Key points are not available for this paper at this time.
The unique climate and topography of the Tibetan Plateau produce an abundant distribution of lakes. These lakes are important indicators of climate change, and changes in lake area have critical implications for water resources and ecological conditions. Lake area change can be monitored using the huge sets of high-resolution remote sensing data available, but this demands an automatic water classification system. This study develops an algorithm for automatic water classification using Chinese GF-1 (or Gaofen-1) wide-field-of-view (WFV) satellite data. The original GF-1 WFV data were automatically preprocessed with radiometric correction and orthorectification. The single-band threshold and two global-local segmentation methods were employed to distinguish water from non-water features. Three methods of determining the optimal thresholds for normalized difference water index (NDWI) images were compared: Iterative Self Organizing Data Analysis Technique (ISODATA); global-local segmentation with thresholds specified by stepwise iteration; and the Otsu method. The water classification from two steps of global-local segmentations showed better performance than the single-band threshold and ISODATA methods. The GF-1 WFV-based lake mapping across the entire Tibetan Plateau in 2015 using the global-local segmentations with thresholds from the Otsu method showed high quality and efficiency in automatic water classification. This method can be extended to other satellite datasets, and makes the high-resolution global monitoring and mapping of lakes possible.
Zhang et al. (Sat,) studied this question.