Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery
The extensive peatlands of the Tibetan Plateau (TP) play a vital role in sustaining the global ecological balance. However, the distribution of peatlands across this region and the related environmental factors remain poorly understood. To address this issue, we created a high-resolution (10 m) map...
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MDPI AG
2025-01-01
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| author | Zihao Pan Hengxing Xiang Xinying Shi Ming Wang Kaishan Song Dehua Mao Chunlin Huang |
| author_facet | Zihao Pan Hengxing Xiang Xinying Shi Ming Wang Kaishan Song Dehua Mao Chunlin Huang |
| author_sort | Zihao Pan |
| collection | DOAJ |
| description | The extensive peatlands of the Tibetan Plateau (TP) play a vital role in sustaining the global ecological balance. However, the distribution of peatlands across this region and the related environmental factors remain poorly understood. To address this issue, we created a high-resolution (10 m) map for peatland distribution in the TP region using 6146 Sentinel-1 and 23,730 Sentinel-2 images obtained through the Google Earth Engine platform in 2023. We employed a random forest algorithm that integrated spatiotemporal features with field training samples. The overall accuracy of the peatland distribution map produced is high, at 86.33%. According to the classification results, the total area of peatlands on the TP is 57,671.55 km<sup>2</sup>, and they are predominantly located in the northeast and southwest, particularly in the Zoige Protected Area. The classification primarily relied on the NDVI, NDWI, and RVI, while the DVI and MNDWI were also used in peatland mapping. B11, B12, NDWI, RVI, NDVI, and slope are the most significant features for peatland mapping, while roughness, correlation, entropy, and ASM have relatively slight significance. The methodology and peatland map developed in this work will enhance the conservation and management of peatlands on the TP while informing policy decisions and supporting sustainable development assessments. |
| format | Article |
| id | doaj-art-4dbe78cc27f24d50b15bb8dd4712e9ec |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-4dbe78cc27f24d50b15bb8dd4712e9ec2025-01-24T13:48:01ZengMDPI AGRemote Sensing2072-42922025-01-0117229210.3390/rs17020292Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel ImageryZihao Pan0Hengxing Xiang1Xinying Shi2Ming Wang3Kaishan Song4Dehua Mao5Chunlin Huang6International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaCollege of Resources and Environment, Anhui Agricultural University, Hefei 230036, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaThe extensive peatlands of the Tibetan Plateau (TP) play a vital role in sustaining the global ecological balance. However, the distribution of peatlands across this region and the related environmental factors remain poorly understood. To address this issue, we created a high-resolution (10 m) map for peatland distribution in the TP region using 6146 Sentinel-1 and 23,730 Sentinel-2 images obtained through the Google Earth Engine platform in 2023. We employed a random forest algorithm that integrated spatiotemporal features with field training samples. The overall accuracy of the peatland distribution map produced is high, at 86.33%. According to the classification results, the total area of peatlands on the TP is 57,671.55 km<sup>2</sup>, and they are predominantly located in the northeast and southwest, particularly in the Zoige Protected Area. The classification primarily relied on the NDVI, NDWI, and RVI, while the DVI and MNDWI were also used in peatland mapping. B11, B12, NDWI, RVI, NDVI, and slope are the most significant features for peatland mapping, while roughness, correlation, entropy, and ASM have relatively slight significance. The methodology and peatland map developed in this work will enhance the conservation and management of peatlands on the TP while informing policy decisions and supporting sustainable development assessments.https://www.mdpi.com/2072-4292/17/2/292peatlandsrandom foresttime-series Sentinel-1 and -2 imagesGoogle Earth EngineTibetan Plateau |
| spellingShingle | Zihao Pan Hengxing Xiang Xinying Shi Ming Wang Kaishan Song Dehua Mao Chunlin Huang Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery Remote Sensing peatlands random forest time-series Sentinel-1 and -2 images Google Earth Engine Tibetan Plateau |
| title | Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery |
| title_full | Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery |
| title_fullStr | Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery |
| title_full_unstemmed | Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery |
| title_short | Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery |
| title_sort | detecting and mapping peatlands in the tibetan plateau region using the random forest algorithm and sentinel imagery |
| topic | peatlands random forest time-series Sentinel-1 and -2 images Google Earth Engine Tibetan Plateau |
| url | https://www.mdpi.com/2072-4292/17/2/292 |
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