A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model
The spectral matching algorithm based on the discrete particle swarm optimization algorithm (SMDPSO) sometimes overestimates extracted surface water areas. Here we constructed a new method (MEDPSO) by coupling discrete particle swarm optimization algorithm with maximum entropy model (MaxEnt) to extr...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | European Journal of Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2022.2062054 |
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| author | Wangping Li Wanchang Zhang Zhihong Li Yu Wang Hao Chen Huiran Gao Zhaoye Zhou Junming Hao Chuanhua Li Xiaodong Wu |
| author_facet | Wangping Li Wanchang Zhang Zhihong Li Yu Wang Hao Chen Huiran Gao Zhaoye Zhou Junming Hao Chuanhua Li Xiaodong Wu |
| author_sort | Wangping Li |
| collection | DOAJ |
| description | The spectral matching algorithm based on the discrete particle swarm optimization algorithm (SMDPSO) sometimes overestimates extracted surface water areas. Here we constructed a new method (MEDPSO) by coupling discrete particle swarm optimization algorithm with maximum entropy model (MaxEnt) to extract water bodies using Landsat 8 Operational Land Imager (OLI) images. To compare the accuracy of the modified normalized difference water index (MNDWI), SMDPSO, and MEDPSO, we selected six areas , i.e. thermokarst lakes, Coongie Lakes National Park, the Amazon River, urban water bodies mixed with buildings, Erhai Lake that is surrounded by mountains, and high-altitude lakes. Our results show that the average overall accuracy of the MEDPSO for the six areas is 97.4%, which is higher than those of MNDWI and SMDPSO. The average commission errors and omission errors of MEDPSO (6.4% and 0.8%) are lower than those of MNDWI and SMDPSO. The MEDPSO has a higher accuracy because the maximum entropy model is a machine learning method that uses all the bands of Landsat imagery and four surface water indices in the calculation of the probability of surface water. Our study established a novel, high-precision water extraction method. |
| format | Article |
| id | doaj-art-b584703ce0cd43c989fbc4eddc3b8db4 |
| institution | OA Journals |
| issn | 2279-7254 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-b584703ce0cd43c989fbc4eddc3b8db42025-08-20T02:38:11ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542022-12-0155130331210.1080/22797254.2022.2062054A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy modelWangping Li0Wanchang Zhang1Zhihong Li2Yu Wang3Hao Chen4Huiran Gao5Zhaoye Zhou6Junming Hao7Chuanhua Li8Xiaodong Wu9School of Civil Engineering, Lanzhou University of Technology, Lanzhou, Gansu, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, Haidian, ChinaSchool of Civil Engineering, Lanzhou University of Technology, Lanzhou, Gansu, ChinaSchool of Civil Engineering, Lanzhou University of Technology, Lanzhou, Gansu, ChinaInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, Nankai, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, Haidian, ChinaSchool of Civil Engineering, Lanzhou University of Technology, Lanzhou, Gansu, ChinaSchool of Civil Engineering, Lanzhou University of Technology, Lanzhou, Gansu, ChinaCollege of Geography and Environmental Science, Northwest Normal University, Lanzhou, Gansu, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Ganus, ChinaThe spectral matching algorithm based on the discrete particle swarm optimization algorithm (SMDPSO) sometimes overestimates extracted surface water areas. Here we constructed a new method (MEDPSO) by coupling discrete particle swarm optimization algorithm with maximum entropy model (MaxEnt) to extract water bodies using Landsat 8 Operational Land Imager (OLI) images. To compare the accuracy of the modified normalized difference water index (MNDWI), SMDPSO, and MEDPSO, we selected six areas , i.e. thermokarst lakes, Coongie Lakes National Park, the Amazon River, urban water bodies mixed with buildings, Erhai Lake that is surrounded by mountains, and high-altitude lakes. Our results show that the average overall accuracy of the MEDPSO for the six areas is 97.4%, which is higher than those of MNDWI and SMDPSO. The average commission errors and omission errors of MEDPSO (6.4% and 0.8%) are lower than those of MNDWI and SMDPSO. The MEDPSO has a higher accuracy because the maximum entropy model is a machine learning method that uses all the bands of Landsat imagery and four surface water indices in the calculation of the probability of surface water. Our study established a novel, high-precision water extraction method.https://www.tandfonline.com/doi/10.1080/22797254.2022.2062054Maximum entropy modelspectral matchingremote sensingLandsat 8_OLIsurface water extractionnormalized difference water index |
| spellingShingle | Wangping Li Wanchang Zhang Zhihong Li Yu Wang Hao Chen Huiran Gao Zhaoye Zhou Junming Hao Chuanhua Li Xiaodong Wu A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model European Journal of Remote Sensing Maximum entropy model spectral matching remote sensing Landsat 8_OLI surface water extraction normalized difference water index |
| title | A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model |
| title_full | A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model |
| title_fullStr | A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model |
| title_full_unstemmed | A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model |
| title_short | A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model |
| title_sort | new method for surface water extraction using multi temporal landsat 8 images based on maximum entropy model |
| topic | Maximum entropy model spectral matching remote sensing Landsat 8_OLI surface water extraction normalized difference water index |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2022.2062054 |
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