550 Geowissenschaften
Refine
Has Fulltext
- yes (112)
Is part of the Bibliography
- yes (112)
Year of publication
Document Type
- Journal article (112) (remove)
Keywords
- remote sensing (16)
- Geographie (8)
- climate change (6)
- time series (6)
- Niger (5)
- earth observation (5)
- forest (5)
- review (5)
- MODIS (4)
- Sentinel-1 (4)
Institute
EU-Project number / Contract (GA) number
- 20-3044-2-11 (1)
- 308377 (1)
- 714087 (1)
- 776019 (1)
Statistical modeling of phenology in Bavaria based on past and future meteorological information
(2020)
Plant phenology is well known to be affected by meteorology. Observed changes in the occurrence of phenological phases arecommonly considered some of the most obvious effects of climate change. However, current climate models lack a representationof vegetation suitable for studying future changes in phenology itself. This study presents a statistical-dynamical modelingapproach for Bavaria in southern Germany, using over 13,000 paired samples of phenological and meteorological data foranalyses and climate change scenarios provided by a state-of-the-art regional climate model (RCM). Anomalies of severalmeteorological variables were used as predictors and phenological anomalies of the flowering date of the test plantForsythiasuspensaas predictand. Several cross-validated prediction models using various numbers and differently constructed predictorswere developed, compared, and evaluated via bootstrapping. As our approach needs a small set of meteorological observationsper phenological station, it allows for reliable parameter estimation and an easy transfer to other regions. The most robust andsuccessful model comprises predictors based on mean temperature, precipitation, wind velocity, and snow depth. Its averagecoefficient of determination and root mean square error (RMSE) per station are 60% and ± 8.6 days, respectively. However, theprediction error strongly differs among stations. When transferred to other indicator plants, this method achieves a comparablelevel of predictive accuracy. Its application to two climate change scenarios reveals distinct changes for various plants andregions. The flowering date is simulated to occur between 5 and 25 days earlier at the end of the twenty-first century comparedto the phenology of the reference period (1961–1990).
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.
Unprecedented urbanization in particular in countries of the global south result in informal urban development processes, especially in mega cities. With an estimated 1 billion slum dwellers globally, the United Nations have made the fight against poverty the number one sustainable development goal. To provide better infrastructure and thus a better life to slum dwellers, detailed information on the spatial location and size of slums is of crucial importance. In the past, remote sensing has proven to be an extremely valuable and effective tool for mapping slums. The nature of used mapping approaches by machine learning, however, made it necessary to invest a lot of effort in training the models. Recent advances in deep learning allow for transferring trained fully convolutional networks (FCN) from one data set to another. Thus, in our study we aim at analyzing transfer learning capabilities of FCNs to slum mapping in various satellite images. A model trained on very high resolution optical satellite imagery from QuickBird is transferred to Sentinel-2 and TerraSAR-X data. While free-of-charge Sentinel-2 data is widely available, its comparably lower resolution makes slum mapping a challenging task. TerraSAR-X data on the other hand, has a higher resolution and is considered a powerful data source for intra-urban structure analysis. Due to the different image characteristics of SAR compared to optical data, however, transferring the model could not improve the performance of semantic segmentation but we observe very high accuracies for mapped slums in the optical data: QuickBird image obtains 86–88% (positive prediction value and sensitivity) and a significant increase for Sentinel-2 applying transfer learning can be observed (from 38 to 55% and from 79 to 85% for PPV and sensitivity, respectively). Using transfer learning proofs extremely valuable in retrieving information on small-scaled urban structures such as slum patches even in satellite images of decametric resolution.
Human health is known to be affected by the physical environment. Various environmental influences have been identified to benefit or challenge people's physical condition. Their heterogeneous distribution in space results in unequal burdens depending on the place of living. In addition, since societal groups tend to also show patterns of segregation, this leads to unequal exposures depending on social status. In this context, environmental justice research examines how certain social groups are more affected by such exposures. Yet, analyses of this per se spatial phenomenon are oftentimes criticized for using “essentially aspatial” data or methods which neglect local spatial patterns by aggregating environmental conditions over large areas. Recent technological and methodological developments in satellite remote sensing have proven to provide highly detailed information on environmental conditions. This narrative review therefore discusses known influences of the urban environment on human health and presents spatial data and applications for analyzing these influences. Furthermore, it is discussed how geographic data are used in general and in the interdisciplinary research field of environmental justice in particular. These considerations include the modifiable areal unit problem and ecological fallacy. In this review we argue that modern earth observation data can represent an important data source for research on environmental justice and health. Especially due to their high level of spatial detail and the provided large-area coverage, they allow for spatially continuous description of environmental characteristics. As a future perspective, ongoing earth observation missions, as well as processing architectures, ensure data availability and applicability of ’big earth data’ for future environmental justice analyses.
No abstract available.
The use of inverse methods allow efficient model calibration. This study employs PEST to calibrate a large catchment scale transient flow model. Results are demonstrated by comparing manually calibrated approaches with the automated approach. An advanced Tikhonov regularization algorithm was employed for carrying out the automated pilot point (PP) method. The results indicate that automated PP is more flexible and robust as compared to other approaches. Different statistical indicators show that this method yields reliable calibration as values of coefficient of determination (R-2) range from 0.98 to 0.99, Nash Sutcliffe efficiency (ME) range from 0.964 to 0.976, and root mean square errors (RMSE) range from 1.68 m to 1.23 m, for manual and automated approaches, respectively. Validation results of automated PP show ME as 0.969 and RMSE as 1.31 m. The results of output sensitivity suggest that hydraulic conductivity is a more influential parameter. Considering the limitations of the current study, it is recommended to perform global sensitivity and linear uncertainty analysis for the better estimation of the modelling results.
Water crises are becoming severe in recent times, further fueled by population increase and climate change. They result in complex and unsustainable water management. Spatial estimation of consumptive water use is vital for performance assessment of the irrigation system using Remote Sensing (RS). For this study, its estimation is done using the Soil Energy Balance Algorithm for Land (SEBAL) approach. Performance indicators including equity, adequacy, and reliability were worked out at various spatiotemporal scales. Moreover, optimization and sustainable use of water resources are not possible without knowing the factors mainly influencing consumptive water use of major crops. For that purpose, random forest regression modelling was employed using various sets of factors for site-specific, proximity, and cropping system. The results show that the system is underperforming both for Kharif (i.e., summer) and Rabi (i.e., winter) seasons. Performance indicators highlight poor water distribution in the system, a shortage of water supply, and unreliability. The results are relatively good for Rabi as compared to Kharif, with an overall poor situation for both seasons. Factors importance varies for different crops. Overall, distance from canal, road density, canal density, and farm approachability are the most important factors for explaining consumptive water use. Auditing of consumptive water use shows the potential for resource optimization through on-farm water management by the targeted approach. The results are based on the present situation without considering future changes in canal water supply and consumptive water use under climate change.
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.
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.
Peatlands located on slopes (herein called slope bogs) are typical landscape units in the Hunsrueck, a low mountain range in Southwestern Germany. The pathways of the water feeding the slope bogs have not yet been documented and analyzed. The identification of the different mechanisms allowing these peatlands to originate and survive requires a better understanding of the subsurface lithology and hydrogeology. Hence, we applied a multi-method approach to two case study sites in order to characterize the subsurface lithology and to image the variable spatio-temporal hydrological conditions. The combination of Electrical Resistivity Tomography (ERT) and an ERT-Monitoring and Ground Penetrating Radar (GPR), in conjunction with direct methods and data (borehole drilling and meteorological data), allowed us to gain deeper insights into the subsurface characteristics and dynamics of the peatlands and their catchment area. The precipitation influences the hydrology of the peatlands as well as the interflow in the subsurface. Especially, the geoelectrical monitoring data, in combination with the precipitation and temperature data, indicate that there are several forces driving the hydrology and hydrogeology of the peatlands. While the water content of the uppermost layers changes with the weather conditions, the bottom layer seems to be more stable and changes to a lesser extent. At the selected case study sites, small differences in subsurface properties can have a huge impact on the subsurface hydrogeology and the water paths. Based on the collected data, conceptual models have been deduced for the two case study sites.
Information about land use/land cover (LULC) and their changes is useful for different stakeholders to assess future pathways of sustainable land use for food production as well as for nature conservation. In this study, we assess LULC changes in the Kilombero catchment in Tanzania, an important area of recent development in East Africa. LULC change is assessed in two ways: first, post-classification comparison (PCC) which allows us to directly assess changes from one LULC class to another, and second, spectral change detection. We perform LULC classification by applying random forests (RF) on sets of multitemporal metrics that account for seasonal within-class dynamics. For the spectral change detection, we make use of the robust change vector analysis (RCVA) and determine those changes that do not necessarily lead to another class. The combination of the two approaches enables us to distinguish areas that show (a) only PCC changes, (b) only spectral changes that do not affect the classification of a pixel, (c) both types of change, or (d) no changes at all. Our results reveal that only one-quarter of the catchment has not experienced any change. One-third shows both, spectral changes and LULC conversion. Changes detected with both methods predominantly occur in two major regions, one in the West of the catchment, one in the Kilombero floodplain. Both regions are important areas of food production and economic development in Tanzania. The Kilombero floodplain is a Ramsar protected area, half of which was converted to agricultural land in the past decades. Therefore, LULC monitoring is required to support sustainable land management. Relatively poor classification performances revealed several challenges during the classification process. The combined approach of PCC and RCVA allows us to detect spatial patterns of LULC change at distinct dimensions and intensities. With the assessment of additional classifier output, namely class-specific per-pixel classification probabilities and derived parameters, we account for classification uncertainty across space. We overlay the LULC change results and the spatial assessment of classification reliability to provide a thorough picture of the LULC changes taking place in the Kilombero catchment.
A new ranking of the world's largest cities—Do administrative units obscure morphological realities?
(2019)
With 37 million inhabitants, Tokyo is the world's largest city in UN statistics. With this work we call this ranking into question. Usually, global city rankings are based on nationally collected population figures, which rely on administrative units. Sprawling urban growth, however, leads to morphological city extents that may surpass conventional administrative units. In order to detect spatial discrepancies between the physical and the administrative city, we present a methodology for delimiting Morphological Urban Areas (MUAs). We understand MUAs as a territorially contiguous settlement area that can be distinguished from low-density peripheral and rural hinterlands. We design a settlement index composed of three indicators (settlement area, settlement area proportion and density within the settlements) describing a gradient of built-up density from the urban center to the periphery applying a sectoral monocentric city model. We assume that the urban-rural transition can be defined along this gradient. With it, we re-territorialize the conventional administrative units. Our data basis are recent mapping products derived from multi-sensoral Earth observation (EO) data – namely the Global Urban Footprint (GUF) and the GUF Density (GUF-DenS) – providing globally consistent knowledge about settlement locations and densities. For the re-territorialized MUAs we calculate population numbers using WorldPop data. Overall, we cover the 1692 cities with >300,000 inhabitants on our planet. In our results we compare the consistently re-territorialized MUAs and the administrative units as well as their related population figures. We find the MUA in the Pearl River Delta the largest morphologically contiguous urban agglomeration in the world with a calculated population of 42.6 million. Tokyo, in this new list ranked number 2, loses its top position. In rank-size distributions we present the resulting deviations from previous city rankings. Although many MUAs outperform administrative units by area, we find that, contrary to what we assumed, in most cases MUAs are considerably smaller than administrative units. Only in Europe we find MUAs largely outweighing administrative units in extent.
Estimating flood risks and managing disasters combines knowledge in climatology, meteorology, hydrology, hydraulic engineering, statistics, planning and geography - thus a complex multi-faceted problem. This study focuses on the capabilities of multi-source remote sensing data to support decision-making before, during and after a flood event. With our focus on urbanized areas, sample methods and applications show multi-scale products from the hazard and vulnerability perspective of the risk framework. From the hazard side, we present capabilities with which to assess flood-prone areas before an expected disaster. Then we map the spatial impact during or after a flood and finally, we analyze damage grades after a flood disaster. From the vulnerability side, we monitor urbanization over time on an urban footprint level, classify urban structures on an individual building level, assess building stability and quantify probably affected people. The results show a large database for sustainable development and for developing mitigation strategies, ad-hoc coordination of relief measures and organizing rehabilitation.
Protection and recovery of natural resource and biodiversity requires accurate monitoring at multiple scales. Airborne Laser Scanning (ALS) provides high-resolution imagery that is valuable for monitoring structural changes to vegetation, providing a reliable reference for ecological analyses and comparison purposes, especially if used in conjunction with other remote-sensing and field products. However, the potential of ALS data has not been fully exploited, due to limits in data availability and validation. To bridge this gap, the global network for airborne laser scanner data (GlobALS) has been established as a worldwide network of ALS data providers that aims at linking those interested in research and applications related to natural resources and biodiversity monitoring. The network does not collect data itself but collects metadata and facilitates networking and collaborative research amongst the end-users and data providers. This letter describes this facility, with the aim of broadening participation in GlobALS.
Geoarchaeological information presented here pertains to a subsidiary Nile channel that once flowed west of the main Sebennitic distributary and discharged its water and sediments at Egypt’s then north-central deltaic coast. Periodical paleoclimatic episodes during the later Middle and Upper Holocene included decreased rainfall and increased aridity that reduced the Nile’s flow levels and thus likely disrupted nautical transport and anthropogenic activity along this channel. Such changes in this deltaic sector, positioned adjacent to the Levantine Basin in the Eastern Mediterranean, can be attributed to climatic shifts triggered as far as the North Atlantic to the west, and African highland source areas of the Egyptian Nile to the south. Of special interest in a study core recovered along the channel are several sediment sequences without anthropogenic material that are interbedded between strata comprising numerous potsherds. The former are interpreted here as markers of increased regional aridity and reduced Nile flow which could have periodically disrupted the regional distribution of goods and nautical activities. Such times occurred ~5000 years B.P., ~4200–4000 years B.P., ~3200–2800 years B.P., ~2300–2200 years B.P., and more recently. Periods comparable to these are also identified by altered proportions of pollen, isotopic and compositional components in different radiocarbon-dated Holocene cores recovered elsewhere in the Nile delta, the Levantine region to the east and north of Egypt, and in the Faiyum depression south of the delta.
The mineralogical and chemical characteristics of fulgurites ( = natural glasses forrned by lightning strikes to the ground) from the southern Centrat Sahara (Niger) are presented. The fulgurites are indicators of thunderstorms. The northernmost important fulgurite formation in the study area reached up to about l8°N, with decreasing fulgurite concentration from south to north. Their distribution pattern and the relative dating of their formation in relation to Iandscape history from the Late Pleistocene onwards (e.g., palaeolakes, palaeosols), and to Neolithic settlement reveals their value as palaeoenvironmental indicators. They indicate: (1) local palaeoenvironmental conditions depending on the topographical situation in a complex dune relief; (2) climatic change during the mid-Holocene from northerly rains to southerly rains; and (3) the northernmost Iimit of important thunderstorrns and rainfall activity since this time in the southern Centrat Sahara.
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.
Silicate and iron crust karst pits and sinkholes in eastern Niger are filled with reworked lateritic sediments or with unconsolidated palaeosoils and aeolian deposits. The fillings facies depend on the environmental conditions during deposition. Geomorphological and sedimentological studies on the karst fillings and the interpretation of various karst/filling associations allow an approach to the chronology of landscape development in eastern Niger plateaus.
Die Karstdepressionen auf den Dachflächen der Schichtstufen in Sahara und Sahel sind im allgemeinen von allochthonen quartären Lockermaterialien verfüllt. Die tieferen Verfüllungsschichten zeigen eine von Norden nach Süden zunehmende Überprägung durch Verwitterungs- und Bodenbildungsprozesse. Die obersten ca. 10 cm der Verfüllungen sind dagegen im ganzen Untersuchungsgebiet relativ uniform ausgebildet. Sie repräsentieren die äolischen Ablagerungen seit der mittelholozänen Aridisierung. Drei Verfiillungsprofile (Seggedim, Termit-Dougoule und Koutous) werden vorgestellt und hinsichtlich ihrer paläoklimatischen Aussagekraft überprüft.
Dans le Niger oriental, des phénomenes karstiques sont fréquents dans les roches siliceuses: gres, silcretes, croûtes ferrugineuses, roches cristallines. A partir des études géomorphologiques et micromorphologiques, on peut conclure a une kartsification, au sense de production de formes par dissolution. Les résultats permettent de dater du Tertiaire inférieur la principale période de karstification. La répartition régionale des formes induites par cette karstification indique une dépendance probable des conditions paléoclimatiques. Actuellement le karst influe encore sur le développement des autres formes de relief.
Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource — if not overexploited — sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.
No abstract available
no abstract available
No abstract available
no abstract available
The Ringgold Knoll pegmatite, a late-stage member of the Granite Harbour Intrusives, crosscuts high-grade Wilson gneisses of the Oates Coast, which forms the westernmost part of the Wilson Terrane at the Pacific end of the Cambro-Ordovician Ross orogenic belt in West Antarctica. The pegmatite mineral assemblage consists of K-feldspar, plagioclase, quartz, garnet (almandinespessartine-pyrope), dark tourmaline (schorl-dravite), muscovite, apatite, monazite, zircon, blue AI-rich tourmaline and dumortierite in order of decreasing abundances. Major, minor and rare earth elements are reported for the greater part of the mineral assemblage. The time of pegmatite emplacement is constrained by Rb-Sr and Sm-Nd isochron ages of 492 ± 8 (2a) Ma and 500 ± 40 (2a) Ma, respectively. High initial 87Sr/86Sr of 0.7315 ± 0.0003 and low E Nd,t of -8.7 ± 1.2 strongly support an origin of the magma from highly evolved crustal source rocks. K-Ar and Ar-Ar model ages of about 470 to 475 Ma for igneous muscovite indicate that the pegmatite together with its wall rocks spent a prolonged period at elevated temperatures before final cooling below about 350 °C. The muscovite dates may give an estimate for the time of exhumation of the Oates Coast crystalline basement along two major late Ross orogenic detachment zones within the Wilson Terrane i.e. the Wilson and the Exiles thrusts (c.f. FLÖTTMANN and KLEINSCHMIDT, 1991).
An Metabasiten der Zone Erbendorf-Vohenstrauß, der Erbendorfer Grünschieferzone und der Zone Tirschenreuth-Mähring wurden petrographische, geochemische und phasenpetrologische Untersuchungen sowie K-Ar-Mineraldatierungen durchgeführt. In der Zone Erbendorf-Vohenstrauß treten in regionaler Abhängigkeit drei Haupttypen von Metabasiten auf: 1. Flaseramphibolite mit der Paragenese Hornblende + Oligoklas/ Andesin + Granat ± Salit und mit Plagioklas- und / oder Kalksilikat-reichen Flasern findet man hauptsächlich im nördlichen und zentralen Teil der Zone Erbendorf-Vohenstrauß. Geochemisch zeigen sie eine deutliche Anreicherung der inkompatiblen Elemente und der leichten Seltenerdelemente und sind vergleichbar mit modernen Tholeiiten von ozeanischen Inseln oder anomalen Abschnitten mittelozeanischer Rükken. Ein kontinentaler Intraplatten-Charakter scheint nicht gegeben. In der Kontaktaureole von Windisch-Eschenbach wurde ein Teil der Flaseramphibolite kontaktmetamorph überprägt. 2. Schiefrige, teils streifige Amphibolite mit der Paragenese Hornblende + Andesin / Labrador ± Salit dominieren im Süd teil der Zone Erbendorf-Vohenstrauß, erscheinen aber in einzelnen Ausstrichen auch im Norden. Die Streifung entsteht durch Plagioklas- und / oder Kalksilikat-reiche Zwischenlagen. Geochemisch sind diese Amphibolite bei flachen Spurenelement- und Seltenerdelement- Mustern vergleichbar mit modernen Tholeiiten von normalen Abschnitten mittelozeanischer Rücken. Sowohl Flaseramphibolite als auch schiefrige, teils streifige Amphibolite zeigen Übergänge zu massigen, homogenen Amphiboliten. 3. Metagabbros mit der Paragenese Hornblende + Plagioklas ± Biotit und einer grobkörnigen, gabbro iden Textur treten hauptsächlich im Bereich der Schuppenzone Michldorf-Kaimling (VOLL 1960) auf. Geochemisch nehmen sie bei einer schwachen Anreicherung der inkompatiblen Elemente eine Zwischenstellung zwischen Flaseramphiboliten und schiefrigen, teils streifigen Amphiboliten ein. In der Erbendorfer Grünschieferzone treten Metagabbros und schiefrige, teils gestreifte Amphibolite mit der Paragenese Hornblende + Oligoklas ± Epidot ± Chlorit auf. Die Amphibolite wurden z. T. stark retrograd überprägt und dabei auch deformiert. Dies wird auf größere, post-regionalmetamorphe, tektonische Bewegungen, möglicherweise Deckenüberschiebungen zurückgeführt. Geochemisch sind die Metabasite der Erbendorfer Grünschieferzone mit modernen tholeiitischen bis kalkalkalischen, subduktionsgebundenen Basalten vergleichbar. Sie unterscheiden sich damit völlig von den südlich angrenzenden Flaseramphiboliten der Zone Erbendorf-Vohenstrauß und den nördlich benachbarten Amphiboliten des Fichtelgebirges. Dies unterstreicht die Stellung der Erbendorfer Grünschieferzone als eigenständige tektonische Einheit. Im Vergleich mit dem Münchberger Komplex zeigt die Erbendorfer Grünschieferzone große Ähnlichkeit mit der Prasinit-Phyllit-Serie hinsichtlich der tektonischen Stellung, der Lithologie und insbesondere des geochemischen Charakters der Metabasite. Zwischen der Zone Erbendorf-Vohen-strauß und den Münchberger Serien konnten bezüglich der Metabasite dagegen keine Parallelen festgestellt werden. Schiefrige, teils streifige Amphibolite der Zone Tirschenreuth-Mähring mit der Paragenese Hornblende + Andesin ± Salit (± Granat) und hellen Plagioklas- oder Kalksilikat-reichen Lagen sind geochemisch mit modernen Basalten normaler mittel ozeanischer Rücken vergleichbar. Die untersuchten Metabasite dürften die Zusammensetzung des basaltischen Ausgangsmaterials zumindest annähernd wiedergeben. In einigen Fällen wurde jedoch eine sekundäre Mobilisation von Elementen festgestellt: Die kontaktmetamorphe Überprägung eines Teils der Flaseramphibolite in der Kontaktaureole von Windisch-Eschenbach führte zu einer deutlichen Anreicherung von Rb, Li und K und zu einer Abreicherung von Ca, Sr und V, insbesondere aber von Cr und Ni. Die sog. immobilen Elemente Nb, Ce, (P), Zr, Ti, Y, Sc streuen in einem deutlich breiteren Bereich als bei den nicht kontaktmetamorphen Flaseramphiboliten. In einer Probe sind die Seltenerdelemente insgesamt angereichert. Eine Mobilisation von P20 S und den leichten Seltenerdelementen in einigen schiefrigen, teils streifigen Amphiboliten der Zone Erbendorf-Vohenstrauß und in einigen kontaktmetamorphen Flaseramphiboliten wird auf einen Abbau von Apatit im Zuge einer post-regionalmetamorphen, möglicherweise postgranitischen, hydrothermalen Überprägung zurückgeführt. Mineralchemische Untersuchungen ergaben für die Zusammensetzung der Hornblenden aus Amphiboliten der Zone Erbendorf-Vohenstrauß deutliche regionale Unterschiede, die weitgehend unabhängig von der regionalen Verteilung der einzelnen Metabasit-Haupttypen sind. Vergleichende Untersuchungen an Granat untermauern eine vermutete, frühere, eklogitfazielle Überprägung eines Granatamphibolits vom Nordrand der Zone Erbendorf-Vohenstrauß bei Hauxdorf. K-Ar-Altersdatierungen an Hornblende- und Glimmerkonzentraten belegen, daß das letzte, (Mitteldruck-) metamorphe Ereignis im Westteil der Zone Erbendorf-Vohenstrauß vor etwa 380 Ma beendet war. Dieses Alter wird korreliert mit einer letzten Metamorphose der höhermetamorphen Serien des Münchberger Komplexes zur gleichen Zeit. Im Ostteil der Zone Erbendorf-Vohenstrauß treten jüngere Altersdaten mit einem Schwerpunkt bei 324 Ma auf, die entweder auf einen Einfluß der etwa gleichalten Niederdruck-Metamorphose im unmittelbar benachbarten Moldanubikum i. e. S. oder auf die Intrusion des 324 Ma alten (KÖHLER et al. 1974) Leuchtenberger Granits zurückzuführen sind. Im Übergangsbereich vom Saxothuringikum zum Moldanubikum bei Mähring zeigen Altersdaten um 320Ma das Ende der letzten (Niederdruck-)Metamorphose an.
No abstract available
Die Kupfer-Zink-Lagerstätte von Kupferberg-Neufang im Bereich der Münchberger Deckenstruktur besteht aus einem etwa 4 km langen Zug einzelner stratiformer Vererzungslinsen, der in NW-SE-Richtung vom anchimetamorphen Paläozoikum "Bayerischer" Faziesentwicklung in die Prasinit-Phyllit-Serie hinüberzieht. Bisherige lagerstättenkundliche Untersuchimgen interpretierten die gesamte Lagerstätte als einheitliche, submarin-exhalativ entstandene stratiforme Bildung in Zusammenhang mit ordovizischem basischem Vulkanismus, der in den Rahmengesteinen der Lagerstätte durch die Diabase der anchimetamorphen Randschieferserie bzw. die Prasinite der Prasinit-Phyllit-Serie dokumentiert ist. Diese Vorstellung steht jedoch in deutlichem Widerspruch zu den Ergebnissen neuerer geochemischer und biostratigraphischer Untersuchungen, die für die Diabase und Prasinite eine völlig unterschiedliche Zusammensetzung ergaben und die zeigten, daß die Prasinit-Phyllit-Serie schon im Oberproterozoikum, die erzführenden Schichten im anchimetamorphen Paläozoikum aber erst im Unterdevon sedimentiert wurden. Für eine Klärung der Situation sind weitere Untersuchungen im Bereich der Lagerstätte erforderlich.
Sufficient plant-available water is one of the most important requirements for vital, stable, and well-growing forest stands. In the face of climate change, there are various approaches to derive recommendations considering tree species selection based on plant-available water provided by measurements or simulations. Owing to the small-parcel management of Central European forests as well as small-spatial variation of soil and stand properties, in situ data collection for individual forest stands of large areas is not feasible, considering time and cost effort. This problem can be addressed using physically based modeling, aiming to numerically simulate the water balance. In this study, we parameterized, calibrated, and verified the hydrological multidimensional WaSiM-ETH model to assess the water balance at a spatial resolution of 30 m in a German forested catchment area (136.4 km2) for the period 2000–2021 using selected in situ data, remote sensing products, and total runoff. Based on the model output, drought-sensitive parameters, such as the difference between potential and effective stand transpiration (Tdiff) and the water balance, were deduced from the model, analyzed, and evaluated. Results show that the modeled evapotranspiration (ET) correlated significantly (R2 = 0.80) with the estimated ET using MODIS data (MOD16A2GFv006). Compared with observed daily, monthly, and annual runoff data, the model shows a good performance (R2: 0.70|0.77|0.73; Kling–Gupta efficiency: 0.59|0.62|0.83; volumetric efficiency: 0.52|0.60|0.83). The comparison with in situ data from a forest monitoring plot, established at the end of 2020, indicated good agreement between observed and simulated interception and soil water content. According to our results, WaSiM-ETH is a potential supplement for forest management, owing to its multidimensionality and the ability to model soil water balance for large areas at comparable high spatial resolution. The outputs offer, compared to non-distributed models (like LWF-Brook90), spatial differentiability, which is important for small-scale parceled forests, regarding stand structure and soil properties. Due to the spatial component offered, additional verification possibilities are feasible allowing a reliable and profound verification of the model and its parameterization.
A 42 m drilling was pertormed in the depresalon of Bilma, Xawar, NE-Niger. The sediment and pollen records show that after an initial deposition of dune sands there were repeated lake phases which terminated by desiccation and consolidation of spring mounds. The pollen record indicates a continuous presence of savanna vegetation. The record probably covers the period between the Upper Pleistocene and the Late Holocene. The climate was characterised by a monssonal summer rain regime giving effective rain fall of about 450-500 mm per year. Groundwater recharge was possible but estimates of the amount of water resources are difficult because of the karstic system of the escarpment and the nearly unknown hydrogeological situation.
Projected climate changes for the 21st century may cause great uncertainties on the hydrology of a river basin. This study explored the impacts of climate change on the water balance and hydrological regime of the Jhelum River Basin using the Soil and Water Assessment Tool (SWAT). Two downscaling methods (SDSM, Statistical Downscaling Model and LARS-WG, Long Ashton Research Station Weather Generator), three Global Circulation Models (GCMs), and two representative concentration pathways (RCP4.5 and RCP8.5) for three future periods (2030s, 2050s, and 2090s) were used to assess the climate change impacts on flow regimes. The results exhibited that both downscaling methods suggested an increase in annual streamflow over the river basin. There is generally an increasing trend of winter and autumn discharge, whereas it is complicated for summer and spring to conclude if the trend is increasing or decreasing depending on the downscaling methods. Therefore, the uncertainty associated with the downscaling of climate simulation needs to consider, for the best estimate, the impact of climate change, with its uncertainty, on a particular basin. The study also resulted that water yield and evapotranspiration in the eastern part of the basin (sub-basins at high elevation) would be most affected by climate change. The outcomes of this study would be useful for providing guidance in water management and planning for the river basin under climate change.
The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR's Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).
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).
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.
Land cover is a key variable in monitoring applications and new processing technologies made deriving this information easier. Yet, classification algorithms remain dependent on samples collected on the field and field campaigns are limited by financial, infrastructural and political boundaries. Here, animal tracking data could be an asset. Looking at the land cover dependencies of animal behaviour, we can obtain land cover samples over places that are difficult to access. Following this premise, we evaluated the potential of animal movement data to map land cover. Specifically, we used 13 White Storks (Cicona cicona) individuals of the same population to map agriculture within three test regions distributed along their migratory track. The White Stork has adapted to foraging over agricultural lands, making it an ideal source of samples to map this land use. We applied a presence-absence modelling approach over a Normalized Difference Vegetation Index (NDVI) time series and validated our classifications, with high-resolution land cover information. Our results suggest White Stork movement is useful to map agriculture, however, we identified some limitations. We achieved high accuracies (F1-scores > 0.8) for two test regions, but observed poor results over one region. This can be explained by differences in land management practices. The animals preferred agriculture in every test region, but our data showed a biased distribution of training samples between irrigated and non-irrigated land. When both options occurred, the animals disregarded non-irrigated land leading to its misclassification as non-agriculture. Additionally, we found difference between the GPS observation dates and the harvest times for non-irrigated crops. Given the White Stork takes advantage of managed land to search for prey, the inactivity of these fields was the likely culprit of their underrepresentation. Including more species attracted to agriculture - with other land-use dependencies and observation times - can contribute to better results in similar applications.
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.
Satellite-derived land surface temperature dynamics in the context of global change — a review
(2023)
Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites.
Central Europe experienced several droughts in the recent past, such as in the year 2018, which was characterized by extremely low rainfall rates and high temperatures, resulting in substantial agricultural yield losses. Time series of satellite earth observation data enable the characterization of past drought events over large temporal and spatial scales. Within this study, Moderate Resolution Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) 250 m time series were investigated for the vegetation periods of 2000 to 2018. The spatial and temporal development of vegetation in 2018 was compared to other dry and hot years in Europe, like the drought year 2003. Temporal and spatial inter- and intra-annual patterns of EVI anomalies were analyzed for all of Germany and for its cropland, forest, and grassland areas individually. While vegetation development in spring 2018 was above average, the summer months of 2018 showed negative anomalies in a similar magnitude as in 2003, which was particularly apparent within grassland and cropland areas in Germany. In contrast, the year 2003 showed negative anomalies during the entire growing season. The spatial pattern of vegetation status in 2018 showed high regional variation, with north-eastern Germany mainly affected in June, north-western parts in July, and western Germany in August. The temporal pattern of satellite-derived EVI deviances within the study period 2000-2018 were in good agreement with crop yield statistics for Germany. The study shows that the EVI deviation of the summer months of 2018 were among the most extreme in the study period compared to other years. The spatial pattern and temporal development of vegetation condition between the drought years differ.
The command area of the Rakh branch canal grows wheat, sugarcane, and rice crops in abundance. The canal water, which is trivial for irrigating these crops, is conveyed to the farms through the network of canals and distributaries. For the maintenance of this vast infrastructure; the end users are charged on a seasonal basis. The present water charges are severely criticized for not being adequate to properly manage the entire infrastructure. We use the residual value to determine the value of the irrigation water and then based on the quantity of irrigation water supplied to farm land coupled with the infrastructure maintenance cost, full cost recovery figures are executed for the study area, and policy recommendations are made for the implementation of the full cost recovery system. The approach is unique in the sense that the pricings are based on the actual quantity of water conveyed to the field for irrigating crops. The results of our analysis showed that the canal water is severely under charged in the culturable command area of selected distributaries, thus negating the plan of having a self-sustainable irrigation system.
A circum-Arctic monitoring framework for quantifying annual erosion rates of permafrost coasts
(2023)
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.
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.