ICESat-2(Ice,Cloud and Land Elevation Satellite-2)是世界首颗采用光子计数模式的激光测高卫星,可快速获得高精度、大尺度地面三维数据。光子探测机制使得数据中除了地面信号外,还包含大气散射等背景信号,需要通过滤波才能获得地形...ICESat-2(Ice,Cloud and Land Elevation Satellite-2)是世界首颗采用光子计数模式的激光测高卫星,可快速获得高精度、大尺度地面三维数据。光子探测机制使得数据中除了地面信号外,还包含大气散射等背景信号,需要通过滤波才能获得地形等信息。为分析ICESat-2背景和信号光子的分布特点及点云滤波算法的效果和适用性,本文首先选取了六种地表覆盖类型(城市、海冰、沙漠、植被、海洋及冰盖/冰川)及不同观测条件的数据,对其背景光子率进行统计分析。分析结果表明:白天观测数据的背景光子率平均为106(点/秒)数量级,远高于夜晚观测数据的背景光子率——104(点/秒)数量级,弱波束的背景光子率与强波束背景光子率相当,六种地表覆盖类型中,冰盖/冰川的背景光子率最高。然后,根据统计结果筛选出21组测高数据,并选取七种具有代表性的点云滤波对其进行去噪实验,分析精度后得出结论:改进局部密度法的去噪效果最佳,算法召回率、精准度和F值均大于0.90,算法较为稳定。最后,对所选取各滤波算法的精度、特点与适用性等性质进行了总结与分析,可为后续该数据的使用和滤波算法的选择提供参考。展开更多
美国NASA于2018年发射的ICESat-2 (The Ice, Cloud, and land Elevation Satellite-2)卫星上搭载的ATLAS (Advanced Topographic Laser Altimeter System)是目前为止全球唯一一个对地观测的星载光子计数激光雷达,具有较高的轨向空间采样...美国NASA于2018年发射的ICESat-2 (The Ice, Cloud, and land Elevation Satellite-2)卫星上搭载的ATLAS (Advanced Topographic Laser Altimeter System)是目前为止全球唯一一个对地观测的星载光子计数激光雷达,具有较高的轨向空间采样率,为用遥感的方法探测海浪要素提供了可能。光子计数激光雷达用于海浪探测的前提是能够准确地提取来自海面的信号光子,并确定瞬时的海面廓线。迄今为止,用星载光子计数激光雷达探测海面形态和海浪要素的研究鲜见报道,也缺少专门针对海面信号光子的提取方法。基于海面信号光子的分布特点,文中提出了一种新的信号提取算法:首先通过直方图统计及自适应的阈值选取完成对海面回波光子的粗去噪;然后基于激光雷达光斑尺寸和海面波动特点,选取合适的搜索邻域计算信号点和噪声点密度,根据两者点密度差异对信号光子和噪声光子分类;最后用高斯函数拟合的方法进一步去除密度较大的后向散射噪声光子,最终得到来自海面反射的信号光子。利用上述算法提取了太平洋7个不同海况区域的海面信号光子和瞬时海面廓线并进一步计算出当地海浪的峰值波长和周期。将计算结果与同期欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts, ECMWF)的全球大气再分析ERA5(ECMWF Re-Analysis5)数据作对比,在不同风速、水深的海域都获得了基本一致的结果,超过半数区域的海浪周期误差在5%以内,初步证明了星载光子计数激光雷达观测成果用于海浪要素计算的可行性。展开更多
针对高空间分辨率遥感影像进行城市不透水面提取时存在的同物异谱、异物同谱及阴影等局限性,提出一种基于WorldView-2高分影像与机载激光雷达数据融合的分层分类估算城市不透水面的方法。该方法首先运用基于雾霾与比值(haze-and-ratio-b...针对高空间分辨率遥感影像进行城市不透水面提取时存在的同物异谱、异物同谱及阴影等局限性,提出一种基于WorldView-2高分影像与机载激光雷达数据融合的分层分类估算城市不透水面的方法。该方法首先运用基于雾霾与比值(haze-and-ratio-based,HR)的融合算法对WorldView-2多光谱波段与全色波段进行数据融合;然后依据LiDAR归一化数字表面模型(normalization digital surface model,nDSM)高度阈值分为地面物体与非地面物体,运用像元尺度上分层支持向量机分类算法进行城市不透水面百分比估算;最后结合特征阈值和GIS空间分析法探测阴影区域不透水面。研究结果表明,与传统的高分影像提取城市不透水面方法相比,该方法可以明显改善材质复杂的建筑物屋顶提取不完整,以及高亮裸土与高反照度屋顶相互混淆的现象,并通过阴影校正可以较好地区分阴影区域的植被与不透水面信息,进而提高城市不透水面估算精度。展开更多
This work uses the canopy height model (CHM) based workflow for individual tree crown delineation from LiDAR point cloud data in an urban environment and evaluates its accuracy by using very high-resolution PAN (spati...This work uses the canopy height model (CHM) based workflow for individual tree crown delineation from LiDAR point cloud data in an urban environment and evaluates its accuracy by using very high-resolution PAN (spatial) and 8-band WorldView-2 imagery. LiDAR point cloud data were used to detect tree features by classifying point elevation values. The workflow includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model, generation of hill-shade image and intensity image, extraction of digital surface model, generation of bare earth digital elevation model and extraction of tree features. Scene dependent extraction criteria were employed to improve the tree feature extraction. LiDAR-based refining/filtering techniques used for bare earth layer extraction were crucial for improving the subsequent tree feature extraction. The PAN-sharpened WV-2 image (with 0.5 m spatial resolution) used to assess the accuracy of LiDAR-based tree features provided an accuracy of 98%. Based on these inferences, we conclude that the LiDAR-based tree feature extraction is a potential application which can be used for understanding vegetation characterization in urban setup.展开更多
为了大范围精确估计空间连续的森林冠层高度,研究使用随机森林回归方法,通过融合冰、云和陆地高程卫星二号(Ice,Cloud and land Elevation Satellite-2,ICESat-2)测量数据和Landsat-8影像,并结合地形、气温等数据来估算森林冠层高度,生...为了大范围精确估计空间连续的森林冠层高度,研究使用随机森林回归方法,通过融合冰、云和陆地高程卫星二号(Ice,Cloud and land Elevation Satellite-2,ICESat-2)测量数据和Landsat-8影像,并结合地形、气温等数据来估算森林冠层高度,生成2020年美国密西西比州30 m空间分辨率的森林冠层最大高度和平均高度图。结果表明,森林覆盖区域冠层最大高度的均值为24.14 m,标准差为4.24 m。森林覆盖区域冠层平均高度的均值为12.04 m,标准差为2.59 m。研究区冠层高度估计值与机载测量值吻合良好(冠层最大高度R2=0.486,HRMSE=4.532 m;冠层平均高度R2=0.467,HRMSE=2.848 m)。利用估算数据进一步对森林冠层垂直结构复杂度进行了分析。研究提出的森林冠层高度制图方案对中国长江三角洲地区的森林管理、物种多样性保护与碳中和评估等具有指导意义。展开更多
The primary goal of this report is to describe the operational concepts of NASA’s ACTIVATE mission. ACTIVATE hopes to improve the understanding of aerosol dispersion and models, provide accurate data for aerosols’ c...The primary goal of this report is to describe the operational concepts of NASA’s ACTIVATE mission. ACTIVATE hopes to improve the understanding of aerosol dispersion and models, provide accurate data for aerosols’ characterization and ozone profiles, and establish knowledge of the relationships between aerosols and water. ACTIVATE’s science objectives are to quantify Na-CCN-Nd relationships and reduce uncertainty in model cloud droplet activation parameterizations, improve process-level understanding and model representation of factors governing cloud micro/macro-physical properties and how they couple with cloud effects on aerosol, plus assess advanced remote sensing capabilities for retrieving aerosol and cloud properties related to aerosol-cloud interactions. ACTIVATE utilizes the fixed-wing B-200 King Air to collect data. Data collected by ACTIVATE is highly relevant for meteorologists and environmental scientists looking to understand more about aerosol-cloud formations. Finally, ACTIVATE is a 5-year mission spanning from January 2019 to December 2023 and has used, and will continue to use, instruments such as the High Spectral Resolution Lidar-2 (HSRL-2), the Research Scanning Polarimeter (RSP), and the Diode Laser Hygrometer (DLH).展开更多
文摘ICESat-2(Ice,Cloud and Land Elevation Satellite-2)是世界首颗采用光子计数模式的激光测高卫星,可快速获得高精度、大尺度地面三维数据。光子探测机制使得数据中除了地面信号外,还包含大气散射等背景信号,需要通过滤波才能获得地形等信息。为分析ICESat-2背景和信号光子的分布特点及点云滤波算法的效果和适用性,本文首先选取了六种地表覆盖类型(城市、海冰、沙漠、植被、海洋及冰盖/冰川)及不同观测条件的数据,对其背景光子率进行统计分析。分析结果表明:白天观测数据的背景光子率平均为106(点/秒)数量级,远高于夜晚观测数据的背景光子率——104(点/秒)数量级,弱波束的背景光子率与强波束背景光子率相当,六种地表覆盖类型中,冰盖/冰川的背景光子率最高。然后,根据统计结果筛选出21组测高数据,并选取七种具有代表性的点云滤波对其进行去噪实验,分析精度后得出结论:改进局部密度法的去噪效果最佳,算法召回率、精准度和F值均大于0.90,算法较为稳定。最后,对所选取各滤波算法的精度、特点与适用性等性质进行了总结与分析,可为后续该数据的使用和滤波算法的选择提供参考。
文摘美国NASA于2018年发射的ICESat-2 (The Ice, Cloud, and land Elevation Satellite-2)卫星上搭载的ATLAS (Advanced Topographic Laser Altimeter System)是目前为止全球唯一一个对地观测的星载光子计数激光雷达,具有较高的轨向空间采样率,为用遥感的方法探测海浪要素提供了可能。光子计数激光雷达用于海浪探测的前提是能够准确地提取来自海面的信号光子,并确定瞬时的海面廓线。迄今为止,用星载光子计数激光雷达探测海面形态和海浪要素的研究鲜见报道,也缺少专门针对海面信号光子的提取方法。基于海面信号光子的分布特点,文中提出了一种新的信号提取算法:首先通过直方图统计及自适应的阈值选取完成对海面回波光子的粗去噪;然后基于激光雷达光斑尺寸和海面波动特点,选取合适的搜索邻域计算信号点和噪声点密度,根据两者点密度差异对信号光子和噪声光子分类;最后用高斯函数拟合的方法进一步去除密度较大的后向散射噪声光子,最终得到来自海面反射的信号光子。利用上述算法提取了太平洋7个不同海况区域的海面信号光子和瞬时海面廓线并进一步计算出当地海浪的峰值波长和周期。将计算结果与同期欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts, ECMWF)的全球大气再分析ERA5(ECMWF Re-Analysis5)数据作对比,在不同风速、水深的海域都获得了基本一致的结果,超过半数区域的海浪周期误差在5%以内,初步证明了星载光子计数激光雷达观测成果用于海浪要素计算的可行性。
文摘针对高空间分辨率遥感影像进行城市不透水面提取时存在的同物异谱、异物同谱及阴影等局限性,提出一种基于WorldView-2高分影像与机载激光雷达数据融合的分层分类估算城市不透水面的方法。该方法首先运用基于雾霾与比值(haze-and-ratio-based,HR)的融合算法对WorldView-2多光谱波段与全色波段进行数据融合;然后依据LiDAR归一化数字表面模型(normalization digital surface model,nDSM)高度阈值分为地面物体与非地面物体,运用像元尺度上分层支持向量机分类算法进行城市不透水面百分比估算;最后结合特征阈值和GIS空间分析法探测阴影区域不透水面。研究结果表明,与传统的高分影像提取城市不透水面方法相比,该方法可以明显改善材质复杂的建筑物屋顶提取不完整,以及高亮裸土与高反照度屋顶相互混淆的现象,并通过阴影校正可以较好地区分阴影区域的植被与不透水面信息,进而提高城市不透水面估算精度。
文摘This work uses the canopy height model (CHM) based workflow for individual tree crown delineation from LiDAR point cloud data in an urban environment and evaluates its accuracy by using very high-resolution PAN (spatial) and 8-band WorldView-2 imagery. LiDAR point cloud data were used to detect tree features by classifying point elevation values. The workflow includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model, generation of hill-shade image and intensity image, extraction of digital surface model, generation of bare earth digital elevation model and extraction of tree features. Scene dependent extraction criteria were employed to improve the tree feature extraction. LiDAR-based refining/filtering techniques used for bare earth layer extraction were crucial for improving the subsequent tree feature extraction. The PAN-sharpened WV-2 image (with 0.5 m spatial resolution) used to assess the accuracy of LiDAR-based tree features provided an accuracy of 98%. Based on these inferences, we conclude that the LiDAR-based tree feature extraction is a potential application which can be used for understanding vegetation characterization in urban setup.
文摘The primary goal of this report is to describe the operational concepts of NASA’s ACTIVATE mission. ACTIVATE hopes to improve the understanding of aerosol dispersion and models, provide accurate data for aerosols’ characterization and ozone profiles, and establish knowledge of the relationships between aerosols and water. ACTIVATE’s science objectives are to quantify Na-CCN-Nd relationships and reduce uncertainty in model cloud droplet activation parameterizations, improve process-level understanding and model representation of factors governing cloud micro/macro-physical properties and how they couple with cloud effects on aerosol, plus assess advanced remote sensing capabilities for retrieving aerosol and cloud properties related to aerosol-cloud interactions. ACTIVATE utilizes the fixed-wing B-200 King Air to collect data. Data collected by ACTIVATE is highly relevant for meteorologists and environmental scientists looking to understand more about aerosol-cloud formations. Finally, ACTIVATE is a 5-year mission spanning from January 2019 to December 2023 and has used, and will continue to use, instruments such as the High Spectral Resolution Lidar-2 (HSRL-2), the Research Scanning Polarimeter (RSP), and the Diode Laser Hygrometer (DLH).
