18 research outputs found

    An integrated deep learning and object-based image analysis approach for mapping debris- covered glaciers

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    Evaluating glacial change and the subsequent water stores in high mountains is becoming increasingly necessary, and in order to do this, models need reliable and consistent glacier data. These often come from global inventories, usually constructed from multi-temporal satellite imagery. However, there are limitations to these datasets. While clean ice can be mapped relatively easily using spectral band ratios, mapping debris-covered ice is more difficult due to the spectral similarity of supraglacial debris to the surrounding terrain. Therefore, analysts often employ manual delineation, a time-consuming and subjective approach to map debris-covered ice extents. Given the increasing prevalence of supraglacial debris in high mountain regions, such as High Mountain Asia, a systematic, objective approach is needed. The current study presents an approach for mapping debris-covered glaciers that integrates a convolutional neural network and object-based image analysis into one seamless classification workflow, applied to freely available and globally applicable Sentinel-2 multispectral, Landsat-8 thermal, Sentinel-1 interferometric coherence, and geomorphometric datasets. The approach is applied to three different domains in the Central Himalayan and the Karakoram ranges of High Mountain Asia that exhibit varying climatic regimes, topographies and debris-covered glacier characteristics. We evaluate the performance of the approach by comparison with a manually delineated glacier inventory, achieving F-score classification accuracies of 89.2%–93.7%. We also tested the performance of this approach on declassified panchromatic 1970 Corona KH-4B satellite imagery in the Manaslu region of Nepal, yielding accuracies of up to 88.4%. We find our approach to be robust, transferable to other regions, and accurate over regional (>4,000 km2) scales. Integrating object-based image analysis with deep-learning within a single workflow overcomes shortcomings associated with convolutional neural network classifications and permits a more flexible and robust approach for mapping debris-covered glaciers. The novel automated processing of panchromatic historical imagery, such as Corona KH-4B, opens the possibility of exploiting a wealth of multi-temporal data to understand past glacier changes.publishedVersio

    Geochemical characterization of supraglacial debris via in situ and optical remote sensing methods: a case study in Khumbu Himalaya, Nepal

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    Surface glacier debris samples and field spectra were collected from the ablation zones of Nepal Himalaya Ngozumpa and Khumbu glaciers in November and December 2009. Geochemical and mineral compositions of supraglacial debris were determined by X-ray diffraction and X-ray fluorescence spectroscopy. This composition data was used as ground truth in evaluating field spectra and satellite supraglacial debris composition and mapping methods. Satellite remote sensing methods for characterizing glacial surface debris include visible to thermal infrared hyper- and multispectral reflectance and emission signature identification, semi-quantitative mineral abundance indicies and spectral image composites. Satellite derived supraglacial debris mineral maps displayed the predominance of layered silicates, hydroxyl-bearing and calcite minerals on Khumbu Himalayan glaciers. Supraglacial mineral maps compared with satellite thermal data revealed correlations between glacier surface composition and glacier surface temperature. Glacier velocity displacement fields and shortwave, thermal infrared false color composites indicated the magnitude of mass flux at glacier confluences. The supraglacial debris mapping methods presented in this study can be used on a broader scale to improve, supplement and potentially reduce errors associated with glacier debris radiative property, composition, areal extent and mass flux quantifications

    Mapping debris-covered glaciers in the Cordillera Blanca, Peru : an object-based image analysis approach.

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    Accurate remote-sensing based inventories of glacial ice are often hindered by the presence of supraglacial debris cover. Attempts at automated mapping of debris-covered glacier areas from remotely-sensed multispectral data have met with limited success due to the spectral similarity of supraglacial debris to nearby bedrock, moraines, and fluvial deposition features. Data-fusion approaches leveraging terrain and/or thermal data with multispectral data have yielded improved results in certain geographic regions, but remain unproven in others. This research builds on the data-fusion approaches from the literature and explores the efficacy of object-based image analysis (OBIA) and tree-based machine learning classifiers using Landsat OLI imagery and SRTM elevation data, in effort to map debris-covered glaciers in the Cordillera Blanca range of Peru. Results suggest that the OBIA and machine learning methods render advantages over traditional methods given the unique morphological settings associated with debris-covered glaciers. Accurate inventories of glacial mass and debris-covered glaciers in the Cordillera Blanca are important for understanding the unique water resource, natural hazards, and climate change implications associated with these tropical mountain glaciers

    Implementing an object-based multi-index protocol for mapping surface glacier facies from Chandra-Bhaga basin, Himalaya

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    Surface glacier facies are superficial expressions of a glacier that are distinguishable based on differing spectral and structural characteristics according to their age and inter-mixed impurities. Increasing bodies of literature suggest that the varying properties of surface glacier facies differentially influence the melt of the glacier, thus affecting the mass balance. Incorporating these variations into distributed mass balance modelling can improve the perceived accuracy of these models. However, detecting and subsequently mapping these facies with a high degree of accuracy is a necessary precursor to such complex modelling. The variations in the reflectance spectra of various glacier facies permit multiband imagery to exploit band ratios for their effective extraction. However, coarse and medium spatial resolution multispectral imagery can delimit the efficacy of band ratioing by muddling the minor spatial and spectral variations of a glacier. Very high-resolution imagery, on the other hand, creates distortions in the conventionally obtained information extracted through pixel-based classification. Therefore, robust and adaptable methods coupled with higher resolution data products are necessary to effectively map glacier facies. This study endeavours to identify and isolate glacier facies on two unnamed glaciers in the Chandra-Bhaga basin, Himalayas, using an established object-based multi-index protocol. Exploiting the very high resolution offered by WorldView-2 and its eight spectral bands, this study implements customized spectral index ratios via an object-based environment. Pixel-based supervised classification is also performed using three popular classifiers to comparatively gauge the classification accuracies. The object-based multi-index protocol delivered the highest overall accuracy of 86.67%. The Minimum Distance classifier yielded the lowest overall accuracy of 62.50%, whereas, the Mahalanobis Distance and Maximum Likelihood classifiers yielded overall accuracies of 77.50% and 70.84% respectively. The results outline the superiority of the object-based method for extraction of glacier facies. Forthcoming studies must refine the indices and test their applicability in wide ranging scenarios

    Supraglacial dust and debris characterization via in situ and optical remote sensing methods

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    Supraglacial dust and debris affects many glaciologic variables, including radiative absorption, ablation, generation of supraglacial melt as well as mass flux. Earth observing satellite technology has advanced greatly in recent decades and allows for unprecedented spatial, temporal and spectral imaging of Earth’s glaciers. While remote sensing of ‘clean’ glacier ice can be done quite successfully, strategies for satellite mapping of supraglacial debris remain in development. This work provides the first visible to thermal infrared full optical spectrum satellite data analysis of supraglacial dust and debris characterization and differentiation. Dust and debris covered glaciers in the following six contrasting study regions were targeted: Iceland, Nepal, New Zealand, southern Norway, Svalbard and Switzerland. A combination of field spectrometry and surface samples of snow, ice and debris were utilized to investigate supraglacial dust and debris diversity. This in situ data served as ground truth for evaluating spaceborne supraglacial debris mapping capabilities. Glacier snow, ice and debris samples were analyzed for mineral composition and inorganic elemental abundances via the following analytical geochemical techniques: X-ray diffraction, X-ray fluorescence spectroscopy and inductively coupled plasma mass spectrometry. A synoptic data set from four contrasting alpine glacier regions – Svalbard, southern Norway, Nepal and New Zealand – and 70 surface snow, ice and debris samples was presented, comparing supraglacial composition variability. Distinct supraglacial geochemical abundances were found in major, trace and rare earth elemental concentrations between the four study regions. Elemental variations were attributed to both natural and anthropogenic processes. Over 8800 glacier surface spectra were collected in Nepal, Svalbard and Switzerland, as well as from Nepal, New Zealand and Switzerland debris samples. Surface glacier debris mineralogy and moisture content were assessed from field spectra. Spaceborne supraglacial dust and debris mineral mapping techniques using visible to shortwave reflective and thermal emissive data were evaluated. Successful methods for mineral identification allowed mapping of volcanic vs. continental supraglacial debris, as well as different mineral classes within one glacier’s supraglacial debris. Granite- vs. schist-dominant debris was mapped on Khumbu glacier in Nepal. Iron-rich vs. iron-poor serpentine debris was mapped on Zmutt glacier in the Swiss Alps. Satellite emissivity derived silica mapping suggested potential use of silica thresholds for delineation of debris covered glacier extent or sediment transport and weathering processes. Satellite derived surface temperatures were compared in Iceland, Nepal, Switzerland and New Zealand glacier study regions, with results demonstrating variations in supraglacial temperatures coincident with changing mineral abundances. Consistently higher surface temperatures with increasing dust and debris cover were mapped at all four glacier study regions. Repeat supraglacial debris imagery was used to estimate ablation area velocities and particulate transport times at debris covered glaciers. Velocity derivations used in conjunction with supraglacial composition variation analysis from shortwave and thermal infrared false color composites, allowed for estimation of glacial mass flux in the Khumbu Himalayas. In short, the visible to thermal infrared satellite spectral analysis, combined with in situ spectral and geochemical ground truth data, proved that glacier dust and debris characterization is possible via satellite spectral data. Furthermore, this supraglacial dust and debris satellite characterization can be applied to a range of glaciologic studies, including thermal, mass balance and surface process interpretations on large spatial and temporal scales

    Glacier Monitoring in Ladakh and Zanskar, northwestern India

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    Glaciers in the Himalaya are often heavily covered with supraglacial debris, making them difficult to study with remotely-sensed imagery alone. Various methods such as band ratios can be used effectively to map clean-ice glaciers; however, a thicker layer of debris often makes it impossible to distinguish between supraglacial debris and the surrounding terrain. Previously, a morphometric approach employing an ASTER-derived digital elevation model (DEM) has been used to map glaciers in the Khumbu Himal and the Tien Shan. This project aims first to test the ability of the morphometric procedure to map small glaciers; second, to use the morphometric approach to map glaciers in Ladakh; and third, to use Landsat and ASTER data and GPS and field measurements to monitor glacier change in Ladakh over the past four decades. Field work was carried out in the summers of 2007 and 2008. For clean ice, a ratio of shortwave infrared (SWIR, 1.6-1.7 µm) and near infrared (NIR, 0.76-0.86 µm) bands from the ASTER dataset was used to distinguish snow and ice. For debris-covered glaciers, morphometric features such as slope, derived from a DEM, were combined with thermal imagery and supervised classifiers to map glacial margins. The method is promising for large glaciers, although problems occurred in the distal and lateral parts and in the forefield of the glaciers. The morphometric approach was inadequate for mapping small glaciers, due to a paucity of unique topographic features on the glaciers which can be used to distinguish them from the surrounding terrain. A multi-temporal analysis of three glaciers in Ladakh found that two of them have receded—one since at least the mid-1970s, the other since at least 2000—while a third glacier, Parkachik Glacier, seemed to have retreated in the 1980s, only to advance in the 1990s and early 2000s. However, from 2004-2008 it showed only negligible change making its current status difficult to determine without further monitoring. The glacier outlines derived during this project will be added to the Global Land Ice Measurements from Space (GLIMS) database. In testing the limits of the morphometric approach, the thesis has provided a valuable contribution to the present literature and knowledge-base regarding the mapping of debris-covered glaciers

    Zjišťování změn polohy ELA ledovců v pohoří Cordillera Blanca, Peru, z dat DPZ

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    Cílem této diplomové práce je zmapovat změny ledovců v Cordillera Blanca v období od roku 1987 do roku 2014. Tato diplomová práce zaměřuje pozornost na tři hory a jedenáct ledovců v severní části Cordillera Blanca. Vstupní data tvoří 29 Landsat scén (Landsat 4,5,7 a 8) a ASTER globální digitální výškový model verze 2. Poloautomatický klasifikační algoritmus je vytvořen na základě prahových hodnot zjištěných spektrální analýzou vybraných typů krajinného pokryvu v Cordillera Blanca. Kromě toho, výšková změna střední sněžné čáry (Equilibrium line altitude - ELA) je vypočtena pro všechny tři hory a jedenáct ledovců. Dále je počítána změna ledovců v závislosti na sklonu a aspektu. Výsledky této práce jsou prezentovány v podobě map, tabulek a grafů. Výsledky klasifikace jsou porovnány s GLIMS databází ledovců a s terénním měřením provedeném Mgr. Adamem Emmerem. Na závěr jsou diskutovány výhody a nevýhody snímků pořízených novým satelitem Landsat 8. Klíčová slova: DPZ, Landsat, klasifikace, mapování sněhu a ledu, ELA, Cordillera BlancaThe aim of this diploma thesis is to monitor glacier change in the Cordillera Blanca in the period from 1987 to 2014. This diploma thesis focuses on three mountains and eleven glaciers in the northern part of the Cordillera Blanca. The input data consist of 29 Landsat scenes (Landsat 4,5,7 and 8) and the ASTER global digital elevation model version 2. Semi-automatic classification algorithm is created based on threshold values detected by spectral analyses of selected land cover types in the Cordillera Blanca. Additionally, the mean snowline (equilibrium line) altitude change is computed for all of the three mountains and eleven glaciers. Besides, glacier change depending on slope and aspect is evaluated. The results of this diploma thesis are presented in maps, tables and charts. The results of the classification are compared with the GLIMS Glacier Database and the field measurements provided by Adam Emmer, MSc. Finally, the advantages and disadvantages of the new Landsat 8 satellite sensor are discussed. Key words: Remote sensing, Landsat, classification, ice and snow detection, ELA, Cordillera BlancaKatedra aplikované geoinformatiky a kartografieDepartment of Applied Geoinformatics and CartographyPřírodovědecká fakultaFaculty of Scienc

    Spatio-Temporal Distribution of Supra-Glacial Ponds and Ice Cliffs on Verde Glacier, Chile

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    Known for their important role in locally enhancing surface melt, supraglacial ponds and ice cliffs are common features on debris-covered glaciers. We use high resolution satellite imagery to describe pond-cliff systems and surface velocity on Verde debris-covered glacier, Monte Tronador, and Southern Chile. Ponds and ice cliffs represent up to 0.4 and 2.7% of the glacier debris-covered area, respectively. Through the analyzed period and the available data, we found a seasonality in the number of detected ponds, with larger number of ponds at the beginning of the ablation season and less at the end of it. Using feature tracking, we determined glacier surface velocity, finding values up to 55 m/yr on the upper part of the debris-covered area, and decreasing almost to stagnation in the terminus. We found that larger ponds develop in glacier zones of low velocity, while zones of high velocity only contain smaller features. Meanwhile, ice cliffs appeared to be less controlled by surface velocity and gradient. Persistent ice cliffs were detected between 2009 and 2019 and backwasting up to 24 m/yr was measured, highlighting significant local glacier wastage.Fil: Loriaux, Thomas. Centro de Estudios Cientificos; ChileFil: Ruiz, Lucas Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentin
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