550 Geowissenschaften
Refine
Has Fulltext
- yes (77)
Is part of the Bibliography
- yes (77)
Year of publication
Document Type
- Journal article (77) (remove)
Keywords
- remote sensing (15)
- climate change (6)
- time series (6)
- earth observation (5)
- forest (5)
- MODIS (4)
- drought (4)
- machine learning (4)
- review (4)
- Earth Observation (3)
- Earth observation (3)
- Landsat (3)
- Sentinel-1 (3)
- South Africa (3)
- dynamics (3)
- land cover (3)
- permafrost (3)
- Antarctic ice sheet (2)
- Antarctica (2)
- Geologie (2)
- Germany (2)
- Google Earth Engine (2)
- Kilombero (2)
- NDVI (2)
- SAR (2)
- Sentinel-2 (2)
- TanDEM-X (2)
- biodiversity (2)
- change detection (2)
- deep learning (2)
- forecast (2)
- forest ecology (2)
- glaciers (2)
- global change (2)
- hydrology (2)
- land use (2)
- movement ecology (2)
- object-based classification (2)
- optical remote sensing (2)
- probability (2)
- random forest (2)
- satellite data (2)
- supraglacial lakes (2)
- time series analysis (2)
- wetland (2)
- 3D (1)
- 3D remote sensing (1)
- 3‐D electrical resistivity imaging (1)
- AVHRR (1)
- Aggeneys (1)
- Alps (1)
- Angola (1)
- Animal Tracking (1)
- Asia (1)
- Bavaria (1)
- Biostratigraphy (1)
- Blue Spot Analysis (1)
- Broken Hill (1)
- CORDEX Africa (1)
- Cambrian (1)
- Covid‐19 (1)
- DEM (1)
- DEUQUA (1)
- DSM (1)
- Dongting Lake (1)
- ERT (1)
- Einzelhandel (1)
- El Niño (1)
- Elissen-Palm flux (1)
- Erholungsplanung (1)
- Europe (1)
- Extreme flows (1)
- Eyjafjallajökull 2010 (1)
- Fractional cover analysis (1)
- GEDI (1)
- GPS-Tracking (1)
- GSV (1)
- Gamsberg (1)
- Ghana (1)
- GlobALS (1)
- Global Ecosystem Dynamics Investigation (1)
- Google Earth Engine (GEE) (1)
- Greenland ice sheet (1)
- Herodotus (1)
- Himalaya Karakoram (1)
- Hunsrueck (1)
- InSAR (1)
- InSAR height (1)
- Indus-Ganges-Brahmaputra-Meghna (1)
- Isheru (1)
- Kunduz River Basin (1)
- LST (1)
- Land Change Modeler (1)
- Landsat archive (1)
- Landsat time series (1)
- Lantana camara (1)
- LiDAR (1)
- MODIS time-series (1)
- Mann-Kendall test (1)
- Markov chains (1)
- Mekong (1)
- Morocco (1)
- NDVI thresholds (1)
- Nachhaltigkeitstransformation (1)
- Namibia (1)
- Nile delta (1)
- Nile flow (1)
- Oshana (1)
- PEST (1)
- Pakistan (1)
- PlanetScope (1)
- R (1)
- Ramsar Convention on Wetlands (1)
- RapidEye (1)
- SBAS (1)
- SDG 11.3.1 (1)
- SOC content prediction (1)
- SPOT-6 (1)
- SWAT (1)
- SWAT model (1)
- Scenario analysis (1)
- Sebennitic (1)
- Sentine-1 (1)
- Sentinel–1 (1)
- Snow Line Elevation (1)
- Soil and Water Assessment Tool (SWAT) (1)
- Southeast Asia (1)
- Systematics (1)
- Sápmi (1)
- TIMELINE (1)
- Tanzania (1)
- Tell Basta (1)
- Tian Shan (1)
- Trilobita (1)
- UAV (1)
- Uzbekistan (1)
- WaSiM-ETH (1)
- West Africa (1)
- West Gondwana (1)
- Western Cape (1)
- Western Europe (1)
- Zambia (1)
- accuracy (1)
- agricultural drought (1)
- agricultural mapping (1)
- agriculture (1)
- air quality (1)
- alpha diversity (1)
- ancient Egypt (1)
- anthroposphere (1)
- aquaculture (1)
- atmospheric circulation (1)
- atmospheric correction (1)
- atmospheric waves (1)
- automatic processing (1)
- base metal deposit (1)
- beech (1)
- beta diversity (1)
- big earth data (1)
- biosphere (1)
- black carbon AOD (1)
- boreholes (1)
- canopy height (1)
- causal networks (1)
- change vector analysis (1)
- circulation patterns (1)
- circulation type (1)
- circum-Arctic (1)
- class homogeneity (1)
- climate extremes (1)
- climate related trends (1)
- climate scenarios (1)
- coal (1)
- coal fire (1)
- coal mining area (1)
- coastal erosion (1)
- coastal zone (1)
- coastline dynamics (1)
- composition (1)
- conservation (1)
- consumptive water use (1)
- convolutional neural network (1)
- crop statistics (1)
- cryosphere (1)
- culturable command area (1)
- damage assessment disaster (1)
- database (1)
- debris-covered glaciers (1)
- digitalisation initiative (1)
- disaster (1)
- distributary (1)
- diurnal (1)
- drainage ratio (1)
- drilling (1)
- driving forces (1)
- drought impact (1)
- drought stress indicators (1)
- eCognition (1)
- earthquake (1)
- electrical resistivity tomography (1)
- emissivity (1)
- energy (1)
- entrainment (1)
- environmental justice (1)
- environmental modeling (1)
- error estimation (1)
- eruption rate (1)
- evapotranspiration (1)
- explosive volcanism (1)
- e‐commerce (1)
- flood (1)
- floodpath lake (1)
- food production (1)
- forest disturbances (1)
- forest hydrology (1)
- forest monitoring (1)
- forest resources inventory (1)
- forest structure Germany (1)
- framing (1)
- function (1)
- galamsey (1)
- gamma diversity (1)
- general circulation model (1)
- geoarchaeology (1)
- geomorphology (1)
- gis (1)
- global (1)
- global warming (1)
- ground penetrating radar (1)
- groundwater (1)
- ground‐penetrating radar (1)
- harmonization (1)
- heat wave (1)
- historical (1)
- hotspot analysis (1)
- human disturbance (1)
- human pressure (1)
- hydrological drought (1)
- hydrological modelling (1)
- hydrological regime (1)
- ice sheet dynamics (1)
- ice sheet hydrology (1)
- image (1)
- image artifacts (1)
- impervious surface (1)
- indicator importance assessment (1)
- infrasound (1)
- integration (1)
- intercomparison (1)
- interferometry (1)
- interpolation (1)
- inundation (1)
- inverse parameterization (1)
- irrigated agriculture (1)
- irrigation (1)
- irrigation pricing (1)
- jet stream (1)
- jets (1)
- land cover change (1)
- land surface (1)
- land surface temperature (1)
- land surface temperature (LST) (1)
- land use change (1)
- land use/cover pattern (LUCP) (1)
- land-use/land-cover change (1)
- landcover changes (1)
- landsat (1)
- landscape metrics (1)
- large‐scale atmospheric circulation modes (1)
- lava (1)
- letzte Meile (1)
- loess plateau (1)
- lokaler Onlinemarktplatz (1)
- loss (1)
- low-cost applications (1)
- management (1)
- mass (1)
- metamorphic sulfidation (1)
- meteorological drought (1)
- mineralization (1)
- mining (1)
- modeling (1)
- models (1)
- mountains (1)
- multi-sensor (1)
- multi-spectral (1)
- multispectral VNIR (1)
- multitemporal metrics (1)
- multi‐model ensemble (1)
- nature conservation (1)
- near-field monitoring (1)
- near-surface geophysics (1)
- networking (1)
- nu SVR (1)
- object-based image analysis (1)
- oil spill (1)
- optical diversity (1)
- optimization (1)
- palaeontology (1)
- paleoclimate (1)
- paleoenvironment (1)
- palsa development (1)
- pan (1)
- partial correlation (1)
- peatland (1)
- penetration bias (1)
- performance assessment (1)
- phenology (1)
- pilot-point-approach (1)
- platform economy (1)
- plumes (1)
- polarimetery (1)
- pollution (1)
- ponds (1)
- population change (1)
- post-classification comparison (1)
- preface (1)
- protection status (1)
- pulsating explosive eruptions (1)
- radar (1)
- random forest regression (1)
- regional climate model (1)
- reliability (1)
- renewable energy (1)
- resource mapping (1)
- resource suitability (1)
- retrogressive thaw slump (1)
- river discharge (1)
- robust change vector analysis (1)
- sacred lakes (1)
- sar (1)
- satellite remote sensing (1)
- scenario analysis (1)
- seasonal (1)
- seasonality (1)
- sedimentology (1)
- segmentation (1)
- semantic segmentation (1)
- sensitivity analysis (1)
- sentinel (1)
- sentinel-2 (1)
- slope bogs (1)
- snow cover area (1)
- snow hydrology (1)
- snow parameters (1)
- snow variability (1)
- snowmelt runoff model (1)
- soil matric potential (1)
- source parameters (1)
- southern annular mode (1)
- spatial analysis (1)
- spatial scale (1)
- spatial water balance (1)
- spatiotemporal slump development (1)
- species (1)
- spectral diversity (1)
- spectral variation hypothesis (1)
- spring flood (1)
- statistical modeling (1)
- storage volume (1)
- stream flow (1)
- structure (1)
- sub-pixel coastline extraction (1)
- subpixel (1)
- subsidence (1)
- subsurface hydrology (1)
- sulfide inclusions (1)
- surface melt (1)
- surface reflectances (1)
- surface urban heat island (SUHI) (1)
- surface water (1)
- surface water area (1)
- sustainable irrigation system (1)
- synthetic aperture RADAR (1)
- tasselled cap (1)
- temperature (1)
- thermal infrared (1)
- tikhonov regularization (1)
- time-series features (1)
- training sample migration (1)
- trend analysis (1)
- trends (1)
- two‐sided markets (1)
- uncertainties (1)
- uncertainty (1)
- uneven-aged mountainous (1)
- urban environments (1)
- urbane Logistik (1)
- vDEUQUA2021 (1)
- validation (1)
- value of water (1)
- variability (1)
- vegetation indices (1)
- vegetation restoration (1)
- volcano (1)
- volcanoes (1)
- water (1)
- water balance (1)
- water dynamics (1)
- water management (1)
- water retention (1)
- water yield (1)
- wetland mapping (1)
- wind speed (1)
Institute
- Institut für Geographie und Geologie (77) (remove)
EU-Project number / Contract (GA) number
- 20-3044-2-11 (1)
- 308377 (1)
- 776019 (1)
Estimating penetration-related X-band InSAR elevation bias: a study over the Greenland ice sheet
(2019)
Accelerating melt on the Greenland ice sheet leads to dramatic changes at a global scale. Especially in the last decades, not only the monitoring, but also the quantification of these changes has gained considerably in importance. In this context, Interferometric Synthetic Aperture Radar (InSAR) systems complement existing data sources by their capability to acquire 3D information at high spatial resolution over large areas independent of weather conditions and illumination. However, penetration of the SAR signals into the snow and ice surface leads to a bias in measured height, which has to be corrected to obtain accurate elevation data. Therefore, this study purposes an easy transferable pixel-based approach for X-band penetration-related elevation bias estimation based on single-pass interferometric coherence and backscatter intensity which was performed at two test sites on the Northern Greenland ice sheet. In particular, the penetration bias was estimated using a multiple linear regression model based on TanDEM-X InSAR data and IceBridge laser-altimeter measurements to correct TanDEM-X Digital Elevation Model (DEM) scenes. Validation efforts yielded good agreement between observations and estimations with a coefficient of determination of R\(^2\) = 68% and an RMSE of 0.68 m. Furthermore, the study demonstrates the benefits of X-band penetration bias estimation within the application context of ice sheet elevation change detection.
The Kunduz River is one of the main tributaries of the Amu Darya Basin in North Afghanistan. Many communities live in the Kunduz River Basin (KRB), and its water resources have been the basis of their livelihoods for many generations. This study investigates climate change impacts on the KRB catchment. Rare station data are, for the first time, used to analyze systematic trends in temperature, precipitation, and river discharge over the past few decades, while using Mann–Kendall and Theil–Sen trend statistics. The trends show that the hydrology of the basin changed significantly over the last decades. A comparison of landcover data of the river basin from 1992 and 2019 shows significant changes that have additional impact on the basin hydrology, which are used to interpret the trend analysis. There is considerable uncertainty due to the data scarcity and gaps in the data, but all results indicate a strong tendency towards drier conditions. An extreme warming trend, partly above 2 °C since the 1960s in combination with a dramatic precipitation decrease by more than −30% lead to a strong decrease in river discharge. The increasing glacier melt compensates the decreases and leads to an increase in runoff only in the highland parts of the upper catchment. The reduction of water availability and the additional stress on the land leads to a strong increase of barren land and a reduction of vegetation cover. The detected trends and changes in the basin hydrology demand an active management of the already scarce water resources in order to sustain water supply for agriculture and ecosystems in the KRB.
The Niger Delta belongs to the largest swamp and mangrove forests in the world hosting many endemic and endangered species. Therefore, its conservation should be of highest priority. However, the Niger Delta is confronted with overexploitation, deforestation and pollution to a large extent. In particular, oil spills threaten the biodiversity, ecosystem services, and local people. Remote sensing can support the detection of spills and their potential impact when accessibility on site is difficult. We tested different vegetation indices to assess the impact of oil spills on the land cover as well as to detect accumulations (hotspots) of oil spills. We further identified which species, land cover types, and protected areas could be threatened in the Niger Delta due to oil spills. The results showed that the Enhanced Vegetation Index, the Normalized Difference Vegetation Index, and the Soil Adjusted Vegetation Index were more sensitive to the effects of oil spills on different vegetation cover than other tested vegetation indices. Forest cover was the most affected land-cover type and oil spills also occurred in protected areas. Threatened species are inhabiting the Niger Delta Swamp Forest and the Central African Mangroves that were mainly affected by oil spills and, therefore, strong conservation measures are needed even though security issues hamper the monitoring and control.
Die Covid-19-Pandemie gilt in vielen gesellschaftlichen Teilbereichen als Beschleuniger für Transformationsprozesse. Auch im Bereich der Organisation urbaner Logistik und Einzelhandelslandschaften etablieren sich neue Akteur*innen und Funktionen. Logistiker*innen integrieren lokale Onlinemarktplätze in ihre Profile und der stationäre Einzelhandel generiert Wettbewerbsfähigkeit gegenüber großen Onlinehändler*innen über die Nutzung lokaler Radlogistiknetzwerke, mittels derer Lieferungen noch am Tag der Bestellung (Same-Day-Delivery) verteilt werden können. Damit leisten die involvierten Akteur*innen potenziell auch einen Beitrag zur Nachhaltigkeitstransformation im Bereich urbaner Logistiksysteme. Im Fokus steht das Fallbeispiel WüLivery, ein Kooperationsprojekt des Stadtmarketingvereins, der Wirtschaftsförderung, Radlogistiker*innen sowie Einzelhändler*innen in Würzburg, welches während des zweiten coronabedingten Lockdowns im November 2020 umgesetzt wurde. Die entstehenden Dynamiken und Organisationsformen werden auf Basis von 11 Expert*inneninterviews dargestellt und analysiert. Es kann gezeigt werden, dass städtische Akteur*innen grundlegende Mediator*innen für Transformationsprozesse darstellen und Einzelhändler*innen und lokale Onlinemarktplätze als Katalysator*innen fungieren können. Das ist auch vor dem Hintergrund planerischer und politischer Kommunikationsprozesse zur Legitimation neuer Verkehrsinfrastrukturen nutzbar, da die einzelnen Akteur*innengruppen in Austausch kommen und ein gesteigertes Bewusstsein für die jeweiligen Bedarfe entsteht.
Numerous ephemeral rivers and thousands of natural pans characterize the transboundary Iishana-System of the Cuvelai Basin between Namibia and Angola. After the rainy season, surface water stored in pans is often the only affordable water source for many people in rural areas. High inter- and intra-annual rainfall variations in this semiarid environment provoke years of extreme flood events and long periods of droughts. Thus, the issue of water availability is playing an increasingly important role in one of the most densely populated and fastest growing regions in southwestern Africa. Currently, there is no transnational approach to quantifying the potential storage and supply functions of the Iishana-System. To bridge these knowledge gaps and to increase the resilience of the local people's livelihood, suitable pans for expansion as intermediate storage were identified and their metrics determined. Therefore, a modified Blue Spot Analysis was performed, based on the high-resolution TanDEM-X digital elevation model. Further, surface area–volume ratio calculations were accomplished for finding suitable augmentation sites in a first step. The potential water storage volume of more than 190,000 pans was calculated at 1.9 km\(^3\). Over 2200 pans were identified for potential expansion to facilitate increased water supply and flood protection in the future.
Via providing various ecosystem services, the old-growth Hyrcanian forests play a crucial role in the environment and anthropogenic aspects of Iran and beyond. The amount of growing stock volume (GSV) is a forest biophysical parameter with great importance in issues like economy, environmental protection, and adaptation to climate change. Thus, accurate and unbiased estimation of GSV is also crucial to be pursued across the Hyrcanian. Our goal was to investigate the potential of ALOS-2 and Sentinel-1's polarimetric features in combination with Sentinel-2 multi-spectral features for the GSV estimation in a portion of heterogeneously-structured and mountainous Hyrcanian forests. We used five different kernels by the support vector regression (nu-SVR) for the GSV estimation. Because each kernel differently models the parameters, we separately selected features for each kernel by a binary genetic algorithm (GA). We simultaneously optimized R\(^2\) and RMSE in a suggested GA fitness function. We calculated R\(^2\), RMSE to evaluate the models. We additionally calculated the standard deviation of validation metrics to estimate the model's stability. Also for models over-fitting or under-fitting analysis, we used mean difference (MD) index. The results suggested the use of polynomial kernel as the final model. Despite multiple methodical challenges raised from the composition and structure of the study site, we conclude that the combined use of polarimetric features (both dual and full) with spectral bands and indices can improve the GSV estimation over mixed broadleaf forests. This was partially supported by the use of proposed evaluation criterion within the GA, which helped to avoid the curse of dimensionality for the applied SVR and lowest over estimation or under estimation.
The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5–5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15–30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm.
Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.
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.
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.