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High rates of land conversion due to urbanization are causing fragmented and dispersed spatial patterns in the wildland-urban interface (WUI) worldwide. The occurrence of anthropogenic fires in the WUI represents an important environmental and social issue, threatening not only vegetated areas but also periurban inhabitants, as is the case in many Latin American cities. However, research has not focused on the dynamics of the local climate in the WUI. This study analyzes whether wildfires contribute to the increase in land surface temperature (LST) in the WUI of the metropolitan area of the city of Guanajuato (MACG), a semi-arid Mexican city. We estimated the pre- and post-fire LST for 2018–2021. Spatial clusters of high LST were detected using hot spot analysis and examined using ANOVA and Tukey’s post-hoc statistical tests to assess whether LST is related to the spatial distribution of wildfires during our study period. Our results indicate that the areas where the wildfires occurred, and their surroundings, show higher LST. This has negative implications for the local ecosystem and human population, which lacks adequate infrastructure and services to cope with the effects of rising temperatures. This is the first study assessing the increase in LST caused by wildfires in a WUI zone in Mexico.
Environmental interlinked problems such as human-induced land cover change, water scarcity, loss in soil fertility, and anthropogenic climate change are expected to affect the viability of agriculture and increase food insecurity in many developing countries. Climate change is certainly the most serious of these challenges for the twenty-first century. The poorest regions of the world – tropical West Africa included – are the most vulnerable due to their high dependence on climate and weather sensitive activities such as agriculture, and the widespread poverty that limits the institutional and economic capacities to adapt to the new stresses brought about by climate change. Climate change is already acting negatively on the poor smallholders of tropical West Africa whose livelihoods dependent mainly on rain-fed agriculture that remains the cornerstone of the economy in the region. Adaptation of the agricultural systems to climate change effects is, therefore, crucial to secure the livelihoods of these rural communities. Since information is a key for decision-making, it is important to provide well-founded information on the magnitude of the impacts in order to design appropriate and sustainable adaptation strategies.
Considering the case of agricultural production in the Republic of Benin, this study aims at using large-scale climatic predictors to assess the potential impacts of past and future climate change on agricultural productivity at a country scale in West Africa. Climate signals from large-scale circulation were used because state-of-the art regional climate models (RCM) still do not perfectly resolve synoptic and mesoscale convective processes. It was hypothesised that in rain-fed systems with low investments in agricultural inputs, yield variations are widely governed by climatic factors. Starting with pineapple, a perennial fruit crops, the study further considered some annual crops such as cotton in the group of fibre crops, maize, sorghum and rice in the group of cereals, cowpeas and groundnuts belonging to the legume crops, and cassava and yams which are root and tuber crops. Thus the selected crops represented the three known groups of photosynthetic pathways (i.e. CAM, C3, and C4 plants).
In the study, use was made of the historical agricultural yield statistics for the Republic of Benin, observed precipitation and mean near-surface air temperature data from the Climatic Research Unit (CRU TS 3.1) and the corresponding variables simulated by the regional climate model (RCM) REMO. REMO RCM was driven at its boundaries by the global climate model ECHAM 5. Simulations with different greenhouse gas concentrations (SRES-A1B and B1 emission scenarios) and transient land cover change scenarios for present-day and future conditions were considered. The CRU data were submitted to empirical orthogonal functions analysis over the north hemispheric part of Africa to obtain large-scale observed climate predictors and associated consistent variability modes. REMO RCM data for the same region were projected on the derived climate patterns to get simulated climate predictors. By means of cross-validated Model Output Statistics (MOS) approach combined with Bayesian model averaging (BMA) techniques, the observed climate predictors and the crop predictand were further on used to derive robust statistical relationships. The robust statistical crop models perform well with high goodness-of-fit coefficients (e.g. for all combined crop models: 0.49 ≤ R2 ≤ 0.99; 0.28 ≤ Brier-Skill-Score ≤ 0.90).
Provided that REMO RCM captures the main features of the real African climate system and thus is able to reproduce its inter-annual variability, the time-independent statistical transfer functions were then used to translate future climate change signal from the simulated climate predictors into attainable crop yields/crop yield changes. The results confirm that precipitation and air temperature governed agricultural production in Benin in general, and particularly, pineapple yield variations are mainly influenced by temperature. Furthermore, the projected yield changes under future anthropogenic climate change during the first-half of the 21st century amount up to -12.5% for both maize and groundnuts, and -11%, -29%, -33% for pineapple, cassava, and cowpeas respectively. Meanwhile yield gain of up to +10% for sorghum and yams, +24% for cotton, and +39% for rice are expected. Over the time period 2001 – 2050, on average the future yield changes range between -3% and -13% under REMO SRES–B1 (GHG)+LCC, -2% and -11% under REMO SRES–A1B (GHG only),and -3% and -14% under REMO SRES–A1B (GHG)+LCC for pineapple, maize, sorghum, groundnuts, cowpeas and cassava. In the meantime for yams, cotton and rice, the average yield gains lie in interval of about +2% to +7% under REMO SRES–B1 (GHG)+LCC, +0.1% and +12% under REMO SRES–A1B (GHG only), and +3% and +10% under REMO SRES–A1B (GHG)+LCC. For sorghum, although the long-term average future yield depicts a reduction there are tendencies towards increasing yields in the future. The results also reveal that the increases in mean air temperature more than the changes in precipitation patterns are responsible for the projected yield changes. As well the results suggest that the reductions in pineapple yields cannot be attributed to the land cover/land use changes across sub-Saharan Africa. The production of groundnuts and in particular yams and cotton will profit from the on-going land use/land cover changes while the other crops will face detrimental effects.
Henceforth, policymakers should take effective measures to limit the on-going land degradation processes and all other anthropogenic actions responsible for temperature increase. Biotechnological improvement of the cultivated crop varieties towards development of set of seed varieties adapted to hotter and dry conditions should be included in the breeding pipeline programs. Amongst other solutions, application of appropriate climate-smart agricultural practices and conservation agriculture are also required to offset the negative impacts of climate change in agriculture.
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.
Information on the state of the terrestrial vegetation cover is important for several ecological, economical, and planning issues. In this regard, vegetation properties such as the type, vitality, or density can be described by means of continuous biophysical parameters. One of these parameters is the leaf area index (LAI), which is defined as half the total leaf area per unit ground surface area. As leaves constitute the interface between the biosphere and the atmosphere, the LAI is used to model exchange processes between plants and their environment. However, to account for the variability of ecosystems, spatially and temporally explicit information on LAI is needed both for monitoring and modeling applications.
Remote sensing aims at providing such information. LAI is commonly derived from remote sensing data by empirical-statistical or physical models. In the first approach, an empirical relationship between LAI measured in situ and the corresponding canopy spectral signature is established. Although this method achieves accurate LAI estimates, these relationships are only valid for the place and time at which the field data were sampled, which hampers automated LAI derivation. The physical approach uses a radiation transfer model to simulate canopy reflectance as a function of the scene’s geometry and of leaf and canopy parameters, from which LAI is derived through model inversion based on remote sensing data. However, this model inversion is not stable, as it is an under-determined and ill-posed problem.
Until now, LAI research focused either on the use of coarse resolution remote sensing data for global applications, or on LAI modeling over a confined area, mostly in forest and crop ecosystems, using medium to high spatial resolution data. This is why to date no study is available in which high spatial resolution data are used for LAI mapping in a heterogeneous, natural landscape such as alpine grasslands, although a growing amount of high spatial and temporal resolution remote sensing data would allow for an improved environmental monitoring. Therefore, issues related to model parameterization and inversion regularization techniques improving its stability have not yet been investigated for this ecosystem.
This research gap was taken up by this thesis, in which the potential of high spatial resolution remote sensing data for grassland LAI estimation based on statistical and radiation transfer modeling is analyzed, and the achieved accuracy and robustness of the two approaches is compared. The objectives were an ecosystem-adapted radiation transfer model set-up and an optimized LAI derivation in mountainous grassland areas. Multi-temporal LAI in situ measurements as well as time series of RapidEye data from 2011 and 2012 over the catchment of the River Ammer in the Bavarian alpine upland were used. In order to obtain accurate in situ data, a comparison of the LAI derivation algorithms implemented in the LAI-2000 PCA instrument with destructively measured LAI was performed first. For optimizing the empirical-statistical approach, it was then analyzed how the selection of vegetation indices and regression models impacts LAI modeling, and how well these models can be transferred to other dates. It was shown that LAI can be derived
with a mean accuracy of 80 % using contemporaneous field data, but that the accuracy decreases to on average 51 % when using these models on remote sensing data from other dates. The combined use of several data sets to create a regression which is used for LAI derivation at different points in time increased the LAI estimation accuracy to on average 65 %. Thus, reduced field measurement labor comes at the cost of LAI error rates being increased by 10 - 30 % as long as at least two campaigns are conducted. Further, it was shown that the use of RapidEye’s red edge channel improves the LAI derivation by on average 5.4 %.
With regard to physical LAI modeling, special interest lay in assessing the accuracy improvements that can be achieved through model set-up and inversion regularization techniques. First, a global sensitivity analysis was applied to the radiation transfer model in order to identify the most important model parameters and most sensitive spectral features. After model parameterization, several inversion regularizations, namely the use of a multiple sample solution, the additional use of vegetation indices, and the addition of noise, were analyzed. Further, an approach to include the local scene’s geometry in the retrieval process was introduced to account for the mountainous topography. LAI modeling accuracies of in average 70 % were achieved using the best combination of regularization techniques, which is in the upper range of accuracies that were achieved in the few existing other grassland studies based on in situ or air-borne measured hyperspectral data. Finally, further physically derived vegetation parameters and inversion uncertainty measures were evaluated in detail to identify challenging modeling conditions, which was mostly neglected in other studies. An increased modeling uncertainty for extremely high and low LAI values was observed. This indicates an insufficiently wide model parameterization and a canopy deviation from model assumptions on some fields. Further, the LAI modeling accuracies varied strongly between the different scenes. From this observation it can be deduced that the radiometric quality of the remote sensing data, which might be reduced by atmospheric effects or unexpected surface reflectances, exerts a high influence on the LAI modeling accuracy.
The major findings of the comparison between the empirical-statistical and physical LAI modeling approaches are the higher accuracies achieved by the empirical-statistical approach as long as contemporaneous field data are available, and the computationally efficiency of the statistical approach. However, when no or temporally unfitting in situ measurements are available, the physical approach achieves comparable or even higher accuracies. Furthermore, radiation transfer modeling enables the derivation of other leaf and canopy variables useful for ecological monitoring and modeling applications, as well as of pixel-wise uncertainty measures indicating the robustness and reliability of the model inversion and LAI derivation procedure. The established look-up tables can be used for further LAI derivation in Central European grassland also in other years.
The use of high spatial resolution remote sensing data for LAI derivation enables a reliable land cover classification and thus a reduced LAI mapping error due to misclassifications. Furthermore, the RapidEye pixels being smaller than individual fields allow for a radiation transfer model inversion over homogeneous canopies in most cases, as canopy gaps or field parcels can be clearly distinguished. However, in case of unexpected local surface conditions such as blooming, litter, or canopy gaps, high spatial resolution data show corresponding strong deviations in reflectance values and hence LAI estimation, which would be reduced using coarser resolution data through the balancing effect of the surrounding surface reflectances. An optimal pixel size with regard to modeling accuracy hence depends on the canopy and landscape structure. Furthermore, a reduced spatial resolution would enable a considerable acceleration of the LAI map derivation.
This illustration of the potential of RapidEye data and of the challenges associated to LAI derivation in heterogeneous grassland areas contributes to the development of robust LAI estimation procedures based on new and upcoming, spatially and temporally high resolution remote sensing imagery such as Landsat 8 and Sentinel-2.
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.
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.
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.
In a three-year study the current aeolian transportation processes were examined in a linear dune area previously used for grazing near Nizzana at the Israeli-Egyptian border. The research area was subject to heavy grazing across the border, which led to the total destruction of the natural vegetation in the period of 1967 to 1982. As a consequence, intensified aeolian activity and significant changes of the morphology of the dunes were observed. After the end of the grazingg on the Israeli side, a rapid return of the vegetation in the interdune corridors and on the footslopes of the dunes took place. In addition also a reduction of obviously active areas on the dune crests was observed. The situation on Egyptian territory west the border remained unchanged until today. This study is aimed at understanding the changed aeolian morphodynamics east the border. The emphasis was placed on the investigation of the spatial and temporal distribution of aeolian sand transport as well as on the influencing factors morphology, surface condition and vegetation.
In der vorliegenden Arbeit werden die Foraminiferenfaunen von 125 Proben, die aus drei Profilen des marinen Mitteljura (Bajocium bis Unteroxfordium) von Kachchh, West-Indien stammen, analysiert: Das Badi Nala-Jhura Village-Profil (ca. 550 m Mächtigkeit) und das Kamaguna-Profil (ca. 365 m Mächtigkeit) wurden am Jhura Hill (23°26’8’’ N; 69°37’00’’ E) ca. 17 km NW von Bhuj aufgenommen, während das dritte Profil durch den Jumara Dome (ca. 365 m Mächtigkeit) (23°40’40’’ N; 69°04’00’’ E) ca. 50 km NW des Jhura Hill liegt. Die Proben wurden lithologisch sowie nach ihren Foraminiferen-Vergesellschaftungen ausgewertet, welche wichtige Daten zur Palökologie lieferten. Die Profile umfassen die mitteljurassische Sedimentabfolge, die der Jhurio-, Patcham- und Chari-Formation des Jhura Dome und Jumara Dome angehört. Die Schichtenfolge des Bajocium und Bathonium besteht aus Karbonaten und gemischt karbonatisch-siliziklastischen Sedimenten der Jhurio- und Patcham-Formation, während im Callovium die Chari-Formation siliziklastisch dominiert ist. Die sedimentäre Abfolge des Kachchh-Beckens zeigt an der Bathonium-Callovium-Grenze signifikante Veränderungen in der Lithologie, im faunistischen Inhalt und in der Faunendiversität. Die lithologischen Veränderungen von einer Karbonat-dominierten Abfolge im mittleren und oberen Bathonium zu siliziklastischen Sedimenten im Callovium gehen mit einer Abnahme der zuvor hohen Diversität einher. Der Grund für den Faunenumschwung liegt vermutlich in einer deutlichen Änderung der Umweltbedingungen von sauerstoffreich im unteren zu sauerstoffarm im oberen Profilabschnitt, was sich in einem Wechsel von einer Kalkschaler- zu einer Sandschaler-dominierten Foraminiferenfauna dokumentiert. Eine weitere Ursache für den Fazies- und Faunenumschwung liegt in einer deutlichen KlimaÄnderung, indem heiße, aride Bedingungen im Bathonium von kühleren, feuchteren Verhältnissen zu Beginn des Callovium abgelöst wurden. Taxonomisch konnten insgesamt 111 Foraminiferentaxa identifiziert werden, die 43 Gattungen angehören. Insgesamt wurden 24 Sandschaler- und 85 benthische Kalkschaler-Taxa sowie 2 Arten planktischer Foraminiferen (Globuligerina) in den untersuchten Profilen bestimmt. Die benthischen Formen dominieren somit bei weitem an Diversität und Häufigkeit, da die planktischen Protoglobigerinen im mittleren Jura global noch keine große Rolle spielten. Aufgrund der spärlich vorhandenen Literatur wurde jedes Taxon inklusive Synonymieliste beschrieben, durch rasterelektronen-mikroskopische Aufnahmen (REM) dokumentiert und auf 9 Tafeln illustriert. In der Foraminiferen-Gesamtfauna dominieren Vertreter der Familie Nodosariidae mit den Gattungen Lenticulina, Astacolus, Citharina, Lingulina, Marginulinopsis, Nodosaria und Vaginulina. Mit der zweitgrößten Häufigkeit folgen die Familien Epistominidae, Textulariidae und Spirillinidae mit den Gattungen Epistomina, Spirillina sowie Reophax, Ammobaculites und Textularia. Die Arten Reophax sterkii, Triplasia althoffi, Verneuilinoides subvitreus, Nubeculinella bigoti, Dentalina filiformis, Saracenaria oxfordiana, Lingulina longiscata, Citharina flabellata, Palmula deslongchampsi, Vaginulina proxima, Ammodiscus asper, Ammodiscus siliceus, Triplasia bartensteini, Spirillina orbicula, Ophthalmidium carinatum, Tubinella inornata, Nodosaria fusiformis, Pyramidulina rara und Ramulina ascissa wurden erstmals in Gesteinen des Kachchh-Beckens nachgewiesen. Die dominierenden Taxa in den untersuchten Sedimenten sind epifaunale Formen, die einen hohen Sauerstoffgehalt im Bodenwasser benötigen. Infaunale, im Sediment lebende Arten treten dagegen etwas seltener auf. Diese lebten bevorzugt in sub- bis dysoxischen Milieus mit geringen Gehalten an gelöstem Sauerstoff im Bodenwasser und konnten auch Sauerstoffminima tolerieren. Aus der benthischen Foraminiferenfauna können folgende Rückschlüsse gezogen werden: • Die wichtigsten Parameter für die Verbreitung der Foraminiferen sind Substrat, Energieniveau und Sauerstoffgehalt. • Die höchste Diversität weisen die epifaunalen Vergesellschaftungen auf. • Mit zunehmender Sedimenttiefe nehmen Häufigkeit und Diversität ab. • Die Diversität der Kalkschaler ist höher als die der Sandschaler. • Minimale Diversitäten liegen in Stressmilieus mit geringen Sauerstoffgehalten vor. • Die hohen Werte des Evenness-Index weisen auf eine annähernd gleichmäßige Verteilung der benthischen Foraminiferen in den Profilen hin. • Epifaunale Arten werden von den physikochemischen Eigenschaften des Bodenwassers gesteuert. Sie sind auf partikuläres organisches Material und hohe Sauerstoffgehalte des Bodenwassers angewiesen. In der vorliegenden Arbeit wurden Probencluster gebildet, um Faunenassoziationen zu erfassen und danach die Umwelt- und Ablagerungsbedingungen zu rekonstruieren. Aus drei Profilen wurden 125 Proben für eine quantitative palökologische Analyse der Foraminiferen ausgewählt. Die W-mode Clusteranalyse zeigt eine große Ähnlichkeit zwischen den Proben, die sich in zwei Hauptcluster mit insgesamt sechs Subclustern gruppieren lassen. Die dadurch gebildeten Probengruppen stellen verschiedene Foraminiferenassoziationen dar, die sich beschreiben und weitgehend interpretieren lassen. • Assoziation A zeichnet sich durch hohe Anteile der Gattung Epistomina mosquensis aus. Diese Assoziation repräsentiert vollmarine Ablagerungsbedingungen mit normalmariner Salinität und guter Durchlüftung des Bodenwassers. • Assoziation B wird durchweg von Reophax metensis dominiert. Die Assoziation charakterisiert einen niedrig-energischen und teilweise sauerstoffarmen Sedimentationsraum. • Assoziation C wird stark von Dorothia prekummi dominiert und kennzeichnet vollmarine Bedingungen. • Assoziation D ist von Lenticulina subalata beherrscht und charakterisiert gutdurchlüftete Flachwasserbereiche. • Assoziation E wird von Spirillina polygyrata dominiert. Sie ist typisch für einen flachen Subtidalbereich unterhalb der Wellenbasis mit vollmarinen Bedingungen, normal mariner Salinität und gut durchlüftetem Bodenwasser. • Assoziation F zeichnet sich durch hohe Gehalte an Lenticulina quenstedti aus. Die Assoziation charakterisiert Bereiche, die überwiegend unterhalb der Schönwetter-Wellenbasis liegen und ein niedriges bis mittleres Sauerstoff-Niveau aufweisen. Aus einem Vergleich zwischen den Gehäusegrößen und der Häufigkeit von Lenticulina subalata und Epistomina mosquensis in den karbonatischen und siliziklastischen Sedimenten ergab sich, dass die Gehäuse von L. subalata und E. mosquensis in den Karbonaten größer sind als in den Siliziklastika. Die mitteljurassischen Ablagerungsräume des Kachchh-Beckens werden anhand der lithologischen und faunistischen Parameter rekonstruiert. Ferner werden die Foraminiferen-Vergesellschaftungen mit den von FÜRSICH et al. (2004) beschriebenen Makrofauna-Assoziationen verglichen. Aus diesen Untersuchungen ergaben sich folgende Schlussfolgerungen: • Die Ablagerungen im Jhura-Profil sind in flacherem Wasser sedimentiert worden als im Kamaguna-Profil und Jumara-Profil. • Die Mikro- und Makrofauna in diesen Profilen führt zu mehr oder weniger identischen palökologischen Schlussfolgerungen.
Two phases of reef sampling were carried out. The first included regular samples taken along the coastline of Aqaba (27km long) at depths of 4-15m, and used to determine spatial distribution of pollution. The second phase included three 20cm-deep cores obtained from within the industrial zone. These cores were drilled from pre-dated communities, where the growth rate was determined earlier to be 10mm y-1, therefore the core obtained represented a period of 20 years (i.e. 1980-2000). The cores were used to reconstruct the metal pollution history at the most heavily used site along the coast (industrial zone).All samples were examined with respect to their metal content of Cd, Pb, Cu, Zn, Ni, and Cr. Almost all of them have shown records above the calculated background values. Mean values of Cd, Pb, Cu, Zn, Ni and Cr recorded along the coast were 1,25; 4,26; 9,76; 11,40; 2,29 and 10,522, µg g-1 respectively, and for core samples 1.4; 4.2; 5.7; 6.4; 2.3 and 8.21 µg g-1 respectively. Spatial distribution of metal enrichment in reef samples have shown a general and clear increasing trend towards the south. Same increasing trend was also in core samples where the six metals have shown a prominent increasing trend towards the core surface indicating an increase of coastal activities during the last twenty years. High and relatively high values were recorded at the oil port, the industrial area and main port, and thus categorized as highly impacted areas. Intermediate metal content were recorded in samples of the north beach, and thus classified as being relatively impacted, where the lowest metal concentrations were observed at the marine reserve, the least impacted site along the coast. The high enrichment of metal is attributed mainly to anthropogenic impacts. The natural inputs of the six metals studied in the Gulf of Aqaba are generally very low, due to the geographic positions and the absence of wadi discharge and as a result of low rainfall. Several potential sources of heavy metals were investigated. The industrial-related activities, port operations and phosphate dust were among the main sources currently threatening the marine ecosystem in Aqaba. Applying the Principle Components Analysis method (PCA) to all samples taken along the coastline has resulted in categorizing three different groups according to their metal enrichment, the first is composed of samples taken from the north beach and the main port with intermediate to high enrichment, the second joined the samples of the marine park and the marine reserve with low and relatively low enrichment, and the last group joined samples of the industrial zone and the oil port with high enrichment. The Principle Component Scores were also utilized to confirm the spatial distribution and relationships of the examined heavy metals along the coast. Two models (interpolated by SURFER  7.0 and ArcView 3.2a) were developed, the first was based on the PC scores of the first component, and shows clearly the positive anomalies in metal concentrations along the coast. The second model was developed by plotting the second factor scores on a landuse map of Aqaba. According to these models, it has shown that the positive anomalies are associated with three different zones; industrial area, the main port and the oil port. The results have shown that coral reefs can be used as good environmental indicator for assessments and monitoring processes, and they can provide data and information on both the spatial distribution of pollution and their history. The present work is the first to document the environmental status along the whole coast of Aqaba and the first to use coral reef as a tool/ indicator.
The coast of Aqaba and the Aqaba region (Jordan) were investigated on their hydrogeo-ecosystem. The results of the research were translated into digits to build a geo-spatial data base. The fillings of the graben aquifer receive indirect type of recharge through the side wadis which drain the highlands. Surface water balance was modeled for a period of 20 years of daily climate records using MODBIL program which attributes direct recharge to wet years only. The hydrodynamic fresh water/seawater interface in the coastal zones was investigated by applying vertical geoelectric surveys and models of several methods to confirm its coincidence with the aquifer’s flow amounts, where human impacts in terms of over-pumping allowed more encroachment of seawater into land, and unintended recharge which led to seaward interface migration. A groundwater balance and solute transport were approached by developing a flow model from the hydrogeological and hydrochemical data. The nature of soil cover and aquifer whose physical properties enhance human impacts indicated the vulnerability of groundwater to pollution. This certainly threatens the marine ecology which forms the sink where the in-excess flow ends. The constructed digital background was exported into GIS to sub-zone the study area in terms of the aquifer’s vulnerability to pollution risks using DRASTIC index. However, it was unable to meet all geo-spatial factors that proved to have significant impacts on the vulnerability. Consequently, a comprehensive index -SALUFT- was developed. This suggests the suitable land use units for each zone in the light of vulnerability grades aiming at protecting the available groundwater resources.
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.
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.