数字正射影像结合LiDAR数据在山区测绘中的应用     

严慧敏 《测绘通报》2020,(1):115-119

随着信息化社会的到来，现代水利测绘已经由传统测绘向信息化测绘发展，无人机技术应用于测绘行业推进了信息化测绘进程。本文探讨了如何有效利用无人机技术解决测绘领域在山区遇到的问题。固定翼无人机能及时获取地面数字正射影像数据，捕获裸露地面的平面和高程，但是无法获取植被覆盖下的地表高程信息，因此，本文通过机载激光雷达获取植被覆盖下的LiDAR点云数据；将二者数据相结合，再通过EPS软件生成三维地表模型，可以快速获取任何测区地物和地形数据，不仅提高了工作效率，还降低了外业劳动强度。  相似文献   

基于LiDAR-DP的机载激光雷达数据处理     

刘华娇  李兵  邵红霞 《地理空间信息》2019,17(3)

基于国产机载LiDAR数据处理软件LiDAR-DP,总结了机载LiDAR数据处理的技术流程,并根据该流程对LiDARDP的相关功能进行了研究。利用LiDAR-DP对实际数据进行了DEM生产试验,并与同类商业软件进行了功能对比。结果表明,LiDAR-DP是一款优秀的国产机载LiDAR数据处理软件,完全能满足DEM的生产需求。  相似文献   

高精度直升机机载LiDAR在高速公路改扩建勘测中的应用可行性研究          下载免费PDF全文

郑伟安 《山东国土资源》2018,34(8)

机载LiDAR在公路勘测方面的用途日益广泛。该文对直升机机载LiDAR在高速公路改扩建中的应用技术路线可行性进行了研究论证,从地面控制测量、点云数据获取、点云数据处理、成果应用等多个方面进行了阐述,通过分析LiDAR点云数据在5种不同地面控制点布设方案校正下的点云数据精度,论证了利用地面控制点对直升机机载LiDAR点云数据进行平面和高程校正的可行性。  相似文献   

机载LiDAR地形特征信息快速提取     

刘智敏  杨安秀  宿殿鹏  刘盼 《大地测量与地球动力学》2018,38(11):1186-1190

为更有效地获取地形特征信息，提出一种机载LiDAR地形特征信息快速提取算法。首先，通过构建二次曲面拟合模型，建立实测LiDAR地形数据与拟合曲面的几何规则；然后，采用LM算法迭代参数寻优，获得最优化结果下的地形拟合参数，计算拟合时间及拟合精度；最后，以地形拟合模型为基础，进行地形特征信息的快速提取。通过机载LiDAR实测数据验证，当最优搜索半径为2 m时，地形曲面的拟合时间仅为0.02 s，RMSE仅为5.09 cm。该算法保证了地形特征信息提取效率和精度，能够有效满足机载LiDAR科学研究和工程应用的技术需求。  相似文献   

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures          下载免费PDF全文

J. L. McCreight  A. G. Slater  H. P. Marshall  B. Rajagopalan 《水文研究》2014,28(3):933-957

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

Exploration of remotely sensed forest structure and ultrasonic range sensor metrics to improve empirical snow models          下载免费PDF全文

Andrés Varhola  Nicholas C. Coops  Younes Alila  Markus Weiler 《水文研究》2014,28(15):4433-4448

Current methods to estimate snow accumulation and ablation at the plot and watershed levels can be improved as new technologies offer alternative approaches to more accurately monitor snow dynamics and their drivers. Here we conduct a meta‐analysis of snow and vegetation data collected in British Columbia to explore the relationships between a wide range of forest structure variables – obtained from Light Detection and Ranging (LiDAR), hemispherical photography (HP) and Landsat Thematic Mapper – and several indicators of snow accumulation and ablation estimated from manual snow surveys and ultrasonic range sensors. By merging and standardizing all the ground plot information available in the study area, we demonstrate how LiDAR‐derived forest cover above 0.5 m was the variable explaining the highest percentage of absolute peak snow water equivalent (SWE) (33%), while HP‐derived leaf area index and gap fraction (45° angle of view) were the best potential predictors of snow ablation rate (explaining 57% of variance). This study reveals how continuous SWE data from ultrasonic sensors are fundamental to obtain statistically significant relationships between snow indicators and structural metrics by increasing mean r² by 20% when compared to manual surveys. The relationships between vegetation and spectral indices from Landsat and snow indicators, not explored before, were almost as high as those shown by LiDAR or HP and thus point towards a new line of research with important practical implications. While the use of different data sources from two snow seasons prevented us from developing models with predictive capacity, a large sample size helped to identify outliers that weakened the relationships and suggest improvements for future research. A concise overview of the limitations of this and previous studies is provided along with propositions to consistently improve experimental designs to take advantage of remote sensing technologies, and better represent spatial and temporal variations of snow. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

机载多光谱LiDAR的随机森林地物分类   总被引：1，自引：0，他引：1  

曹爽  潘锁艳  管海燕 《测绘通报》2019,(11):79-84

机载多光谱LiDAR技术利用激光进行探测和测距，不仅可以快速获取地面物体的三维坐标，还可以获得多个波段的地物光谱信息，可广泛用于地形测绘、土地覆盖分类、环境建模、森林资源调查等。本文提出了多光谱LiDAR的随机森林地物分类方法。该方法通过对LiDAR强度数据和高程数据提取分类特征，完成多光谱LiDAR的随机森林地物分类；并分析随机森林的特征贡献度特性，采用后向特征选择方法实现分类特征选择。通过对加拿大Optech Titan多光谱LiDAR数据的试验表明：随机森林方法可以获得较好的地物分类精度，而且可以适当地去除部分冗余和相关的特征，从而有效提高分类精度。  相似文献   

无人机机载LiDAR在沿海滩涂大比例尺地形测绘中的应用     

郭春海  张英明  丁忠明 《测绘通报》2019,(9):155-158

以南京市以下长江12.5 m深水航道建设工程中的白茆沙整治建筑物工程的年度监测为例，介绍了利用无人机航摄和机载LiDAR技术进行大比例尺高精度的滩涂地理信息数据采集的关键技术。研究案例表明，采用无人机搭载数码相机和LiDAR联合进行数据采集，在小区域、通行困难地区的高分辨率影像和地面高程数据快速获取方面具有明显优势，为沿海滩涂开发中的工程用图测绘工作提供了一种新的有效快捷的技术手段。  相似文献   

机载LiDAR点云获取与高精度DEM建设关键技术探讨   总被引：1，自引：0，他引：1  

杨辉山  雷丽珍 《测绘通报》2019,(3):124-127

结合广东省机载LiDAR点云获取与高精度DEM建设项目，介绍了项目总体技术路线，针对项目难点，从设备选择、点云密度设计、植被覆盖密集山区数据获取方法、点云数据分类组合算法、空白区处理等5个方面的关键技术进行了探讨，并提出解决方案，为同类项目的设计与实施提供参考。  相似文献   

多尺度邻域特征下的机载LiDAR点云电力线分类   总被引：1，自引：0，他引：1  

王艳军  李凯  路立娟 《测绘通报》2019,(4):21-25

利用机载激光雷达技术三维测量精度高且获取快速的优点进行电力线自动分类提取已成为点云数据处理与电力应用的重要领域。针对电力线分类模型的自动化和高精度需求，本文提出了基于三维多尺度邻域特征的机载LiDAR点云电力线分类提取模型框架，主要包括4个步骤：电力线候选点滤波、多尺度邻域类型选取、形状结构特征提取和支持向量机分类。通过对2个复杂城市区域的试验数据集和8种不同邻域类型的详细结果对比分析，发现基于多尺度圆球邻域形状结构特征的分类模型结果准确率、召回率和质量分别达到97%、94%和93%，同时整体处理时间在2个试验数据中分别从366、256 s减少到274、160 s。试验结果表明，该方法在多种复杂城市场景中能够实现机载LiDAR数据的电力线较高精度分类提取。  相似文献