Suchergebnisse

The growth of settlements and the associated increase of the exploitation of natural resources is an ongoing trend in the Arctic. Buildings and other infrastructure are endangered by destabilization and collaps due to the climate change induced thawing of permafrost in northern regions. The majority of human activity in the Arctic is located near permafrost coasts. Coastal settlements are additionally vulnerable because of coastal erosion, caused by rapid warming and thawing of coastal permafrost. The European Union (EU) Horizon2020 project "Nunataryuk" aims to assess the impacts of thawing land, coast and subsea permafrost on the climate and on local communities in the Arctic. One task of the project is to determine the impacts of permafrost thaw on coastal Arctic infrastructures and to provide appropriate adaptation and mitigation strategies. For that purpose, a circumpolar account of infrastructure is needed. The two polar-orbiting Sentinel-2 satellites of the Copernicus program of the EU are continuously providing multi-spectral images with high spatial and temporal resolution. Sentinel-2 data is of high value for mapping land cover. However, most traditional land cover classifications only contain one class for built-up areas. By using a multi-sensor approach, such as the combination of multispectral and Synthetic Aperture Radar (SAR) data, additional information can be derived that goes beyond the identification of built-up areas. Different types of infrastructure can be distinguished, as it is commonly needed. We assess the potential of combining Sentinel-2 multispectral data with Sentinel-1 (Synthetic Aperture Radar) data for mapping and characterizing Arctic infrastructure. Settlement characteristics (building properties, surface types) have been collected for sites in Greenland and Longyearbyen on Svalbard, Norway. First results based on machine learning methods show that the available resolution (10m) allows the identification of narrow features such as roads, which were not previously identifiable by ...

The climate change induced increased warming of the Arctic is leading to an accelerated thawing of permafrost, which can cause ground subsidence. In consequence, buildings and other infrastructure of local settlements are endangered from destabilization and collapsing in many Arctic regions. The increase of the exploitation of Arctic natural resources has led to the establishment of large industrial infrastructures that are at risk likewise. Most of the human activity in the Arctic is located near permafrost coasts. The thawing of coastal permafrost additionally leads to coastal erosion, which makes Arctic coastal settlements even more vulnerable. The European Union (EU) Horizon2020 project "Nunataryuk" aims to assess the impacts of thawing land, coast and subsea permafrost on the climate and on local communities in the Arctic. One task of the project is to determine the impacts of permafrost thaw on coastal Arctic infrastructures and to provide appropriate adaptation and mitigation strategies. For that purpose, a circumpolar account of infrastructure is needed. During recent years, the two polar-orbiting Sentinel-2 satellites of the Copernicus program of the EU have been acquiring multi-spectral imagery at high spatial and temporal resolution. Sentinel-2 data is a common choice for land cover mapping. Most land cover products only include one class for built-up areas, however. The fusion of optical and Synthetic Aperture Radar (SAR) data for land cover mapping has gained more and more attention over the last years. By combining Sentinel-2 and Sentinel-1 SAR data, the classification of multiple types of infrastructure can be anticipated. Another emerging trend is the application machine learning and deep learning methods for land cover mapping. We present an automated workflow for downloading, processing and classifying Sentinel-2 and Sentinel-1 data in order to map coastal infrastructure with circum-Arctic extent, developed on a highly performant virtual machine (VM) provided by the Copernicus Research and User ...

In this study, the combined surface status and surface soil moisture products retrieved by the ASCAT sensor within the ESA-DUE Permafrost project are compared to the hydrological outputs of the land surface model ORCHIDEE over Northern Eurasia. The objective is to derive broad conclusions as to the strengths and weaknesses of hydrological modelling and, to a minor extent, remote sensing of soil moisture over an area where data is rare and hydrological modelling is though crucial for climate and ecological applications. The spatial and temporal resolutions of the ASCAT products make them suitable for comparison with model outputs. Modelled and remotely-sensed surface frozen and unfrozen statuses agree reasonably well, which allows for a seasonal comparison between modelled and observed (liquid) surface soil moisture. The atmospheric forcing and the snow scheme of the land surface model are identified as causes of moderate model-to-data divergence in terms of surface status. For unfrozen soils, the modelled and remotely-sensed surface soil moisture signals are positively correlated over most of the study area. The correlation deteriorates in the North-Eastern Siberian regions, which is consistent with the lack of accurate model parameters and the scarcity of meteorological data. The model shows a reduced ability to capture the main seasonal dynamics and spatial patterns of observed surface soil moisture in Northern Eurasia, namely a characteristic spring surface moistening resulting from snow melt and flooding. We hypothesize that these weak performances mainly originate from the non-representation of flooding and surface ponding in the model. Further identified limitations proceed from the coarse treatment of the hydrological specificities of mountainous areas and spatial inaccuracies in the meteorological forcing in remote, North-Eastern Siberian areas. Investigations are currently underway to determine to which extent plausible inaccuracies in the satellite data could also contribute to the diagnosed model-to-data discrepancies. ; The European Union FP7-ENV

Knowledge of surface hydrology is essential for many applications, including studies that aim to understand permafrost response to changing climate and the associated feedback mechanisms. Advanced remote sensing techniques make it possible to retrieve a range of land-surface variables, including radar retrieved soil moisture (SSM). It has been pointed out before that soil moisture retrieval from satellite data can be challenging at high latitudes, which correspond to remote areas where ground data are scarce and the applicability of satellite data of this type is essential. This study investigates backscatter variability other than associated with changing soil moisture in order to examine the possible impact on soil moisture retrieval. It focuses on issues specific to SSM retrieval in the Arctic, notably variations related to tundra lakes. ENVISAT Advanced Synthetic Aperture Radar (ASAR) Wide Swath (WS, 120 m) data are used to understand and quantify impacts on Metop (AAdvanced Scatterometer (ASCAT, 25 km) soil moisture retrieval during the snow free period. Sites of interest are chosen according to ASAR WS availability, high or low agreement between output from the land surface model ORCHIDEE and ASCAT derived SSM. Backscatter variations are analyzed with respect to the ASCAT footprint area. It can be shown that the low model agreement is related to water fraction in most cases. No difference could be detected between periods with floating ice (in snow off situation) and ice free periods at the chosen sites. The mean footprint backscatter is however impacted by partial short term surface roughness change. The water fraction correlates with backscatter deviations (relative to a smooth water surface reference image) within the ASCAT footprint areas (R = 0.91) ; European Union FP7-ENV project PAGE21(Changing Permafrost in the Arctic and its Global effects in the 21st century) ; The European Space Agency (ESA) support to Science Element (STSE) program (ESRIN)

Remote sensing of Arctic water bodies is an essential method for monitoring the dynamics of frozen ground. Thaw lake change provides insight into the state of permafrost. In the vast Arctic and sub-Arctic areas capturing changes in lake extent is assisted by satellite data. In particular, active microwave sensors can be used in a straightforward manner for water body classifications. This study uses the pan-Siberian datasets that are provided under the ESA STSE-ALANIS methane project. Surface water classifications in 10-day intervals have been produced using Envisat ASAR (Advanced Synthetic Aperture Radar) operating in wide swath mode. The high temporal frequency of these data allows an investigation of surface hydrology on an intra-annual basis. The current study applies a post-processing algorithm to the ALANIS products in order to investigate changes in surface inundation across the Yamalo-Nenets Autonomous District over the summer period of 2007. Multiple areas are found to exhibit changes in surface inundation. Strong seasonal variations occur in areas where previous investigations determined disappearing lakes. Spring floods associated with the depletion of snow-cover and melt waters as well as floodplain dynamics can be identified. On the Yamal peninsula, these changes occur most dominantly in the west; an area subject to anthropogenic land-use change. Changes in water body extent for each hot spot of seasonal variations are quantified and discussed. ; European Space Agency (ESA) Support to Science Element (STSE) programme (ESRIN) ; the European Union FP7-ENV ; Austrian Science Fund (FWF)