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   

基于车载激光点云的街景立面自动提取     

耿雨馨  钟若飞  彭宝江 《地球信息科学学报》2018,20(4):480-488

街道景观图是城市规划设计和城市管理的重要参考依据,车载点云数据能够提供沿街建筑的三维点信息,精度高,覆盖范围广泛,为街景立面整治提供了新的解决方案。为此,本文提出一种适用于车载点云的街景立面的自动提取方法,提取立面点云的具体步骤为：对原始数据去噪滤波;选取非地面点构建规则格网并二值化,依据语义特征筛选出建筑物点云;用POS数据拟合直线段帮助选取参考向量与参考平面;计算点云到参考面的距离,按距离分类点云数据,并对前述步骤中未分类点另行提取,合并面点集得到以沿街建筑物立面为主的街景立面点云。为了验证这一方法的可行性和有效性,采用点云数据进行实验,实验结果表明本方法在一定程度上提高了数据处理效率,能得到较理想的结果。  相似文献   

Google Earth Engine平台支持下的赣南柑橘果园遥感提取研究   总被引：1，自引：0，他引：1  

徐晗泽宇  刘冲  王军邦  齐述华 《地球信息科学学报》2018,20(3):396-404

赣南地区是中国柑橘主产区,柑橘种植产业经数十年发展已具较大规模。本文利用Google Earth Engine平台,使用2140景Landsat影像进行像元级融合,重构目标年份季节最小云量影像集,构建多维分类特征集,利用随机森林分类算法,实现了1990、1995、2000、2005、2010和2016年赣南柑橘果园的分布制图。结果表明：利用Google Earth Engine平台可实现大量遥感影像数据的高效处理;最小云量影像合成方法能够有效解决多云多雨地区高质量光学影像获取困难的问题;以最小云量影像合成构建的数据集,使用随机森林分类算法能够有效提取赣南柑橘果园,分类平均总体精度和Kappa系数分别为93.15%和0.90,分类效果良好;赣南柑橘果园面积由1990年9.77 km²扩大为2016年2200.34 km²,2005年以后呈大规模扩张趋势,果园分布由零星分布,逐步形成连片化的聚集分布特点,柑橘果园用地的主要来源为林地、灌丛和耕地。  相似文献   

Galileo广播星历评估及其对定位精度的影响     

韩德强  党亚民  庞辉  薛树强  杨强  张龙平 《大地测量与地球动力学》2018,38(11):1165-1169

分析现阶段伽利略空间信号的精度，并评估伽利略广播星历及其对单点定位的影响。统计30 d健康卫星广播星历，结果表明，FOC和IOV两种类型的广播星历卫星轨道误差优于0.8 m，并且径向误差最小，法向误差次之，切向误差最大；伽利略卫星的钟差误差精度达到1.0 ns；从空间信号测距误差对伽利略卫星进行整体分析，广播星历精度优于1.0 m，FOC与IOV卫星精度相当；用广播星历进行事后单点定位的精度可以达到dm级。  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   

Nine thousand years of upper montane soil/vegetation dynamics from the summit of Caratuva Peak,Southern Brazil     

《Journal of South American Earth Sciences》2014

Biodiversity loss, climate change, and increased freshwater consumption are some of the main environmental problems on Earth. Mountain ecosystems can reduce these threats by providing several positive influences, such as the maintenance of biodiversity, water regulation, and carbon storage, amongst others. The knowledge of the history of these environments and their response to climate change is very important for management, conservation, and environmental monitoring programs. The genesis of the soil organic matter of the current upper montane vegetation remains unclear and seems to be quite variable depending on location. Some upper montane sites in the very extensive coastal Sea Mountain Range present considerable organic matter from the late Pleistocene and other from only the Holocene. Our study was carried out on three soil profiles (two cores in grassland and one in forest) on the Caratuva Peak of the Serra do Ibitiraquire (a sub-range of Sea Mountain Range – Serra do Mar) in Southern Brazil. The δ¹³C isotopic analyses of organic matter in soil horizons were conducted to detect whether C₃ or C₄ plants dominated the past communities. Complementarily, we performed a pollen analysis and ¹⁴C dating of the humin fraction to obtain the age of the studied horizons. Except for a short and probably drier period (between 6000 and 4500 cal yr BP), C₃ plants, including ombrophilous grasses and trees, have dominated the highlands of the Caratuva Peak (Pico Caratuva), as well as the other uppermost summits of the Serra do Ibitiraquire, since around 9000 cal yr BP. The Caratuva region represents a landscape of high altitude grasslands (campos de altitude altomontanos or campos altomontanos) and upper montane rain/cloud forests with soils that most likely contain some organic matter from the late Pleistocene, as has been reported in Southern and Southeastern Brazil for other sites. However, our results indicate that the studied deposits (near the summit) are from the early to late Holocene, when somewhat wetter and warmer conditions (since around 9000 cal yr BP) enabled a stronger colonization of the ridge of Pico Caratuva by mainly C₃ plants, especially grassland species. However, at the same time, even near the summit, the soil core from the forest site already presented the current physiognomy (or a shrubby/elfin or successional forest), indicating that the colonization of the neighboring uppermost saddles and valleys were probably populated mainly by upper montane forest species.  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   

钻探参数实时采集系统研制与应用          下载免费PDF全文

汤小仁  孟义泉  訾兵  梁日旺 《探矿工程》2020,47(6):46-53

钻探参数实时采集系统研制的目的是向钻探技术及管理人员提供直观的钻探实时参数，通过该系统可全天候的掌握钻探工况。钻探参数采集的方法是通过传感器采集压力、转速、位移、流量、液位等物理参数，使用配套的软硬件设备进行数据转换和处理，在人机交互界面显示直观的工程参数。该系统根据野外现场不同工作环境可以使用互联网、GPRS终端或者北斗卫星终端把现场实时工程数据无线传输至远程服务器上，用户通过手机或电脑即可查看到现场的实时或历史工程数据。采用本系统可以预防和减少钻探事故，为事故处理提供数据支撑，为实现钻探自动化、智能化提供基础数据。  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