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RSS FeedsRemote Sensing, Vol. 11, Pages 183: 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 Sensing)

 
 

20 january 2019 05:00:02

 
Remote Sensing, Vol. 11, Pages 183: 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 Sensing)
 




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.


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18 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 185: Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification (Remote Sensing)
Remote Sensing, Vol. 11, Pages 184: Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes (Remote Sensing)
 
 
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