Lidar-aided analysis of boreal forest backscatter at Ku band     

《International Journal of Applied Earth Observation and Geoinformation》2020

This paper analyzes the backscatter of the microwave signal in a boreal forest environment based on a Ku -band airborne Frequency-Modulated Continuous Waveform (FMCW) profiling radar—Tomoradar. We selected a half-managed boreal forest in the southern part of Finland for a field test. By decomposing the waveform collected by the Tomoradar, the vertical canopy structure was achieved. Based on the amplitude of the waveform, the Backscattered Energy Ratio of Canopy-to-Total (BERCT) was calculated. Meanwhile, the canopy fraction was derived from the corresponding point cloud recorded by a Velodyne VLP-16 LiDAR mounted on the same platform. Lidar-derived canopy fraction was obtained by counting the number of the first/ the strongest returns versus the total amount of returns. Qualitative and quantitative analysis of radar-derived BERCT on lidar-derived canopy fraction and canopy height are investigated. A fitted model is derived to describe the Ku-band microwave backscatter in the boreal forest to numerically analyze the proportion contributed by four factors: lidar-derived canopy fraction, radar-derived canopy height, the radar-derived distance between trees and radar sensor and other factors, from co-polarization Tomoradar measurements. The Root Mean Squared Error (RMSE) of the proposed model was 0.0958, and the coefficient of determination R2 was 0.912. The fitted model reveals that the correlation coefficient between radar-derived BERCT and lidar-derived canopy fraction is 0.84, which illustrates that lidar surface reflection explains the majority of the profiling /waveform radar response. Thus, vertical canopy structure derived from lidar can be used for the benefit of radar analysis.  相似文献   

Integration of hyperspectral and LiDAR data for mapping small water bodies     

《International Journal of Applied Earth Observation and Geoinformation》2020

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   

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的农田地形环境三维重建方法     

郎朗  冯晓蓉  刘浪 《测绘通报》2020,(2):55-60

为克服传统农田土地平整测量方法耗时费力的特点,提出采用LiDAR技术对农田地形进行重建的探索性研究。通过HDL-32E型激光雷达等搭建了系统的硬件平台,应用C++语言编写了系统数据的采集程序;在此基础上对激光雷达所采集数据进行了标定,研究了农田地形重建系统中不同坐标系的转换方法;同时基于最小值去噪法设计了更适用于农田地形点云去噪的均值限差去噪法。通过对比在农田起伏较大区域不同坡度范围内RTK与激光雷达所测单元个数,对系统精度进行了评价;最后实现了车载农田地形重建系统的界面显示、应用与精度评估。结果表明,在10°~15°、25°~30°大坡度范围内激光雷达所获农田地形更为丰富,精度更高。该方法重建的农田地形模型点云数据和原始农田地形点云数据投影面积逼近度可达93%,验证了本文研究方法应用于农田地形环境重建的可行性,同时为今后的土地精细平整工作提供了理论参考与依据。  相似文献   

The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity     

《International Journal of Applied Earth Observation and Geoinformation》2020

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   

电力巡线LiDAR点云电塔自动定位和提取算法     

黄陆君  陈光平  袁帅  涂朴  乔杰 《测绘通报》2020,(4):53

针对电力巡线机载激光雷达（LiDAR）激光点云电塔自动提取问题,提出了一种电塔自动定位和点云提取算法。首先,基于点云进行二维空间网格划分,利用网格点云高程偏差和方差特征提取潜在电塔网格;其次,基于电塔点云的高程连续特性完成电塔自动定位和点云粗提取;然后,利用点云分层密度信息和图像开运算,实现电塔精细提取;最后,利用轻小型无人机载激光雷达数据验证本文算法的有效性。试验结果表明,本文所提出的自动提取算法,能够有效解决LiDAR数据中电塔自动定位和点云提取问题,在LiDAR数据质量较差时仍能够取得良好效果,算法对于噪点数据具有较强的稳健性。本文所提出的电塔自动提取算法在LiDAR电力巡检数据处理中具有一定的应用价值。  相似文献   

旋翼无人机载激光雷达在农村不动产权籍调查中的应用     

吉绪发 《测绘通报》2020,(7):152

我国越来越重视农村的发展,目前各地都在积极开展农村不动产权籍调查工作,该工作时间紧迫,对数据精度要求较高。在农村不动产权籍调查中,本文针对传统外业全野外调绘所带来的低效率问题,提出了利用旋翼无人机载激光雷达进行调绘的方法,并且通过实例验证了该方法相较于传统测绘方式能够提高测绘的效率且测绘精度符合最终要求。  相似文献   

徕卡激光雷达技术在铁路全生命周期中的应用     

李海亮 《测绘通报》2020,(10):160-163

为了将激光雷达技术应用到铁路全生命周期中,充分发挥激光雷达技术的优势,本文以徕卡公司相关激光雷达产品为例,系统分析了不同类型的激光雷达的特点和铁路工程不同阶段的测量要求,总结了激光技术在铁路全生命周期中的应用方法。通过典型工程应用分析表明,激光雷达技术可显著提高铁路工程测量的效率和质量,具有良好的应用前景。  相似文献   

基于深度残差网络的机载LiDAR点云分类     

赵传  郭海涛  卢俊  余东行  张保明 《测绘学报》2020,49(2):202-213

机载LiDAR点云的分类是利用其进行城市场景三维重建的关键步骤之一。为充分利用现有的图像领域性能较好的深度学习网络模型,提高点云分类精度,并降低训练时间和对训练样本数量的要求,本文提出一种基于深度残差网络的机载LiDAR点云分类方法。首先提取归一化高程、表面变化率、强度和归一化植被指数4种具有较高区分度的点云低层次特征;然后通过设置不同的邻域大小和视角,利用所提出的点云特征图生成策略,得到多尺度和多视角点云特征图;再将点云特征图输入到预训练的深度残差网络,提取多尺度和多视角深层次特征;最后构建并训练神经网络分类器,利用训练的模型对待分类点云进行预测,经后处理得到分类结果。利用ISPRS三维语义标记竞赛的公开标准数据集进行试验,结果表明,本文方法可有效区分建筑物、地面、车辆等8类地物,分类结果的总体精度为87.1%,可为城市场景三维重建提供可靠的信息。  相似文献   

基于TerrSolid对机载雷达DEM重提取方法研究     

王瑚  周光耀  周海龙 《测绘与空间地理信息》2020,(3):127-130

DEM的提取是激光雷达数据采集的重要用途之一,但由于目前算法的局限性,无法高精度地自动分类,如何在算法分类的基础上重新进行人工分类,以一定的原则和标准进行重提取从而得到更高精度的DEM,仍然是专业领域的一大难题。本文对某.LAS数据重提取,极大提高了其DEM的精度,为今后相关单位和个人使用TerrSolid软件对机载雷达DEM重提取提供一些经验。  相似文献