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The area northeast of Sudbury, Ontario, is known for one of the largest unexplained geophysical anomalies on the Canadian Shield, the 1,200 km2 Temagami Anomaly. The geological cause of this regional magnetic, conductive and gravity feature has previously been modelled to be a mafic-ultramafic body at relatively great depth (2–15 km) of unknown age and origin, which may or may not be related to the meteorite impact-generated Sudbury Igneous Complex in its immediate vicinity. However, with a profound lack of outcrops and drill holes, the geological cause of the anomaly remains elusive, a genetic link to the 1.85 Ga Sudbury impact event purely speculative.
In search for any potential surface expression of the deep-seated cause of the Temagami Anomaly, this study provides a first, yet comprehensive petrological and geochemical assessment of exotic igneous dykes recently discovered in outcrops above, and drill cores into, the Temagami Anomaly. Based on cross-cutting field relations, petrographic studies, lithogeochemistry, whole-rock Nd-Sr-Pb isotope systematics, and U-Pb geochronology, it was possible to identify, and distinguish between, at least six different groups of igneous dykes: (i) Calc-alkaline quartz diorite dykes related to the 1.85 Ga Sudbury Igneous Complex (locally termed Offset Dykes); (ii) tholeiitic quartz diabase of the regional 2.22 Ga Nipissing Suite/Senneterre Dyke Swarm; (iii) calc-alkaline quartz diabase of the regional 2.17 Ga Biscotasing Dyke Swarm; (iv) alkaline ultrabasic dykes correlated with the 1.88–1.86 Ga Circum-Superior Large Igneous Province (LIP); and (v) aplitic dykes as well as (vi) a hornblende syenite, the latter two of more ambiguous age and stratigraphic position.
The findings presented in this study – the discovery of three new Offset Dykes in particular – offer some unexpected insights into the geology and economic potential of one of the least explored areas of the world-class Sudbury Mining Camp as well as into the nature and distribution of both allochthonous and autochthonous impactites within one of the oldest and largest impact structures known on Earth. Not only do the geometric patterns of dyke (and breccia) distribution reaffirm previous notions of the existence of discrete ring structures in the sense of a ~200-km multi-ring basin, but they provide critical constraints as to the pre-erosional thickness and extent of the impact melt sheet, thus helping to identity new areas for Ni-Cu-PGE exploration. Furthermore, this study provides important insights into the pre-impact stratigraphy and the magmatic evolution of the region in general, which reveals to be much more complex, compositionally divers, and protracted than initially assumed. Of note is the discovery of rocks related to the 2.17 Ga Biscotasing and the 1.88–1.86 Ga Circum-Superior magmatic events, as these were not previously known to occur on the southeast margin of the Superior Craton. Shortly predating the Sudbury impact and being contemporaneous with ore-forming events at Thompson (Manitoba) and Raglan (Cape Smith), these magmatic rocks could provide the missing link between unusual mafic, pre-enriched, crustal target rocks, and the unique metal endowment of the Sudbury Impact Structure.
The actual geological cause of the Temagami Anomaly remains open to debate and requires the downward extension of existing bore holes as well as more detailed geophysical investigations. The hypothesis of a genetic relationship between Sudbury impact event and Temagami Anomaly is neither borne out by any evidence nor particularly realistic, even in case of an oblique impact, and should thus be abandoned. It is instead proposed, based on circumstantial evidence, that the anomaly might be explained by an ultramafic complex of the 1.88–1.86 Ga Circum-Superior LIP.
Optical remote sensing is an important tool in the study of animal behavior providing ecologists with the means to understand species-environment interactions in combination with animal movement data. However, differences in spatial and temporal resolution between movement and remote sensing data limit their direct assimilation. In this context, we built a data-driven framework to map resource suitability that addresses these differences as well as the limitations of satellite imagery. It combines seasonal composites of multiyear surface reflectances and optimized presence and absence samples acquired with animal movement data within a cross-validation modeling scheme. Moreover, it responds to dynamic, site-specific environmental conditions making it applicable to contrasting landscapes. We tested this framework using five populations of White Storks (Ciconia ciconia) to model resource suitability related to foraging achieving accuracies from 0.40 to 0.94 for presences and 0.66 to 0.93 for absences. These results were influenced by the temporal composition of the seasonal reflectances indicated by the lower accuracies associated with higher day differences in relation to the target dates. Additionally, population differences in resource selection influenced our results marked by the negative relationship between the model accuracies and the variability of the surface reflectances associated with the presence samples. Our modeling approach spatially splits presences between training and validation. As a result, when these represent different and unique resources, we face a negative bias during validation. Despite these inaccuracies, our framework offers an important basis to analyze species-environment interactions. As it standardizes site-dependent behavioral and environmental characteristics, it can be used in the comparison of intra- and interspecies environmental requirements and improves the analysis of resource selection along migratory paths. Moreover, due to its sensitivity to differences in resource selection, our approach can contribute toward a better understanding of species requirements.
By 2050, two-third of the world’s population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was extracted to generate labelled data for ten classes, including three classes representing low, middle, and high level of imperviousness. We used the output of the 2017 classification and information based on radiometric bi-temporal change detection for retrospective classification. Besides spectral bands, we calculated several indices and various temporal composites, which were used as an input for Random Forest classification. The results provide information on three imperviousness classes with accuracies exceeding 75%. According to our results, the imperviousness areas grew continuously from 1985 to 2017, with a high imperviousness area growth of more than 167,000 ha, comprising around 30% increase. The information on the expansion of urban areas was integrated with population dynamics data to estimate the progress towards SDG 11. With the intensity analysis and the integration of population data, the spatial heterogeneity of urban expansion and population growth was analysed, showing that the urban expansion rates considerably excelled population growth rates in some regions in NRW. The study highlights the applicability of earth observation data for accurately quantifying spatio-temporal urban dynamics for sustainable urbanization and targeted planning.
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.
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.
The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600%, a severe loss of cropland (−24%), a moderate reduction in meadows (−4%), and a small gain in forests (+4%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65% for historical maps of poor quality and the maximum was 91% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services.
Nella regione alpina, si può osservare il continuo consumo di spazi aperti a causa dell’aumento di aree di insediamento e di infrastrutture tecniche e la conseguente impermeabilizzazione del suolo. Questo fenomeno porta principalmente alla perdita di suolo agricolo. A seconda dell'estensione dello sviluppo, si riscontra anche una maggiore frammentazione del paesaggio, che è associata all'isolamento degli habitat naturali e alla perdita della connettività ecologica, così come ad altre conseguenze negative.
Il progetto OpenSpaceAlps ha affrontato questo problema e, sulla base di procedure cooperative partecipate attuate in diverse regioni pilota alpine, ha sviluppato approcci e strategie di soluzione per la salvaguardia sostenibile degli spazi aperti. Questo manuale supporta le attività e il processo decisionale di vari stakeholder, in primo luogo i pianificatori delle autorità pubbliche di pianificazione. Sulla base di un'analisi delle sfide e delle condizioni generali nella regione alpina, il manuale presenta e confronta i "principi" centrali della pianificazione degli spazi aperti. Inoltre, vengono discusse strategie di pianificazione integrata per diverse categorie spaziali.
Forests in Germany cover around 11.4 million hectares and, thus, a share of 32% of Germany's surface area. Therefore, forests shape the character of the country's cultural landscape. Germany's forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany’s forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps.
Forecasting spatio-temporal dynamics on the land surface using Earth Observation data — a review
(2020)
Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.
In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.