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    GEE,ISPRS,2020

    The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) is the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). The Journal provides a channel of communication for scientists and professionals in all countries working in the many disciplines that employ photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The Journal is designed to serve as a source reference and archive of advancements in these disciplines. The P&RS objective is to publish high quality, peer-reviewed, preferably previously unpublished papers of a scientific/research, technological development or application/practical nature. P&RS will publish papers, including those based on ISPRS meeting presentations*, which are regarded as significant contributions in the above-mentioned fields. We especially encourage papers: of broad scientific interest; on innovative applications, particularly in new fields; of an interdisciplinary nature; on topics that have not been dealt with (or to a small degree) by P&RS or related journals; and on topics related to new possible scientific/professional directions. Preferably, theoretical papers should include applications, and papers dealing with systems and applications should include theoretical background. The scope of the journal is extensive and covers sensors, theory and algorithms, systems, experiments, developments and applications. Topics of interest include but are not limited to: Sensors: • Airborne and spaceborne multispectral and hyperspectral imaging systems • Airborne and terrestrial cameras • Airborne, terrestrial and mobile laser scanning • Range imaging • Active and passive imaging sensor characterisation • Sensor calibration and standardisation • Geosensor networks • Internet of Things Methods and procedures: • Spatial data handling technologies • Integrated sensor calibration and orientation • Surface and object reconstruction, modelling and interpretation • GIS data modelling, representation and structur

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    快速测绘和量化地球表面边缘变化的GEE工具-数字化工具(GEEDiT)和边缘变化量化工具(MaQiT)

    利用遥感卫星影像来研究边缘变化是环境过程和地球表面驱动因素的定量化指标,例如冰川边缘消退或海平面上升导致的沿海变化。这里介绍了三种新的、可免费使用的工具,它们可以一起用于处理和可视化,Landsat 4-8和Sentinel 1-2卫星存档数据,能够在很短的时间内实现高效的绘图(通过手动数字化)和自动量化边缘变化。这些工具对各种遥感专家的用户都是高度可访问的,在访问方面几乎没有计算、许可和知识方面的障碍。谷歌地球引擎数字化工具(GEEDiT)允许用户定义地球上任何地方的一个点,并通过一个简单的图形用户界面(GUI)对每个卫星的数据进行过滤,以获得用户定义的时间框架、最大可接受的云量,以及预定义或自定义图像波段组合的选项。GEEDiT允许从每个图像快速地绘制地理参考向量,图像元数据和用户注释自动追加到每个向量,然后可以导出用于后续分析。GEEDiT Reviewer工具允许用户对自己/他人的数据进行质量控制,并根据其特定研究问题的空间/时间要求过滤现有的数据集。边缘变化量化工具(MaQiT)是GEEDiT和GEEDiT Reviewer的补充,允许通过使用两种已建立的方法(以前用于测量冰川边缘变化)和两种新的方法,通过类似的简单GUI快速量化这些边缘变化。MaQiT的开发初衷是量化潮汐冰川末端的变化,尽管工具中包含的方法有可能广泛应用于地球表面科学的多个领域(例如,沿海和植被范围的变化)。这些工具将使地球科学领域的广泛研究人员和学生能够有效地绘制、分析和访问大量数据。

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    Google Earth Engine APP(GEE) ——秘鲁和厄瓜多尔流域的高分辨率网格化降水数据集(1981-2015)

    秘鲁和厄瓜多尔流域的高分辨率网格化降水数据集(1981-2015) RAIN4PE是一个新型的日网格降水数据集,它通过随机森林回归法将多源降水数据(基于卫星的气候灾害组红外降水,CHIRP(Funk等人,2015),再分析ERA5(Hersbach等人,2020),以及地面降水)与地形高程合并而获得。此外,RAIN4PE通过逆向水文,在降水低估的集水区使用溪流数据进行水文校正。因此,RAIN4PE是秘鲁和厄瓜多尔唯一的网格化降水产品,它得益于最大限度的现有原地观测、多种降水来源、高程数据,并辅以溪流数据来校正帕拉莫斯和山地流域的降水低估。前言 – 床长人工智能教程

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