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Periglacial environments are facing dramatic changes. Warming air temperatures and strong snow cover variations fundamentally affect landforming processes in this hotspot region of Climate Change. But before we can assess the response of landform development to a changing climate, we need to enhance our understanding of the internal structure of those landforms. Within this study, a broad scope of landform types from alpine and subarctic regions is investigated: rock glaciers, solifluction lobes, palsas and patterned ground. By using the geophysical methods 2-D and 3-D ERI, as well as GPR surveying, structural differences and similarities between landform units of different or the same landform types are highlighted. This enables a reconstruction of their past and a projection of their future development.
The 2010 eruption of Eyjafjallajokull volcano was characterized by pulsating activity. Discrete ash bursts merged at higher altitude and formed a sustained quasi-continuous eruption column. High-resolution near-field videos were recorded on 8-10 May, during the second explosive phase of the eruption, and supplemented by contemporary aerial observations. In the observed period, pulses occurred at intervals of 0.8 to 23.4 s (average, 4.2 s). On the basis of video analysis, the pulse volume and the velocity of the reversely buoyant jets that initiated each pulse were determined. The expansion history of jets was tracked until the pulses reached the height of transition from a negatively buoyant jet to a convective buoyant plume about 100 m above the vent. Based on the assumption that the density of the gas-solid mixture making up the pulse approximates that of the surrounding air at the level of transition from the jet to the plume, a mass flux ranging between 2.2 and 3.5 . 10\(^4\) kg/s was calculated. This mass eruption rate is in good agreement with results obtained with simple models relating plume height with mass discharge at the vent. Our findings indicate that near-field measurements of eruption source parameters in a pulsating eruption may prove to be an effective monitoring tool. A comparison of the observed pulses with those generated in calibrated large-scale experiments reveals very similar characteristics and suggests that the analysis of near-field sensors could in the future help to constrain the triggering mechanism of explosive eruptions.
A completely revised and enhanced version of the water balance model MODBIL of the regional water balance dynamics of Cyprus was developed for this study. The model is based on a physical, process-oriented, spatially distributed concept and is applied for the calculation of all important water balance components of the island for the time period of 1961-2004. The calibrated results are statistically analysed and visualised for the whole island area, and evaluated with respect to the renewability of natural water resources. Climate variability and changes of the past decades are analysed with regard to their influence on water balances. A further part of the study focusses on the simulation of impacts of potential climate change. The water balances are simulated under changing climatic conditions on the base of theoretical precipitation, temperature and relative humidity changes and the revealed impacts on the water balances and renewable resources are discussed. Furthermore, a first principal water balance scenario is developed for the assessment of the regional hydrological changes expected for Cyprus by the end of the 21st century. The scenarios are based on recently calculated climate change assessments for this part of the Mediterranean, under an assumed further increase of greenhouse gasses in the atmosphere.
During the Mesoproterozoic large volumes of magma were repeatedly emplaced within the basement of NW Namibia. Magmatic activity started with the intrusion of the anorthositic rocks of the Kunene Intrusive Complex (KIC) at 1,385-1,347 Ma. At its south-eastern margin the KIC was invaded by syenite dykes (1,380-1,340 Ma) and younger carbonatites (1,140-1,120 Ma) along ENE and SE trending faults. Older ferrocarbonatite intrusions, the ‘carbonatitic breccia’, frequently contain wallrock fragments, whereas subordinate ferrocarbonatite veins are almost xenolith-free. Metasomatic interaction between carbonatite-derived fluids and the neighbouring and incorporated anorthosites led to the formation of economically important sodalite deposits. Investigated anorthosite samples display the magmatic mineral assemblage of Pl (An37-75) ± Ol ± Opx ± Cpx + Ilm + Mag + Ap ± Zrn. Ilmenite and pyroxene are surrounded by narrow reaction rims of biotite and pargasite. During the subsolidus stage sporadic coronitic garnet-orthopyroxene-quartz assemblages were produced. Thermobarometry studies on amphiboles yield temperatures of 985-950°C whereas the chemical composition of coronitic garnet and orthopyroxene indicate a subsolidus re-equilibration of the KIC at conditions of 760 ± 100°C and 7.3 ± 1 kbar. In the syenites Kfs, Pl, Hbl and/or Cpx crystallized first, followed by a second generation of Kfs, Hbl, Fe-Ti oxides and Ttn. Crystallization of potassium feldspar occurred under temperatures of 890-790°C. For the crystallization of hastingsite pressures of 6.5 ± 0.6 kbar are obtained. In order to constrain the source rocks of the two suites, oxygen isotope analyses of feldspar as well as geochemical bulk rock analyses were carried out. In case of the anorthosites, the general geochemical characteristics are in excellent agreement with their derivation from fractionated basaltic liquids, with the d18O values (5.88 ± 0.19 ‰) proving their derivation from mantle-derived magmas. The results obtained for the felsic suite, provide evidence against consanguinity of the anorthosites and the syenites, i.e. (1) compositional gaps between the geochemical data of the two suites, (2) trace element data of the felsic suite points to a mixed crustal-mantle source, (3) syenites do not exhibit ubiquitous negative Eu-anomalies in their REE patterns, which would be expected from fractionation products of melts that previously formed plagioclase cumulates and (4) feldspar d18O values from the syenites fall in a range of 7.20-7.92 ‰, which, however, is about 1.6 ‰ higher than the average d18O of the anorthosites. Conformably, the crustal-derived felsic and the mantle-derived anorthositic suite are suggested to be coeval but not consanguineous. Their spatial and temporal association can be accounted for, if the heat necessary for crustal melting is provided by the upwelling and emplacement of mantle-derived melts, parental to the anorthosites. In order to constrain the source of the 1,140-1,120 Ma carbonatites and to elucidate the fenitizing processes, which led to the formation of the sodalite, detailed mineralogical and geochemical investigations, stable isotope (C,O,S) analyses and fluid inclusion measurements (microthermometrical studies and synchrotron-micro-XRF analyses) have been combined. There is striking evidence that carbonatites of both generations are magmatic in origin. They occur as dykes with cross-cutting relationships and margins disturbed by fenitic aureoles, and contain abundant flow-oriented xenoliths. The mineral assemblage of both carbonatite generations of Ank + Cal + Ilm + Mag + Bt ± Ap ± pyrochlore ± sulphides in the main carbonatite body and Ank + Cal + Mag ± pyrochlore ± rutile in the ferrocarbonatite veins, their geochemical characteristics and the O and C isotope values of ankerite (8.91 to 9.73 and –6.73 to –6.98, respectively) again indicate igneous derivation, with the 18O values suggesting minor subsolidus alteration. NaCl-rich fluids, released from the carbonatite melt mainly caused the fenitization of both, the incorporated and the bordering anorthosite. This process is characterized by the progressive transformation of Ca-rich plagioclase into albite and sodalite. Applying conventional geothermobarometry combined with fluid-inclusion isochore data, it was possible to reconstruct the P-T conditions for the carbonatite emplacement and crystallization (1200-630°C, 4-5 kbar) and for several mineral-forming processes during metasomatism (e.g. formation of sodalite: 800-530°C). The composition and evolutionary trends of the fenitizing solution were estimated from both the sequence of metasomatic reactions within wallrock xenoliths in the carbonatitic breccia and fluid inclusion data. The fenitizing solutions responsible for the transformation of albite into sodalite can be characterised as of NaCl-rich aqueous brines (19-30 wt.% NaCl eq.), that contained only minor amounts of Sr, Ba, Fe, Nb, and LREE.
The surface urban heat island (SUHI) affects the quality of urban life. Because varying urban structures have varying impacts on SUHI, it is crucial to understand the impact of land use/land cover characteristics for improving the quality of life in cities and urban health. Satellite-based data on land surface temperatures (LST) and derived land use/cover pattern (LUCP) indicators provide an efficient opportunity to derive the required data at a large scale. This study explores the seasonal and diurnal variation of spatial associations from LUCP and LST employing Pearson correlation and ordinary least squares regression analysis. Specifically, Landsat-8 images were utilized to derive LSTs in four seasons, taking Berlin as a case study. The results indicate that: (1) in terms of land cover, hot spots are mainly distributed over transportation, commercial and industrial land in the daytime, while wetlands were identified as hot spots during nighttime; (2) from the land composition indicators, the normalized difference built-up index (NDBI) showed the strongest influence in summer, while the normalized difference vegetation index (NDVI) exhibited the biggest impact in winter; (3) from urban morphological parameters, the building density showed an especially significant positive association with LST and the strongest effect during daytime.
The Bafoussam area in west Cameroon is located within the Cameroon Neoproterozoic orogenic belt (north of the Congo craton) which is part of the Central African Fold Belt (CAFB).The evolution of the CAFB is related to the collision between the convergent West African craton, the São Francisco – Congo cratons and the Sahara Metacraton. The outcrop area stretches over a surface of ~1000 km2 and dominantly consists of granitoids which intruded wall-rocks of gneiss and migmatite during the Pan-African orogeny. The Bafoussam granitoid emplacement was influenced by the N 30 °E strike-slip shear zone in the prolongation of the Cameroon Volcanic Line, but also by the N 70 °E Central Cameroon Shear Zone. In the field, these two shear directions are expressed in the schistosity and foliation trajectories, fault orientation and the alignment of the volcanic cones as well. In the Bafoussam area, four types of granitoids can be distinguished, including: (i) the biotite granitoid, (ii) the deformed biotite granitoid, (iii) the mega feldspar granitoid, and (iv) the two-mica granitoid. These granitoids occur as elongated plutons hosting irregular mafic enclaves (amphibole-bearing, biotite-rich, and metagabbroic types) and are frequently cut by late pegmatites, aplite dykes and quartz veins. Petrographically, they range in composition from syenogranite (major), alkali-feldspar granite, granodiorite, monzogranite, quartz-syenite, quartzmonzonite to quartz-monzodiorite. Potassium feldspar, quartz, plagioclase and biotite are the principal phases, in cases accompanied by amphibole and accessory minerals such as apatite,zircon, monazite, titanite, allanite, ilmenite and magnetite. Sericite, epidote and chlorite are secondary minerals. In addition, the two-mica granitoid contains primary muscovite and sometimes igneous garnet. In the granitoids, potassium feldspar is orthoclase (microcline and orthoclase: Or81–97Ab19–3), and plagioclase is mainly oligoclase with some albite and andesine (An3–35Ab96–64).Biotite is Fe-rich (meroxene and lepidomelane, with some siderophyllite), having high Fe2+/(Fe2+ + Mg) ratios of 0.40–0.80. It is a re-equilibrated primary biotite and suggests calc-alkaline and peraluminous nature of the host granitoids. Amphibole is edenitic and magnesian hastingsitic hornblende, with high Mg/(Mg + Fe2+) ratios of 0.50–0.62. The evolution of the hornblende was dominated by the edenitic, tschermakitic, pargasitic and hastingsitic substitution types. Primary muscovite is iron-rich [Fe2+/(Fe2+ + Mg) = 0.52–0.82] and has experienced celadonite and paragonite substitutions. Igneous garnet is almandine–spessartine (XFe = 0.99 and XMn = 0.46–0.56). The euhedral grain shapes of garnet crystals and the absence of inclusions coupled with the high Mn and Fe2+contents (2.609–3.317 a.p.f.u and 2.646–3.277 a.p.f.u,respectively) and low Mg contents (0.012–0.038 a.p.f.u) clearly point to its plutonic origin. The Mn-depletion crystallization model is suggested for the origin of the analyzed garnet, i.e. initial crystallization of garnet inducing early decrease of Mn in the original melt. Aluminum-in-hornblende and phengite barometric estimates show that the granitoids crystallized at 4.2 ± 1.1 to 6.6 ± 1.0 kbar, corresponding to emplacement depths of 15–24 km.Zircon and apatite saturation temperature calibrations and hornblende–plagioclase thermometry yielded emplacement temperatures between 772 ± 41 and 808 ± 34 °C. Except the two-mica granitoid, the titanite–magnetite–quartz assemblage gives oxygen fugacities ranging from 10–17 to 10–13, suggesting that the granitoids were produced by an oxidized magma. Since the twomica granitoid lacks magnetite, it was originated from a magma under reducing conditions, below the quartz–fayalite–magnetite buffer. Fluid inclusions in quartz from hydrothermal veins are secondary in nature and are found in trails along healed microcracks or in clusters. Two types of fluid inclusion have been recognized, mixed aqueous–non-aqueous volatile fluid inclusions subdivided into aqueous-rich mixed and non-aqueous volatile-rich mixed fluid inclusions, and pure aqueous fluid inclusions.The non-aqueous volatile-rich mixed fluid inclusions are one-, two-, or three-phase inclusions, whereas the aqueous-rich mixed fluid inclusions are exclusively three-phase inclusions. Both have similar low to moderate salinities (1 to 10 equiv. wt. %). The total homogenization temperatures of the aqueous-rich mixed fluid inclusions are slightly lower than those of the nonaqueous volatile-rich mixed fluid inclusions, ranging from 150 to 250 °C and 170 to 300 °C,respectively. They contain nearly pure CO2, or CO2 with addition of 4.1–13.5 mole % CH4 as volatile constituents. Pure aqueous fluid inclusions are two-phase with lower total homogenization temperatures (130–150 °C) and salinities ranging from 3 to 8 equiv. wt. %. They display mixing salt system characteristics, having NaCl as the dominant salt and considerable amounts of other divalent cations. Aqueous-rich mixed fluid inclusions and pure aqueous fluid inclusions exhibit a low geothermal gradient value of 18 °C/km, whereas the non-aqueous volatiles-rich mixed fluid inclusions have a high density which correspond to high geothermal gradient of 68 °C/km. The studied granitoids are intermediate to felsic in compositions (56.9–74.6 wt. % SiO2)and have high contents of alkalis K2O (1.73–7.32 wt. %) and Na2O (1.25–5.13 wt. %) but low abundances in MnO (0.01–0.20 wt. %), MgO (0.10–3.97 wt. %), CaO (0.37–4.85 wt. %), P2O5(up to 0.90 wt. %). They display variable contents in TiO2 (0.07–0.91 wt. %), Fe2O3* (total Fe = 0.96–7.79 wt. %) and Al2O3 (12.0–17.6 wt. %) contents. The granitoids show a wide range of high-field-strength elements (HFSE) and large ion lithophile elements (LILE) contents, with felsic granitoids being enriched in HFSE and the intermediate granitoids displaying in contrast high LILE concentrations. They exhibit chemical characteristics of non-alkaline to mid-alkaline, alkali-calcic, calc-alkaline, K-rich to shoshonitic, ferriferous affinities. Chondrite-normalized rare earth element (REE) patterns are characterized by a strong enrichment in light compared to heavy REEs [(La/Sm)N = 3.23–9.65 and (Ga/Lu)N = 1.45–5.54, respectively], with small to significant negative Eu anomalies (Eu/Eu* = 0.28–1.08). Ocean ridge granites (ORG)normalized multi-elements spidergrams display typical collision-related granites pattern, with characteristic negative anomalies of Ba, Nb and Y, and positive anomalies in Rb, Th and Sm. The granitoids under study are genetically I-type granitoids (biotite granitoid, deformed biotite granitoid and mega feldspar granitoid) and one S-type granitoid (two-mica granitoid). The I-type granitoids are metaluminous (ASI: 0.70–1.00) or moderately peraluminous if highly fractionated (ASI: 1.01–1.06). The geochemistry and petrological features of these I-type granitoids argue for close genetic relationships and it is suggest that they originated from a single parent magma. The observed variability in mineralogy and major and trace element compositions in these granitoids are then the reflection of the fractional crystallization that evolved separation of plagioclase, biotite, K-feldspar and accessory minerals at the level of emplacement. The two mica S-type granitoid is exclusively peraluminous (ASI: 1.07–1.25) and classified as a peraluminous leucocratic granitoid or leucogranite. It is marked in its CIPW normative composition by the permanent presence of corundum, ranging between 0.12 and 3.03. The Bafoussam granitoids were emplaced in a syn- to post-collisional tectonic environment. The observed deformational features and the concentrations in Y, less than 40 ppm, confirm that they are related to an orogenesis. Whole-rock Rb–Sr isochrons defines an igneous crystallization ages of 540 ± 27 Ma for the biotite granitoid and 587 ± 41 Ma for the mega feldspar granitoid. These ages fit with the range of Pan-African granitoid ages (650–530 Ma) in West Cameroon and correspond to the Pan-African D2 deformation event in the Neoproterozoic Cameroon orogenic belt. The two-mica granitoid yields an older Rb–Sr isochron age of 663 ± 62 Ma which is considered to be probably a mixing age. The Nd–Sr isotopic compositions indicate that the I-type granitoids have been produced by partial melting of a tonalite–granodiorite source in the lower crust. This is supported by their initial 87Sr/86Sr(600 Ma) ratios (0.705–0.709) and by their WNd(600 Ma) values (0.2 to –6.3, mainly < 0). The two-mica granitoid was generated by partial melting of a greywacke-dominated source involving biotite-limited, biotite dehydration melting. Chemical data of the two-mica granitoid that support this hypothesis are low CaO/Na2O (0.11–0.38) and Sr/Ba (0.20–0.30), the high Rb/Sr (2.26–7.00), the high initial 87Sr/86Sr(600 Ma) ratios ranging from 0.708 to 0.720, the large range in Al2O3/TiO2 (47–204) and the negative WNd(600 Ma) values (–9.9 to –14.0). Moreover,the higher initial 87Sr/86Sr(600 Ma) ratios of the two-mica granitoid are consistent with an upper crust origin. The depleted mantle Nd model ages (TDM) of 1.3–2.3 Ga indicate that the studied granitoids originated by partial melting of Paleoproterozoic and Mesoproterozoic crust, with limited mantle-derived magma contribution. The high initial 87Sr/86Sr(600 Ma) ratios of these granitoids coupled with the wide negative WNd(600 Ma) values strongly suggest a very long residence time in the crust of their protoliths before the melting event. The petrologic signatures of the Bafoussam granitoids are similar to those described in other Pan-African belts of western Gondwanaland such as the neighbouring provinces of Nigeria and the Central African Republic, as well as in the Borborema Province of northeastern Brazil. This supports the previous hypothesis that the Central African fold Belt including Cameroon, Nigeria and the Central African Republic provinces has a continuation in Brazil.
Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017–2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km\(^2\)). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further methodological developments using Sentinel-1 SAR data in order to characterize intraannual supraglacial meltwater dynamics also during polar night and independent of meteorological conditions. In summary, the implementation of the Random Forest classifier enabled the development of the first automated mapping method applied to Sentinel-2 data distributed across all three Antarctic regions.
Supraglacial meltwater accumulation on ice sheets can be a main driver for accelerated ice discharge, mass loss, and global sea-level-rise. With further increasing surface air temperatures, meltwater-induced hydrofracturing, basal sliding, or surface thinning will cumulate and most likely trigger unprecedented ice mass loss on the Greenland and Antarctic ice sheets. While the Greenland surface hydrological network as well as its impacts on ice dynamics and mass balance has been studied in much detail, Antarctic supraglacial lakes remain understudied with a circum-Antarctic record of their spatio-temporal development entirely lacking. This study provides the first automated supraglacial lake extent mapping method using Sentinel-1 synthetic aperture radar (SAR) imagery over Antarctica and complements the developed optical Sentinel-2 supraglacial lake detection algorithm presented in our companion paper. In detail, we propose the use of a modified U-Net for semantic segmentation of supraglacial lakes in single-polarized Sentinel-1 imagery. The convolutional neural network (CNN) is implemented with residual connections for optimized performance as well as an Atrous Spatial Pyramid Pooling (ASPP) module for multiscale feature extraction. The algorithm is trained on 21,200 Sentinel-1 image patches and evaluated in ten spatially or temporally independent test acquisitions. In addition, George VI Ice Shelf is analyzed for intra-annual lake dynamics throughout austral summer 2019/2020 and a decision-level fused Sentinel-1 and Sentinel-2 maximum lake extent mapping product is presented for January 2020 revealing a more complete supraglacial lake coverage (~770 km\(^2\)) than the individual single-sensor products. Classification results confirm the reliability of the proposed workflow with an average Kappa coefficient of 0.925 and a F\(_1\)-score of 93.0% for the supraglacial water class across all test regions. Furthermore, the algorithm is applied in an additional test region covering supraglacial lakes on the Greenland ice sheet which further highlights the potential for spatio-temporal transferability. Future work involves the integration of more training data as well as intra-annual analyses of supraglacial lake occurrence across the whole continent and with focus on supraglacial lake development throughout a summer melt season and into Antarctic winter.
With accelerating global climate change, the Antarctic Ice Sheet is exposed to increasing ice dynamic change. During 1992 and 2017, Antarctica contributed ~7.6 mm to global sea-level-rise mainly due to ocean thermal forcing along West Antarctica and atmospheric warming along the Antarctic Peninsula (API). Together, these processes caused the progressive retreat of glaciers and ice shelves and weakened their efficient buttressing force causing widespread ice flow accelerations. Holding ~91% of the global ice mass and 57.3 m of sea-level-equivalent, the Antarctic Ice Sheet is by far the largest potential contributor to future sea-level-rise.
Despite the improved understanding of Antarctic ice dynamics, the future of Antarctica remains difficult to predict with its contribution to global sea-level-rise representing the largest uncertainty in current projections. Given that recent studies point towards atmospheric warming and melt intensification to become a dominant driver for future Antarctic ice mass loss, the monitoring of supraglacial lakes and their impacts on ice dynamics is of utmost importance. In this regard, recent progress in Earth Observation provides an abundance of high-resolution optical and Synthetic Aperture Radar (SAR) satellite data at unprecedented spatial and temporal coverage and greatly supports the monitoring of the Antarctic continent where ground-based mapping efforts are difficult to perform. As an automated mapping technique for supraglacial lake extent delineation in optical and SAR satellite imagery as well as a pan-Antarctic inventory of Antarctic supraglacial lakes at high spatial and temporal resolution is entirely missing, this thesis aims to advance the understanding of Antarctic surface hydrology through exploitation of spaceborne remote sensing.
In particular, a detailed literature review on spaceborne remote sensing of Antarctic supraglacial lakes identified several research gaps including the lack of (1) an automated mapping technique for optical or SAR satellite data that is transferable in space and time, (2) high-resolution supraglacial lake extent mappings at intra-annual and inter-annual temporal resolution and (3) large-scale mapping efforts across the entire Antarctic continent. In addition, past method developments were found to be restricted to purely visual, manual or semi-automated mapping techniques hindering their application to multi-temporal satellite imagery at large-scale. In this context, the development of automated mapping techniques was mainly limited by sensor-specific characteristics including the similar appearance of supraglacial lakes and other ice sheet surface features in optical or SAR data, the varying temporal signature of supraglacial lakes throughout the year as well as effects such as speckle noise and wind roughening in SAR data or cloud coverage in optical data. To overcome these limitations, this thesis exploits methods from artificial intelligence and big data processing for development of an automated processing chain for supraglacial lake extent delineation in Sentinel-1 SAR and optical Sentinel-2 satellite imagery. The combination of both sensor types enabled to capture both surface and subsurface lakes as well as to acquire data during cloud cover or wind roughening of lakes. For Sentinel-1, a deep convolutional neural network based on residual U-Net was trained on the basis of 21,200 labeled Sentinel-1 SAR image patches covering 13 Antarctic regions. Similarly, optical Sentinel-2 data were collected over 14 Antarctic regions and used for training of a Random Forest classifier. Optical and SAR classification products were combined through decision-level fusion at bi-weekly temporal scale and unprecedented 10 m spatial resolution. Finally, the method was implemented as part of DLR’s High-Performance Computing infrastructure allowing for an automated processing of large amounts of data including all required pre- and postprocessing steps. The results of an accuracy assessment over independent test scenes highlighted the functionality of the classifiers returning accuracies of 93% and 95% for supraglacial lakes in Sentinel-1 and Sentinel-2 satellite imagery, respectively.
Exploiting the full archive of Sentinel-1 and Sentinel-2, the developed framework for the first time enabled the monitoring of seasonal characteristics of Antarctic supraglacial lakes over six major ice shelves in 2015-2021. In particular, the results for API ice shelves revealed low lake coverage during 2015-2018 and particularly high lake coverage during the 2019-2020 and 2020-2021 melting seasons. On the contrary, East Antarctic ice shelves were characterized by high lake coverage during 2016-2019 and
extremely low lake coverage during the 2020-2021 melting season. Over all six investigated ice shelves, the development of drainage systems was revealed highlighting an increased risk for ice shelf instability. Through statistical correlation analysis with climate data at varying time lags as well as annual data on Southern Hemisphere atmospheric modes, environmental drivers for meltwater ponding were revealed. In addition, the influence of the local glaciological setting was investigated through computation of annual recurrence times of lakes. Over both ice sheet regions, the complex interplay between local, regional and large-scale environmental drivers was found to control supraglacial lake formation despite local to regional discrepancies, as revealed through pixel-based correlation analysis. Local control factors included the ice surface topography, the ice shelf geometry, the presence of low-albedo features as well as a reduced firn air content and were found to exert strong control on lake distribution. On the other hand, regional controls on lake evolution were revealed to be the amount of incoming solar radiation, air temperature and wind occurrence. While foehn winds were found to dictate lake evolution over the API, katabatic winds influenced lake ponding in East Antarctica. Furthermore, the regional near-surface climate was shown to be driven by large-scale atmospheric modes and teleconnections with the tropics. Overall, the results highlight that similar driving factors control supraglacial lake formation on the API and EAIS pointing towards their transferability to other Antarctic regions.
The eminent importance of snow cover for climatic, hydrologic, anthropogenic, and economic reasons has been widely discussed in scientific literature. Up to 50% of the Northern Hemisphere is covered by snow at least temporarily, turning snow to the most prevalent land cover types at all. Depending on regular precipitation and temperatures below freezing point it is obvious that a changing climate effects snow cover characteristics fundamentally. Such changes can have severe impacts on local, national, and even global scale. The region of Central Asia is not an exception from this general rule, but are the consequences accompanying past, present, and possible future changes in snow cover parameters of particular importance. Being characterized by continental climate with hot and dry summers most precipitation accumulates during winter and spring months in the form of snow. The population in this 4,000,000 km² vast area is strongly depending on irrigation to facilitate agriculture. Additionally, electricity is often generated by hydroelectric power stations. A large proportion of the employed water originates from snow melt during spring months, implying that changes in snow cover characteristics will automatically affect both the total amount of obtainable water and the time when this water becomes available. The presented thesis explores the question how the spatial extent of snow covered surface has evolved since the year 1986. This investigation is based on the processing of medium resolution remote sensing data originating from daily MODIS and AVHRR sensors, thus forming a unique approach of snow cover analysis in terms of temporal and spatial resolution. Not only duration but also onset and melt of snow coverage are tracked over time, analyzing for systematic changes within this 26 years lasting time span. AVHRR data are processed from raw Level 1B orbit data to Level 3 thematic snow cover products. Both, AVHRR and MODIS snow maps undergo a further post-processing, producing daily full-area mosaics while completely eliminating inherent cloud cover. Snow cover parameters are derived based on these daily and cloud-free time series, allowing for a detailed analysis of current status and changes. The results confirm the predictions made by coarse resolution predictions from climate models: Central Asian snow cover is changing, posing new challenges for the ecosystem and future water supply. The changes, however, are not aimed at only one direction. Regions with decreasing snow cover exist as well as those where the duration of snow cover increases. A shift towards earlier snow cover start and melt can be observed, posing a serious challenge to water management authorities due to a changed runoff regime.
Der Pulvermaar-Vulkan
(2000)
Das etwa 20 000 Jahre alte Pulvermaar in der Westeifel besitzt einen 72 m tiefen, zentral liegenden See und einen stellenweise mindestens 45 m mächtigen Tuffwall. Durch seinen außergewöhnlich guten Erhaltungszustand nimmt es eine Sonderstellung ein. Um auch den Tiefbau dieser Struktur besser kennenzulernen, wurden geophysikalische Messungen mit dem Ziel einer dreidimensionalen Modellierung durchgeführt. Über beides wird in dieser Arbeit berichtet. Den Schwerpunkt der geophysikalischen Untersuchungen bildet ein Gravimetrieprogramm mit der Erstellung einer Schwerekarte des Pulvermaars und seiner Umgebung. Im Rahmen dieser Schweremessungen hat der Einsatz des GPS-Systems zur Vermessung ein besonderes Gewicht. Die Magnetfeldmessungen der Totalintensität konzentrieren sich mit einem dichten Meßnetz auf den Seebereich (Messungen im Boot). Mit Widerstands-Tiefensondierungen der Geoelektrik wird versucht, zu einer präziseren Bestimmung der Tuffmächtigkeiten zu gelangen. Die gewonnene Schwerekarte dient einer dreidimensionalen Modellierung auf der Basis der Freiluftanomalie mit dem Programm IGMAS. Die verhältnismäßig kleine (negative) Schwere-anomalie von 1 - 2 mGal über dem Pulvermaar läßt vermuten, daß ein Basaltkörper in das Diatrem eingebettet ist und zur kleinen Amplitude beiträgt. Die Magnetfeldmessungen er-härten diese Vorstellung; das Ergebnis einer einfachen Modellierung für ein diametrales Profil ist mit einem 40 m mächtigen Basaltkörper grob 120 m unter Seeoberfläche ver-träglich. Die Ergebnisse der gravimetrischen und magnetischen Modellierung, die Mächtigkeitsab-schätzungen für die pyroklastischen Ablagerungen aufgrund der Geoelektrik-Messungen sowie die Einbeziehung einer Volumenkalkulation für die Pyroklastika führen zu einem detaillierten Modell für das Pulvermaar, das sich insbesondere durch ein 2000 m tief reichendes Diatrem auszeichnet. Eine Bearbeitung des Schwerefeldes mit der Berechnung von Gradientenfeldern führt zu einem bisher von Maaren nicht bekannten Ergebnis: Um das Pulvermaar herum existiert ein Hof erniedrigter Dichte mit einem Durchmesser von grob 2 km. Als Ursache wird eine Auf-lockerung des Gesteins durch Streß-Wellen angenommen, die ihren Ursprung in den wiederholten starken Eruptionen der Maar-Entstehung haben. Ebenfalls die Gradienten-felder der Gravimetrie zeigen Zusammenhänge zwischen der Struktur des Maares und der regionalen Tektonik auf.
Accurate crop monitoring in response to climate change at a regional or field scale
plays a significant role in developing agricultural policies, improving food security,
forecasting, and analysing global trade trends. Climate change is expected to
significantly impact agriculture, with shifts in temperature, precipitation patterns, and
extreme weather events negatively affecting crop yields, soil fertility, water availability,
biodiversity, and crop growing conditions. Remote sensing (RS) can provide valuable
information combined with crop growth models (CGMs) for yield assessment by
monitoring crop development, detecting crop changes, and assessing the impact of
climate change on crop yields. This dissertation aims to investigate the potential of RS
data on modelling long-term crop yields of winter wheat (WW) and oil seed rape (OSR)
for the Free State of Bavaria (70,550 km2
), Germany. The first chapter of the dissertation
describes the reasons favouring the importance of accurate crop yield predictions for
achieving sustainability in agriculture. Chapter second explores the accuracy
assessment of the synthetic RS data by fusing NDVIs of two high spatial resolution data
(high pair) (Landsat (30 m, 16-days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low
spatial resolution data (low pair) (MOD13Q1 (250 m, 16-days), MCD43A4 (500 m, one
day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, 8-days)) using the spatial
and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud
or shadow gaps without losing spatial information. The chapter finds that both L-MOD13Q1 (R2 = 0.62, RMSE = 0.11) and S-MOD13Q1 (R2 = 0.68, RMSE = 0.13) are more
suitable for agricultural monitoring than the other synthetic products fused. Chapter
third explores the ability of the synthetic spatiotemporal datasets (obtained in chapter
2) to accurately map and monitor crop yields of WW and OSR at a regional scale. The
chapter investigates and discusses the optimal spatial (10 m, 30 m, or 250 m), temporal
(8 or 16-day) and CGMs (World Food Studies (WOFOST), and the semi-empiric light
use efficiency approach (LUE)) for accurate crop yield estimations of both crop types.
Chapter third observes that the observations of high temporal resolution (8-day)
products of both S-MOD13Q1 and L-MOD13Q1 play a significant role in accurately
measuring the yield of WW and OSR. The chapter investigates that the simple light use
efficiency (LUE) model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) that required fewer input parameters to simulate crop yield is highly accurate, reliable, and more
precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher
input parameters. Chapter four researches the relationship of spatiotemporal fusion
modelling using STRAFM on crop yield prediction for WW and OSR using the LUE
model for Bavaria from 2001 to 2019. The chapter states the high positive correlation
coefficient (R) = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and
modelled yield accuracy for WW and OSR from 2001 to 2019, respectively. The chapter
analyses the impact of climate variables on crop yield predictions by observing an
increase in R2
(0.79 (WW)/0.86 (OSR)) and a decrease in RMSE (4.51/2.57 dt/ha) when
the climate effect is included in the model. The fifth chapter suggests that the coupling
of the LUE model to the random forest (RF) model can further reduce the relative root
mean square error (RRMSE) from -8% (WW) and -1.6% (OSR) and increase the R2 by
14.3% (for both WW and OSR), compared to results just relying on LUE. The same
chapter concludes that satellite-based crop biomass, solar radiation, and temperature
are the most influential variables in the yield prediction of both crop types. Chapter six
attempts to discuss both pros and cons of RS technology while analysing the impact of
land use diversity on crop-modelled biomass of WW and OSR. The chapter finds that
the modelled biomass of both crops is positively impacted by land use diversity to the
radius of 450 (Shannon Diversity Index ~0.75) and 1050 m (~0.75), respectively. The
chapter also discusses the future implications by stating that including some dependent
factors (such as the management practices used, soil health, pest management, and
pollinators) could improve the relationship of RS-modelled crop yields with
biodiversity. Lastly, chapter seven discusses testing the scope of new sensors such as
unmanned aerial vehicles, hyperspectral sensors, or Sentinel-1 SAR in RS for achieving
accurate crop yield predictions for precision farming. In addition, the chapter highlights
the significance of artificial intelligence (AI) or deep learning (DL) in obtaining higher
crop yield accuracies.
Accurate crop monitoring in response to climate change at a regional or field scale plays a significant role in developing agricultural policies, improving food security, forecasting, and analysing global trade trends. Climate change is expected to significantly impact agriculture, with shifts in temperature, precipitation patterns, and extreme weather events negatively affecting crop yields, soil fertility, water availability, biodiversity, and crop growing conditions. Remote sensing (RS) can provide valuable information combined with crop growth models (CGMs) for yield assessment by monitoring crop development, detecting crop changes, and assessing the impact of climate change on crop yields. This dissertation aims to investigate the potential of RS data on modelling long-term crop yields of winter wheat (WW) and oil seed rape (OSR) for the Free State of Bavaria (70,550 km2), Germany. The first chapter of the dissertation describes the reasons favouring the importance of accurate crop yield predictions for achieving sustainability in agriculture. Chapter second explores the accuracy assessment of the synthetic RS data by fusing NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16-days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16-days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, 8-days)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud or shadow gaps without losing spatial information. The chapter finds that both L-MOD13Q1 (R2 = 0.62, RMSE = 0.11) and S-MOD13Q1 (R2 = 0.68, RMSE = 0.13) are more suitable for agricultural monitoring than the other synthetic products fused. Chapter third explores the ability of the synthetic spatiotemporal datasets (obtained in chapter 2) to accurately map and monitor crop yields of WW and OSR at a regional scale. The chapter investigates and discusses the optimal spatial (10 m, 30 m, or 250 m), temporal (8 or 16-day) and CGMs (World Food Studies (WOFOST), and the semi-empiric light use efficiency approach (LUE)) for accurate crop yield estimations of both crop types. Chapter third observes that the observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 play a significant role in accurately measuring the yield of WW and OSR. The chapter investigates that the simple light use efficiency (LUE) model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) that required fewer input parameters to simulate crop yield is highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Chapter four researches the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for WW and OSR using the LUE model for Bavaria from 2001 to 2019. The chapter states the high positive correlation coefficient (R) = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR from 2001 to 2019, respectively. The chapter analyses the impact of climate variables on crop yield predictions by observing an increase in R2 (0.79 (WW)/0.86 (OSR)) and a decrease in RMSE (4.51/2.57 dt/ha) when the climate effect is included in the model. The fifth chapter suggests that the coupling of the LUE model to the random forest (RF) model can further reduce the relative root mean square error (RRMSE) from -8% (WW) and -1.6% (OSR) and increase the R2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. The same chapter concludes that satellite-based crop biomass, solar radiation, and temperature are the most influential variables in the yield prediction of both crop types. Chapter six attempts to discuss both pros and cons of RS technology while analysing the impact of land use diversity on crop-modelled biomass of WW and OSR. The chapter finds that the modelled biomass of both crops is positively impacted by land use diversity to the radius of 450 (Shannon Diversity Index ~0.75) and 1050 m (~0.75), respectively. The chapter also discusses the future implications by stating that including some dependent factors (such as the management practices used, soil health, pest management, and pollinators) could improve the relationship of RS-modelled crop yields with biodiversity. Lastly, chapter seven discusses testing the scope of new sensors such as unmanned aerial vehicles, hyperspectral sensors, or Sentinel-1 SAR in RS for achieving accurate crop yield predictions for precision farming. In addition, the chapter highlights the significance of artificial intelligence (AI) or deep learning (DL) in obtaining higher crop yield accuracies.
Previous work on Jurassic bivalves from the Iberian Range is reviewed, whereby emphasis is placed on Callovian-Kimmeridgian species. The taxonomy, distribution pattern and ecology of the bivalve fauna occurring in Middle and Upper Jurassic rocks of the Aragonian Branch of the Iberian Range have been analysed. For this purpose 14 sections and 5 additional outcrops, selected according to the abundance of bivalves, were measured in detail and sampled. The rocks studied belong to the Chelva, Yátova, Sot de Chera and Loriguilla formations of Callovian-Kimmeridgian age. The distribution of species of bivalves is given for each section. More than 3000 specimens of bivalves representing 83 species that belong to 46 genera and subgenera of the subclasses Palaeotaxodonta, Pteriomorphia, Isofilibranchia. Palaeoheterodonta, Heterodonta and Anomaldesmata have been used for the taxonomic analysis. One species is new: Plagiostoma fuersichi from the Callovian of the Chelva Fm. The autecology (trophic group and life habit) of each bivalve has been discussed. 49 samples of four sections habe been selected for a quantitative palaeoecological analysis of the bivalve fraction of the benthic fauna. Five bivalve associations and two assemblages are recognised by a Q-mode hierarchical cluster analysis (Ward method). The main environmental factors controlling bivalve associations are thought to be substrate, water energy and distribution of organic matter. The bivalves exhibit a distinct spatial and temporal distribution pattern within the Aragonian Branch. Four of the bivalve associations occur in the Upper Oxfordian (Sot de Chera Fm) and one association in the Lower Callovian (Chelva Fm). In the Sot de Chera and Loriguilla formations, the abundance of bivalves decreases from NW to SE i.e., from relatively close to the shore line towards the distal-most part of the carbonate platform. In the Chelva Fm. bivalves are abundant in the Ariño region, interpreted as a palaeogeographic high. The distribution of bivalves might have been largely controlled by the availability of nutrients.
Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.
Current changes of biodiversity result almost exclusively from human activities. This anthropogenic conversion of natural ecosystems during the last decades has led to the so-called ‘biodiversity crisis’, which comprises the loss of species as well as changes in the global distribution patterns of organisms. Species richness is unevenly distributed worldwide. Altogether, 17 so-called ‘megadiverse’ nations cover less than 10% of the earth’s land surface but support nearly 70% of global species richness. Mexico, the study area of this thesis, is one of those countries. However, due to Mexico’s large extent and geographical complexity, it is impossible to conduct reliable and spatially explicit assessments of species distribution ranges based on these collection data and field work alone. In the last two decades, Species distribution models (SDMs) have been established as important tools for extrapolating such in situ observations. SDMs analyze empirical correlations between geo-referenced species occurrence data and environmental variables to obtain spatially explicit surfaces indicating the probability of species occurrence. Remote sensing can provide such variables which describe biophysical land surface characteristics with high effective spatial resolutions. Especially during the last three to five years, the number of studies making use of remote sensing data for modeling species distributions has therefore multiplied. Due to the novelty of this field of research, the published literature consists mostly of selective case studies. A systematic framework for modeling species distributions by means of remote sensing is still missing. This research gap was taken up by this thesis and specific studies were designed which addressed the combination of climate and remote sensing data in SDMs, the suitability of continuous remote sensing variables in comparison with categorical land cover classification data, the criteria for selecting appropriate remote sensing data depending on species characteristics, and the effects of inter-annual variability in remotely sensed time series on the performance of species distribution models. The corresponding novel analyses were conducted with the Maximum Entropy algorithm developed by Phillips et al. (2004). In this thesis, a more comprehensive set of remote sensing predictors than in the existing literature was utilized for species distribution modeling. The products were selected based on their ecological relevance for characterizing species distributions. Two 1 km Terra-MODIS Land 16-day composite standard products including the Enhanced Vegetation Index (EVI), Reflectance Data, and Land Surface Temperature (LST) were assembled into enhanced time series for the time period of 2001 to 2009. These high-dimensional time series data were then transformed into 18 phenological and 35 statistical metrics that were selected based on an extensive literature review. Spatial distributions of twelve tree species were modeled in a hierarchical framework which integrated climate (WorldClim) and MODIS remote sensing data. The species are representative of the major Mexican forest types and cover a variety of ecological traits, such as range size and biotope specificity. Trees were selected because they have a high probability of detection in the field and since mapping vegetation has a long tradition in remote sensing. The result of this thesis showed that the integration of remote sensing data into species distribution models has a significant potential for improving and both spatial detail and accuracy of the model predictions.
The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5–5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15–30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm.
Die Bewässerungslandwirtschaft in Mittelasien ist geprägt von schwerwiegenden ökologischen und ökonomischen Problemen. Zur Verbesserung der Situation auf dem hydrologischen Sektor wird daher seitens der mittelasiatischen Interstate Commission for Water Coordination (ICWC) die Einführung des Integrated Water Resource Management (IWRM) gefordert. Wichtige Herausforderungen zur Optimierung der Wassernutzung im Aralsee-Becken sind dabei die Schaffung von Transparenz sowie von Möglichkeiten zur Überwachung der Landnutzung und der Wasserentnahme in den Bewässerungssystemen. Im Detail fokussierte diese Arbeit auf das Bewässerungssystem der Region Khorezm im Unterlauf des Amu Darya südlich des Aralsees. Die Arbeit zielte darauf ab, (1) objektive und konsistente Datengrundlagen zum Monitoring der Landnutzung und des Wasserverbrauchs innerhalb des Bewässerungslandes zu schaffen und (2) auf Basis dieser Ergebnisse die Funktionsweise des Bewässerungssystems zu verstehen sowie die Land- und Wassernutzung der Region zu bewerten. Um diese Ziele zu erreichen, wurden Methoden der Fernerkundung und der Hydrologie miteinander kombiniert. Fernerkundliche Schlüsselgrößen der Arbeit waren die Kartierung der agrarischen Landnutzung und die Modellierung der saisonalen tatsächlichen Evapotranspiration. Es wurde eine Methode vorgestellt, die eine Unterscheidung verschiedener Landnutzungen und Fruchtfolgen der Region durch die temporale Segmentierung von Zeitserien aus 8-tägigen Kompositen von 250 m-Daten des MODIS-Sensors ermöglicht. Durch die mehrfache Anwendung von Recursive Partitioning And Regression Trees auf deskriptive Statistiken von Zeitseriensegmenten konnte eine hohe Stabilität erzielt werden (overall accuracy: 91 %, Kappa-Koeffizient: 0,9). Täglich von MODIS aufgezeichnete Landoberflächentemperaturen (LST) bildeten die Basis zur fernerkundungsbasierten Modellierung der saisonalen tatsächlichen Evapotranspiration (ETact) für die sommerliche Vegetationsperiode. Aufgrund der hohen zeitlichen und groben räumlichen Auflösung der verwendeten MODIS-Daten von 1 km waren leichte Modifikationen des zur Modellierung eingesetzten Surface Energy Balance Algortihm for Land (SEBAL) erforderlich. Zur Modellierung von ETact wurden MODIS-Produkte (LST, Emissionsgrad, Albedo, NDVI und Blattflächenindex) und meteorologische Stationsdaten aus Khorezm verwendet. Die Modellierung des fühlbaren Wärmeflusses, einer Komponente der Energiebilanzgleichung an der Erdoberfläche, erfolgte mittels METRIC (High Resolution and Internalized Calibration), einer Variante des SEBAL. Die Landnutzungsklassifikation fungierte als zentraler Eingangsparameter, um eine automatisierte Auswahl der Ankerpunkte des Models sicherzustellen. Da innerhalb der MODIS-Auflösung aufgrund der Mischpixelproblematik keine homogen feuchten oder trockenen Bedingungen im Bewässerungsgebiet gefunden werden konnten, wurden die Landnutzungsklassifikation, der NDVI und die ASCE-Referenz-Evapotranspiration zur Abschätzung des tatsächlichen Zustands an den Ankerpunkten herangezogen. Weiterhin wurden umfassende Geländemessungen durchgeführt, um in der Vegetationsperiode 2005 die Zu- und Abflussmengen des Wasser von und nach Khorezm zu bestimmen. Die abschließende Bewertung der Land- und Wassernutzung basierte letztendlich auf der Bildung von Wasserbilanzen und der Berechnung anerkannter Performanceindikatoren wie der Ratio aus Drainage und Wasserentnahme oder der depleted fraction. Für die landwirtschaftliche Nutzung im Rayon Khorezm wurde für die Sommersaison 2005 eine Wasserentnahme von 5,38 km3 ermittelt. Damit übertrafen die Messergebnisse die offiziell verfügbaren Daten der ICWC um durchschnittlich 37 %. Auf die landwirtschaftliche Fläche bezogen ergab sich für Khorezm im Jahr 2005 eine mittlere Wasserentnahme von 22.782 m3/ha. In den Subsystemen schwankten diese Werte zwischen 17.000 m3/ha und 30.000 m3/ha. Allerdings konnte an den Systemgrenzen, an denen die Messungen durchgeführt werden, der aus den fernerkundungsbasierten Modellierungen auf WUA-Level erwartete abnehmende Gradient der Wasserentnahme zwischen Oberlauf und Unterlauf nicht nachvollzogen werden. Als Ursache für diese Diskrepanz sind vor allem die Versickerungsverluste im Kanalsystem zu nennen, die den Grundwasserkörper großräumig auffüllen und auf Feldebene nicht zur oberflächlichen Bewässerung zur Verfügung stehen. Monatliche Bilanzierungen und die Analyse der Performanceindikatoren führten zu denselben Ergebnissen. In dieser Arbeit konnte gezeigt werden, dass sich mit Methoden der Fernerkundung objektive und konsistente Daten der agrarischen Landnutzung und des Wasserverbrauchs für ein regionales Monitoring erstellen lassen. Da in den benachbarten Regionen gleiche atmosphärische Bedingungen und ähnliche Anbausorten anzutreffen sind, ist anzunehmen, dass beide Verfahren auch auf der Planungsebene in einem IWRM für die übrigen Mittel- und Unterläufe von Amu Darya und Syr Darya ein hohes Anwendungspotenzial besitzen.
Processes of the Earth’s surface occur at different scales of time and intensity. Climate in particular determines the activity and seasonal development of vegetation. These dynamics are predominantly driven by temperature in the humid mid-latitudes and by the availability of water in semi-arid regions. Human activities are a modifying parameter for many ecosystems and can become the prime force in well-developed regions with an intensively managed environment. Accounting for these dynamics, i.e. seasonal dynamics of ecosystems and short- to long-term changes in land-cover composition, requires multiple measurements in time. With respect to the characterization of the Earth surface and its transformation due to global warming and human-induced global change, there is a need for appropriate data and methods to determine the activity of vegetation and the change of land cover. Space-borne remote sensing is capable of monitoring the activity and development of vegetation as well as changes of the land surface. In many instances, satellite images are the only means to comprehensively assess the surface characteristics of large areas. A high temporal frequency of image acquisition, forming a time series of satellite data, can be employed for mapping the development of vegetation in space and time. Time series allow for detecting and assessing changes and multi-year transformation processes of high and low intensity, or even abrupt events such as fire and flooding. The operational processing of satellite data and automated information-extraction techniques are the basis for consistent and continuous long-term product generation. This provides the potential for directly using remote-sensing data and products for analyzing the land surface in relation to global warming and global change, including deforestation and land transformation. This study aims at the development of an advanced approach to time-series generation using data-quality indicators. A second goal focuses on the application of time series for automated land-cover classification and update, using fractional cover estimates to accommodate for the comparatively coarse spatial resolution. Requirements of this study are the robustness and high accuracy of the approaches as well as the full transferability to other regions and datasets. In this respect, the developments of this study form a methodological framework, which can be filled with appropriate modules for a specific sensor and application. In order to attain the first goal, time-series compilation, a stand-alone software application called TiSeG (Time Series Generator) has been developed. TiSeG evaluates the pixel-level quality indicators provided with each MODIS land product. It computes two important data-availability indicators, the number of invalid pixels and the maximum gap length. Both indices are visualized in time and space, indicating the feasibility of temporal interpolation. The level of desired data quality can be modified spatially and temporally to account for distinct environments in a larger study area and for seasonal differences. Pixels regarded as invalid are either masked or interpolated with spatial or temporal techniques.