Integrated approach for effective debris mapping in glacierized regions of Chandra River Basin, Western Himalayas, India

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole.

Section: Summary Of Cryosphere Studies From Spacementioning

confidence: 99%

Remote Sensing and Modeling of the Cryosphere in High Mountain Asia: A Multidisciplinary Review

Ye,

Wang,

Liu

et al. 2024

“…Extraction of surface facies has been demonstrated using methods incorporating data from a single sensor [2][3][4][5] as well as multiple sensors and data products [6][7][8][9][10][11]. Many of the persistent problems of mapping mountain glaciers revolve around solving the spectral complexities of supraglacial debris [6,[12][13][14]. Debris characterization utilizes the shortwave infrared (SWIR), visible to near infrared (VNIR), and thermal infrared (TIR) range of optical satellite data [15].…”

Section: Introductionmentioning

confidence: 99%

Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard) through Investigations of Pixel and Object-Based Mapping Using Variable Processing Routines

Jawak¹,

Wankhede

Luis

et al. 2022

Fundamental image processing methods, such as atmospheric corrections and pansharpening, influence the signal of the pixel. This morphs the spectral signature of target features causing a change in both the final spectra and the way different mapping methods may assign thematic classes. In the current study, we aim to identify the variations induced by popular image processing methods in the spectral reflectance and final thematic maps of facies. To this end, we have tested three different atmospheric corrections: (a) Quick Atmospheric Correction (QUAC), (b) Dark Object Subtraction (DOS), and (c) Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods: (a) Hyperspherical Color Sharpening (HCS) and (b) Gram–Schmidt (GS). WorldView-2 and WorldView-3 satellite images over Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard are tested via spectral subsets in traditional (BGRN1), unconventional (CYRN2), visible to near-infrared (VNIR), and the complete available spectrum (VNIR_SWIR). Thematic mapping was comparatively performed using 12 pixel-based (PBIA) algorithms and 3 object-based (GEOBIA) rule sets. Thus, we test the impact of varying image processing routines, effectiveness of specific spectral bands, utility of PBIA, and versatility of GEOBIA for mapping facies. Our findings suggest that the image processing routines exert an extreme impact on the end spectral reflectance. DOS delivers the most reliable performance (overall accuracy = 0.64) averaged across all processing schemes. GEOBIA delivers much higher accuracy when the QUAC correction is employed and if the image is enhanced by GS pansharpening (overall accuracy = 0.79). SWIR bands have not enhanced the classification results and VNIR band combination yields superior performance (overall accuracy = 0.59). The maximum likelihood classifier (PBIA) delivers consistent and reliable performance (overall accuracy = 0.61) across all processing schemes and can be used after DOS correction without pansharpening, as it deteriorates spectral information. GEOBIA appears to be robust against modulations in atmospheric corrections but is enhanced by pansharpening. When utilizing GEOBIA, we find that a combination of spatial and spectral object features (rule set 3) delivers the best performance (overall accuracy = 0.86), rather than relying only on spectral (rule set 1) or spatial (rule set 2) object features. The multiresolution segmentation parameters used here may be transferable to other very high resolution (VHR) VNIR mapping of facies as it yielded consistent objects across all processing schemes.

“…Seasonal snow cover and debris also increase the difficulty and uncertainty of glacier boundary identification [25,26]. These uncertain factors cause significant interference in research on glacier distribution, water storage, glacier changes, hydrological modeling, and information extraction on the local and regional scales, which is the focus [27] of current research on glaciers [21].…”

Section: Introductionmentioning

confidence: 99%

“…By combining machine learning methods to analyze and extract features based on the different dimensions of the glacier remote sensing images in each pixel, a rapid automatic classification of glaciers can be achieved, clearly reflecting the various glacier types [27,28]. The concept underlying technology that performs specific target segmentation in highresolution remote sensing images is to construct the object-relational map between remote sensing images and the target space [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers

Zhang

Shangguan

et al. 2021

Glaciers in High Mountain Asia (HMA) have a significant impact on human activity. Thus, a detailed and up-to-date inventory of glaciers is crucial, along with monitoring them regularly. The identification of debris-covered glaciers is a fundamental and yet challenging component of research into glacier change and water resources, but it is limited by spectral similarities with surrounding bedrock, snow-affected areas, and mountain-shadowed areas, along with issues related to manual discrimination. Therefore, to use fewer human, material, and financial resources, it is necessary to develop better methods to determine the boundaries of debris-covered glaciers. This study focused on debris-covered glacier mapping using a combination of related technologies such as random forest (RF) and convolutional neural network (CNN) models. The models were tested on Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), selecting Eastern Pamir and Nyainqentanglha as typical glacier areas on the Tibetan Plateau to construct a glacier classification system. The performances of different classifiers were compared, the different classifier construction strategies were optimized, and multiple single-classifier outputs were obtained with slight differences. Using the relationship between the surface area covered by debris and the machine learning model parameters, it was found that the debris coverage directly determined the performance of the machine learning model and mitigated the issues affecting the detection of active and inactive debris-covered glaciers. Various classification models were integrated to ascertain the best model for the classification of glaciers.