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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Lake Water Footprint Identification From Time-Series ICESat/GLAS Data
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Lake Water Footprint Identification From Time-Series ICESat/GLAS Data

机译：从时间序列ICESat / GLAS数据识别湖泊水足迹

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摘要

To provide high-quality data for time-series change detection of lake water level, an automatic and robust algorithm for lake water footprint (LWF) identification is developed. Based on the Ice, Cloud, and Land Elevation Satellite GLA14 data file, six parameters were taken as features of an algorithm for LWF identification, and they are elevation difference between adjacent footprints, waveform width, number of peaks, reflectivity, kurtosis, and skewness of laser echoes. The sensitivity of each parameter was discussed, and elevation difference between adjacent footprints was proved to be most effective. The algorithm was described as a combination of these six parameters, and the thresholds of each parameter were set through statistics of LWF covering Peiku Co in Tibet, China, from 2003 to 2009. The performance of this classification algorithm was evaluated by the user's accuracy and producer's accuracy. Greater than 94% is achieved for all four tested lakes with 97% being the best result of producer's accuracy, and the user's accuracy ranges from 97.9% to 90% for these four lakes.
机译：为了为湖泊水位的时间序列变化检测提供高质量的数据，开发了一种自动且鲁棒的湖泊水足迹（LWF）识别算法。基于冰，云和陆地高程卫星GLA14数据文件，六个参数被用作LWF识别算法的特征，它们是相邻覆盖区之间的高度差，波形宽度，峰数，反射率，峰度​​和偏度回波。讨论了每个参数的灵敏度，并证明相邻足迹之间的高度差是最有效的。将该算法描述为这六个参数的组合，并通过2003年至2009年覆盖西藏佩库公司的LWF的统计数据来设置每个参数的阈值。通过用户的准确性和生产者的准确性。对于所有四个测试湖泊，获得的结果均高于94％，其中97％是生产者准确度的最佳结果，这四个湖泊的用户准确度范围为97.9％至90％。

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