Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau
An accurate and detailed vegetation map is of crucial significance for understanding the spatial heterogeneity of subsurfaces, which can help to characterize the thermal state of permafrost. The absence of an alpine swamp meadow (ASM) type, or an insufficient resolution (usually km-level) to capture...
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MDPI AG
2022-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/1/232 |
| _version_ | 1827667733295136768 |
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| author | Defu Zou Lin Zhao Guangyue Liu Erji Du Guojie Hu Zhibin Li Tonghua Wu Xiaodong Wu Jie Chen |
| author_facet | Defu Zou Lin Zhao Guangyue Liu Erji Du Guojie Hu Zhibin Li Tonghua Wu Xiaodong Wu Jie Chen |
| author_sort | Defu Zou |
| collection | DOAJ |
| description | An accurate and detailed vegetation map is of crucial significance for understanding the spatial heterogeneity of subsurfaces, which can help to characterize the thermal state of permafrost. The absence of an alpine swamp meadow (ASM) type, or an insufficient resolution (usually km-level) to capture the spatial distribution of the ASM, greatly limits the availability of existing vegetation maps in permafrost modeling of the Qinghai-Tibet Plateau (QTP). This study generated a map of the vegetation type at a spatial resolution of 30 m on the central QTP. The random forest (RF) classification approach was employed to map the vegetation based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. Validation using a train-test split (i.e., 70% of the samples were randomly selected to train the RF model, while the remaining 30% were used for validation and a total of 1000 runs) showed that the average overall accuracy and Kappa coefficient of the RF approach were 0.78 (0.68–0.85) and 0.69 (0.64–0.74), respectively. The confusion matrix showed that the overall accuracy and Kappa coefficient of the predicted vegetation map reached 0.848 (0.844–0.852) and 0.790 (0.785–0.796), respectively. The user accuracies for the ASM, alpine meadow, alpine steppe, and alpine desert were 95.0%, 83.3%, 82.4%, and 86.7%, respectively. The most important variables for vegetation type prediction were two vegetation indices, i.e., NDVI and EVI. The surface reflectance of visible and shortwave infrared bands showed a secondary contribution, and the brightness temperature and the surface temperature of the thermal infrared bands showed little contribution. The dominant vegetation in the study area is alpine steppe and alpine desert. The results of this study can provide an accurate and detailed vegetation map, especially for the distribution of the ASM, which can help to improve further permafrost studies. |
| first_indexed | 2024-03-10T03:23:23Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:23:23Z |
| publishDate | 2022-01-01 |
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| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ad14b07833224df28686c0ab30a3cf6e2023-11-23T12:15:12ZengMDPI AGRemote Sensing2072-42922022-01-0114123210.3390/rs14010232Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet PlateauDefu Zou0Lin Zhao1Guangyue Liu2Erji Du3Guojie Hu4Zhibin Li5Tonghua Wu6Xiaodong Wu7Jie Chen8Cryosphere Research Station on Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco–Environment and Resources, Chinese Academy of Sciences (CAS), Lanzhou 730000, ChinaSchool of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaCryosphere Research Station on Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco–Environment and Resources, Chinese Academy of Sciences (CAS), Lanzhou 730000, ChinaCryosphere Research Station on Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco–Environment and Resources, Chinese Academy of Sciences (CAS), Lanzhou 730000, ChinaCryosphere Research Station on Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco–Environment and Resources, Chinese Academy of Sciences (CAS), Lanzhou 730000, ChinaSchool of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaCryosphere Research Station on Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco–Environment and Resources, Chinese Academy of Sciences (CAS), Lanzhou 730000, ChinaCryosphere Research Station on Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco–Environment and Resources, Chinese Academy of Sciences (CAS), Lanzhou 730000, ChinaCryosphere Research Station on Qinghai–Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco–Environment and Resources, Chinese Academy of Sciences (CAS), Lanzhou 730000, ChinaAn accurate and detailed vegetation map is of crucial significance for understanding the spatial heterogeneity of subsurfaces, which can help to characterize the thermal state of permafrost. The absence of an alpine swamp meadow (ASM) type, or an insufficient resolution (usually km-level) to capture the spatial distribution of the ASM, greatly limits the availability of existing vegetation maps in permafrost modeling of the Qinghai-Tibet Plateau (QTP). This study generated a map of the vegetation type at a spatial resolution of 30 m on the central QTP. The random forest (RF) classification approach was employed to map the vegetation based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. Validation using a train-test split (i.e., 70% of the samples were randomly selected to train the RF model, while the remaining 30% were used for validation and a total of 1000 runs) showed that the average overall accuracy and Kappa coefficient of the RF approach were 0.78 (0.68–0.85) and 0.69 (0.64–0.74), respectively. The confusion matrix showed that the overall accuracy and Kappa coefficient of the predicted vegetation map reached 0.848 (0.844–0.852) and 0.790 (0.785–0.796), respectively. The user accuracies for the ASM, alpine meadow, alpine steppe, and alpine desert were 95.0%, 83.3%, 82.4%, and 86.7%, respectively. The most important variables for vegetation type prediction were two vegetation indices, i.e., NDVI and EVI. The surface reflectance of visible and shortwave infrared bands showed a secondary contribution, and the brightness temperature and the surface temperature of the thermal infrared bands showed little contribution. The dominant vegetation in the study area is alpine steppe and alpine desert. The results of this study can provide an accurate and detailed vegetation map, especially for the distribution of the ASM, which can help to improve further permafrost studies.https://www.mdpi.com/2072-4292/14/1/232vegetation mappingalpine swamp meadowrandom forestpermafrost regionQinghai-Tibet Plateau |
| spellingShingle | Defu Zou Lin Zhao Guangyue Liu Erji Du Guojie Hu Zhibin Li Tonghua Wu Xiaodong Wu Jie Chen Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau Remote Sensing vegetation mapping alpine swamp meadow random forest permafrost region Qinghai-Tibet Plateau |
| title | Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau |
| title_full | Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau |
| title_fullStr | Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau |
| title_full_unstemmed | Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau |
| title_short | Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau |
| title_sort | vegetation mapping in the permafrost region a case study on the central qinghai tibet plateau |
| topic | vegetation mapping alpine swamp meadow random forest permafrost region Qinghai-Tibet Plateau |
| url | https://www.mdpi.com/2072-4292/14/1/232 |
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