49 research outputs found

    Rock Glacier Characteristics Under Semiarid Climate Conditions in the Western Nyainqêntanglha Range, Tibetan Plateau

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    Abstract Rock glaciers are receiving increased attention as a potential source of water and indicator of climate change in periglacial landscapes. They consist of an ice‐debris mixture, which creeps downslope. Although rock glaciers are a wide‐spread feature on the Tibetan Plateau, characteristics such as its ice fraction are unknown as a superficial debris layer inhibits remote assessments. We investigate one rock glacier in the semiarid western Nyainqêntanglha range (WNR) with a multi‐method approach, which combines geophysical, geological and geomorphological field investigations with remote sensing techniques. Long‐term kinematics of the rock glacier are detected by 4‐year InSAR time series analysis. The ice content and the active layer are examined by electrical resistivity tomography, ground penetrating radar, and environmental seismology. Short‐term activity (11‐days) is captured by a seismic network. Clast analysis shows a sorting of the rock glacier's debris. The rock glacier has three zones, which are defined by the following characteristics: (a) Two predominant lithology types are preserved separately in the superficial debris patterns, (b) heterogeneous kinematics and seismic activity, and (c) distinct ice fractions. Conceptually, the studied rock glacier is discussed as an endmember of the glacier—debris‐covered glacier—rock glacier continuum. This, in turn, can be linked to its location on the semiarid lee‐side of the mountain range against the Indian summer monsoon. Geologically preconditioned and glacially overprinted, the studied rock glacier is suggested to be a recurring example for similar rock glaciers in the WNR. This study highlights how geology, topography and climate influence rock glacier characteristics and development.Plain Language Summary Climate change has begun to impact all regions of our planet. In cold regions, such as high‐mountain areas, rising temperatures lead to massive melting of glaciers. Besides this evident loss of ice, permafrost, a long‐term ice resource hidden in the subsurface, has started to thaw. Rock glaciers as visible permafrost‐related landforms consist of an ice‐debris mixture, which makes them creep downslope. Due to this movement and their recognizable shape, rock glaciers are permafrost indicators in high‐mountain areas. We investigate one rock glacier in the western Nyaingêntanglha Range (Tibetan Plateau) using field and remote sensing methods to understand its development and to know the current state of its ice core. Our main outcome is, that the heterogeneous creeping behavior, the properties of the debris cover as well as the internal distribution of ice are the results of a continuous development from a glacier into today's rock glacier. In particular, the high ice content in particular sections points to such a glacial precondition. The debris layer covering the internal ice attenuates the effect of climate warming. This makes the rock glacier and similar rock glaciers found in the northern part of the mountain range important future water resources for the semiarid region.Key Points Geophysical and remote sensing methods in concert reveal the morphostructure, ice fraction, and kinematics of the studied rock glacier Rock glacier characteristics are controlled by geology, topography and climate on the Tibetan Plateau The studied rock glacier is conceptually interpreted as the endmember of a glacier—debris‐covered glacier—rock glacier continuu

    Book of Abstracts, ACOP2017 : 2nd Asian Conference on Permafrost

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    One Decade of Glacier Mass Changes on the Tibetan Plateau Derived from Multisensoral Remote Sensing Data

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    The Tibetan Plateau (TP) with an average altitude of 4,500 meters above sea level is characterized by many glaciers and ice caps. Glaciers are a natural indicator for climate variability in this high mountain environment where meteorological stations are rare or non-existent. In addition, the melt water released from the Tibetan glaciers is feeding the headwaters of the major Asian river systems and contributes to the rising levels of endorheic lakes on the plateau. As many people directly rely on the glacier melt water a continuous glacier monitoring program is necessary in this region. In situ measurements of glaciers are important, but are spatial limited due to large logistical efforts, physical constrains and high costs. Remote sensing techniques can overcome this gap and are suitable to complement in situ measurements on a larger scale. In the last decade several remote sensing studies dealt with areal changes of glaciers on the TP. However, glacier area changes only provide a delayed signal to a changing climate and the amount of melt water released from the glaciers cannot be quantified. Therefore it is important to measure the glacier mass balance. In order to estimate glacier mass balances and their spatial differences on the TP, several remote sensing techniques and sensors were synthesized in this thesis. In a first study data from the Ice Cloud and Elevation Satellite (ICESat) mission were employed. ICESat was in orbit between 2003 and 2009 and carried a laser altimeter which recorded highly accurate surface elevation measurements. As in mid-latitudes these measurements are rather sparse glaciers on the TP were grouped into eight climatological homogeneous sub-regions in order to perform a statistical sound analysis of glacier elevation changes. To assess surface elevation changes of a single mountain glacier from ICESat data, an adequate spatial sampling of ICESat measurements need to be present. This is the case for the Grosser Aletschgletscher, located in the Swiss Alps which served as a test site in this thesis. In another study data from the current TanDEM-X satellite mission and from the Shuttle Radar Topography Mission (SRTM) conducted in February 2000 were employed to calculate glacier elevation changes. In a co-authored study, these estimates could be compared with glacier elevation changes obtained from the current French Pléiades satellite mission. In order to calculate glacier mass balances, the derived elevation changes were combined with assumptions about glacier area and ice density in all studies. In this thesis contrasting patterns of glacier mass changes were found on the TP. With an ICESat derived estimate of -15.6±10.1 Gt/a between 2003 and 2009 the average glacier mass balance on the TP was clearly negative. However, some glaciers in the central and north-western part of the TP showed a neutral mass balance or a slightly positive anomaly which was also confirmed by data from the current TanDEM-X satellite mission. A possible explanation of this anomaly in mass balance could be a compensation of the temperature driven glacier melt due to an increase in precipitation

    Remote Sensing of Environmental Changes in Cold Regions

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    This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

    CONTRIBUTIONS OF OPTICAL REMOTE SENSING TO PERMAFROST MAPPING IN DONNELLY TRAINING AREA, ALASKA

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    AN ABSTRACT OF THE THESIS OFKiran Thapa, for the Master of Science degree in Geography and Environmental Resources, presented on April 8, 2020, at Southern Illinois University Carbondale.TITLE: CONTRIBUTIONS OF OPTICAL REMOTE SENSING TO PERMAFROST MAPPING IN DONNELLY TRAINING AREA, ALASKA MAJOR PROFESSOR: Dr. Guangxing Wang Permafrost occupies about a quarter of the northern hemisphere land with 25.5 million ha. Global warming and anthropogenic activities affect the dynamics of permafrost. Snow and permafrost, in turn, serve as an indicator of climate change and human activity disturbance. The dynamics of permafrost are often estimated using interferometric Synthetic Aperture Radar (InSAR) methods. However, acquiring and processing InSAR images is costly and computation intensive. Due to various spectral variables and indices available from optical images, Landsat satellite images that are free-downloadable provide the potential for studying and monitoring changes of permafrost. The overall objective of this study was to explore the use of optical images as a cost-effective method to map permafrost in Donnelly Training Area (DTA) - an installation located in Alaska. First, Landsat 8 OLI/TIRS images from January 2014 to December 2018 were used to calculate various remote sensing variables. The variables included Land Surface Temperature (LST), albedo, Soil Moisture index (SMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Snow Index (NDSI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water index (NDWI), Simple Ratio (SR), Soil Adjusted Vegetation Index (SAVI), Normalized Burn Ratio (NBR), Triangular Vegetation Index(TVI), Visible Atmospherically Resistant Index (VARI), and Active Layer Thickness (ALT). Moreover, elevation, slope, and aspect were obtained from a digital elevation model (DEM). The variables were used to estimate the probabilities of permafrost presence (POP) for DTA. The logistic and linear models were respectively selected and optimized based on logistic and linear stepwise regression for the estimation of and ALT. A total of 414 field observations that were collected from 1994 to 2012 were utilized for validation of models.The results showed that the POP in DTA was significantly affected by all the factors except aspect and EVI. The factor that was most correlated with ln((1-POP)/POP) was elevation, then NDVI, albedo, ALT, LST, NDWI, NDSI, slope, TVI, RSR, SMI, NDBI, SR, SAVI, NBR and VARI. A total of six prediction models were obtained. The elevation, NDVI, LST, TVI, ALT, SLOPE, RSR, SMI, NBR, and NDSI were finally chosen in the best model 5.6 with the smallest relative root mean square error (RMSE) and Akaike information criterion (AIC). The albedo used in previous studies was excluded in the final model, implying that the albedo was not critical to the prediction of POP. In addition to the previously used elevation, NDVI and SMI, other predictors including LST, TVI, ALT, SLOPE, RSR, NBR, and NDSI could not be ignored in the prediction of POP. The model generated reasonable spatial distribution of POP in which POP had greater values in the east, northeast, north, and northwest parts and smaller in the south and southwest parts. Except for NDVI, NDWI, NDSI, aspect, and RSR, moreover, all other predictors showed significant contributions to the prediction of ALT. The SMI, ELEVATION, SAVI, NDBI, SLOPE, LST, SR, EVI, VARI, and TVI were finally selected in the best model 5.14 with the smallest relative RMSE and AIC. The ALT highly varied over the study area with the spatial patterns inversely consistent with those of POP.The results are essential for the governments, policymakers, and other concerned stakeholders to estimate the degradation of permafrost in DTA and minimize the risk of policy decision-making for land use management and planning. This study will help to understand the global climate change, changing ecosystems, increasing concentration in the atmosphere, and human activity-induced disturbance

    Spatial variability of aircraft-measured surface energy fluxes in permafrost landscapes

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    Arctic ecosystems are undergoing a very rapid change due to global warming and their response to climate change has important implications for the global energy budget. Therefore, it is crucial to understand how energy fluxes in the Arctic will respond to any changes in climate related parameters. However, attribution of these responses is challenging because measured fluxes are the sum of multiple processes that respond differently to environmental factors. Here, we present the potential of environmental response functions for quantitatively linking energy flux observations over high latitude permafrost wetlands to environmental drivers in the flux footprints. We used the research aircraft POLAR 5 equipped with a turbulence probe and fast temperature and humidity sensors to measure turbulent energy fluxes along flight tracks across the Alaskan North Slope with the aim to extrapolate the airborne eddy covariance flux measurements from their specific footprint to the entire North Slope. After thorough data pre-processing, wavelet transforms are used to improve spatial discretization of flux observations in order to relate them to biophysically relevant surface properties in the flux footprint. Boosted regression trees are then employed to extract and quantify the functional relationships between the energy fluxes and environmental drivers. Finally, the resulting environmental response functions are used to extrapolate the sensible heat and water vapor exchange over spatio-temporally explicit grids of the Alaskan North Slope. Additionally, simulations from the Weather Research and Forecasting (WRF) model were used to explore the dynamics of the atmospheric boundary layer and to examine results of our extrapolation

    Advances on the investigation of landslides by space-borne synthetic aperture radar interferometry

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    Landslides are destructive geohazards to people and infrastructure, resulting in hundreds of deaths and billions of dollars of damage every year. Therefore, mapping the rate of deformation of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated risks. In this paper, the main outcomes relevant to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon-5 initiative cooperation project ID 59,339 “Earth observation for seismic hazard assessment and landslide early warning system” are reported. The primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks, detect potential landslides in wide regions, and demonstrate EO-based landslide early warning system over selected landslides. This work only focuses on the landslide hazard content of the project, and thus, in order to achieve these objectives, the following tasks were developed up to now: a) a procedure for phase unwrapping errors and tropospheric delay correction; b) an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements; c) the application of polarimetric SAR interferometry (PolInSAR) to increase the number and quality of monitoring points in landslide-prone areas; d) the semiautomatic mapping and preliminary classification of active displacement areas on wide regions; e) the modeling and identification of landslides in order to identify triggering factors or predict future displacements; and f) the application of an InSAR-based landslide early warning system on a selected site. The achieved results, which mainly focus on specific sensitive regions, provide essential assets for planning present and future scientific activities devoted to identifying, mapping, characterizing, monitoring and predicting landslides, as well as for the implementation of early warning systems.This work was supported by the ESA-MOST China DRAGON-5 project with ref. 59339, by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI), and the European Funds for Regional Development under grant [grant number PID2020-117303GB-C22], by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital in the framework of the project CIAICO/2021/335, by the Natural Science Foundation of China [grant numbers 41874005 and 41929001], the Fundamental Research Funds for the Central University [grant numbers 300102269712 and 300102269303], and China Geological Survey Project [grant numbers DD20190637 and DD20190647]. Xiaojie Liu and Liuru Hu have been funded by Chinese Scholarship Council Grants Ref. [grant number 202006560031] and [grant number 202004180062], respectively
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