Automatic Extraction of Open Water Using Imagery of Landsat Series
Open surface freshwater is an important resource for terrestrial ecosystems. However, climate change, seasonal precipitation cycling, and anthropogenic activities add high variability to its availability. Thus, timely and accurate mapping of open surface water is necessary. In this study, a methodol...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/12/7/1928 |
| _version_ | 1797563155146080256 |
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| author | Dandan Xu Dong Zhang Dan Shi Zhaoqing Luan |
| author_facet | Dandan Xu Dong Zhang Dan Shi Zhaoqing Luan |
| author_sort | Dandan Xu |
| collection | DOAJ |
| description | Open surface freshwater is an important resource for terrestrial ecosystems. However, climate change, seasonal precipitation cycling, and anthropogenic activities add high variability to its availability. Thus, timely and accurate mapping of open surface water is necessary. In this study, a methodology based on the concept of spatial autocorrelation was developed for automatic water extraction from Landsat series images using Taihu Lake in south-eastern China as an example. The results show that this method has great potential to extract continuous open surface water automatically, even when the water surface is covered by floating vegetation or algal blooms. The results also indicate that the second shortwave-infrared band (SWIR2) band performs best for water extraction when water is turbid or covered by surficial vegetation. Near-infrared band (NIR), first shortwave-infrared band (SWIR1), and SWIR2 have consistent extraction success when the water surface is not covered by vegetation. Low filter image processing greatly overestimated extracted water bodies, and cloud and image salt and pepper issues have a large impact on water extraction using the methods developed in this study. |
| first_indexed | 2024-03-10T18:38:36Z |
| format | Article |
| id | doaj.art-827f1005bd2f43b3b8f91410eaa725b3 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T18:38:36Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-827f1005bd2f43b3b8f91410eaa725b32023-11-20T06:00:13ZengMDPI AGWater2073-44412020-07-01127192810.3390/w12071928Automatic Extraction of Open Water Using Imagery of Landsat SeriesDandan Xu0Dong Zhang1Dan Shi2Zhaoqing Luan3Department of Ecology, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, ChinaDepartment of Ecology, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Forest Science, Nanjing Forestry University, Nanjing 210037, ChinaDepartment of Ecology, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, ChinaOpen surface freshwater is an important resource for terrestrial ecosystems. However, climate change, seasonal precipitation cycling, and anthropogenic activities add high variability to its availability. Thus, timely and accurate mapping of open surface water is necessary. In this study, a methodology based on the concept of spatial autocorrelation was developed for automatic water extraction from Landsat series images using Taihu Lake in south-eastern China as an example. The results show that this method has great potential to extract continuous open surface water automatically, even when the water surface is covered by floating vegetation or algal blooms. The results also indicate that the second shortwave-infrared band (SWIR2) band performs best for water extraction when water is turbid or covered by surficial vegetation. Near-infrared band (NIR), first shortwave-infrared band (SWIR1), and SWIR2 have consistent extraction success when the water surface is not covered by vegetation. Low filter image processing greatly overestimated extracted water bodies, and cloud and image salt and pepper issues have a large impact on water extraction using the methods developed in this study.https://www.mdpi.com/2073-4441/12/7/1928Landsat serieswater extractionspatial autocorrelationTaihu Lakelow filter |
| spellingShingle | Dandan Xu Dong Zhang Dan Shi Zhaoqing Luan Automatic Extraction of Open Water Using Imagery of Landsat Series Water Landsat series water extraction spatial autocorrelation Taihu Lake low filter |
| title | Automatic Extraction of Open Water Using Imagery of Landsat Series |
| title_full | Automatic Extraction of Open Water Using Imagery of Landsat Series |
| title_fullStr | Automatic Extraction of Open Water Using Imagery of Landsat Series |
| title_full_unstemmed | Automatic Extraction of Open Water Using Imagery of Landsat Series |
| title_short | Automatic Extraction of Open Water Using Imagery of Landsat Series |
| title_sort | automatic extraction of open water using imagery of landsat series |
| topic | Landsat series water extraction spatial autocorrelation Taihu Lake low filter |
| url | https://www.mdpi.com/2073-4441/12/7/1928 |
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