Detecting Rock Glacier Displacement in the Central Himalayas Using Multi-Temporal InSAR

Abstract

Rock glaciers represent typical periglacial landscapes and are distributed widely in alpine mountain environments. Rock glacier activity represents a critical indicator of water reserves state, permafrost distribution, and landslide disaster susceptibility. The dynamics of rock glacier activity in alpine periglacial environments are poorly quantified, especially in the central Himalayas. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been shown to be a useful technique for rock glacier deformation detection. In this study, we developed a multi-baseline persistent scatterer (PS) and distributed scatterer (DS) combined MT-InSAR method to monitor the activity of rock glaciers in the central Himalayas. In periglacial landforms, the application of the PS interferometry (PSI) method is restricted by insufficient PS due to large temporal baseline intervals and temporal decorrelation, which hinder comprehensive measurements of rock glaciers. Thus, we first evaluated the rock glacier interferometric coherence of all possible interferometric combinations and determined a multi-baseline network based on rock glacier coherence; then, we constructed a Delaunay triangulation network (DTN) by exploiting both PS and DS points. To improve the robustness of deformation parameters estimation in the DTN, we combined the Nelder–Mead algorithm with the M-estimator method to estimate the deformation rate variation at the arcs of the DTN and introduced a ridge-estimator-based weighted least square (WLR) method for the inversion of the deformation rate from the deformation rate variation. We applied our method to Sentinel-1A ascending and descending geometry data (May 2018 to January 2019) and obtained measurements of rock glacier deformation for 4327 rock glaciers over the central Himalayas, at least more than 15% detecting with single geometry data. The line-of-sight (LOS) deformation of rock glaciers in the central Himalayas ranged from −150 mm to 150 mm. We classified the active deformation area (ADA) of all individual rock glaciers with the threshold determined by the standard deviation of the deformation map. The results show that 49% of the detected rock glaciers (monitoring rate greater than 30%) are highly active, with an ADA ratio greater than 10%. After projecting the LOS deformation to the steep slope direction and classifying the rock glacier activity following the IPA Action Group guideline, 12% of the identified rock glaciers were classified as active and 86% were classified as transitional. This research is the first multi-baseline, PS, and DS network-based MT-InSAR method applied to detecting large-scale rock glaciers activity.

Monitoring and Prediction of Glacier Deformation in the Meili Snow Mountain Based on InSAR Technology and GA-BP Neural Network Algorithm

“…Relevant research has suggested [ 5 , 7 , 8 , 15 , 17 ] that the main factors for the climate and environment of mountain glaciers on the Qinghai-Tibet Plateau ice sheet and thus glacier morphology [ 47 , 48 ] include altitude, slope, slope orientation, rainfall, as well as glacier albedo. In addition, changes in surface organic carbon density due to human and natural activities can cause regional climatic and environmental changes [ 49 ], thus affecting glacier morphology.…”

“…However, the effective range of temperate glacier monitoring by conventional means is less than 1% due to the extreme climatic conditions and complex topography of the alpine ice sheet environment [ 15 ]. Currently, optical remote sensing is one of the critical tools adopted to generate regional inventories of mountain glaciers and extract glacier boundaries and area changes [ 16 , 17 ], whereas it is susceptible to natural cloud conditions, especially under the extreme climatic conditions of the ice sheet environment in alpine regions. Moreover, the effectiveness of optical remote sensing for mountain glacier monitoring has been poor [ 15 ].…”

“…InSAR technology is based on active synthetic aperture radar (SAR) technology, which shows the advantages of a large range, all-day, all-weather, good stability, strong dynamics, high accuracy, as well as high resolution [ 18 ]. It has been extensively employed in urban surface subsidence [ 19 , 20 ], seismic activity [ 21 , 22 ], volcanic hazards [ 23 , 24 ], landslide hazards [ 25 , 26 , 27 ], as well as glacier displacement monitoring [ 15 , 16 , 17 , 18 ]. The earliest application in surface deformation monitoring is the differential InSAR (D-InSAR) technique, which has been well-adopted to invert glacier surface displacements under short-time baselines [ 28 ].…”

“…The technique has been proposed to fuse the ascending and descending orbit D-InSAR and azimuthal AZO or MAI deformation results to build a 3D deformation field of mountain glaciers and obtain the morphological change components of glaciers in vertical, east–west, and north–south directions, such that a more comprehensive insight is provided into glacier morphological changes compared with the line-of-sight deformation results. The current applications of InSAR technology in glaciers primarily have the following aspects: (1) monitoring glacier deformation using D-InSAR technology [ 16 , 35 , 36 ]; (2) obtaining time series of glacier deformation using MT-InSAR [ 6 , 7 , 15 , 17 , 28 ]; (3) and fusing LOS and AZI directional deformation information to build glacier 3D deformation fields [ 34 , 37 , 38 ]. The above methods confirm that the InSAR technique can effectively acquire glacier deformation information in extremely complex ice sheet environments, and it has greater potential for application in monitoring glacier flow velocity and morphology.…”

Evaluating Permafrost Degradation in the Tuotuo River Basin by MT-InSAR and LSTM Methods

“…The sinusoidal model or the square root of the cumulative degree day of the freeze-thaw model has been commonly used to approximate seasonal oscillations. However, it is difficult to characterize the freeze-thaw process using a simple physical sinusoidal model, and the square root of the cumulative degree day model is generally constructed by a single point temperature, which cannot represent the deformation process on a large-scale [ 30 ]. Due to snow melt or vegetation dynamics (e.g., in alpine meadows), strong spatio-temporal decorrelation may occur in an interferometry network.…”