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Lake Water Footprint Identification From Time-Series ICESat/GLAS Data

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7 Author(s)
Xianwei Wang ; State Key Lab. of Remote Sensing Sci., Beijing Normal Univ., Beijing, China ; Xiao Cheng ; Zhan Li ; Huabing Huang
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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.

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Geoscience and Remote Sensing Letters, IEEE  (Volume:9 ,  Issue: 3 )