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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ftdoajarticles:oai:doaj.org/article:ad14b07833224df28686c0ab30a3cf6e 2023-05-15T17:56:58+02:00 Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau Defu Zou Lin Zhao Guangyue Liu Erji Du Guojie Hu Zhibin Li Tonghua Wu Xiaodong Wu Jie Chen 2022-01-01T00:00:00Z https://doi.org/10.3390/rs14010232 https://doaj.org/article/ad14b07833224df28686c0ab30a3cf6e EN eng MDPI AG https://www.mdpi.com/2072-4292/14/1/232 https://doaj.org/toc/2072-4292 doi:10.3390/rs14010232 2072-4292 https://doaj.org/article/ad14b07833224df28686c0ab30a3cf6e Remote Sensing, Vol 14, Iss 232, p 232 (2022) vegetation mapping alpine swamp meadow random forest permafrost region Qinghai-Tibet Plateau Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14010232 2022-12-31T16:32:38Z 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. Article in Journal/Newspaper permafrost Directory of Open Access Journals: DOAJ Articles Remote Sensing 14 1 232 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
vegetation mapping alpine swamp meadow random forest permafrost region Qinghai-Tibet Plateau Science Q |
spellingShingle |
vegetation mapping alpine swamp meadow random forest permafrost region Qinghai-Tibet Plateau Science Q 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 |
topic_facet |
vegetation mapping alpine swamp meadow random forest permafrost region Qinghai-Tibet Plateau Science Q |
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. |
format |
Article in Journal/Newspaper |
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 |
title |
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_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_sort |
vegetation mapping in the permafrost region: a case study on the central qinghai-tibet plateau |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14010232 https://doaj.org/article/ad14b07833224df28686c0ab30a3cf6e |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Remote Sensing, Vol 14, Iss 232, p 232 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/1/232 https://doaj.org/toc/2072-4292 doi:10.3390/rs14010232 2072-4292 https://doaj.org/article/ad14b07833224df28686c0ab30a3cf6e |
op_doi |
https://doi.org/10.3390/rs14010232 |
container_title |
Remote Sensing |
container_volume |
14 |
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1 |
container_start_page |
232 |
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1766165309489676288 |