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Institute
- Institut für Geographie und Geologie (59) (remove)
Sonstige beteiligte Institutionen
- The Italian Federation of Parks and Nature Reserves (3)
- ALPARC - The Alpine Network of Protected Areas (2)
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- Salzburg Institute for Regional Planning and Housing (2)
- Urban Planning Institute of the Republic of Slovenia (2)
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- Eurac Research – Istituto per lo sviluppo regionale (1)
EU-Project number / Contract (GA) number
Die imperiale Lebensweise westlicher Industrienationen, die sich durch ein permanentes Streben nach Wirtschaftswachstum ausdrückt, bringt den Planeten an die Grenzen seiner Tragfähigkeit. In den letzten Jahren wurden jedoch – bestärkt durch die Weltwirtschaftskrise 2007/08 – Alternativen zum Modell des permanenten Wachstums immer populärer, die sich anstatt auf ökonomischen Wohlstand vermehrt auf soziale und ökologische Belange des gesellschaftlichen Zusammenlebens fokussierten. Unter dem Begriff der Postwachstumsbewegung sammelten sich Ansätze, Ideen und Akteure, die gemeinsam für eine Zukunft fernab jeglicher Wachstumszwänge und innerhalb der planetaren Grenzen kämpfen.
Vor dem Hintergrund der zunehmenden sozialen und ökologischen Herausforderungen wurden nun erstmals sozial-ökologische Nischenakteure aus drei unterschiedlichen Bereichen der Postwachstumsbewegung gemeinsam in einem Forschungsvorhaben – unter besonderer Berücksichtigung gesellschaftlicher, organisatorischer und territorialer Einbettungsprozesse – untersucht. Eingebettet ist diese Untersuchung in den theoretisch-konzeptionellen Ansatz der sozial-ökologischen Transformation, deren inkrementeller Wandel mithilfe der Multi-Level-Perspektive beschrieben werden kann. Die Kombination dieses spezifischen theoretisch-konzeptionellen Ansatzes und der empirischen Erhebung ist das Alleinstellungsmerkmal der vorliegenden Untersuchung.
Es zeigte sich, dass alle untersuchten Nischenakteure eine deutlich progressive Unternehmungsphilosophie vertreten, die häufig in einer Unternehmungsorganisation mit flachen Hierarchien und konsensbasierten Entscheidungsfindungen mündet. Besonders gesellschaftliche Einbettungsprozesse bedingen den Erfolg oder Misserfolg der Nischenentwicklung. Organisatorische Einbettung kommt derweil vor allem im Aufbau weitreichender Netzwerkstrukturen zum Tragen, die die Innovationsfähigkeit und Stabilität der Nische unterstützen. Eine starke territoriale Einbettung steigert den lokal-regionalen Einfluss der Nischeninnovationen und generiert Rückhalt in der Bevölkerung.
The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI.
The expansion of renewable energies is being driven by the gradual phaseout of fossil fuels in order to reduce greenhouse gas emissions, the steadily increasing demand for energy and, more recently, by geopolitical events. The offshore wind energy sector is on the verge of a massive expansion in Europe, the United Kingdom, China, but also in the USA, South Korea and Vietnam. Accordingly, the largest marine infrastructure projects to date will be carried out in the upcoming decades, with thousands of offshore wind turbines being installed. In order to accompany this process globally and to provide a database for research, development and monitoring, this dissertation presents a deep learning-based approach for object detection that enables the derivation of spatiotemporal developments of offshore wind energy infrastructures from satellite-based radar data of the Sentinel-1 mission.
For training the deep learning models for offshore wind energy infrastructure detection, an approach is presented that makes it possible to synthetically generate remote sensing data and the necessary annotation for the supervised deep learning process. In this synthetic data generation process, expert knowledge about image content and sensor acquisition techniques is made machine-readable. Finally, extensive and highly variable training data sets are generated from this knowledge representation, with which deep learning models can learn to detect objects in real-world satellite data.
The method for the synthetic generation of training data based on expert knowledge offers great potential for deep learning in Earth observation. Applications of deep learning based methods can be developed and tested faster with this procedure. Furthermore, the synthetically generated and thus controllable training data offer the possibility to interpret the learning process of the optimised deep learning models.
The method developed in this dissertation to create synthetic remote sensing training data was finally used to optimise deep learning models for the global detection of offshore wind energy infrastructure. For this purpose, images of the entire global coastline from ESA's Sentinel-1 radar mission were evaluated. The derived data set includes over 9,941 objects, which distinguish offshore wind turbines, transformer stations and offshore wind energy infrastructures under construction from each other. In addition to this spatial detection, a quarterly time series from July 2016 to June 2021 was derived for all objects. This time series reveals the start of construction, the construction phase and the time of completion with subsequent operation for each object.
The derived offshore wind energy infrastructure data set provides the basis for an analysis of the development of the offshore wind energy sector from July 2016 to June 2021. For this analysis, further attributes of the detected offshore wind turbines were derived. The most important of these are the height and installed capacity of a turbine. The turbine height was calculated by a radargrammetric analysis of the previously detected Sentinel-1 signal and then used to statistically model the installed capacity. The results show that in June 2021, 8,885 offshore wind turbines with a total capacity of 40.6 GW were installed worldwide. The largest installed capacities are in the EU (15.2 GW), China (14.1 GW) and the United Kingdom (10.7 GW). From July 2016 to June 2021, China has expanded 13 GW of offshore wind energy infrastructure. The EU has installed 8 GW and the UK 5.8 GW of offshore wind energy infrastructure in the same period. This temporal analysis shows that China was the main driver of the expansion of the offshore wind energy sector in the period under investigation.
The derived data set for the description of the offshore wind energy sector was made publicly available. It is thus freely accessible to all decision-makers and stakeholders involved in the development of offshore wind energy projects. Especially in the scientific context, it serves as a database that enables a wide range of investigations. Research questions regarding offshore wind turbines themselves as well as the influence of the expansion in the coming decades can be investigated. This supports the imminent and urgently needed expansion of offshore wind energy in order to promote sustainable expansion in addition to the expansion targets that have been set.
"Die Innenstadt braucht den Handel, der Handel aber nicht die Innenstadt", lautet eine oft formulierte These bezüglich des Verhältnisses von Handel und Innenstadt – nicht erst seit der Covid-19-Pandemie. Die Krise hat die Herausforderungen des Strukturwandels im Einzelhandel erneut offengelegt und teils Entwicklungen beschleunigt. Besonders hervorzuheben sind zum einen Handlungsbedarfe im Bereich der Digitalisierung sowie die dringende Notwendigkeit einer überdachten Auseinandersetzung über das Verhältnis von Innenstadt und Einzelhandel. Neben Fragen zur zukünftigen Gestaltung des Einzelhandels und seiner Bedeutung für Innenstädte, sind auch Fragen zur Bedeutung anderer Branchen/Einrichtungen/Angebote (z.B. Gastronomie, Handwerk, Kultureinrichtungen, Kitas, Sport- und Bildungseinrichtungen, aber auch Freiräume, Grünflächen, verkehrsberuhigte Bereiche oder lokale Kurierdienste) für den Einzelhandel vermehrt aus Perspektive der geographischen Handelsforschung zu beantworten. Mit der Krise wurden Defizite und Handlungsfelder in den Blick gerückt, deren Bearbeitung schon lange ansteht. Die Chance liegt darin, diesen Aufmerksamkeitsschub konstruktiv zu nutzen und realistische fall- und standortspezifische Perspektiven für Innenstädte und ihre Akteur*innen jetzt zu verhandeln und nicht weiter auf die lange Bank zu schieben. Der vorliegende Band vereint neun handelsgeographische Beiträge von Wissenschaftler*innen und Praktiker*innen, die die Auswirkungen der Covid-19-Pandemie erörtern und damit einen wichtigen Beitrag für die notwendige Diskussion der Zukunft von Innenstädten und Handel leisten.
Individual mobility and human patterns analyses is receiving increasing attention in numerous interdisciplinary studies and publications using the concept of time-geography but is largely unknown to the subdiscipline of sports geography. Meanwhile the visualization and evaluation of large data of individual patterns are still a major challenge. While a qualitative, microscale view on spatial-temporal topics is more common in today's pattern research using mostly 24h time intervals, this work examines a quantitative approach focusing on an extended period of life. This paper presents a combination of time-geographic approaches with 3D-geoinformation systems and demonstrates their value for analysing individual mobility by implementing a path-homogeneity factor (HPA). Using the example of professional athletes, it is shown which groups display greater similarities in their career paths. While a high homogeneity suggests that groups make similar decisions through socially influenced processes, low values allow the assumption that external processes provide stronger, independent individual structures.
The area northeast of Sudbury, Ontario, is known for one of the largest unexplained geophysical anomalies on the Canadian Shield, the 1,200 km2 Temagami Anomaly. The geological cause of this regional magnetic, conductive and gravity feature has previously been modelled to be a mafic-ultramafic body at relatively great depth (2–15 km) of unknown age and origin, which may or may not be related to the meteorite impact-generated Sudbury Igneous Complex in its immediate vicinity. However, with a profound lack of outcrops and drill holes, the geological cause of the anomaly remains elusive, a genetic link to the 1.85 Ga Sudbury impact event purely speculative.
In search for any potential surface expression of the deep-seated cause of the Temagami Anomaly, this study provides a first, yet comprehensive petrological and geochemical assessment of exotic igneous dykes recently discovered in outcrops above, and drill cores into, the Temagami Anomaly. Based on cross-cutting field relations, petrographic studies, lithogeochemistry, whole-rock Nd-Sr-Pb isotope systematics, and U-Pb geochronology, it was possible to identify, and distinguish between, at least six different groups of igneous dykes: (i) Calc-alkaline quartz diorite dykes related to the 1.85 Ga Sudbury Igneous Complex (locally termed Offset Dykes); (ii) tholeiitic quartz diabase of the regional 2.22 Ga Nipissing Suite/Senneterre Dyke Swarm; (iii) calc-alkaline quartz diabase of the regional 2.17 Ga Biscotasing Dyke Swarm; (iv) alkaline ultrabasic dykes correlated with the 1.88–1.86 Ga Circum-Superior Large Igneous Province (LIP); and (v) aplitic dykes as well as (vi) a hornblende syenite, the latter two of more ambiguous age and stratigraphic position.
The findings presented in this study – the discovery of three new Offset Dykes in particular – offer some unexpected insights into the geology and economic potential of one of the least explored areas of the world-class Sudbury Mining Camp as well as into the nature and distribution of both allochthonous and autochthonous impactites within one of the oldest and largest impact structures known on Earth. Not only do the geometric patterns of dyke (and breccia) distribution reaffirm previous notions of the existence of discrete ring structures in the sense of a ~200-km multi-ring basin, but they provide critical constraints as to the pre-erosional thickness and extent of the impact melt sheet, thus helping to identity new areas for Ni-Cu-PGE exploration. Furthermore, this study provides important insights into the pre-impact stratigraphy and the magmatic evolution of the region in general, which reveals to be much more complex, compositionally divers, and protracted than initially assumed. Of note is the discovery of rocks related to the 2.17 Ga Biscotasing and the 1.88–1.86 Ga Circum-Superior magmatic events, as these were not previously known to occur on the southeast margin of the Superior Craton. Shortly predating the Sudbury impact and being contemporaneous with ore-forming events at Thompson (Manitoba) and Raglan (Cape Smith), these magmatic rocks could provide the missing link between unusual mafic, pre-enriched, crustal target rocks, and the unique metal endowment of the Sudbury Impact Structure.
The actual geological cause of the Temagami Anomaly remains open to debate and requires the downward extension of existing bore holes as well as more detailed geophysical investigations. The hypothesis of a genetic relationship between Sudbury impact event and Temagami Anomaly is neither borne out by any evidence nor particularly realistic, even in case of an oblique impact, and should thus be abandoned. It is instead proposed, based on circumstantial evidence, that the anomaly might be explained by an ultramafic complex of the 1.88–1.86 Ga Circum-Superior LIP.
Durch die globale Organisation von Lebensmittelwarenketten steht Konsument*innen heute ein vielfältiges, ganzjährig nahezu gleichbleibendes Angebot an frischem Obst und Gemüse im Lebensmitteleinzelhandel zur Verfügung. Damit einher geht eine erhöhte Komplexität beim Lebensmitteleinkauf und ein verändertes Wissen von Konsument*innen, über die Waren: Das eigene Erfahren der Lebensmittelproduktion ist im Alltag heute nicht mehr möglich. Statt praktischem Wissen gewinnt damit explizites und objektiviertes Wissen über die Waren, z.B. in Form von Siegeln an Bedeutung. Viele Produkt- und Produktionseigenschaften entziehen sich zudem der Kenntnis der Konsument*innen, während gleichzeitig das Bewusstsein für Fragen sozialer und ökologischer Nachhaltigkeit steigt.
Die vorliegende Studie geht vor diesem Hintergrund am Beispiel des Einkaufs von frischem Obst und Gemüse der Frage nach, welche Bedeutung die Herkunftsangabe als Hinweis auf die Geographien der Waren für die Bewertung von frischem Obst und Gemüse hat und welches Wissen Konsument*innen über Waren und deren Biographien haben. Es wird zudem aufgezeigt, welche Rolle Nichtwissen beim Lebensmittelkonsum spielt.
Die Studie liefert Erkenntnisse für die bislang im deutschsprachigen Raum noch vergleichsweise wenig repräsentierte Konsumgeographie und macht Konzepte aus der Wissens- und Organisationssoziologie für die wirtschaftsgeographische Forschung fruchtbar. Aus einer Praxisperspektive bietet sie Anschlusspunkte für Fragen des nachhaltigen Konsums sowie des Verbraucherschutzes.
The recently observed consistent loss of β-diversity across ecosystems indicates increasingly homogeneous communities in patches of landscapes, mainly caused by increasing land-use intensity. Biodiversity is related to numerous ecosystem functions and stability. Therefore, decreasing β-diversity is also expected to reduce multifunctionality. To assess the impact of homogenization and to develop guidelines to reverse its potentially negative effects, we combine expertise from forest science, ecology, remote sensing, chemical ecology and statistics in a collaborative and experimental β-diversity approach. Specifically, we will address the question whether the Enhancement of Structural Beta Complexity (ESBC) in forests by silviculture or natural disturbances will increase biodiversity and multifunctionality in formerly homogeneously structured production forests. Our approach will identify potential mechanisms behind observed homogenization-diversity-relationships and show how these translate into effects on multifunctionality. At eleven forest sites throughout Germany, we selected two districts as two types of small ‘forest landscapes’. In one of these two districts, we established ESBC treatments (nine differently treated 50x50 m patches with a focus on canopy cover and deadwood features). In the second, the control district, we will establish nine patches without ESBC. By a comprehensive sampling, we will monitor 18 taxonomic groups and measure 21 ecosystem functions, including key functions in temperate forests, on all patches. The statistical framework will allow a comprehensive biodiversity assessment by quantifying the different aspects of multitrophic biodiversity (taxonomical, functional and phylogenetic diversity) on different levels of biodiversity (α-, β-, γ-diversity). To combine overall diversity, we will apply the concept of multidiversity across the 18 taxa. We will use and develop new approaches for quantification and partitioning of multifunctionality at α- and β- scales. Overall, our study will herald a new research avenue, namely by experimentally describing the link between β-diversity and multifunctionality. Furthermore, we will help to develop guidelines for improved silvicultural concepts and concepts for management of natural disturbances in temperate forests reversing past homogenization effects.
Snow cover (SC) and timing of snowmelt are key regulators of a wide range of Arctic ecosystem functions. Both are strongly influenced by the amplified Arctic warming and essential variables to understand environmental changes and their dynamics. This study evaluates the potential of Sentinel-1 (S-1) synthetic aperture radar (SAR) time series for monitoring SC depletion and snowmelt with high spatiotemporal resolution to capture their understudied small-scale heterogeneity. We use 97 dual-polarized S-1 SAR images acquired over northeastern Greenland and 94 over southwestern Greenland in the interferometric wide swath mode from the years 2017 and 2018. Comparison of S-1 intensity against SC fraction maps derived from orthorectified terrestrial time-lapse imagery indicates that SAR backscatter can increase before a decrease in SC fraction is observed. Hence, the increase in backscatter is related to changing snowpack properties during the runoff phase as well as decreasing SC fraction. We here present a novel empirical approach based on the temporal evolution of the SAR signal to identify start of runoff (SOR), end of snow cover (EOS) and SC extent for each S-1 observation date during melt using backscatter thresholds as well as the derivative. Comparison of SC with orthorectified time-lapse imagery indicates that HV polarization outperforms HH when using a global threshold. The derivative avoids manual selection of thresholds and adapts to different environmental settings and seasonal conditions. With a global configuration (threshold: 4 dB; polarization: HV) as well as with the derivative, the overall accuracy of SC maps was in all cases above 75 % and in more than half of cases above 90 %. Based on the physical principle of SAR backscatter during snowmelt, our approach is expected to work well in other low-vegetation areas and, hence, could support large-scale SC monitoring at high spatiotemporal resolution (20 m, 6 d) with high accuracy.
While the place of birth plays a crucial role for women’s birth experiences, the interest in out-of-hospital births has increased during the Covid-19 pandemic. Related to this, various international policies recommend enabling women to choose where to give birth. We aimed to analyze Swiss women’s choice between birth hospitals and birth centers. Employing spatial accessibility analysis, we incorporated four data types: highly disaggregated population data, administrative data, street network data, addresses of birth hospitals and birth centers. 99.8% of Swiss women of childbearing age were included in the analysis (N = 1.896.669). We modelled car travel times from a woman’s residence to the nearest birth hospital and birth center. If both birth settings were available within 30 minutes, a woman was considered to have a true choice. Only 58.2% of women had a true choice. This proportion varied considerably across Swiss federal states. The main barrier to a true choice was limited accessibility of birth centers. Median travel time to birth hospitals was 9.8 (M = 12.5), to birth centers 23.9 minutes (M = 28.5). Swiss women are insufficiently empowered to exercise their reproductive autonomy as their choice of place of birth is significantly limited by geographical constraints. It is an ethical and medical imperative to provide women with a true choice. We provide high-resolution insights into the accessibility of birth settings and strong arguments to (re-)examine the need for further birth centers (and birth hospitals) in specific geographical areas. Policy-makers are obligated to improve the accessibility of birth centers to advance women’s autonomy and enhance maternal health outcomes after childbirth. The Covid-19 pandemic offers an opportunity to shift policy.
OpenSpaceAlps Planungshandbuch: Perspektiven für eine konsistente Freiraumsicherung im Alpenraum
(2022)
Im Alpenraum lässt sich nach wie vor die kontinuierliche Inanspruchnahme von Freiräumen für Siedlungsflächen und technische Infrastrukturen und die damit verbundene Bodenversiegelung beobachten. Dies führt in erster Linie zum Verlust von landwirtschaftlichen Flächen. Je nach Ausmaß der Bebauung kommt es auch zu einer verstärkten Landschaftszerschneidung, die zur Isolierung natürlicher Lebensräume und zur Einschränkung des ökologischen Verbundes sowie zu weiteren negativen Folgewirkungen führt. Das OpenSpaceAlps Projekt hat sich dieser Thematik angenommen und, basierend auf kooperativen Verfahren in mehreren Pilotregionen, Handlungsansätze und Strategien für eine nachhaltige Sicherung von Freiräumen entwickelt. Dieses Handbuch stellt eine Handlungs- und Entscheidungshilfe für verschiedene Akteure/Akteurinnen dar, allen voran Planer*innen in öffentlichen Planungsbehörden. Ausgehend von einer Analyse der Herausforderungen und Rahmenbedingungen im Alpenraum, werden in diesem Handbuch zentrale „Prinzipien“ der Freiraumplanung vorgestellt und verglichen. Außerdem werden integrierte Planungsstrategien für verschiedene Raumkategorien diskutiert.
Landslide susceptibility assessment in the Chiconquiaco Mountain Range area, Veracruz (Mexico)
(2022)
In Mexico, numerous landslides occur each year and Veracruz represents the state with the third highest number of events. Especially the Chiconquiaco Mountain Range, located in the central part of Veracruz, is highly affected by landslides and no detailed information on the spatial distribution of existing landslides or future occurrences is available. This leaves the local population exposed to an unknown threat and unable to react appropriately to this hazard or to consider the potential landslide occurrence in future planning processes.
Thus, the overall objective of the present study is to provide a comprehensive assessment of the landslide situation in the Chiconquiaco Mountain Range area. Here, the combination of a site-specific and a regional approach enables to investigate the causes, triggers, and process types as well as to model the landslide susceptibility for the entire study area.
For the site-specific approach, the focus lies on characterizing the Capulín landslide, which represents one of the largest mass movements in the area. In this context, the task is to develop a multi-methodological concept, which concentrates on cost-effective, flexible and non-invasive methods. This approach shows that the applied methods complement each other very well and their combination allows for a detailed characterization of the landslide.
The analyses revealed that the Capulín landslide is a complex mass movement type. It comprises rotational movement in the upper parts and translational movement in the lower areas, as well as flow processes at the flank and foot area and therefore, is classified as a compound slide-flow according to Cruden and Varnes (1996). Furthermore, the investigations show that the Capulín landslide represents a reactivation of a former process. This is an important new information, especially with regard to the other landslides identified in the study area. Both the road reconstructed after the landslide, which runs through the landslide mass, and the stream causing erosion processes at the foot of the landslide severely affect the stability of the landslide, making it highly susceptible to future reactivation processes. This is particularly important as the landslide is located only few hundred meters from the village El Capulín and an extension of the landslide area could cause severe damage.
The next step in the landslide assessment consists of integrating the data obtained in the site-specific approach into the regional analysis. Here, the focus lies on transferring the generated data to the entire study area. The developed methodological concept yields applicable results, which is supported by different validation approaches.
The susceptibility modeling as well as the landslide inventory reveal that the highest probability of landslides occurrence is related to the areas with moderate slopes covered by slope deposits. These slope deposits comprise material from old mass movements and erosion processes and are highly susceptible to landslides. The results give new insights into the landslide situation in the Chiconquiaco Mountain Range area, since previously landslide occurrence was related to steep slopes of basalt and andesite.
The susceptibility map is a contribution to a better assessment of the landslide situation in the study area and simultaneously proves that it is crucial to include specific characteristics of the respective area into the modeling process, otherwise it is possible that the local conditions will not be represented correctly.
This study compares the performance of three bias correction (BC) techniques in adjusting simulated precipitation estimates over Germany. The BC techniques are the multivariate quantile delta mapping (MQDM) where the grids are used as variables to incorporate the spatial dependency structure of precipitation in the bias correction; empirical quantile mapping (EQM) and, the linear scaling (LS) approach. Several metrics that include first to fourth moments and extremes characterized by the frequency of heavy wet days and return periods during boreal summer were applied to score the performance of the BC techniques. Our results indicate a strong dependency of the relative performances of the BC techniques on the choice of the regional climate model (RCM), the region, the season, and the metrics of interest. Hence, each BC technique has relative strengths and weaknesses. The LS approach performs well in adjusting the first moment but tends to fall short for higher moments and extreme precipitation during boreal summer. Depending on the season, the region and the RCM considered, there is a trade-off between the relative performances of the EQM and the MQDM in adjusting the simulated precipitation biases. However, the MQDM performs well across all considered metrics. Overall, the MQDM outperforms the EQM in improving the higher moments and in capturing the observed return level of extreme summer precipitation, averaged over Germany.
The investigation of the Earth system and interplays between its components is of utmost importance to enhance the understanding of the impacts of global climate change on the Earth's land surface. In this context, Earth observation (EO) provides valuable long-term records covering an abundance of land surface variables and, thus, allowing for large-scale analyses to quantify and analyze land surface dynamics across various Earth system components. In view of this, the geographical entity of river basins was identified as particularly suitable for multivariate time series analyses of the land surface, as they naturally cover diverse spheres of the Earth. Many remote sensing missions with different characteristics are available to monitor and characterize the land surface. Yet, only a few spaceborne remote sensing missions enable the generation of spatio-temporally consistent time series with equidistant observations over large areas, such as the MODIS instrument.
In order to summarize available remote sensing-based analyses of land surface dynamics in large river basins, a detailed literature review of 287 studies was performed and several research gaps were identified. In this regard, it was found that studies rarely analyzed an entire river basin, but rather focused on study areas at subbasin or regional scale. In addition, it was found that transboundary river basins remained understudied and that studies largely focused on selected riparian countries. Moreover, the analysis of environmental change was generally conducted using a single EO-based land surface variable, whereas a joint exploration of multivariate land surface variables across spheres was found to be rarely performed.
To address these research gaps, a methodological framework enabling (1) the preprocessing and harmonization of multi-source time series as well as (2) the statistical analysis of a multivariate feature space was required. For development and testing of a methodological framework that is transferable in space and time, the transboundary river basins Indus, Ganges, Brahmaputra, and Meghna (IGBM) in South Asia were selected as study area, having a size equivalent to around eight times the size of Germany. These basins largely depend on water resources from monsoon rainfall and High Mountain Asia which holds the largest ice mass outside the polar regions. In total, over 1.1 billion people live in this region and in parts largely depend on these water resources which are indispensable for the world's largest connected irrigated croplands and further domestic needs as well. With highly heterogeneous geographical settings, these river basins allow for a detailed analysis of the interplays between multiple spheres, including the anthroposphere, biosphere, cryosphere, hydrosphere, lithosphere, and atmosphere.
In this thesis, land surface dynamics over the last two decades (December 2002 - November 2020) were analyzed using EO time series on vegetation condition, surface water area, and snow cover area being based on MODIS imagery, the DLR Global WaterPack and JRC Global Surface Water Layer, as well as the DLR Global SnowPack, respectively. These data were evaluated in combination with further climatic, hydrological, and anthropogenic variables to estimate their influence on the three EO land surface variables. The preprocessing and harmonization of the time series was conducted using the implemented framework. The resulting harmonized feature space was used to quantify and analyze land surface dynamics by means of several statistical time series analysis techniques which were integrated into the framework. In detail, these methods involved (1) the calculation of trends using the Mann-Kendall test in association with the Theil-Sen slope estimator, (2) the estimation of changes in phenological metrics using the Timesat tool, (3) the evaluation of driving variables using the causal discovery approach Peter and Clark Momentary Conditional Independence (PCMCI), and (4) additional correlation tests to analyze the human influence on vegetation condition and surface water area.
These analyses were performed at annual and seasonal temporal scale and for diverse spatial units, including grids, river basins and subbasins, land cover and land use classes, as well as elevation-dependent zones. The trend analyses of vegetation condition mostly revealed significant positive trends. Irrigated and rainfed croplands were found to contribute most to these trends. The trend magnitudes were particularly high in arid and semi-arid regions. Considering surface water area, significant positive trends were obtained at annual scale. At grid scale, regional and seasonal clusters with significant negative trends were found as well. Trends for snow cover area mostly remained stable at annual scale, but significant negative trends were observed in parts of the river basins during distinct seasons. Negative trends were also found for the elevation-dependent zones, particularly at high altitudes. Also, retreats in the seasonal duration of snow cover area were found in parts of the river basins. Furthermore, for the first time, the application of the causal discovery algorithm on a multivariate feature space at seasonal temporal scale revealed direct and indirect links between EO land surface variables and respective drivers. In general, vegetation was constrained by water availability, surface water area was largely influenced by river discharge and indirectly by precipitation, and snow cover area was largely controlled by precipitation and temperature with spatial and temporal variations. Additional analyses pointed towards positive human influences on increasing trends in vegetation greenness. The investigation of trends and interplays across spheres provided new and valuable insights into the past state and the evolution of the land surface as well as on relevant climatic and hydrological driving variables. Besides the investigated river basins in South Asia, these findings are of great value also for other river basins and geographical regions.
Arctic permafrost coasts become increasingly vulnerable due to environmental drivers such as the reduced sea-ice extent and duration as well as the thawing of permafrost itself. A continuous quantification of the erosion process on large to circum-Arctic scales is required to fully assess the extent and understand the consequences of eroding permafrost coastlines. This study presents a novel approach to quantify annual Arctic coastal erosion and build-up rates based on Sentinel-1 (S1) Synthetic Aperture RADAR (SAR) backscatter data, in combination with Deep Learning (DL) and Change Vector Analysis (CVA). The methodology includes the generation of a high-quality Arctic coastline product via DL, which acted as a reference for quantifying coastal erosion and build-up rates from annual median and standard deviation (sd) backscatter images via CVA. The analysis was applied on ten test sites distributed across the Arctic and covering about 1038 km of coastline. Results revealed maximum erosion rates of up to 160 m for some areas and an average erosion rate of 4.37 m across all test sites within a three-year temporal window from 2017 to 2020. The observed erosion rates within the framework of this study agree with findings published in the previous literature. The proposed methods and data can be applied on large scales and, prospectively, even for the entire Arctic. The generated products may be used for quantifying the loss of frozen ground, estimating the release of stored organic material, and can act as a basis for further related studies in Arctic coastal environments.
Na območju Alp že dalj časa poteka preobrazba odprtega prostora zaradi gradnje in širjenja naselij ter tehnične infrastrukture. Navedeni procesi povzročajo zlasti izgubo kmetijskih zemljišč, stalno pokritje površine tal z nepropustnimi snovmi in razdrobljenost krajine. Razdrobljenost je odvisna od vrst posegov in stopnje pozidanosti prostora, poglavitna negativna učinka pa sta izolacija naravnih habitatov in slabšanje ekološke povezljivosti. Opisana problematika je bila glavna tema projekta OpenSpaceAlps, v katerem so bili ob sodelovanju z deležniki na več pilotnih območjih razviti pristopi in rešitve, ki omogočajo trajnostno ohranjanje odprtega prostora. Načrtovalski priročnik povzema del rezultatov projekta. Namenjen je različnim deležnikom, zlasti načrtovalcem v javnih službah, kot pripomoček pri izvajanju načrtovalskih nalog in odločanju. V priročniku so predstavljeni analiza izzivov in okvirnih pogojev v Alpah ter opis in primerjava poglavitnih načel načrtovanja odprtega prostora, obravnavane pa so tudi celostne načrtovalske strategije za različne kategorije prostora.
Globale Wertschöpfungsketten stellen nicht nur hochkomplexe Beziehungsgefüge dar, sondern unterliegen auch einem ständigen Wandlungsprozess. Ein zentraler Treiber dieser Wandlungsprozesse ist der technologische Fortschritt. Moderne Informations- und Kommunikationstechnologien, insbesondere die Phänomene der Digitalisierung und des Online-Handels, sind derzeit von besonderer Bedeutung für Wertschöpfungsketten, da unterschiedliche Fortschritte in der Digitalisierung nicht nur zu wirtschaftlichen Vor- und Nachteilen von Unternehmen führen können, sondern auch zu Up- bzw. Downgradingprozessen innerhalb der Wertschöpfungsketten.
In der vorliegenden Studie wird der Fokus auf den handels- bzw. konsumentennahen Teil von Wertschöpfungsketten gelegt, um die Folgen der Digitalisierung für Hersteller, Händler und Konsumenten näher zu betrachten. Als konkretes Forschungsbeispiel dient die deutsche Schuhbranche, da sich diese gegenwärtig – von Industrie bis Handel – in einem umfassenden Strukturwandel befindet. Die Analyse zeigt, dass sich die Komplexität von Wertschöpfungsketten im Zuge der Digitalisierung deutlich erhöht (hat). In der Schuhbranche drängen neue Akteure auf den Markt, bestehende Akteure müssen sich anpassen. Direkte Folgen sind nicht nur eine neue Akteurskonstellation, sondern auch ein sich neu bildendes Machtgefüge. Es kommt somit zur Restrukturierung bisheriger Wertschöpfungsketten.
Impacts of climate variability and change on Maize (\(Zea\) \(mays\)) production in tropical Africa
(2022)
Climate change is undeniable and constitutes one of the major threats of the 21st century. It impacts sectors of our society, usually negatively, and is likely to worsen towards the middle and end of the century. The agricultural sector is of particular concern, for it is the primary source of food and is strongly dependent on the weather. Considerable attention has been given to the impact of climate change on African agriculture because of the continent’s high vulnerability, which is mainly due to its low adaptation capac- ity. Several studies have been implemented to evaluate the impact of climate change on this continent. The results are sometimes controversial since the studies are based on different approaches, climate models and crop yield datasets. This study attempts to contribute substantially to this large topic by suggesting specific types of climate pre- dictors. The study focuses on tropical Africa and its maize yield. Maize is considered to be the most important crop in this region. To estimate the effect of climate change on maize yield, the study began by developing a robust cross-validated multiple linear regression model, which related climate predictors and maize yield. This statistical trans- fer function is reputed to be less prone to overfitting and multicollinearity problems. It is capable of selecting robust predictors, which have a physical meaning. Therefore, the study combined: large-scale predictors, which were derived from the principal component analysis of the monthly precipitation and temperature; traditional local-scale predictors, mainly, the mean precipitation, mean temperature, maximum temperature and minimum temperature; and the Water Requirement Satisfaction Index (WRSI), derived from the specific crop (maize) water balance model. The projected maize-yield change is forced by a regional climate model (RCM) REMO under two emission scenarios: high emission scenario (RCP8.5) and mid-range emission scenario (RCP4.5). The different effects of these groups of predictors in projecting the future maize-yield changes were also assessed. Furthermore, the study analysed the impact of climate change on the global WRSI. The results indicate that almost 27 % of the interannual variability of maize production of the entire region is explained by climate variables. The influence of climate predictors on maize-yield production is more pronounced in West Africa, reaching 55 % in some areas. The model projection indicates that the maize yield in the entire region is expected to decrease by the middle of the century under an RCP8.5 emission scenario, and from the middle of the century to the end of the century, the production will slightly recover but will remain negative (around -10 %). However, in some regions of East Africa, a slight increase in maize yield is expected. The maize-yield projection under RCP4.5 remains relatively unchanged compared to the baseline period (1982-2016). The results further indicate that large-scale predictors are the most critical drivers of the global year-to-year maize-yield variability, and ENSO – which is highly correlated with the most important predictor (PC2) – seems to be the physical process underlying this variability. The effects of local predictors are more pronounced in the eastern parts of the region. The impact of the future climate change on WRSI reveals that the availability of maize water is expected to decrease everywhere, except in some parts of eastern Africa.
Nationwide and consistent information on agricultural land use forms an important basis for sustainable land management maintaining food security, (agro)biodiversity, and soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 and Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, and spectral characteristics that have great potential for mapping and monitoring agricultural crops. This paper presents an approach which synergistically uses these multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018. Input data for the Random Forest (RF) classification are monthly statistics of Sentinel-1 and Sentinel-2 time series. This approach reduces the amount of input data and pre-processing steps while retaining phenological information, which is crucial for crop type discrimination. For training and validation, Land Parcel Identification System (LPIS) data were available covering 15 of the 16 German Federal States. An overall map accuracy of 75.5% was achieved, with class-specific F1-scores above 80% for winter wheat, maize, sugar beet, and rapeseed. By combining optical and SAR data, overall accuracies could be increased by 6% and 9%, respectively, compared to single sensor approaches. While no increase in overall accuracy could be achieved by stratifying the classification in natural landscape regions, the class-wise accuracies for all but the cereal classes could be improved, on average, by 7%. In comparison to census data, the crop areas could be approximated well with, on average, only 1% of deviation in class-specific acreages. Using this streamlined approach, similar accuracies for the most widespread crop types as well as for smaller permanent crop classes were reached as in other Germany-wide crop type studies, indicating its potential for repeated nationwide crop type mapping.
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 Northern Bald Ibis (Geronticus eremita, NBI) is an endangered migratory species, which went extinct in Europe in the 17th century. Currently, a translocation project in the frame of the European LIFE program is carried out, to reintroduce a migratory population with breeding colonies in the northern and southern Alpine foothills and a common wintering area in southern Tuscany. The population meanwhile consists of about 200 individuals, with about 90% of them carrying a GPS device on their back. We used biologging data from 2021 to model the habitat suitability for the species in the northern Alpine foothills. To set up a species distribution model, indices describing environmental conditions were calculated from satellite images of Landsat-8, and in addition to the well-proven use of optical remote sensing data, we also included Sentinel-1 actively sensed observation data, as well as climate and urbanization data. A random forest model was fitted on NBI GPS positions, which we used to identify regions with high predicted foraging suitability within the northern Alpine foothills. The model resulted in 84.5% overall accuracy. Elevation and slope had the highest predictive power, followed by grass cover and VV intensity of Sentinel-1 radar data. The map resulting from the model predicts the highest foraging suitability for valley floors, especially of Inn, Rhine, and Salzach-Valley as well as flatlands, like the Swiss Plateau and the agricultural areas surrounding Lake Constance. Areas with a high suitability index largely overlap with known historic breeding sites. This is particularly noteworthy because the model only refers to foraging habitats without considering the availability of suitable breeding cliffs. Detailed analyses identify the transition zone from extensive grassland management to intensive arable farming as the northern range limit. The modeling outcome allows for defining suitable areas for further translocation and management measures in the frame of the European NBI reintroduction program. Although required in the international IUCN translocation guidelines, the use of models in the context of translocation projects is still not common and in the case of the Northern Bald Ibis not considered in the present Single Species Action Plan of the African-Eurasian Migratory Water bird Agreement. Our species distribution model represents a contemporary snapshot, but sustainability is essential for conservation planning, especially in times of climate change. In this regard, a further model could be optimized by investigating sustainable land use, temporal dynamics, and climate change scenarios.
In the Alpine region, the continuous consumption of open spaces for settlement areas and technical infrastructure and the associated soil sealing can be observed. This leads primarily to the loss of agricultural land. Depending on the extent of development, there is also increased landscape fragmentation, which is associated with the isolation of natural habitats and the restriction of ecological connectivity, as well as other negative consequences. The OpenSpaceAlps project has addressed this issue and, based on cooperative procedures in several pilot regions, has developed approaches and solution strategies for the sustainable safeguarding of open spaces. This handbook supports the activities and decision-making of various stakeholders, first and foremost planners in public planning authorities. Based on an analysis of the challenges and framework conditions in the Alpine region, the handbook presents and compares central "principles" of open space planning. Furthermore, integrated planning strategies for different spatial categories are discussed.
Comparing PlanetScope and Sentinel-2 imagery for mapping mountain pines in the Sarntal Alps, Italy
(2022)
The mountain pine (Pinus mugo ssp. Mugo Turra) is an important component of the alpine treeline ecotone and fulfills numerous ecosystem functions. To understand and quantify the impacts of increasing logging activities and climatic changes in the European Alps, accurate information on the occurrence and distribution of mountain pine stands is needed. While Earth observation provides up-to-date information on land cover, space-borne mapping of mountain pines is challenging as different coniferous species are spectrally similar, and small-structured patches may remain undetected due to the sensor’s spatial resolution. This study uses multi-temporal optical imagery from PlanetScope (3 m) and Sentinel-2 (10 m) and combines them with additional features (e.g., textural statistics (homogeneity, contrast, entropy, spatial mean and spatial variance) from gray level co-occurrence matrix (GLCM), topographic features (elevation, slope and aspect) and canopy height information) to overcome the present challenges in mapping mountain pine stands. Specifically, we assessed the influence of spatial resolution and feature space composition including the GLCM window size for textural features. The study site is covering the Sarntal Alps, Italy, a region known for large stands of mountain pine. Our results show that mountain pines can be accurately mapped (PlanetScope (90.96%) and Sentinel-2 (90.65%)) by combining all features. In general, Sentinel-2 can achieve comparable results to PlanetScope independent of the feature set composition, despite the lower spatial resolution. In particular, the inclusion of textural features improved the accuracy by +8% (PlanetScope) and +3% (Sentinel-2), whereas accuracy improvements of topographic features and canopy height were low. The derived map of mountain pines in the Sarntal Alps supports local forest management to monitor and assess recent and ongoing anthropogenic and climatic changes at the treeline. Furthermore, our study highlights the importance of freely available Sentinel-2 data and image-derived textural features to accurately map mountain pines in Alpine environments.
In the Spessart, a low mountain range in central Germany, a feud during the Middle Ages led to the construction of numerous castles in this region. This study analyzes the mutual influence of (paleo-)relief development and medieval building activity using a geomorphological and geoarchaeological multimethod approach to expand the knowledge of human-environmental interactions during this time. For this purpose, GIS-based terrain analysis and geophysical measurements were conducted and combined with sedimentological information to create 1D-3D models of the subsurface and to assess knowledge of the landscape and relief evolution at various medieval castle and mining sites. The interpretation of all these data led to the answering of numerous site-specific questions on various geomorphological, geoarchaeological, geologic, and archaeological topics that have been explored in this work and have greatly increased our knowledge of each study site. In addition to these key contributions to the archaeological and geomorphological interpretation of individual study sites, a quantification of the anthropogenic influence on the relief development was conducted, a generalized model of the influence was derived, and new methodological and interpretative approaches were developed. Overall, this study links geomorphological/geological and (geo-)archaeological investigations at five medieval sites and delivers important information on human-environmental interactions within the Spessart and beyond.
Forest conservation is of particular concern in tropical regions where a large refuge of biodiversity is still existing. These areas are threatened by deforestation, forest degradation and fragmentation. Especially, pressures of anthropogenic activities adjacent to these areas significantly influence conservation effectiveness. Ecuador was chosen as study area since it is a globally relevant center of forest ecosystems and biodiversity. We identified hotspots of deforestation on the national level of continental Ecuador between 1990 and 2018, analyzed the most significant drivers of deforestation on national and biome level (the Coast, the Andes, The Amazon) as well as inside protected areas in Ecuador by using multiple regression analysis. We separated the national system of protected areas (SNAP) into higher and lower protection levels. Besides SNAP, we also considered Biosphere Reserves (BRs) and Ramsar sites. In addition, we investigated the rates and spatial patterns of deforestation in protected areas and buffer zones (5 km and 10 km outwards the protected area boundaries) using landscape metrics. Between 1990 and 2018, approximately 4% of the accumulated deforestation occurred within the boundaries of SNAP, and up to 25.5% in buffer zones. The highest rates of deforestation have been found in the 5 km buffer zone around the protected areas with the highest protection level. Protected areas and their buffer zones with higher protection status were identified as the most deforested areas among SNAP. BRs had the highest deforestation rates among all protected areas but most of these areas just became BRs after the year 2000. The most important driver of deforestation is agriculture. Other relevant drivers differ between the biomes. The results suggest that the SNAP is generally effective to prevent deforestation within their protection boundaries. However, deforestation around protected areas can undermine conservation strategies to sustain biodiversity. Actions to address such dynamics and patterns of deforestation and forest fragmentation, and developing conservation strategies of their landscape context are urgently needed especially in the buffer zones of areas with the highest protection status.
A first assessment of canopy cover loss in Germany's forests after the 2018–2020 drought years
(2022)
Central Europe was hit by several unusually strong periods of drought and heat between 2018 and 2020. These droughts affected forest ecosystems. Cascading effects with bark beetle infestations in spruce stands were fatal to vast forest areas in Germany. We present the first assessment of canopy cover loss in Germany for the period of January 2018–April 2021. Our approach makes use of dense Sentinel-2 and Landsat-8 time-series data. We computed the disturbance index (DI) from the tasseled cap components brightness, greenness, and wetness. Using quantiles, we generated monthly DI composites and calculated anomalies in a reference period (2017). From the resulting map, we calculated the canopy cover loss statistics for administrative entities. Our results show a canopy cover loss of 501,000 ha for Germany, with large regional differences. The losses were largest in central Germany and reached up to two-thirds of coniferous forest loss in some districts. Our map has high spatial (10 m) and temporal (monthly) resolution and can be updated at any time.
Das Ziel dieser Arbeit war neue Eingangsdaten für die Landoberflächenbeschreibung des regionalen Klimamodells REMO zu finden und ins Modell zu integrieren, um die Vorhersagequalität des Modells zu verbessern. Die neuen Daten wurden so in das Modell eingebaut, dass die bisherigen Daten weiterhin als Option verfügbar sind. Dadurch kann überprüft werden, ob und in welchem Umfang sich die von jedem Klimamodell benötigten Rahmendaten auf Modellergebnisse auswirken. Im Zuge der Arbeit wurden viele unterschiedliche Daten und Methoden zur Generierung neuer Parameter miteinander verglichen, denn neben dem Ersetzen der konstanten Eingangswerte für verschiedene Oberflächenparameter und den damit verbundenen Änderungen wurden als zusätzliche Verbesserung auch Veränderungen an der Parametrisierung des Bodens speziell in Hinblick auf die Bodentemperaturen in REMO vorgenommen. Im Rahmen dieser Arbeit wurden die durch die verschiedenen Änderungen ausgelösten Auswirkungen für das CORDEX-Gebiet EUR-44 mit einer Auflösung von ca. 50km und für das in dem darin eingebetteten neu definierten Deutschlandgebiet GER-11 mit einer Auflösung von ca. 12km getestet sowie alle Änderungen anhand von verschiedenen Beobachtungsdatensätzen validiert.
Die vorgenommenen Arbeiten gliederten sich in drei Hauptteile. Der erste Teil bestand in dem vom eigentlichen Klimamodell unabhängigen Vergleich der verschiedenen Eingangsdaten auf unterschiedlichen Auflösungen und deren Performanz in allen Teilen der Erde, wobei ein besonderer Fokus auf der Qualität in den späteren Modellgebieten lag. Unter Berücksichtigung der Faktoren, wie einer globalen Verfügbarkeit der Daten, einer verbesserten räumlichen Auflösung und einer kostenlosen Nutzung der Daten sowie verschiedener Validationsergebnissen von anderen Studien, wurden in dieser Arbeit vier neue Topographiedatensätze (SRTM, ALOS, TANDEM und ASTER) und drei neue Bodendatensätze (FAOn, Soilgrid und HWSD) für die Verwendung im Präprozess von REMO aufbereitet und miteinander sowie mit den bisher in REMO verwendeten Daten verglichen. Auf Grundlage dieser Vergleichsstudien schieden bei den Topographiedaten die verwendeten Datensatz-Versionen von SRTM, ALOS und TANDEM für die in dieser Arbeit durchgeführten REMO-Läufe aus. Bei den neuen Bodendatensätzen wurde ausgenutzt, dass diese verschiedenen Bodeneigenschaften für unterschiedliche Tiefen als Karten zur Verfügung stellen. In REMO wurden bisher alle benötigten Bodenparameter abhängig von fünf verschiedenen Bodentexturklassen und einer zusätzlichen Torfklasse ausgewiesen und als konstant über die gesamte Modellbodensäule (bis ca. 10m) angenommen. Im zweiten Teil wurden auf Basis der im ersten Teil ausgewählten neuen Datensätze und den neu verfügbaren Bodenvariablen verschiedene Sensitivitätsstudien über das Beispieljahr 2000 durchgeführt. Dabei wurden verschiedene neue Parametrisierungen für die bisher aus der Textur abgeleiteten Bodenvariablen und die Parametrisierung von weiteren hydrologischen und thermalen Bodeneigenschaften verglichen. Ferner wurde aufgrund der neuen nicht über die Tiefe konstanten Bodeneigenschaften eine neue numerische Methode zur Berechnung der Bodentemperaturen der fünf Schichten in REMO getestet, welche wiederum andere Anpassungen erforderte. Der Test und die Auswahl der verschiedenen Datensatz- und Parametrisierungsversionen auf die Modellperformanz wurde in drei Experimentpläne unterteilt. Im ersten Plan wurden die Auswirkungen der ausgewählten Topographie- und Bodendatensätze überprüft. Der zweite Plan behandelte die Unterschiede der verschiedenen Parametrisierungsarten der Bodenvariablen hinsichtlich der verwendeten Variablen zur Berechnung der Bodeneigenschaften, der über die Tiefe variablen oder konstanten Eigenschaften und der verwendeten Berechnungsmethode der Bodentemperaturänderungen. Durch die Erkenntnisse aus diesen beiden Experimentplänen, die für beide Untersuchungsgebiete durchgeführt wurden, ergaben sich im dritten Plan weitere Parametrisierungsänderungen. Alle Änderungen dieses dritten Experimentplans wurden sukzessiv getestet, sodass der paarweise Vergleich von zwei aufeinanderfolgenden Modellläufen die Auswirkungen der Neuerung im jeweils zweiten Lauf widerspiegelt. Der letzte Teil der Arbeit bestand aus der Analyse von fünf längeren Modellläufen (2000-2018), die zur Überprüfung der Ergebnisse aus den Sensitivitätsstudien sowie zur Einschätzung der Performanz in weiteren teilweise extremen atmosphärischen Bedingungen durchgeführt wurden. Hierfür wurden die bisherige Modellversion von REMO (id01) für die beiden Untersuchungsgebiete EUR-44 und GER-11 als Referenzläufe, zwei aufgrund der Vergleichsergebnisse von Experimentplan 3 selektierte Modellversionen (id06 und id15a für GER-11) sowie die finale Version (id18a für GER-11), die alle vorgenommenen Änderungen dieser Arbeit enthält, ausgewählt.
Es stellte sich heraus, dass sowohl die neuen Topographiedaten als auch die neuen Bodendaten große Differenzen zu den bisherigen Daten in REMO haben. Zudem änderten sich die von diesen konstanten Eingangsdaten abgeleiteten Hilfsvariablen je nach verwendeter Parametrisierung sehr deutlich. Dies war besonders gut anhand der Bodenparameter zu erkennen. Sowohl die räumliche Verteilung als auch der Wertebereich der verschiedenen Modellversionen unterschieden sich stark. Eine Einschätzung der Qualität der resultierenden Parameter wurde jedoch dadurch erschwert, dass auch die verschiedenen zur Validierung herangezogenen Bodendatensätze für diese Parameter deutlich voneinander abweichen. Die finale Modellversion id18a ähnelte trotz der umfassenden Änderungen in den meisten Variablen den Ergebnissen der bisherigen REMO-Version. Je nach zeitlicher und räumlicher Aggregation sowie unterschiedlichen Regionen und Jahreszeiten wurden leichte Verbesserungen, aber auch leichte Verschlechterungen im Vergleich zu den klimatologischen Validationsdaten festgestellt. Größere Veränderungen im Vergleich zur bisherigen Modellversion konnten in den tieferen Bodenschichten aufgezeigt werden, welche allerdings aufgrund von fehlenden Validationsdaten nicht beurteilt werden konnten. Für alle 2m-Temperaturen konnte eine tendenzielle leichte Erwärmung im Vergleich zum bisherigen Modelllauf beobachtet werden, was sich einerseits negativ auf die ohnehin durchschnittlich zu hohe Minimumtemperatur, aber andererseits positiv auf die bisher zu niedrige Maximumtemperatur des Modells in den betrachteten Gebieten auswirkte. Im Niederschlagssignal und in den 10m-Windvariablen konnten keine signifikanten Änderungen nachgewiesen werden, obwohl die neue Topographie an manchen Stellen im Modellgebiet deutlich von der bisherigen abweicht. Des Weiteren variierte das Ranking der verschiedenen Modellversionen jeweils nach dem angewendeten Qualitätsindex.
Um diese Ergebnisse besser einordnen zu können, muss berücksichtigt werden, dass die neuen Daten für Modellgebiete mit 50 bzw. 12km räumlicher Auflösung und der damit verbundenen hydrostatischen Modellversion getestet wurden. Zudem sind vor allem in Fall der Topographie die bisher enthaltenen GTOPO-Daten (1km Auflösung) für die Aggregation auf diese gröbere Modellauflösung geeignet. Die bisherigen Bodendaten stoßen jedoch mit 50km Auflösung bereits an ihre Grenzen. Zusätzlich ist zu beachten, dass nicht nur die Mittelwerte dieser Daten, sondern auch deren Subgrid-Variabilität als Variablen im Modell für verschiedene Parametrisierungen verwendet werden. Daher ist es essentiell, dass die Eingangsdaten eine deutlich höhere Auflösung bereitstellen als die zur Modellierung definierte Auflösung. Für lokale Klimasimulationen mit Auflösungen im niedrigen Kilometerbereich spielen auch die Vertikalbewegungen (nicht-hydrostatische Modellversion) eine wichtige Rolle, die stark von der Topographie sowie deren horizontaler und vertikaler Änderungsrate beeinflusst werden, was die in dieser Arbeit eingebauten wesentlich höher aufgelösten Daten für die zukünftige Weiterentwicklung von REMO wertvoll machen kann.
Im Zuge der Aufbereitung von Kalirohsalzen fallen weltweit feste Rückstände an, die auf Großhalden entsorgt werden. Die Aufhaldung und die von den Rückstandshalden ausgehenden Umweltauswirkungen unterliegen in Deutschland der Kontrolle durch die zuständigen Bergbehörden. Um die Emissionen besser quantifizieren zu können und die Eignung technischer Minderungsmaßnahmen zu beurteilen, erfolgte im Rahmen der Genehmigungsverfahren zur Erweiterung der Rückstandshalden an den Standorten Hattorf und Wintershall die Erkundung des Haldenkörpers durch Bohrungen mit Fokus auf die darin ablaufenden Strömungsprozesse. Eine Modellvorstellung zur Zonierung der Halde im Hinblick auf Strömungsprozesse war zunächst anhand von Haldenbohrungen am Standort Hattorf entwickelt worden. Das Ziel dieser Arbeit war, mittels der Bohrergebnisse einer weiteren Haldenbohrung am Standort Wintershall die Übertragbarkeit der zuvor in Hattorf gefundenen Gegebenheiten zu überprüfen und den Kenntnisstand zu Strömungs- und Alterationsprozessen innerhalb der Halde zu verbessern. Im Zuge der Haldenbohrungen erfolgten bohrbegleitende Untersuchungen (Abflussmessungen, Kamerabefahrungen, geophysikalische und geohydraulische Untersuchungen), und an dem Bohrkernmaterial und den Haldenlösungen wurde ein umfangreiches Laboruntersuchungsprogramm ausgeführt, das chemische und mineralogische Analysen sowie Untersuchungen der physikalischen und hydraulischen Eigenschaften des Rückstands umfasste. Zusätzlich wurden ergänzende Gefügeuntersuchungen (Dünnschliffuntersuchungen am Rasterelektronen-Mikroskop und exemplarische CT-Untersuchungen) an Probenmaterial der Halde Hattorf durchgeführt, um mittels visueller Untersuchungen insbesondere die Rolle überschütteter ehemaliger Haldenoberflächen für das Strömungsgeschehen im Haldenkörper zu erkunden. Unter Berücksichtigung aller Ergebnisse wurden die Strömungs- und Alterationsprozesse im Rückstand beschrieben. Die maßgebliche Erkenntnis im Hinblick auf das Strömungsgeschehen im Haldenkörper ist, dass in dessen Porenraum keine Sättigung besteht und dieser in allen Bereichen mit einem Gemisch aus Lösung und Luft gefüllt ist, so dass die Gesetzmäßigkeiten der Zweiphasenströmung maßgeblich sind. Die bislang zur Bewertung von Strömungsprozessen üblichen Durchlässigkeitsbeiwerte sind damit ungeeignet, da sie für gesättigte Bereiche gelten. Übereinstimmend wurde mit einer Reihe von Ergebnissen belegt, dass die Strömungsprozesse im Haldenkörper an bevorzugte Wegsamkeiten gebunden sind, bei denen es sich ausweislich der Gefügeuntersuchungen um ein System miteinander verbundenen Sekundärporen handelt. Der Rückstand ist zu charakterisieren als ein Nebeneinander aus diesen Wegsamkeiten und unbeeinflussten, aggregierten Bereichen. Des Weiteren wurde gezeigt, dass der Niederschlagseinfluss zur Teufe hin abnimmt, und es wurden Kriterien zum Nachweis von Niederschlagsunbeeinflussten Bereichen formuliert. Die Arbeit hat damit auch gezeigt, dass die Modellvorstellung, welche die Halde in eine für die Strömungsprozesse maßgebliche Haldenmantelzone, eine gering durchlässige Kernzone und eine dazwischen befindliche Übergangszone mit gradueller Änderung der Eigenschaften unterteilt, grundsätzlich auch auf den Standort Wintershall übertragbar ist. Das Modell des Haldenkörpers wurde weiter detailliert und zusätzliche Kriterien zur Verortung der einzelnen Zonen im Haldenkörper abgeleitet. Insbesondere wurde im Haldenmantelbereich eine charakteristische Randzone ausgehalten, welche im Ergebnis einer intensiven Durchströmung mit un- bzw. teilgesättigten Lösungen selektiv an Wertstoff-Restgehalten abgereichert ist. Sie 2 wird von den unterlagernden reaktiven Zonen durch die Lösungsfronten für Kalium und Magnesium abgegrenzt. Aufbauend auf der erweiterten Modellvorstellung wurde die Zonierung für die Haldenbohrung am Standort Wintershall abgeleitet. Besonderes Augenmerk galt im Rahmen aller Untersuchungen der Wirkung von überschütteten ehemaligen Haldenoberflächen, die als Schüttflächen bezeichnet werden. Es zeigte sich anhand der Untersuchungen, dass die Relevanz von Schüttflächen für das Strömungsgeschehen abhängig von der Schütthistorie ist, und dass diese, selbst wenn sie aktiv am Strömungsgeschehen teilnehmen, die Gegebenheiten im Haldenkörper nur lokal überprägen. Das Ziel der Aufstellung eines Modells zu Strömungsprozessen im Haldenkörper besteht in der Beurteilung der von diesen Halden ausgehenden Umweltauswirkungen. Darüber hinaus dienen die Erkenntnisse der Einschätzung der Wirksamkeit der bereits ergriffenen bzw. noch zu ergreifenden Schutz- und Emissionsminderungsmaßnahmen sowie der Planung zukünftiger Maßnahmen zur Wiedernutzbarmachung der Tagesoberfläche und zur Erstellung von Prognosen. In diesem Sinne wurden aus den Ergebnissen der Arbeit abschließend Empfehlungen für technische Konzepte und den Haldenbetrieb abgeleitet.
In the past decades, various Earth observation-based time series products have emerged, which have enabled studies and analysis of global change processes. Besides their contribution to understanding past processes, time series datasets hold enormous potential for predictive modeling and thereby meet the demands of decision makers on future scenarios. In order to further exploit these data, a novel pixel-based approach has been introduced, which is the spatio-temporal matrix (STM). The approach integrates the historical characteristics of a specific land cover at a high temporal frequency in order to interpret the spatial and temporal information for the neighborhood of a given target pixel. The provided information can be exploited with common predictive models and algorithms. In this study, this approach was utilized and evaluated for the prediction of future urban/built-settlement growth. Random forest and multi-layer perceptron were employed for the prediction. The tests have been carried out with training strategies based on a one-year and a ten-year time span for the urban agglomerations of Surat (India), Ho-Chi-Minh City (Vietnam), and Abidjan (Ivory Coast). The slope, land use, exclusion, urban, transportation, hillshade (SLEUTH) model was selected as a baseline indicator for the performance evaluation. The statistical results from the receiver operating characteristic curve (ROC) demonstrate a good ability of the STM to facilitate the prediction of future settlement growth and its transferability to different cities, with area under the curve (AUC) values greater than 0.85. Compared with SLEUTH, the STM-based model achieved higher AUC in all of the test cases, while being independent of the additional datasets for the restricted and the preferential development areas.
Mapping of lava flows in unvegetated areas of active volcanoes using optical satellite data is challenging due to spectral similarities of volcanic deposits and the surrounding background. Using very high-resolution PlanetScope data, this study introduces a novel object-oriented classification approach for mapping lava flows in both vegetated and unvegetated areas during several eruptive phases of three Indonesian volcanoes (Karangetang 2018/2019, Agung 2017, Krakatau 2018/2019). For this, change detection analysis based on PlanetScope imagery for mapping loss of vegetation due to volcanic activity (e.g., lava flows) is combined with the analysis of changes in texture and brightness, with hydrological runoff modelling and with analysis of thermal anomalies derived from Sentinel-2 or Landsat-8. Qualitative comparison of the mapped lava flows showed good agreement with multispectral false color time series (Sentinel-2 and Landsat-8). Reports of the Global Volcanism Program support the findings, indicating the developed lava mapping approach produces valuable results for monitoring volcanic hazards. Despite the lack of bands in infrared wavelengths, PlanetScope proves beneficial for the assessment of risk and near-real-time monitoring of active volcanoes due to its high spatial (3 m) and temporal resolution (mapping of all subaerial volcanoes on a daily basis).
In vielen mediterranen Küstenniederungen entstand seit 1950 infolge von Gebirgsentvölkerung, Infrastrukturausbau, neuer Gewerbe sowie illegaler Bautätigkeit ein fast lückenloses Verstädterungsband. Am Golf von Neapel konnte dieser Landschaftswandel über eine lange Zeit beobachtet und durch zahlreiche Vergleichsfotos, Kartierungen, Luft- und Satellitenbilder und Interviews dokumentiert werden. Horst-Günter Wagner zeigt in diesem Band die Veränderungen der Küstenebene und erläutert ihre Ursachen.
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.
In most countries, freight is predominantly transported by road cargo trucks. We present a new satellite remote sensing method for detecting moving trucks on roads using Sentinel-2 data. The method exploits a temporal sensing offset of the Sentinel-2 multispectral instrument, causing spatially and spectrally distorted signatures of moving objects. A random forest classifier was trained (overall accuracy: 84%) on visual-near-infrared-spectra of 2500 globally labelled targets. Based on the classification, the target objects were extracted using a developed recursive neighbourhood search. The speed and the heading of the objects were approximated. Detections were validated by employing 350 globally labelled target boxes (mean F\(_1\) score: 0.74). The lowest F\(_1\) score was achieved in Kenya (0.36), the highest in Poland (0.88). Furthermore, validated at 26 traffic count stations in Germany on in sum 390 dates, the truck detections correlate spatio-temporally with station figures (Pearson r-value: 0.82, RMSE: 43.7). Absolute counts were underestimated on 81% of the dates. The detection performance may differ by season and road condition. Hence, the method is only suitable for approximating the relative truck traffic abundance rather than providing accurate absolute counts. However, existing road cargo monitoring methods that rely on traffic count stations or very high resolution remote sensing data have limited global availability. The proposed moving truck detection method could fill this gap, particularly where other information on road cargo traffic are sparse by employing globally and freely available Sentinel-2 data. It is inferior to the accuracy and the temporal detail of station counts, but superior in terms of spatial coverage.
The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results.
This study investigates the surroundings of Munigua (municipium Flavium Muniguense), a small Roman town in the ancient province of Hispania Baetica (SW Spain). The city's economy was based primarily on copper and iron mining, which brought financial prosperity to its citizens. Local production of agricultural goods is thought to have been of little importance, as the regional soil conditions do not seem to be suitable for extensive agriculture.
To evaluate the recent soil agro-potential and to find evidence for prehistoric and historic land use in the surroundings of Munigua, we applied a pedo-geomorphological approach based on the physico-chemical analysis of 14 representative soil and sediment exposures. Selected samples were analyzed for bulk chemistry, texture and phytoliths. The chronostratigraphy of the sequences was based on radiocarbon dating of charcoal samples. The site evaluation of the present-day soil agro-potential was carried out according to standard procedures and included evaluation of potential rootability, available water-storage capacity and nutrient budget within the uppermost 1 m.
The results show that moderate to very good soil agro-potential prevails in the granitic and floodplain areas surrounding Munigua. Clearly, recent soil agro-potential in these areas allows the production of basic agricultural goods, and similar limited agricultural use should also have been possible in ancient times. In contrast, weak to very weak present-day soil agro-potential prevails in the metamorphic landscape due to the occurrence of shallow and sandy to stony soils.
In addition, the study provides pedo-geomorphological evidence for prehistoric and historic land use in pre-Roman, Roman and post-Roman times. Catenary soil mapping in the vicinity of a Roman house complex reveals multi-layered colluvial deposits. They document phases of hillslope erosion mainly triggered by human land use between 4063 ± 82 and 3796 ± 76 cal BP, around 2601 ± 115 cal BP, and between 1424 ± 96 and 421 ± 88 cal BP. Moreover, geochemical and phytolith analyses of a Roman hortic Anthrosol indicate the local cultivation of agricultural products that contributed to the food supply of Munigua.
Overall, the evidence of Roman agricultural use in the Munigua area indicates that the city's economy was by no means focused solely on mining. The production of basic agricultural products was also part of Munigua's economic portfolio. Our geoarcheological study thus supports the archeological concept of economically diversified Roman cities in the province of Baetica and in Hispania.
Climate and land-use change are key drivers of environmental degradation in the Anthropocene, but too little is known about their interactive effects on biodiversity and ecosystem services. Long-term data on biodiversity trends are currently lacking. Furthermore, previous ecological studies have rarely considered climate and land use in a joint design, did not achieve variable independence or lost statistical power by not covering the full range of environmental gradients.
Here, we introduce a multi-scale space-for-time study design to disentangle effects of climate and land use on biodiversity and ecosystem services. The site selection approach coupled extensive GIS-based exploration (i.e. using a Geographic information system) and correlation heatmaps with a crossed and nested design covering regional, landscape and local scales. Its implementation in Bavaria (Germany) resulted in a set of study plots that maximise the potential range and independence of environmental variables at different spatial scales.
Stratifying the state of Bavaria into five climate zones (reference period 1981–2010) and three prevailing land-use types, that is, near-natural, agriculture and urban, resulted in 60 study regions (5.8 × 5.8 km quadrants) covering a mean annual temperature gradient of 5.6–9.8°C and a spatial extent of ~310 × 310 km. Within these regions, we nested 180 study plots located in contrasting local land-use types, that is, forests, grasslands, arable land or settlement (local climate gradient 4.5–10°C). This approach achieved low correlations between climate and land use (proportional cover) at the regional and landscape scale with |r ≤ 0.33| and |r ≤ 0.29| respectively. Furthermore, using correlation heatmaps for local plot selection reduced potentially confounding relationships between landscape composition and configuration for plots located in forests, arable land and settlements.
The suggested design expands upon previous research in covering a significant range of environmental gradients and including a diversity of dominant land-use types at different scales within different climatic contexts. It allows independent assessment of the relative contribution of multi-scale climate and land use on biodiversity and ecosystem services. Understanding potential interdependencies among global change drivers is essential to develop effective restoration and mitigation strategies against biodiversity decline, especially in expectation of future climatic changes. Importantly, this study also provides a baseline for long-term ecological monitoring programs.
Nella regione alpina, si può osservare il continuo consumo di spazi aperti a causa dell’aumento di aree di insediamento e di infrastrutture tecniche e la conseguente impermeabilizzazione del suolo. Questo fenomeno porta principalmente alla perdita di suolo agricolo. A seconda dell'estensione dello sviluppo, si riscontra anche una maggiore frammentazione del paesaggio, che è associata all'isolamento degli habitat naturali e alla perdita della connettività ecologica, così come ad altre conseguenze negative.
Il progetto OpenSpaceAlps ha affrontato questo problema e, sulla base di procedure cooperative partecipate attuate in diverse regioni pilota alpine, ha sviluppato approcci e strategie di soluzione per la salvaguardia sostenibile degli spazi aperti. Questo manuale supporta le attività e il processo decisionale di vari stakeholder, in primo luogo i pianificatori delle autorità pubbliche di pianificazione. Sulla base di un'analisi delle sfide e delle condizioni generali nella regione alpina, il manuale presenta e confronta i "principi" centrali della pianificazione degli spazi aperti. Inoltre, vengono discusse strategie di pianificazione integrata per diverse categorie spaziali.
Detection of grassland mowing events for Germany by combining Sentinel-1 and Sentinel-2 time series
(2022)
Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany.
Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource — if not overexploited — sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.
Vietnam's 3260 km coastline is densely populated, experiences rapid urban and economic growth, and faces at the same time a high risk of coastal hazards. Satellite archives provide a free and powerful opportunity for long-term area-wide monitoring of the coastal zone. This paper presents an automated analysis of coastline dynamics from 1986 to 2021 for Vietnam's entire coastal zone using the Landsat archive. The proposed method is implemented within the cloud-computing platform Google Earth Engine to only involve publicly and globally available datasets and tools. We generated annual coastline composites representing the mean-high water level and extracted sub-pixel coastlines. We further quantified coastline change rates along shore-perpendicular transects, revealing that half of Vietnam's coast did not experience significant change, while the remaining half is classified as erosional (27.7%) and accretional (27.1%). A hotspot analysis shows that coastal segments with the highest change rates are concentrated in the low-lying deltas of the Mekong River in the south and the Red River in the north. Hotspots with the highest accretion rates of up to +47 m/year are mainly associated with the construction of artificial coastlines, while hotspots with the highest erosion rates of −28 m/year may be related to natural sediment redistribution and human activity.
An increasing amount of Brazilian rainforest is being lost or degraded for various reasons, both anthropogenic and natural, leading to a loss of biodiversity and further global consequences. Especially in the Brazilian state of Mato Grosso, soy production and large-scale cattle farms led to extensive losses of rainforest in recent years. We used a spectral mixture approach followed by a decision tree classification based on more than 30 years of Landsat data to quantify these losses. Research has shown that current methods for assessing forest degradation are lacking accuracy. Therefore, we generated classifications to determine land cover changes for each year, focusing on both cleared and degraded forest land. The analyses showed a decrease in forest area in Mato Grosso by 28.8% between 1986 and 2020. In order to measure changed forest structures for the selected period, fragmentation analyses based on diverse landscape metrics were carried out for the municipality of Colniza in Mato Grosso. It was found that forest areas experienced also a high degree of fragmentation over the study period, with an increase of 83.3% of the number of patches and a decrease of the mean patch area of 86.1% for the selected time period, resulting in altered habitats for flora and fauna.
Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990–2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R\(^2\) > 0.69, p < 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg\(^{−1}\) in 1990 to 9.23 gC·kg\(^{−1}\) in 2010 and then declined to 5.31 gC·Kg\(^{−1}\) in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone.
Within the Spessart low mountain range in central Germany, numerous castle ruins of the 13th century ce exist. Their construction and destruction were often determined by the struggle for political and economic supremacy in the region and for control over the Spessart's natural resources. Wahlmich Castle is located in a relatively uncommon strategic and geomorphological position, characterized by a fairly remote position and atypical rough relief. In order to reconstruct the local relief development and possible human impact, a multi-method approach was applied combining two-dimensional geoelectrical measurements, geomorphological mapping and stratigraphic-sedimentological investigations. This provides new insights into the influence of landscape characteristics on choices of castle locations.
The combined geoelectrical, geomorphological and stratigraphic-sedimentological data show that the rough relief is of natural origin and influenced by regional faulting, which triggered sliding and slumping as well as weathering and dissection of the surface deposits. The rough relief and the lithology permitted intensive land use and building activities. However, the location of the castle offered access to and possibly control over important medieval traffic routes and also represented certain ownership claims in the Aschaff River valley.
The economic situation combined with rivalry between different elites led to the castle being built in a geomorphological challenging and strategically less valuable location. Focusing on castles located in rare and challenging geomorphological positions may therefore lead to a better understanding of castle siting in the future.
Digital platforms, such as Amazon, represent the major beneficiaries of the Covid‐19 crisis. This study examines the role of digital platforms and their engagement in digitalisation initiatives targeting (small) brick‐and‐mortar retailers in Germany, thereby contributing to a better understanding of how digital platforms augment, substitute or reorganise physical retail spaces. This study applies a mixed‐method approach based on qualitative interviews, participant observation as well as media analysis. First, the study illustrates the controversial role of digital platforms by positioning themselves as supporting partners of the (offline) retailers, while simultaneously shifting power towards the platforms themselves. Second, digital platforms have established themselves not only as infrastructure providers but also as actors within these infrastructures, framing digital as well as physical retail spaces, inter alia due to their role as publicly legitimised retail advisers. Third, while institutions want to help retailers to survive, they simultaneously enhance retailers' dependency on digital platforms.
Forests are essential for global environmental well-being because of their rich provision of ecosystem services and regulating factors. Global forests are under increasing pressure from climate change, resource extraction, and anthropologically-driven disturbances. The results are dramatic losses of habitats accompanied with the reduction of species diversity. There is the urgent need for forest biodiversity monitoring comprising analysis on α, β, and γ scale to identify hotspots of biodiversity. Remote sensing enables large-scale monitoring at multiple spatial and temporal resolutions. Concepts of remotely sensed spectral diversity have been identified as promising methodologies for the consistent and multi-temporal analysis of forest biodiversity. This review provides a first time focus on the three spectral diversity concepts “vegetation indices”, “spectral information content”, and “spectral species” for forest biodiversity monitoring based on airborne and spaceborne remote sensing. In addition, the reviewed articles are analyzed regarding the spatiotemporal distribution, remote sensing sensors, temporal scales and thematic foci. We identify multispectral sensors as primary data source which underlines the focus on optical diversity as a proxy for forest biodiversity. Moreover, there is a general conceptual focus on the analysis of spectral information content. In recent years, the spectral species concept has raised attention and has been applied to Sentinel-2 and MODIS data for the analysis from local spectral species to global spectral communities. Novel remote sensing processing capacities and the provision of complementary remote sensing data sets offer great potentials for large-scale biodiversity monitoring in the future.
Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria
(2022)
The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region’s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses 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, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions’ cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R\(^2\) = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R\(^2\) = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R\(^2\) = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R\(^2\) = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R\(^2\) = 0.60, RMSE = 0.05) and S-MOD13Q1 (R\(^2\) = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution.
A novel method for detecting and delineating coppice trees in UAV images to monitor tree decline
(2022)
Monitoring tree decline in arid and semi-arid zones requires methods that can provide up-to-date and accurate information on the health status of the trees at single-tree and sample plot levels. Unmanned Aerial Vehicles (UAVs) are considered as cost-effective and efficient tools to study tree structure and health at small scale, on which detecting and delineating tree crowns is the first step to extracting varied subsequent information. However, one of the major challenges in broadleaved tree cover is still detecting and delineating tree crowns in images. The frequent dominance of coppice structure in degraded semi-arid vegetation exacerbates this problem. Here, we present a new method based on edge detection for delineating tree crowns based on the features of oak trees in semi-arid coppice structures. The decline severity in individual stands can be analyzed by extracting relevant information such as texture from the crown area. Although the method presented in this study is not fully automated, it returned high performances including an F-score = 0.91. Associating the texture indices calculated in the canopy area with the phenotypic decline index suggested higher correlations of the GLCM texture indices with tree decline at the tree level and hence a high potential to be used for subsequent remote-sensing-assisted tree decline studies.
The Moroccan locust has been considered one of the most dangerous agricultural pests in the Mediterranean region. The economic importance of its outbreaks diminished during the second half of the 20th century due to a high degree of agricultural industrialization and other human-caused transformations of its habitat. Nevertheless, in Sardinia (Italy) from 2019 on, a growing invasion of this locust species is ongoing, being the worst in over three decades. Locust swarms destroyed crops and pasture lands of approximately 60,000 ha in 2022. Drought, in combination with increasing uncultivated land, contributed to forming the perfect conditions for a Moroccan locust population upsurge. The specific aim of this paper is the quantification of land cover land use (LCLU) influence with regard to the recent locust outbreak in Sardinia using remote sensing data. In particular, the role of untilled, fallow, or abandoned land in the locust population upsurge is the focus of this case study. To address this objective, LCLU was derived from Sentinel-2A/B Multispectral Instrument (MSI) data between 2017 and 2021 using time-series composites and a random forest (RF) classification model. Coordinates of infested locations, altitude, and locust development stages were collected during field observation campaigns between March and July 2022 and used in this study to assess actual and previous land cover situation of these locations. Findings show that 43% of detected locust locations were found on untilled, fallow, or uncultivated land and another 23% within a radius of 100 m to such areas. Furthermore, oviposition and breeding sites are mostly found in sparse vegetation (97%). This study demonstrates that up-to-date remote sensing data and target-oriented analyses can provide valuable information to contribute to early warning systems and decision support and thus to minimize the risk concerning this agricultural pest. This is of particular interest for all agricultural pests that are strictly related to changing human activities within transformed habitats.
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.