Spatial Distribution of Permafrost in the Xing’an Mountains of Northeast China from 2001 to 2018

Yanyu Zhang, Shuying Zang, Miao Li, Xiangjin Shen and Yue Lin
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Yanyu Zhang: Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China
Shuying Zang: Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China
Miao Li: Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China
Xiangjin Shen: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Yue Lin: Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China

Land, 2021, vol. 10, issue 11, 1-13

Abstract: Permafrost is a key element of the cryosphere and sensitive to climate change. High-resolution permafrost map is important to environmental assessment, climate modeling, and engineering application. In this study, to estimate high-resolution Xing’an permafrost map (up to 1 km 2 ), we employed the surface frost number ( SFN ) model and ground temperature at the top of permafrost (TTOP) model for the 2001–2018 period, driven by remote sensing data sets (land surface temperature and land cover). Based on the comparison of the modeling results, it was found that there was no significant difference between the two models. The performances of the SFN model and TTOP model were evaluated by using a published permafrost map. Based on statistical analysis, both the SFN model and TTOP model efficiently estimated the permafrost distribution in Northeast China. The extent of Xing’an permafrost distribution simulated by the SFN model and TTOP model were 6.88 × 10 5 km 2 and 6.81 × 10 5 km 2 , respectively. Ground-surface characteristics were introduced into the permafrost models to improve the performance of models. The results provided a basic reference for permafrost distribution research at the regional scale.

Keywords: Xing’an permafrost; permafrost distribution; SFN; TTOP (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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