Mapping lakes on the Tibetan Plateau with landsat imagery and object-based image analyis
The Tibetan Plateau, the world’s largest orogenic plateau, hosts thousands of lakes that play a prominent role as water resources, environmental archives, and sources of natural hazards such as glacier lake outburst floods. Previous studies reported that the size of the lakes on the Tibetan Plateau...
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| Tác giả chính: | |
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| Định dạng: | BB |
| Ngôn ngữ: | eng |
| Thông tin xuất bản: |
2020
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| Chủ đề: | |
| Truy cập trực tuyến: | http://tailieuso.tlu.edu.vn/handle/DHTL/4810 |
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| Tóm tắt: | The Tibetan Plateau, the world’s largest orogenic plateau, hosts thousands of lakes that play a prominent role as water resources, environmental archives, and sources of natural hazards such as glacier lake outburst floods. Previous studies reported that the size of the lakes on the Tibetan Plateau has been changing rapidly in recent years, possibly because of contemporary global warming. Tracking these changes systematically from remote sensing data offers new challenges and opportunities for automated classification methods such as object-based image analysis (OBIA). We present a method for an automatic mapping of lakes combining LANDSAT images, Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and OBIA. We tested our method for most of the area of the Tibetan Plateau where lakes formed in tectonic depressions or blocked by glaciers and sediments have different spectral colours, and different physical states of water (frozen and not-frozen). We analysed images obtained in 1995 and 2015. For detecting the lakes we used the Modified Normalized Difference Water Index (MNDWI) combined with OBIA and a slope map derived from the DEM. Our classification of lakes with an area >10 km² derived 323 water bodies with a total area of 31,258 km², which is 2.6% of the analysed area in 2015. The same number of lakes had covered only 24,892 km² in 1995, so that lakes grew by ~26% in the past two decades on average. The classification had an overall, producer’s and user’s accuracies of 0.98, and a Cohen’s kappa of 0.98, and may be useful step towards quantifying regional-scale hydrological budgets |
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