TY - JOUR A1 - Philipp, Marius A1 - Dietz, Andreas A1 - Ullmann, Tobias A1 - Kuenzer, Claudia T1 - A circum-Arctic monitoring framework for quantifying annual erosion rates of permafrost coasts JF - Remote Sensing N2 - This study demonstrates a circum-Arctic monitoring framework for quantifying annual change of permafrost-affected coasts at a spatial resolution of 10 m. Frequent cloud coverage and challenging lighting conditions, including polar night, limit the usability of optical data in Arctic regions. For this reason, Synthetic Aperture RADAR (SAR) data in the form of annual median and standard deviation (sd) Sentinel-1 (S1) backscatter images covering the months June–September for the years 2017–2021 were computed. Annual composites for the year 2020 were hereby utilized as input for the generation of a high-quality coastline product via a Deep Learning (DL) workflow, covering 161,600 km of the Arctic coastline. The previously computed annual S1 composites for the years 2017 and 2021 were employed as input data for the Change Vector Analysis (CVA)-based coastal change investigation. The generated DL coastline product served hereby as a reference. Maximum erosion rates of up to 67 m per year could be observed based on 400 m coastline segments. Overall highest average annual erosion can be reported for the United States (Alaska) with 0.75 m per year, followed by Russia with 0.62 m per year. Out of all seas covered in this study, the Beaufort Sea featured the overall strongest average annual coastal erosion of 1.12 m. Several quality layers are provided for both the DL coastline product and the CVA-based coastal change analysis to assess the applicability and accuracy of the output products. The predicted coastal change rates show good agreement with findings published in previous literature. The proposed methods and data may act as a valuable tool for future analysis of permafrost loss and carbon emissions in Arctic coastal environments. KW - permafrost KW - coastal erosion KW - circum-Arctic KW - deep learning KW - change vector analysis KW - Google Earth Engine KW - synthetic aperture RADAR Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-304447 SN - 2072-4292 VL - 15 IS - 3 ER - TY - JOUR A1 - Uereyen, Soner A1 - Bachofer, Felix A1 - Kuenzer, Claudia T1 - A framework for multivariate analysis of land surface dynamics and driving variables — a case study for Indo-Gangetic river basins JF - Remote Sensing N2 - The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI. KW - time series analysis KW - trends KW - seasonality KW - partial correlation KW - causal networks KW - NDVI KW - snow cover area KW - surface water area KW - Indus-Ganges-Brahmaputra-Meghna KW - Himalaya Karakoram Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-255295 SN - 2072-4292 VL - 14 IS - 1 ER - TY - JOUR A1 - Dirscherl, Mariel A1 - Dietz, Andreas J. A1 - Kneisel, Christof A1 - Kuenzer, Claudia T1 - A novel method for automated supraglacial lake mapping in Antarctica using Sentinel-1 SAR imagery and deep learning JF - Remote Sensing N2 - Supraglacial meltwater accumulation on ice sheets can be a main driver for accelerated ice discharge, mass loss, and global sea-level-rise. With further increasing surface air temperatures, meltwater-induced hydrofracturing, basal sliding, or surface thinning will cumulate and most likely trigger unprecedented ice mass loss on the Greenland and Antarctic ice sheets. While the Greenland surface hydrological network as well as its impacts on ice dynamics and mass balance has been studied in much detail, Antarctic supraglacial lakes remain understudied with a circum-Antarctic record of their spatio-temporal development entirely lacking. This study provides the first automated supraglacial lake extent mapping method using Sentinel-1 synthetic aperture radar (SAR) imagery over Antarctica and complements the developed optical Sentinel-2 supraglacial lake detection algorithm presented in our companion paper. In detail, we propose the use of a modified U-Net for semantic segmentation of supraglacial lakes in single-polarized Sentinel-1 imagery. The convolutional neural network (CNN) is implemented with residual connections for optimized performance as well as an Atrous Spatial Pyramid Pooling (ASPP) module for multiscale feature extraction. The algorithm is trained on 21,200 Sentinel-1 image patches and evaluated in ten spatially or temporally independent test acquisitions. In addition, George VI Ice Shelf is analyzed for intra-annual lake dynamics throughout austral summer 2019/2020 and a decision-level fused Sentinel-1 and Sentinel-2 maximum lake extent mapping product is presented for January 2020 revealing a more complete supraglacial lake coverage (~770 km\(^2\)) than the individual single-sensor products. Classification results confirm the reliability of the proposed workflow with an average Kappa coefficient of 0.925 and a F\(_1\)-score of 93.0% for the supraglacial water class across all test regions. Furthermore, the algorithm is applied in an additional test region covering supraglacial lakes on the Greenland ice sheet which further highlights the potential for spatio-temporal transferability. Future work involves the integration of more training data as well as intra-annual analyses of supraglacial lake occurrence across the whole continent and with focus on supraglacial lake development throughout a summer melt season and into Antarctic winter. KW - Antarctica KW - Antarctic ice sheet KW - supraglacial lakes KW - ice sheet hydrology KW - Sentinel-1 KW - remote sensing KW - machine learning KW - deep learning KW - semantic segmentation KW - convolutional neural network Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-222998 SN - 2072-4292 VL - 13 IS - 2 ER - TY - JOUR A1 - Ha, Tuyen V. A1 - Huth, Juliane A1 - Bachofer, Felix A1 - Kuenzer, Claudia T1 - A review of Earth observation-based drought studies in Southeast Asia JF - Remote Sensing N2 - Drought is a recurring natural climatic hazard event over terrestrial land; it poses devastating threats to human health, the economy, and the environment. Given the increasing climate crisis, it is likely that extreme drought phenomena will become more frequent, and their impacts will probably be more devastating. Drought observations from space, therefore, play a key role in dissimilating timely and accurate information to support early warning drought management and mitigation planning, particularly in sparse in-situ data regions. In this paper, we reviewed drought-related studies based on Earth observation (EO) products in Southeast Asia between 2000 and 2021. The results of this review indicated that drought publications in the region are on the increase, with a majority (70%) of the studies being undertaken in Vietnam, Thailand, Malaysia and Indonesia. These countries also accounted for nearly 97% of the economic losses due to drought extremes. Vegetation indices from multispectral optical remote sensing sensors remained a primary source of data for drought monitoring in the region. Many studies (~21%) did not provide accuracy assessment on drought mapping products, while precipitation was the main data source for validation. We observed a positive association between spatial extent and spatial resolution, suggesting that nearly 81% of the articles focused on the local and national scales. Although there was an increase in drought research interest in the region, challenges remain regarding large-area and long time-series drought measurements, the combined drought approach, machine learning-based drought prediction, and the integration of multi-sensor remote sensing products (e.g., Landsat and Sentinel-2). Satellite EO data could be a substantial part of the future efforts that are necessary for mitigating drought-related challenges, ensuring food security, establishing a more sustainable economy, and the preservation of the natural environment in the region. KW - drought KW - drought impact KW - agricultural drought KW - hydrological drought KW - meteorological drought KW - earth observation KW - remote sensing KW - Southeast Asia KW - Mekong Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-286258 SN - 2072-4292 VL - 14 IS - 15 ER - TY - JOUR A1 - Fekri, Erfan A1 - Latifi, Hooman A1 - Amani, Meisam A1 - Zobeidinezhad, Abdolkarim T1 - A training sample migration method for wetland mapping and monitoring using Sentinel data in Google Earth Engine JF - Remote Sensing N2 - Wetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient archived images over different spatial scales. However, a lack of sufficient consistent training samples at different times is a significant limitation of multi-temporal wetland monitoring. In this study, a new training sample migration method was developed to identify unchanged training samples to be used in wetland classification and change analyses over the International Shadegan Wetland (ISW) areas of southwestern Iran. To this end, we first produced the wetland map of a reference year (2020), for which we had training samples, by combining Sentinel-1 and Sentinel-2 images and the Random Forest (RF) classifier in Google Earth Engine (GEE). The Overall Accuracy (OA) and Kappa coefficient (KC) of this reference map were 97.93% and 0.97, respectively. Then, an automatic change detection method was developed to migrate unchanged training samples from the reference year to the target years of 2018, 2019, and 2021. Within the proposed method, three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the mean Standard Deviation (SD) of the spectral bands, along with two similarity measures of the Euclidean Distance (ED) and Spectral Angle Distance (SAD), were computed for each pair of reference–target years. The optimum threshold for unchanged samples was also derived using a histogram thresholding approach, which led to selecting the samples that were most likely unchanged based on the highest OA and KC for classifying the test dataset. The proposed migration sample method resulted in high OAs of 95.89%, 96.83%, and 97.06% and KCs of 0.95, 0.96, and 0.96 for the target years of 2018, 2019, and 2021, respectively. Finally, the migrated samples were used to generate the wetland map for the target years. Overall, our proposed method showed high potential for wetland mapping and monitoring when no training samples existed for a target year. KW - wetland KW - Google Earth Engine (GEE) KW - training sample migration KW - sentinel Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-248542 SN - 2072-4292 VL - 13 IS - 20 ER - TY - JOUR A1 - Schamel, Johannes T1 - Ableitung von Präferenzen aus GPS-Trajektorien bei landschaftsbezogenen Erholungsaktivitäten JF - AGIT - Journal für Angewandte Geoinformatik N2 - No abstract available. KW - GPS-Tracking KW - Erholungsplanung Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-153590 VL - 2015 IS - 1 ER - TY - JOUR A1 - Kumar, Navneet A1 - Khamzina, Asia A1 - Knöfel, Patrick A1 - Lamers, John P. A. A1 - Tischbein, Bernhard T1 - Afforestation of degraded croplands as a water-saving option in irrigated region of the Aral Sea Basin JF - Water N2 - Climate change is likely to decrease surface water availability in Central Asia, thereby necessitating land use adaptations in irrigated regions. The introduction of trees to marginally productive croplands with shallow groundwater was suggested for irrigation water-saving and improving the land’s productivity. Considering the possible trade-offs with water availability in large-scale afforestation, our study predicted the impacts on water balance components in the lower reaches of the Amudarya River to facilitate afforestation planning using the Soil and Water Assessment Tool (SWAT). The land-use scenarios used for modeling analysis considered the afforestation of 62% and 100% of marginally productive croplands under average and low irrigation water supply identified from historical land-use maps. The results indicate a dramatic decrease in the examined water balance components in all afforestation scenarios based largely on the reduced irrigation demand of trees compared to the main crops. Specifically, replacing current crops (mostly cotton) with trees on all marginal land (approximately 663 km\(^2\)) in the study region with an average water availability would save 1037 mln m\(^3\) of gross irrigation input within the study region and lower the annual drainage discharge by 504 mln m\(^3\). These effects have a considerable potential to support irrigation water management and enhance drainage functions in adapting to future water supply limitations. KW - drainage ratio KW - irrigation KW - spatial water balance KW - Soil and Water Assessment Tool (SWAT) KW - scenario analysis KW - stream flow KW - water yield Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-239626 SN - 2073-4441 VL - 13 IS - 10 ER - TY - JOUR A1 - Huth, Juliane A1 - Gessner, Ursula A1 - Klein, Igor A1 - Yesou, Hervé A1 - Lai, Xijun A1 - Oppelt, Natascha A1 - Kuenzer, Claudia T1 - Analyzing water dynamics based on Sentinel-1 time series — a study for Dongting Lake wetlands in China JF - Remote Sensing N2 - In China, freshwater is an increasingly scarce resource and wetlands are under great pressure. This study focuses on China's second largest freshwater lake in the middle reaches of the Yangtze River — the Dongting Lake — and its surrounding wetlands, which are declared a protected Ramsar site. The Dongting Lake area is also a research region of focus within the Sino-European Dragon Programme, aiming for the international collaboration of Earth Observation researchers. ESA's Copernicus Programme enables comprehensive monitoring with area-wide coverage, which is especially advantageous for large wetlands that are difficult to access during floods. The first year completely covered by Sentinel-1 SAR satellite data was 2016, which is used here to focus on Dongting Lake's wetland dynamics. The well-established, threshold-based approach and the high spatio-temporal resolution of Sentinel-1 imagery enabled the generation of monthly surface water maps and the analysis of the inundation frequency at a 10 m resolution. The maximum extent of the Dongting Lake derived from Sentinel-1 occurred in July 2016, at 2465 km\(^2\), indicating an extreme flood year. The minimum size of the lake was detected in October, at 1331 km\(^2\). Time series analysis reveals detailed inundation patterns and small-scale structures within the lake that were not known from previous studies. Sentinel-1 also proves to be capable of mapping the wetland management practices for Dongting Lake polders and dykes. For validation, the lake extent and inundation duration derived from the Sentinel-1 data were compared with excerpts from the Global WaterPack (frequently derived by the German Aerospace Center, DLR), high-resolution optical data, and in situ water level data, which showed very good agreement for the period studied. The mean monthly extent of the lake in 2016 from Sentinel-1 was 1798 km\(^2\), which is consistent with the Global WaterPack, deviating by only 4%. In summary, the presented analysis of the complete annual time series of the Sentinel-1 data provides information on the monthly behavior of water expansion, which is of interest and relevance to local authorities involved in water resource management tasks in the region, as well as to wetland conservationists concerned with the Ramsar site wetlands of Dongting Lake and to local researchers. KW - Earth observation KW - SAR KW - Sentinel–1 KW - time series KW - Dongting Lake KW - water dynamics KW - floodpath lake KW - Ramsar Convention on Wetlands Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-205977 SN - 2072-4292 VL - 12 IS - 11 ER - TY - JOUR A1 - Ayala-Carrillo, Mariana A1 - Farfán, Michelle A1 - Cárdenas-Nielsen, Anahí A1 - Lemoine-Rodríguez, Richard T1 - Are wildfires in the wildland-urban interface increasing temperatures? A land surface temperature assessment in a semi-arid Mexican city JF - Land N2 - High rates of land conversion due to urbanization are causing fragmented and dispersed spatial patterns in the wildland-urban interface (WUI) worldwide. The occurrence of anthropogenic fires in the WUI represents an important environmental and social issue, threatening not only vegetated areas but also periurban inhabitants, as is the case in many Latin American cities. However, research has not focused on the dynamics of the local climate in the WUI. This study analyzes whether wildfires contribute to the increase in land surface temperature (LST) in the WUI of the metropolitan area of the city of Guanajuato (MACG), a semi-arid Mexican city. We estimated the pre- and post-fire LST for 2018–2021. Spatial clusters of high LST were detected using hot spot analysis and examined using ANOVA and Tukey’s post-hoc statistical tests to assess whether LST is related to the spatial distribution of wildfires during our study period. Our results indicate that the areas where the wildfires occurred, and their surroundings, show higher LST. This has negative implications for the local ecosystem and human population, which lacks adequate infrastructure and services to cope with the effects of rising temperatures. This is the first study assessing the increase in LST caused by wildfires in a WUI zone in Mexico. KW - fire KW - grassland KW - urban climate KW - burned area KW - periurban Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-297308 SN - 2073-445X VL - 11 IS - 12 ER - TY - JOUR A1 - Dirscherl, Mariel A1 - Dietz, Andreas J. A1 - Kneisel, Christof A1 - Kuenzer, Claudia T1 - Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach JF - Remote Sensing N2 - Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017–2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km\(^2\)). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further methodological developments using Sentinel-1 SAR data in order to characterize intraannual supraglacial meltwater dynamics also during polar night and independent of meteorological conditions. In summary, the implementation of the Random Forest classifier enabled the development of the first automated mapping method applied to Sentinel-2 data distributed across all three Antarctic regions. KW - Antarctica KW - Antarctic ice sheet KW - supraglacial lakes KW - surface melt KW - hydrology KW - ice sheet dynamics KW - sentinel-2 KW - remote sensing KW - random forest KW - machine learning Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203735 SN - 2072-4292 VL - 12 IS - 7 ER - TY - JOUR A1 - Schüssler, Ulrich A1 - Skinner, D. N. B. A1 - Roland, N. T1 - Basischer bis intermediärer Plutonismus im NW-Teil des Wilson Terrane, Nordvictorialand, Antarktis N2 - no abstract available KW - Wilson Terrane KW - Nordvictorialand KW - Antarktis Y1 - 1990 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-81850 ER - TY - JOUR A1 - Sponholz, Barbara T1 - Beobachtungen zur Morphodynamik an Koris des südlichen Air-Vorlandes (Niger) N2 - Am Beispiel von vier Koris l) (Téloua, Barghot, Tazolé, Oufaguédout) des südlichen Air-Vorlandes werden Beobachtungen zum aktuellen morphodynamischen Geschehen dieser Region vorgestellt. Durch die regionalen Unterschiede der durchschnittlichen jährlichen Niederschlagshöhen und durch unterschiedliche Charakteristika ihrer Einzugsgebiete können diese vier Koris als typische Vertreter verschiedener fluviatil/äolischer Formungsgruppen angesehen werden. Der Übergang von vorherrschend fluviatiler zu vorherrschend äolischer Formung liegt dabei im Bereich des Kori Oufaguédout, im östlichen Teil des Untersuchungsraumes. Hier konnte durch mehrere Schürfe auch der zeitliche Übergang zwischen beiden Formungskreisen in der jüngeren Reliefgeschichte nachgewiesen werden. N2 - Four koris (Téloua, Barghot, Tazolé, Oufaguédout) in the southern foreland of the Air-Mountains were studied regarding their recent morphological dynamics. Caused by regional differences in mean annual rainfall and the different characteristics of their drainage areas each of these koris represents one group of typical fluvial/eolian influence complex. The transition between fluvial and eolian processes is located in the area of the kori Oufaguédout, in the eastern part of the study area. Also the transition in time between the both processes during younger relief formation periods was proven there. KW - Geographie Y1 - 1988 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-53579 ER - TY - JOUR A1 - Paeth, Heiko A1 - Pollinger, Felix T1 - Changes in mean flow and atmospheric wave activity in the North Atlantic sector JF - Quarterly Journal of the Royal Meteorological Society N2 - In recent years, the midlatitudes are characterized by more intense heatwaves in summer and sometimes severe cold spells in winter that might emanate from changes in atmospheric circulation, including synoptic‐scale and planetary wave activity in the midlatitudes. In this study, we investigate the heat and momentum exchange between the mean flow and atmospheric waves in the North Atlantic sector and adjacent continents by means of the physically consistent Eliassen–Palm flux diagnostics applied to reanalysis and forced climate model data. In the long‐term mean, momentum is transferred from the mean flow to atmospheric waves in the northwest Atlantic region, where cyclogenesis prevails. Further downstream over Europe, eddy fluxes return momentum to the mean flow, sustaining the jet stream against friction. A global climate model is able to reproduce this pattern with high accuracy. Atmospheric variability related to atmospheric wave activity is much more expressed at the intraseasonal rather than the interannual time‐scale. Over the last 40 years, reanalyses reveal a northward shift of the jet stream and a weakening of intraseasonal weather variability related to synoptic‐scale and planetary wave activity. This pertains to the winter and summer seasons, especially over central Europe, and correlates with changes in the North Atlantic Oscillation as well as regional temperature and precipitation. A very similar phenomenon is found in a climate model simulation with business‐as‐usual scenario, suggesting an anthropogenic trigger in the weakening of intraseasonal weather variability in the midlatitudes. KW - atmospheric waves KW - climate change KW - Elissen-Palm flux KW - jet stream Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-208079 VL - 145 IS - 725 ER - TY - JOUR A1 - Yang, Xuting A1 - Yao, Wanqiang A1 - Li, Pengfei A1 - Hu, Jinfei A1 - Latifi, Hooman A1 - Kang, Li A1 - Wang, Ningjing A1 - Zhang, Dingming T1 - Changes of SOC content in China's Shendong coal mining area during 1990–2020 investigated using remote sensing techniques JF - Sustainability N2 - Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990–2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R\(^2\) > 0.69, p < 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg\(^{−1}\) in 1990 to 9.23 gC·kg\(^{−1}\) in 2010 and then declined to 5.31 gC·Kg\(^{−1}\) in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone. KW - loess plateau KW - coal mining area KW - SOC content prediction KW - human disturbance KW - vegetation restoration KW - climate change Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-278939 SN - 2071-1050 VL - 14 IS - 12 ER - TY - JOUR A1 - Ibebuchi, Chibuike Chiedozie T1 - Circulation pattern controls of wet days and dry days in Free State, South Africa JF - Meteorology and Atmospheric Physics N2 - Atmospheric circulation is a vital process in the transport of heat, moisture, and pollutants around the globe. The variability of rainfall depends to some extent on the atmospheric circulation. This paper investigates synoptic situations in southern Africa that can be associated with wet days and dry days in Free State, South Africa, in addition to the underlying dynamics. Principal component analysis was applied to the T-mode matrix (variable is time series and observation is grid points at which the field was observed) of daily mean sea level pressure field from 1979 to 2018 in classifying the circulation patterns in southern Africa. 18 circulation types (CTs) were classified in the study region. From the linkage of the CTs to the observed rainfall data, from 11 stations in Free State, it was found that dominant austral winter and late austral autumn CTs have a higher probability of being associated with dry days in Free State. Dominant austral summer and late austral spring CTs were found to have a higher probability of being associated with wet days in Free State. Cyclonic/anti-cyclonic activity over the southwest Indian Ocean, explained to a good extent, the inter-seasonal variability of rainfall in Free State. The synoptic state associated with a stronger anti-cyclonic circulation at the western branch of the South Indian Ocean high-pressure, during austral summer, leading to enhanced low-level moisture transport by southeast winds was found to have the highest probability of being associated with above-average rainfall in most regions in Free State. On the other hand, the synoptic state associated with enhanced transport of cold dry air, by the extratropical westerlies, was found to have the highest probability of being associated with (winter) dryness in Free State. KW - South Africa KW - atmospheric circulation KW - circulation patterns Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-268552 SN - 1436-5065 VL - 133 IS - 5 ER - TY - JOUR A1 - Latifi, Hooman A1 - Valbuena, Ruben T1 - Current trends in forest ecological applications of three-dimensional remote sensing: Transition from experimental to operational solutions? JF - Forests N2 - The alarming increase in the magnitude and spatiotemporal patterns of changes in composition, structure and function of forest ecosystems during recent years calls for enhanced cross-border mitigation and adaption measures, which strongly entail intensified research to understand the underlying processes in the ecosystems as well as their dynamics. Remote sensing data and methods are nowadays the main complementary sources of synoptic, up-to-date and objective information to support field observations in forest ecology. In particular, analysis of three-dimensional (3D) remote sensing data is regarded as an appropriate complement, since they are hypothesized to resemble the 3D character of most forest attributes. Following their use in various small-scale forest structural analyses over the past two decades, these sources of data are now on their way to be integrated in novel applications in fields like citizen science, environmental impact assessment, forest fire analysis, and biodiversity assessment in remote areas. These and a number of other novel applications provide valuable material for the Forests special issue “3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function”, which shows the promising future of these technologies and improves our understanding of the potentials and challenges of 3D remote sensing in practical forest ecology worldwide. KW - 3D remote sensing KW - composition KW - forest ecology KW - function KW - structure Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193282 SN - 1999-4907 VL - 10 IS - 10 ER - TY - JOUR A1 - Buchelt, Sebastian A1 - Blöthe, Jan Henrik A1 - Kuenzer, Claudia A1 - Schmitt, Andreas A1 - Ullmann, Tobias A1 - Philipp, Marius A1 - Kneisel, Christof T1 - Deciphering small-scale seasonal surface dynamics of rock glaciers in the Central European Alps using DInSAR time series JF - Remote Sensing N2 - The Essential Climate Variable (ECV) Permafrost is currently undergoing strong changes due to rising ground and air temperatures. Surface movement, forming characteristic landforms such as rock glaciers, is one key indicator for mountain permafrost. Monitoring this movement can indicate ongoing changes in permafrost; therefore, rock glacier velocity (RGV) has recently been added as an ECV product. Despite the increased understanding of rock glacier dynamics in recent years, most observations are either limited in terms of the spatial coverage or temporal resolution. According to recent studies, Sentinel-1 (C-band) Differential SAR Interferometry (DInSAR) has potential for monitoring RGVs at high spatial and temporal resolutions. However, the suitability of DInSAR for the detection of heterogeneous small-scale spatial patterns of rock glacier velocities was never at the center of these studies. We address this shortcoming by generating and analyzing Sentinel-1 DInSAR time series over five years to detect small-scale displacement patterns of five high alpine permafrost environments located in the Central European Alps on a weekly basis at a range of a few millimeters. Our approach is based on a semi-automated procedure using open-source programs (SNAP, pyrate) and provides East-West displacement and elevation change with a ground sampling distance of 5 m. Comparison with annual movement derived from orthophotos and unpiloted aerial vehicle (UAV) data shows that DInSAR covers about one third of the total movement, which represents the proportion of the year suited for DInSAR, and shows good spatial agreement (Pearson R: 0.42–0.74, RMSE: 4.7–11.6 cm/a) except for areas with phase unwrapping errors. Moreover, the DInSAR time series unveils spatio-temporal variations and distinct seasonal movement dynamics related to different drivers and processes as well as internal structures. Combining our approach with in situ observations could help to achieve a more holistic understanding of rock glacier dynamics and to assess the future evolution of permafrost under changing climatic conditions. KW - Sentinel-1 KW - DInSAR KW - rock glaciers KW - seasonal dynamics KW - periglacial KW - feature tracking Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-362939 SN - 2072-4292 VL - 15 IS - 12 ER - TY - JOUR A1 - Kleemann, Janina A1 - Zamora, Camilo A1 - Villacis-Chiluisa, Alexandra Belen A1 - Cuenca, Pablo A1 - Koo, Hongmi A1 - Noh, Jin Kyoung A1 - Fürst, Christine A1 - Thiel, Michael T1 - Deforestation in continental Ecuador with a focus on protected areas JF - Land N2 - Forest conservation is of particular concern in tropical regions where a large refuge of biodiversity is still existing. These areas are threatened by deforestation, forest degradation and fragmentation. Especially, pressures of anthropogenic activities adjacent to these areas significantly influence conservation effectiveness. Ecuador was chosen as study area since it is a globally relevant center of forest ecosystems and biodiversity. We identified hotspots of deforestation on the national level of continental Ecuador between 1990 and 2018, analyzed the most significant drivers of deforestation on national and biome level (the Coast, the Andes, The Amazon) as well as inside protected areas in Ecuador by using multiple regression analysis. We separated the national system of protected areas (SNAP) into higher and lower protection levels. Besides SNAP, we also considered Biosphere Reserves (BRs) and Ramsar sites. In addition, we investigated the rates and spatial patterns of deforestation in protected areas and buffer zones (5 km and 10 km outwards the protected area boundaries) using landscape metrics. Between 1990 and 2018, approximately 4% of the accumulated deforestation occurred within the boundaries of SNAP, and up to 25.5% in buffer zones. The highest rates of deforestation have been found in the 5 km buffer zone around the protected areas with the highest protection level. Protected areas and their buffer zones with higher protection status were identified as the most deforested areas among SNAP. BRs had the highest deforestation rates among all protected areas but most of these areas just became BRs after the year 2000. The most important driver of deforestation is agriculture. Other relevant drivers differ between the biomes. The results suggest that the SNAP is generally effective to prevent deforestation within their protection boundaries. However, deforestation around protected areas can undermine conservation strategies to sustain biodiversity. Actions to address such dynamics and patterns of deforestation and forest fragmentation, and developing conservation strategies of their landscape context are urgently needed especially in the buffer zones of areas with the highest protection status. KW - conservation KW - driving forces KW - forest KW - loss KW - human pressure KW - land use change KW - landscape metrics KW - protection status KW - spatial analysis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-262078 SN - 2073-445X VL - 11 IS - 2 ER - TY - JOUR A1 - Rösch, Moritz A1 - Plank, Simon T1 - Detailed mapping of lava and ash deposits at Indonesian volcanoes by means of VHR PlanetScope change detection JF - Remote Sensing N2 - Mapping of lava flows in unvegetated areas of active volcanoes using optical satellite data is challenging due to spectral similarities of volcanic deposits and the surrounding background. Using very high-resolution PlanetScope data, this study introduces a novel object-oriented classification approach for mapping lava flows in both vegetated and unvegetated areas during several eruptive phases of three Indonesian volcanoes (Karangetang 2018/2019, Agung 2017, Krakatau 2018/2019). For this, change detection analysis based on PlanetScope imagery for mapping loss of vegetation due to volcanic activity (e.g., lava flows) is combined with the analysis of changes in texture and brightness, with hydrological runoff modelling and with analysis of thermal anomalies derived from Sentinel-2 or Landsat-8. Qualitative comparison of the mapped lava flows showed good agreement with multispectral false color time series (Sentinel-2 and Landsat-8). Reports of the Global Volcanism Program support the findings, indicating the developed lava mapping approach produces valuable results for monitoring volcanic hazards. Despite the lack of bands in infrared wavelengths, PlanetScope proves beneficial for the assessment of risk and near-real-time monitoring of active volcanoes due to its high spatial (3 m) and temporal resolution (mapping of all subaerial volcanoes on a daily basis). KW - lava KW - volcanoes KW - PlanetScope KW - change detection KW - object-based image analysis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-262232 SN - 2072-4292 VL - 14 IS - 5 ER - TY - JOUR A1 - Riyas, Moidu Jameela A1 - Syed, Tajdarul Hassan A1 - Kumar, Hrishikesh A1 - Kuenzer, Claudia T1 - Detecting and analyzing the evolution of subsidence due to coal fires in Jharia coalfield, India using Sentinel-1 SAR data JF - Remote Sensing N2 - Public safety and socio-economic development of the Jharia coalfield (JCF) in India is critically dependent on precise monitoring and comprehensive understanding of coal fires, which have been burning underneath for more than a century. This study utilizes New-Small BAseline Subset (N-SBAS) technique to compute surface deformation time series for 2017–2020 to characterize the spatiotemporal dynamics of coal fires in JCF. The line-of-sight (LOS) surface deformation estimated from ascending and descending Sentinel-1 SAR data are subsequently decomposed to derive precise vertical subsidence estimates. The most prominent subsidence (~22 cm) is observed in Kusunda colliery. The subsidence regions also correspond well with the Landsat-8 based thermal anomaly map and field evidence. Subsequently, the vertical surface deformation time-series is analyzed to characterize temporal variations within the 9.5 km\(^2\) area of coal fires. Results reveal that nearly 10% of the coal fire area is newly formed, while 73% persisted throughout the study period. Vulnerability analyses performed in terms of the susceptibility of the population to land surface collapse demonstrate that Tisra, Chhatatanr, and Sijua are the most vulnerable towns. Our results provide critical information for developing early warning systems and remediation strategies. KW - coal fire KW - InSAR KW - subsidence KW - remote sensing KW - coal KW - interferometry KW - SBAS Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-236703 SN - 2072-4292 VL - 13 IS - 8 ER -