对激光雷达数据进行了处理，并且使用多种指标对处理的结果进行了比较，证明本算法既保持了传统中值滤波的优点，又改进了对弱边缘目标的保护。 相似文献