Identifying Grassland Distribution in a Mountainous Region in Southwest China Using Multi-Source Remote Sensing Images
Southwest China has abundant grassland resources, but they are mainly scattered across fragmented mountainous terrain with frequently cloudy and rainy weather, making their accurate identification by remote sensing challenging. Therefore, the goal of this study was to generate prefecture-level city-...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/6/1472 |
| _version_ | 1797442649006800896 |
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| author | Yixin Yuan Qingke Wen Xiaoli Zhao Shuo Liu Kunpeng Zhu Bo Hu |
| author_facet | Yixin Yuan Qingke Wen Xiaoli Zhao Shuo Liu Kunpeng Zhu Bo Hu |
| author_sort | Yixin Yuan |
| collection | DOAJ |
| description | Southwest China has abundant grassland resources, but they are mainly scattered across fragmented mountainous terrain with frequently cloudy and rainy weather, making their accurate identification by remote sensing challenging. Therefore, the goal of this study was to generate prefecture-level city-scale mountainous grassland distribution data to support the development of sustainable grassland husbandry. Here, we proposed a sample selection method and comprehensively utilized multi-source data to obtain the quasi-10 m southwest grassland distribution data. The sample selection method was to first determine the sample selection range based on multi-source land use/cover database, and then to randomly select the samples under the constraint of secondary land use types, multiple factors of terrain and pure pixels. This method can deal with the difficulty in identifying the fragmented grassland distribution caused by steep mountains and hills. In addition, a multispectral time series dataset was constructed based on the fusion of Landsat 8 OLI and Sentinel-2A/B data due to cloudy and rainy weather and was used as one of the input features along with synthetic aperture radar Sentinel-1 time series data and the terrain multi-factor data. Finally, a remote sensing method to accurately identify grassland distribution in southwest China was constructed based on the Google Earth Engine (GEE) platform. Taking Zhaotong City, a prefecture-level city in Yunnan Province, as an example, a thematic map of grassland distribution with an overall accuracy of 88.21% was obtained using the above method. This map has been used by the local government of Zhaotong City in their planning of the development of sustainable grassland husbandry. |
| first_indexed | 2024-03-09T12:45:00Z |
| format | Article |
| id | doaj.art-88447ef179b44c0182a3c58a9e7256f2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:45:00Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-88447ef179b44c0182a3c58a9e7256f22023-11-30T22:13:32ZengMDPI AGRemote Sensing2072-42922022-03-01146147210.3390/rs14061472Identifying Grassland Distribution in a Mountainous Region in Southwest China Using Multi-Source Remote Sensing ImagesYixin Yuan0Qingke Wen1Xiaoli Zhao2Shuo Liu3Kunpeng Zhu4Bo Hu5National Engineering Research Center for Geomatics (NCG), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaNational Engineering Research Center for Geomatics (NCG), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaNational Engineering Research Center for Geomatics (NCG), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaNational Engineering Research Center for Geomatics (NCG), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaNational Engineering Research Center for Geomatics (NCG), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaNational Engineering Research Center for Geomatics (NCG), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaSouthwest China has abundant grassland resources, but they are mainly scattered across fragmented mountainous terrain with frequently cloudy and rainy weather, making their accurate identification by remote sensing challenging. Therefore, the goal of this study was to generate prefecture-level city-scale mountainous grassland distribution data to support the development of sustainable grassland husbandry. Here, we proposed a sample selection method and comprehensively utilized multi-source data to obtain the quasi-10 m southwest grassland distribution data. The sample selection method was to first determine the sample selection range based on multi-source land use/cover database, and then to randomly select the samples under the constraint of secondary land use types, multiple factors of terrain and pure pixels. This method can deal with the difficulty in identifying the fragmented grassland distribution caused by steep mountains and hills. In addition, a multispectral time series dataset was constructed based on the fusion of Landsat 8 OLI and Sentinel-2A/B data due to cloudy and rainy weather and was used as one of the input features along with synthetic aperture radar Sentinel-1 time series data and the terrain multi-factor data. Finally, a remote sensing method to accurately identify grassland distribution in southwest China was constructed based on the Google Earth Engine (GEE) platform. Taking Zhaotong City, a prefecture-level city in Yunnan Province, as an example, a thematic map of grassland distribution with an overall accuracy of 88.21% was obtained using the above method. This map has been used by the local government of Zhaotong City in their planning of the development of sustainable grassland husbandry.https://www.mdpi.com/2072-4292/14/6/1472southwest grasslandGoogle Earth Enginetime seriesmulti-source remote sensing identificationrandom forest |
| spellingShingle | Yixin Yuan Qingke Wen Xiaoli Zhao Shuo Liu Kunpeng Zhu Bo Hu Identifying Grassland Distribution in a Mountainous Region in Southwest China Using Multi-Source Remote Sensing Images Remote Sensing southwest grassland Google Earth Engine time series multi-source remote sensing identification random forest |
| title | Identifying Grassland Distribution in a Mountainous Region in Southwest China Using Multi-Source Remote Sensing Images |
| title_full | Identifying Grassland Distribution in a Mountainous Region in Southwest China Using Multi-Source Remote Sensing Images |
| title_fullStr | Identifying Grassland Distribution in a Mountainous Region in Southwest China Using Multi-Source Remote Sensing Images |
| title_full_unstemmed | Identifying Grassland Distribution in a Mountainous Region in Southwest China Using Multi-Source Remote Sensing Images |
| title_short | Identifying Grassland Distribution in a Mountainous Region in Southwest China Using Multi-Source Remote Sensing Images |
| title_sort | identifying grassland distribution in a mountainous region in southwest china using multi source remote sensing images |
| topic | southwest grassland Google Earth Engine time series multi-source remote sensing identification random forest |
| url | https://www.mdpi.com/2072-4292/14/6/1472 |
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