Extraction of Mountain Grasslands in Yunnan, China, from Sentinel-2 Data during the Optimal Phenological Period Using Feature Optimization
The timely and accurate mapping of the spatial distribution of grasslands is crucial for maintaining grassland habitats and ensuring the sustainable utilization of resources. We used Google Earth Engine (GEE) and Sentinel-2 data for mountain grassland extraction in Yunnan, China. The differences in...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/8/1948 |
| _version_ | 1797440000604766208 |
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| author | Xinmeng Cheng Wendou Liu Junhong Zhou Zizhi Wang Shuqiao Zhang Shengxi Liao |
| author_facet | Xinmeng Cheng Wendou Liu Junhong Zhou Zizhi Wang Shuqiao Zhang Shengxi Liao |
| author_sort | Xinmeng Cheng |
| collection | DOAJ |
| description | The timely and accurate mapping of the spatial distribution of grasslands is crucial for maintaining grassland habitats and ensuring the sustainable utilization of resources. We used Google Earth Engine (GEE) and Sentinel-2 data for mountain grassland extraction in Yunnan, China. The differences in the normalized vegetation index in the time-series data of different ground objects were compared. February to March, during grassland senescence, was the optimum phenological stage for grassland extraction. The spectral, textural of Sentinel-2, and topographic features of the Shuttle Radar Topography Mission (SRTM) were used for the classification. The features were optimized using the recursive feature elimination (RFE) feature importance selection algorithm. The overall accuracy of the random forest (RF) classification algorithm was 91.2%, the producer’s accuracy of grassland was 96.7%, and the user’s accuracy of grassland was 89.4%, exceeding that of the cart classification (Cart), support vector machine (SVM), and minimum distance classification (MDC). The SWIR1 and elevation were the most important features. The results show that Yunnan has abundant grassland resources, accounting for 18.99% of the land area; most grasslands are located in the northwest at altitudes above 3200 m and in the Yuanjiang River regions. This study provides a new approach for feature optimization and grassland extraction in mountainous areas, as well as essential data for the further investigation, evaluation, protection, and utilization of grassland resources. |
| first_indexed | 2024-03-09T12:01:47Z |
| format | Article |
| id | doaj.art-89233be725374385b622ecd15b807107 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T12:01:47Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-89233be725374385b622ecd15b8071072023-11-30T23:03:11ZengMDPI AGAgronomy2073-43952022-08-01128194810.3390/agronomy12081948Extraction of Mountain Grasslands in Yunnan, China, from Sentinel-2 Data during the Optimal Phenological Period Using Feature OptimizationXinmeng Cheng0Wendou Liu1Junhong Zhou2Zizhi Wang3Shuqiao Zhang4Shengxi Liao5Institute of Highland Forest Science, Chinese Academy of Forestry, Kunming 650216, ChinaInstitute of Highland Forest Science, Chinese Academy of Forestry, Kunming 650216, ChinaInstitute of Highland Forest Science, Chinese Academy of Forestry, Kunming 650216, ChinaInstitute of Highland Forest Science, Chinese Academy of Forestry, Kunming 650216, ChinaInstitute of Highland Forest Science, Chinese Academy of Forestry, Kunming 650216, ChinaInstitute of Highland Forest Science, Chinese Academy of Forestry, Kunming 650216, ChinaThe timely and accurate mapping of the spatial distribution of grasslands is crucial for maintaining grassland habitats and ensuring the sustainable utilization of resources. We used Google Earth Engine (GEE) and Sentinel-2 data for mountain grassland extraction in Yunnan, China. The differences in the normalized vegetation index in the time-series data of different ground objects were compared. February to March, during grassland senescence, was the optimum phenological stage for grassland extraction. The spectral, textural of Sentinel-2, and topographic features of the Shuttle Radar Topography Mission (SRTM) were used for the classification. The features were optimized using the recursive feature elimination (RFE) feature importance selection algorithm. The overall accuracy of the random forest (RF) classification algorithm was 91.2%, the producer’s accuracy of grassland was 96.7%, and the user’s accuracy of grassland was 89.4%, exceeding that of the cart classification (Cart), support vector machine (SVM), and minimum distance classification (MDC). The SWIR1 and elevation were the most important features. The results show that Yunnan has abundant grassland resources, accounting for 18.99% of the land area; most grasslands are located in the northwest at altitudes above 3200 m and in the Yuanjiang River regions. This study provides a new approach for feature optimization and grassland extraction in mountainous areas, as well as essential data for the further investigation, evaluation, protection, and utilization of grassland resources.https://www.mdpi.com/2073-4395/12/8/1948classificationfeature spacegrasslandrandom forestSentinel-2 |
| spellingShingle | Xinmeng Cheng Wendou Liu Junhong Zhou Zizhi Wang Shuqiao Zhang Shengxi Liao Extraction of Mountain Grasslands in Yunnan, China, from Sentinel-2 Data during the Optimal Phenological Period Using Feature Optimization Agronomy classification feature space grassland random forest Sentinel-2 |
| title | Extraction of Mountain Grasslands in Yunnan, China, from Sentinel-2 Data during the Optimal Phenological Period Using Feature Optimization |
| title_full | Extraction of Mountain Grasslands in Yunnan, China, from Sentinel-2 Data during the Optimal Phenological Period Using Feature Optimization |
| title_fullStr | Extraction of Mountain Grasslands in Yunnan, China, from Sentinel-2 Data during the Optimal Phenological Period Using Feature Optimization |
| title_full_unstemmed | Extraction of Mountain Grasslands in Yunnan, China, from Sentinel-2 Data during the Optimal Phenological Period Using Feature Optimization |
| title_short | Extraction of Mountain Grasslands in Yunnan, China, from Sentinel-2 Data during the Optimal Phenological Period Using Feature Optimization |
| title_sort | extraction of mountain grasslands in yunnan china from sentinel 2 data during the optimal phenological period using feature optimization |
| topic | classification feature space grassland random forest Sentinel-2 |
| url | https://www.mdpi.com/2073-4395/12/8/1948 |
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