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首页> 外文学位 >An Algorithm for Automated Satellite-Based River Ice Identification Using a Local Cloud Mask: Application over the Lower Susquehanna River Using VIIRS and MODIS
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An Algorithm for Automated Satellite-Based River Ice Identification Using a Local Cloud Mask: Application over the Lower Susquehanna River Using VIIRS and MODIS

机译：一种使用局部云掩码的基于卫星的河冰自动识别算法：使用VIIRS和MODIS在萨斯奎哈纳河下游应用

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摘要

Spatially detailed characterization of the distribution, amounts and timing of river ice is important for identifying and predicting potential ice hazards. Although information on ice cover over inland water bodies is provided within the VIIRS and MODIS snow products, this information has little practical value for river ice monitoring. First, rivers may not be properly resolved in the respective land/water masks. Second, cloud masks incorporated in the products can be too conservative or contain local errors which may potentially result in reduced effective area and temporal coverage.;This thesis presents an algorithm that identifies river ice as primary product. Notably, it also consists of a two-tiered cloud identification scheme that does not require information from cloud products for accurate cloud characterization. Generally, the developed cloud screening is more liberal than that of other masks, and a larger portion of reasonably cloud-free grid cells can be retrieved at a given time when compared to the MODIS or VIIRS cloud product. This approach avoids cloud masking differences due to snow/cloud confusions and makes it possible to produce ice maps that are highly consistent between the observing platforms. The algorithm is tested with MODIS-Terra (TR), MODIS-Aqua (AQ) and VIIRS (VR) between 2001 and 2016 over part of the Lower Susquehanna River. The algorithm features several unique characteristics and it (1) avoids potential cloud-product related errors by incorporating novel and robust spatiotemporal contrasting and k-means-like data clustering and as result generally samples from high-quality grid cells; (2) uses improved land/water as input, generated from on multi-spectral and multi-temporal data using maximum likelihood classifications to delineate the river channel; (3) produces binary ice maps, one for each confidence level defined by concurrent requirements that must be met by the visible and short wave infrared bands; (4) incorporates viewing geometry information incorporated within the MODIS daily reflectance products for better data clustering, further improving classification accuracy; (5) produces several auxiliary datasets, in particular at-grid ice cover durations, reflectance differences and time-series data that may be used in models.;The algorithm first estimates cloudiness via "test 1" (T1) within the region, and if it fails no data is obtained for that day. If T1 conditions are met, river grid cells are classified as water, cloud and ice cover. Owing to this approach the algorithm observes 23% (MODIS-Terra), 45% (MODIS-Aqua) and 10% (VIIRS) more of the river on days T1 is passed. Although the algorithm frequently rejects data outright, an overall improvement in the amount of available data for MODIS-Aqua (39%) is realized in comparison to the incorporated cloud mask, which substantially reduces its effective revisit time. However, when applied to MODIS-Terra and VIIRS the algorithm has fewer overall data by 6% and 26%, respectively. The effective revisit times in days are 4.0 & 4.2 (MODIS-Terra), 5.2 & 3.7 (MODIS-Aqua) and 2.9 & 3.6 (VIIRS), for the cloud mask and algorithm, respectively. Daily effective revisit times may be further improved by compositing ice maps between the platforms, in particular between the AM and PM observations. While the sample involving VIIRS is limited to 2015 and 2016, using multiple platforms (i.e. TR vs TR/AQ, AQ vs AQ/VR and TR vs TR/VR) significantly increases the number of unique days the algorithm may observe by 37%, 23% and 42%, respectively.;Observations are highly consistent between the independently observing platforms. Comparisons of the normalized river ice cover fraction (RIF) and river ice amount (RIA) between platforms show correlations of 95% or more and mean absolute differences (MAD) near 5%, with the best agreement between MODIS-Aqua and VIIRS. Ice cover outputs were evaluated against the discharge data quality flag (DQF) at the USGS gauge, taken to suggest ice cover. While this may lead to occasional errors, river observations by traffic cameras for 2016 support that the DQF are quite accurate. Visual comparisons with Landsat 8 and the CRIOS river ice product also show good correspondence. RIF time series for the algorithm and its equivalent for CRIOS are nearly identical. The POD for ice and PC range from 87--91% and 91--94%. Despite the liberal cloud screening and inclusion of lower likelihood ice layers, errors are few. False detections range from 4--7% while non-detections range between 0--2%. The higher false detection rate is due to the chosen approach being more liberal with cloud screening, resulting in occasional misclassification of cloud as ice. Produced river ice maps are also consistent and in good agreement with traffic camera imagery.;Reflectance difference maps (DeltaR) show some promise in helping distinguish between solid ice cover and mobile ice, and also generally indicate regions of ice accumulations. (Abstract shortened by ProQuest.).
机译：对河冰的分布，数量和时间进行空间详细的表征对于识别和预测潜在的冰害非常重要。尽管VIIRS和MODIS雪产品中提供了内陆水体冰盖的信息，但该信息对河冰监测没有实际价值。首先，河流可能无法在相应的土地/水域中得到适当解决。其次，产品中集成的云罩可能过于保守或包含局部误差，这有可能导致有效面积和时间覆盖范围减小。;本文提出了一种将河冰识别为主要产品的算法。值得注意的是，它还包含两层云识别方案，该方案不需要云产品的信息即可进行准确的云表征。通常，与MODIS或VIIRS云产品相比，已开发的云筛选比其他蒙版更为宽松，并且可以在给定的时间检索到较大比例的无云网格单元。这种方法避免了由于雪/云混淆而造成的云遮罩差异，并使生成观测平台之间高度一致的冰图成为可能。在2001年至2016年之间，对下萨斯奎哈纳河的部分地区的MODIS-Terra（TR），MODIS-Aqua（AQ）和VIIRS（VR）对该算法进行了测试。该算法具有几个独特的特征，并且（1）通过合并新颖且健壮的时空对比和类似k-means的数据聚类避免了潜在的云产品相关错误，因此通常从高质量网格单元中采样； （2）使用改进的土地/水作为输入，该输入是使用最大似然分类从多光谱和多时相数据生成的，以划定河道； （3）生成二元冰图，每个图由并发要求定义的每个置信度，可见和短波红外波段必须满足； （4）结合MODIS日反射产品中包含的查看几何信息，以更好地进行数据聚类，从而进一步提高分类精度； （5）产生几个辅助数据集，特别是可用于模型的网格冰盖持续时间，反射率差异和时间序列数据。；该算法首先通过“ test 1”（T1）估算区域内的云量，以及如果失败，则无法获取当天的数据。如果满足T1条件，则河流网格单元将分为水，云和冰盖。由于采用了这种方法，该算法在T1天通过时观察到的河流增加了23％（MODIS-Terra），45％（MODIS-Aqua）和10％（VIIRS）。尽管该算法经常彻底拒绝数据，但与合并的云掩码相比，MODIS-Aqua的可用数据量得到了总体改善（39％），从而大大减少了其有效的重访时间。但是，当应用于MODIS-Terra和VIIRS时，该算法的总数据分别减少了6％和26％。对于云掩码和算法，以天为单位的有效重访时间分别为4.0和4.2（MODIS-Terra），5.2和3.7（MODIS-Aqua）和2.9和3.6（VIIRS）。通过在平台之间，尤其是在AM和PM观测之间合成冰图，可以进一步改善每日有效重访时间。虽然涉及VIIRS的样本仅限于2015年和2016年，但使用多个平台（即TR vs TR / AQ，AQ vs AQ / VR和TR vs TR / VR）显着增加了算法可观察到的唯一天数，独立观测平台之间的观测高度一致，分别为23％和42％。平台之间的归一化河冰覆盖率（RIF）和河冰量（RIA）的比较显示，相关性达到95％或更高，平均绝对差（MAD）接近5％，MODIS-Aqua与VIIRS的最佳一致性。根据USGS仪表上的排放数据质量标志（DQF）对冰盖的输出进行了评估，以此来建议冰盖。尽管这可能会导致偶尔的错误，但2016年交通摄像机对河流的观测表明DQF非常准确。与Landsat 8和CRIOS河冰产品的视觉比较也显示出良好的对应关系。该算法的RIF时间序列和CRIOS的等效时间序列几乎相同。冰块和PC的POD范围为87--91％和91--94％。尽管进行了自由的云筛查并包括了可能性较低的冰层，但误差很少。错误检测的范围是4--7％，而未检测的范围是0--2％。较高的错误检测率是由于选择的方法在进行云筛查时更加自由，导致偶尔将云分类为冰。生成的河流冰图也与交通摄像机图像保持一致，并且非常吻合。反射率差异图（DeltaR）在帮助区分固态冰盖和流动冰上显示出一些希望，通常也表示冰块堆积的区域。 （摘要由ProQuest缩短。）。

著录项

  • 作者

    Kraatz, Simon G.;

  • 作者单位

    The City College of New York.;

  • 授予单位 The City College of New York.;
  • 学科 Civil engineering.;Hydrologic sciences.;Remote sensing.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 147 p.
  • 总页数 147
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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