Mapping Frozen Ground in the Qilian Mountains in 2004–2019 Using Google Earth Engine Cloud Computing

Abstract

The permafrost in the Qilian Mountains (QLMs), the northeastern margin of the Qinghai–Tibet Plateau, changed dramatically in the context of climate warming and increasing anthropogenic activities, which poses significant influences on the stability of the ecosystem, water resources, and greenhouse gas cycles. Yet, the characteristics of the frozen ground in the QLMs are largely unclear regarding the spatial distribution of active layer thickness (ALT), the maximum frozen soil depth (MFSD), and the temperature at the top of the permafrost or the bottom of the MFSD (TTOP). In this study, we simulated the dynamics of the ALT, TTOP, and MFSD in the QLMs in 2004–2019 in the Google Earth Engine (GEE) platform. The widely-adopted Stefan Equation and TTOP model were modified to integrate with the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) in GEE. The N-factors, the ratio of near-surface air to ground surface freezing and thawing indices, were assigned to the freezing and thawing indices derived with MODIS LST in considerations of the fractional vegetation cover derived from MODIS normalized difference vegetation index (NDVI). The results showed that the GEE platform and remote sensing imagery stored in Google cloud could be quickly and effectively applied to obtain the spatial and temporal variation of permafrost distribution. The area with TTOP < 0 °C is 8.4 × 104 km2 (excluding glaciers and lakes) and accounts for 46.6% of the whole QLMs, the regional mean ALT is 2.43 ± 0.44 m, while the regional mean MFSD is 2.54 ± 0.45 m. The TTOP and ALT increase with the decrease of elevation from the sources of the sub-watersheds to middle and lower reaches. There is a strong correlation between TTOP and elevation (slope = −1.76 °C km−1, p < 0.001). During 2004–2019, the area of permafrost decreased by 20% at an average rate of 0.074 × 104 km2·yr−1. The regional mean MFSD decreased by 0.1 m at a rate of 0.63 cm·yr−1, while the regional mean ALT showed an exception of a decreasing trend from 2.61 ± 0.45 m during 2004–2005 to 2.49 ± 0.4 m during 2011–2015. Permafrost loss in the QLMs in 2004–2019 was accelerated in comparison with that in the past several decades. Compared with published permafrost maps, this study shows better calculation results of frozen ground in the QLMs.

Machine Learning-Based Active Layer Thickness Estimation Over Permafrost Landscapes by Upscaling Airborne Remote Sensing Measurements with Cloud-Computing Geotechnologies

“…Thus, the use of GEE removed the need for powerful and expensive local computing power (i.e., high-performance computing; HCP), which is otherwise necessary for scaling high-resolution predictions over large areas with RS big data [35]. This study now joins a dense collection of literature demonstrating the large-scale modeling capabilities of GEE for environmental applications and information generation over permafrost rich landscapes [33,[68][69][70][71]. As such, the use of GEE, along with other cloud-based geospatial platforms (e.g., Microsoft Azure, Amazon Web Services, etc.…”

The Spatio-Temporal Changes of Small Lakes of the Qilian Mountains from 1987 to 2020 and Their Driving Mechanisms

“…As a result of glacial shrinkage, small glacial lakes are distributed in the QMR [11,12,33]. The QMR is also dotted with mountain permafrost and is representative of the mountainous permafrost regions of the QTP [34]. The melting rate of permafrost in mountainous areas is expected to accelerate in a warming climate [35,36].…”

Biochemical composition and function of subalpine shrubland and meadow soil microbiomes in the Qilian Mountains, Qinghai–Tibetan plateau, China

“…The Qilian Mountains, on the northeastern edge of the Qinghai–Tibetan Plateau, play a significant role in retaining water, mitigating climate change, and maintaining biodiversity ( Qian et al, 2019 ). Shrublands, comprising evergreen shrubs and alpine deciduous shrubs, and meadows are the primary vegetation type in high elevation regions ( Qi et al, 2021 ). A combination of environmental factors and anthropogenic activities have substantially reduced shrubland area since the 1950s ( Sun et al, 2016 ).…”