Land Use/Land Cover Mapping Based on GEE for the Monitoring of Changes in Ecosystem Types in the Upper Yellow River Basin over the Tibetan Plateau
The upper Yellow River basin over the Tibetan Plateau (TP) is an important ecological barrier in northwestern China. Effective LULC products that enable the monitoring of changes in regional ecosystem types are of great importance for their environmental protection and macro-control. Here, we combin...
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| Format: | Article |
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MDPI AG
2022-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/21/5361 |
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| author | Senyao Feng Wenlong Li Jing Xu Tiangang Liang Xuanlong Ma Wenying Wang Hongyan Yu |
| author_facet | Senyao Feng Wenlong Li Jing Xu Tiangang Liang Xuanlong Ma Wenying Wang Hongyan Yu |
| author_sort | Senyao Feng |
| collection | DOAJ |
| description | The upper Yellow River basin over the Tibetan Plateau (TP) is an important ecological barrier in northwestern China. Effective LULC products that enable the monitoring of changes in regional ecosystem types are of great importance for their environmental protection and macro-control. Here, we combined an 18-class LULC classification scheme based on ecosystem types with Sentinel-2 imagery, the Google Earth Engine (GEE) platform, and the random forest method to present new LULC products with a spatial resolution of 10 m in 2018 and 2020 for the upper Yellow River Basin over the TP and conducted monitoring of changes in ecosystem types. The results indicated that: (1) In 2018 and 2020, the overall accuracy (OA) of LULC maps ranged between 87.45% and 93.02%. (2) Grassland was the main LULC first-degree class in the research area, followed by wetland and water bodies and barren land. For the LULC second-degree class, the main LULC was grassland, followed by broadleaf shrub and marsh. (3) In the first-degree class of changes in ecosystem types, the largest area of progressive succession (positive) was grassland–shrubland (451.13 km<sup>2</sup>), whereas the largest area of retrogressive succession (negative) was grassland–barren (395.91 km<sup>2</sup>). In the second-degree class, the largest areas of progressive succession (positive) were grassland–broadleaf shrub (344.68 km<sup>2</sup>) and desert land–grassland (302.02 km<sup>2</sup>), whereas the largest areas of retrogressive succession (negative) were broadleaf shrubland–grassland (309.08 km<sup>2</sup>) and grassland–bare rock (193.89 km<sup>2</sup>). The northern and southwestern parts of the study area showed a trend towards positive succession, whereas the south-central Huangnan, northeastern Gannan, and central Aba Prefectures showed signs of retrogressive succession in their changes in ecosystem types. The purpose of this study was to provide basis data for basin-scale ecosystem monitoring and analysis with more detailed categories and reliable accuracy. |
| first_indexed | 2024-03-09T18:41:43Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:41:43Z |
| publishDate | 2022-10-01 |
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| series | Remote Sensing |
| spelling | doaj.art-0fe114a0f4274efeb9fbd8ae2957e8662023-11-24T06:37:48ZengMDPI AGRemote Sensing2072-42922022-10-011421536110.3390/rs14215361Land Use/Land Cover Mapping Based on GEE for the Monitoring of Changes in Ecosystem Types in the Upper Yellow River Basin over the Tibetan PlateauSenyao Feng0Wenlong Li1Jing Xu2Tiangang Liang3Xuanlong Ma4Wenying Wang5Hongyan Yu6State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, ChinaState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, ChinaSchool of Agriculture and Forestry Economic and Management, Lanzhou University of Finance and Economics, Lanzhou 730020, ChinaState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, ChinaCollege of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730020, ChinaDepartment of Life Sciences, Qinghai Normal University, Xining 810008, ChinaService Guarantee Center of Qilian Mountain National Park in Qinghai, Xining 810008, ChinaThe upper Yellow River basin over the Tibetan Plateau (TP) is an important ecological barrier in northwestern China. Effective LULC products that enable the monitoring of changes in regional ecosystem types are of great importance for their environmental protection and macro-control. Here, we combined an 18-class LULC classification scheme based on ecosystem types with Sentinel-2 imagery, the Google Earth Engine (GEE) platform, and the random forest method to present new LULC products with a spatial resolution of 10 m in 2018 and 2020 for the upper Yellow River Basin over the TP and conducted monitoring of changes in ecosystem types. The results indicated that: (1) In 2018 and 2020, the overall accuracy (OA) of LULC maps ranged between 87.45% and 93.02%. (2) Grassland was the main LULC first-degree class in the research area, followed by wetland and water bodies and barren land. For the LULC second-degree class, the main LULC was grassland, followed by broadleaf shrub and marsh. (3) In the first-degree class of changes in ecosystem types, the largest area of progressive succession (positive) was grassland–shrubland (451.13 km<sup>2</sup>), whereas the largest area of retrogressive succession (negative) was grassland–barren (395.91 km<sup>2</sup>). In the second-degree class, the largest areas of progressive succession (positive) were grassland–broadleaf shrub (344.68 km<sup>2</sup>) and desert land–grassland (302.02 km<sup>2</sup>), whereas the largest areas of retrogressive succession (negative) were broadleaf shrubland–grassland (309.08 km<sup>2</sup>) and grassland–bare rock (193.89 km<sup>2</sup>). The northern and southwestern parts of the study area showed a trend towards positive succession, whereas the south-central Huangnan, northeastern Gannan, and central Aba Prefectures showed signs of retrogressive succession in their changes in ecosystem types. The purpose of this study was to provide basis data for basin-scale ecosystem monitoring and analysis with more detailed categories and reliable accuracy.https://www.mdpi.com/2072-4292/14/21/5361Google Earth Engineland use/land cover mappingmachine learningupper Yellow River basinSentinel-2ecosystem types |
| spellingShingle | Senyao Feng Wenlong Li Jing Xu Tiangang Liang Xuanlong Ma Wenying Wang Hongyan Yu Land Use/Land Cover Mapping Based on GEE for the Monitoring of Changes in Ecosystem Types in the Upper Yellow River Basin over the Tibetan Plateau Remote Sensing Google Earth Engine land use/land cover mapping machine learning upper Yellow River basin Sentinel-2 ecosystem types |
| title | Land Use/Land Cover Mapping Based on GEE for the Monitoring of Changes in Ecosystem Types in the Upper Yellow River Basin over the Tibetan Plateau |
| title_full | Land Use/Land Cover Mapping Based on GEE for the Monitoring of Changes in Ecosystem Types in the Upper Yellow River Basin over the Tibetan Plateau |
| title_fullStr | Land Use/Land Cover Mapping Based on GEE for the Monitoring of Changes in Ecosystem Types in the Upper Yellow River Basin over the Tibetan Plateau |
| title_full_unstemmed | Land Use/Land Cover Mapping Based on GEE for the Monitoring of Changes in Ecosystem Types in the Upper Yellow River Basin over the Tibetan Plateau |
| title_short | Land Use/Land Cover Mapping Based on GEE for the Monitoring of Changes in Ecosystem Types in the Upper Yellow River Basin over the Tibetan Plateau |
| title_sort | land use land cover mapping based on gee for the monitoring of changes in ecosystem types in the upper yellow river basin over the tibetan plateau |
| topic | Google Earth Engine land use/land cover mapping machine learning upper Yellow River basin Sentinel-2 ecosystem types |
| url | https://www.mdpi.com/2072-4292/14/21/5361 |
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