Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s
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 acqu...
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ftdoajarticles:oai:doaj.org/article:0f9f2a582f62493f906e12fab5742922 2023-05-15T17:55:19+02:00 Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s Shangmin Zhao Shifang Zhang Weiming Cheng Chenghu Zhou 2019-01-01T00:00:00Z https://doi.org/10.3390/rs11020183 https://doaj.org/article/0f9f2a582f62493f906e12fab5742922 EN eng MDPI AG http://www.mdpi.com/2072-4292/11/2/183 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs11020183 https://doaj.org/article/0f9f2a582f62493f906e12fab5742922 Remote Sensing, Vol 11, Iss 2, p 183 (2019) mountain permafrost logistic regression model model simulation and prediction mean decadal air temperature data Qilian Mountains Science Q article 2019 ftdoajarticles https://doi.org/10.3390/rs11020183 2022-12-31T15:21:16Z 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. Article in Journal/Newspaper permafrost Directory of Open Access Journals: DOAJ Articles The Bench ENVELOPE(-53.181,-53.181,49.767,49.767) Remote Sensing 11 2 183 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
mountain permafrost logistic regression model model simulation and prediction mean decadal air temperature data Qilian Mountains Science Q |
spellingShingle |
mountain permafrost logistic regression model model simulation and prediction mean decadal air temperature data Qilian Mountains Science Q Shangmin Zhao Shifang Zhang Weiming Cheng Chenghu Zhou Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s |
topic_facet |
mountain permafrost logistic regression model model simulation and prediction mean decadal air temperature data Qilian Mountains Science Q |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Shangmin Zhao Shifang Zhang Weiming Cheng Chenghu Zhou |
author_facet |
Shangmin Zhao Shifang Zhang Weiming Cheng Chenghu Zhou |
author_sort |
Shangmin Zhao |
title |
Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s |
title_short |
Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s |
title_full |
Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s |
title_fullStr |
Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s |
title_full_unstemmed |
Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s |
title_sort |
model simulation and prediction of decadal mountain permafrost distribution based on remote sensing data in the qilian mountains from the 1990s to the 2040s |
publisher |
MDPI AG |
publishDate |
2019 |
url |
https://doi.org/10.3390/rs11020183 https://doaj.org/article/0f9f2a582f62493f906e12fab5742922 |
long_lat |
ENVELOPE(-53.181,-53.181,49.767,49.767) |
geographic |
The Bench |
geographic_facet |
The Bench |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Remote Sensing, Vol 11, Iss 2, p 183 (2019) |
op_relation |
http://www.mdpi.com/2072-4292/11/2/183 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs11020183 https://doaj.org/article/0f9f2a582f62493f906e12fab5742922 |
op_doi |
https://doi.org/10.3390/rs11020183 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
2 |
container_start_page |
183 |
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1766163232706265088 |