کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6344999 | 1621215 | 2016 | 13 صفحه PDF | دانلود رایگان |

- Continental-scale circa-2015 lake map using ~Â 3300 Landsat 8 images
- First effort addressing seasonal variability in Landsat 8 continental lake mapping
- The compositing scheme can reduce seasonal variability and cloud contamination.
- Proposed procedures can reduce manual workload in the sequent QA/QC process.
- The proposed strategies and methods are readily applicable to other continents.
Inland lakes, important water resources, play a crucial role in the global water cycle and are sensitive to climate change and human activities. There clearly is a pressing need to understand temporal and spatial variations of lakes at global and continental scales. The recent operation of Landsat 8 extends the unprecedented Landsat record to over 40Â years, allowing long-term, large-scale lake dynamics mapping at high resolutions. Using our circa-2000 lake product derived from Landsat 7 images as a reference, this research produces a circa-2015 map of representative lake extents and distributions, and addresses seasonal and inter-annual lake area variability using Landsat 8 images acquired in lake stable seasons at a continental scale. Oceania is chosen here as a case study as it contains a large group of salt lakes that exhibit high area variability and has the most intensive image coverage during the first 2.5-year operation of Landsat 8. Accordingly, this paper describes an adaptive algorithm to automate lake mapping for various surface conditions using images acquired during lake stable seasons and a compositing scheme in the vector domain to generate a representative continental mosaic of lake extents from multi-temporal mapping. Our results demonstrate that these strategies and methods produce a highly reliable and representative composite of highly-variable lake extents across Oceania, and are potentially applicable to other large-scale lake mapping projects using multi-temporal data.
Journal: Remote Sensing of Environment - Volume 185, November 2016, Pages 129-141