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Remote Sens. 2019, 11(2), 183; https://doi.org/10.3390/rs11020183

Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s

1
Department of Surveying and Mapping, College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2
State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Received: 1 October 2018 / Revised: 26 December 2018 / Accepted: 16 January 2019 / Published: 18 January 2019
(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions)
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Abstract

Based on the results of remote sensing data interpretation, this paper aims to simulate and predict the mountain permafrost distribution changes affected by the mean decadal air temperature (MDAT), from the 1990s to the 2040s, in the Qilian Mountains. A bench-mark map is visually interpreted to acquire a mountain permafrost distribution from the 1990s, based on remote sensing images. Through comparison and estimation, a logistical regression model (LRM) is constructed using the bench-mark map, topographic and land coverage factors and MDAT data from the 1990s. MDAT data from the 2010s to the 2040s are predicted according to survey data from meteorological stations. Using the LRM, MDAT data and the factors, the probabilities (p) of decadal mountain permafrost distribution from the 1990s to the 2040s are simulated and predicted. According to the p value, the permafrost distribution statuses are classified as ‘permafrost probable’ (p > 0.7), ‘permafrost possible’ (0.7 ≥ p ≥ 0.3) and ‘permafrost improbable’ (p < 0.3). From the 1990s to the 2040s, the ‘permafrost probable’ type mainly degrades to that of ‘permafrost possible’, with the total area degenerating from 73.5 × 103 km2 to 66.5 × 103 km2. The ‘permafrost possible’ type mainly degrades to that of ‘permafrost impossible’, with a degradation area of 6.5 × 103 km2, which accounts for 21.3% of the total area. Meanwhile, the accuracy of the simulation results can reach about 90%, which was determined by the validation of the simulation results for the 1990s, 2000s and 2010s based on remote sensing data interpretation results. This research provides a way of understanding the mountain permafrost distribution changes affected by the rising air temperature rising over a long time, and can be used in studies of other mountains with similar topographic and climatic conditions. View Full-Text
Keywords: mountain permafrost; logistic regression model; model simulation and prediction; mean decadal air temperature data; Qilian Mountains mountain permafrost; logistic regression model; model simulation and prediction; mean decadal air temperature data; Qilian Mountains
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhao, S.; Zhang, S.; Cheng, W.; Zhou, C. Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s. Remote Sens. 2019, 11, 183.

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