干旱区生态环境知识资源中心(Arid): Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau

Arid

DOI	10.3390/rs14010232
	Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau
	Zou, Defu; Zhao, Lin; Liu, Guangyue; Du, Erji; Hu, Guojie; Li, Zhibin; Wu, Tonghua; Wu, Xiaodong; Chen, Jie
通讯作者	Zhao, L (corresponding author)，Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China. ; Zhao, L (corresponding author)，Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:1
英文摘要	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.
英文关键词	vegetation mapping alpine swamp meadow random forest permafrost region Qinghai-Tibet Plateau
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000741384800001
WOS关键词	RANDOM FOREST CLASSIFIER ; ACTIVE-LAYER ; SOILS ; MAP ; GRADIENT ; IMPACT ; CHINA
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376814
作者单位	[Zou, Defu; Liu, Guangyue; Du, Erji; Hu, Guojie; Wu, Tonghua; Wu, Xiaodong; Chen, Jie] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, State Key Lab Cryospher Sci, Cryosphere Res Stn Qinghai Tibet Plateau, Lanzhou 730000, Peoples R China; [Zhao, Lin; Li, Zhibin] Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China; [Zhao, Lin] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Zou, Defu,Zhao, Lin,Liu, Guangyue,et al. Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau[J],2022,14(1).
APA	Zou, Defu.,Zhao, Lin.,Liu, Guangyue.,Du, Erji.,Hu, Guojie.,...&Chen, Jie.(2022).Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau.REMOTE SENSING,14(1).
MLA	Zou, Defu,et al."Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau".REMOTE SENSING 14.1(2022).