Permafrost Distribution along the Qinghai-Tibet Engineering Corridor, China Using High-Resolution Statistical Mapping and Modeling Integrated with Remote Sensing and GIS

Abstract

Permafrost distribution in the Qinghai-Tibet Engineering Corridor (QTEC) is of growing interest due to the increase in infrastructure development in this remote area. Empirical models of mountain permafrost distribution have been established based on field sampled data, as a tool for regional-scale assessments of its distribution. This kind of model approach has never been applied for a large portion of this engineering corridor. In the present study, this methodology is applied to map permafrost distribution throughout the QTEC. After spatial modelling of the mean annual air temperature distribution from MODIS-LST and DEM, using high-resolution satellite image to interpret land surface type, a permafrost probability index was obtained. The evaluation results indicate that the model has an acceptable performance. Conditions highly favorable to permafrost presence (≥70%) are predicted for 60.3% of the study area, declaring a discontinuous permafrost distribution in the QTEC. This map is useful for the infrastructure development along the QTEC. In the future, local ground-truth observations will be required to confirm permafrost presence in favorable areas and to monitor permafrost evolution under the influence of climate change.

Permafrost zonation index map and statistics over the Qinghai–Tibet Plateau based on field evidence

“…18 Generalized linear models (GLMs) are a class of empirical-statistical model which have been applied to derive permafrost distribution at both global and regional scales (e.g. 14,[19][20][21][22] ). Compared with traditional categorical maps, GLMs can estimate permafrost distribution more precisely by introducing continuous permafrost probability indices which range from 0 to 1.…”

“…Furthermore, these local factors normally could provide some hints on techniques and measures we can use to artificially simulate their similar effects and hence could further improve mapping accuracy. 14 The PZI global map represents an improvement over the IPA map by adding detail and accurately representing major topographic features lacking in the IPA map through the use of high-resolution MAAT and topography data sets. Unfortunately, its model parameters calibrated using 1990s-era measurements of the IPA map, and up-to-date ground-truth information is absent from this calibration.…”

“…). These maps were usually produced at very small scales (from 1:600,000 to 1:10,000,000) and used a categorical classification (such as permafrost, seasonally frozen ground and unfrozen ground) to delineate permafrost regions . More recently, due to the increased availability of high‐resolution meteorological data sets, several higher resolution (∼ 1–10 km) maps were developed which represent permafrost distribution as a binary presence or absence classification .…”

“…However, because the PZI global map uses MAAT as its single predictor variable, it may underestimate the strong spatial heterogeneity of permafrost distribution over the QTP caused by local factors including snow cover and vegetation. Furthermore, these local factors normally could provide some hints on techniques and measures we can use to artificially simulate their similar effects and hence could further improve mapping accuracy . The PZI global map represents an improvement over the IPA map by adding detail and accurately representing major topographic features lacking in the IPA map through the use of high‐resolution MAAT and topography data sets.…”

“…These maps were usually produced at very small scales (from 1:600,000 to 1:10,000,000) and used a categorical classification (such as permafrost, seasonally frozen ground and unfrozen ground) to delineate permafrost regions. 14…”