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More Than Infrastructure Providers – Digital Platforms' Role and Power in Retail Digitalisation in Germany (2022)
Hardaker, Sina
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.
Global Dynamics of the Offshore Wind Energy Sector Derived from Earth Observation Data - Deep Learning Based Object Detection Optimised with Synthetic Training Data for Offshore Wind Energy Infrastructure Extraction from Sentinel-1 Imagery (2022)
Höser, Thorsten
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.
Holocene aridity-induced interruptions of human activity along a fluvial channel in Egypt's northern delta (2021)
Stanley, Jean-Daniel ; Ullmann, Tobias ; Lange-Athinodorou, Eva
Geoarchaeological information presented here pertains to a subsidiary Nile channel that once flowed west of the main Sebennitic distributary and discharged its water and sediments at Egypt’s then north-central deltaic coast. Periodical paleoclimatic episodes during the later Middle and Upper Holocene included decreased rainfall and increased aridity that reduced the Nile’s flow levels and thus likely disrupted nautical transport and anthropogenic activity along this channel. Such changes in this deltaic sector, positioned adjacent to the Levantine Basin in the Eastern Mediterranean, can be attributed to climatic shifts triggered as far as the North Atlantic to the west, and African highland source areas of the Egyptian Nile to the south. Of special interest in a study core recovered along the channel are several sediment sequences without anthropogenic material that are interbedded between strata comprising numerous potsherds. The former are interpreted here as markers of increased regional aridity and reduced Nile flow which could have periodically disrupted the regional distribution of goods and nautical activities. Such times occurred ~5000 years B.P., ~4200–4000 years B.P., ~3200–2800 years B.P., ~2300–2200 years B.P., and more recently. Periods comparable to these are also identified by altered proportions of pollen, isotopic and compositional components in different radiocarbon-dated Holocene cores recovered elsewhere in the Nile delta, the Levantine region to the east and north of Egypt, and in the Faiyum depression south of the delta.
Simplified and hybrid remote sensing-based delineation of management zones for nitrogen variable rate application in wheat (2021)
Rokhafrouz, Mohammad ; Latifi, Hooman ; Abkar, Ali A. ; Wojciechowski, Tomasz ; Czechlowski, Mirosław ; Naieni, Ali Sadeghi ; Maghsoudi, Yasser ; Niedbała, Gniewko
Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample T-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times.
BETA-FOR: Enhancing the structural diversity between patches for improving multidiversity and multifunctionality in production forests. Proposal for DFG Research Unit FOR 5375 (2022)
Müller, Jörg ; Scherer-Lorenzen, Michael ; Ammer, Christian ; Eisenhauer, Nico ; Seidel, Dominik ; Schuldt, Bernhard ; Biedermann, Peter ; Schmitt, Thomas ; Künzer, Claudia ; Wegmann, Martin ; Cesarz, Simone ; Peters, Marcell ; Feldhaar, Heike ; Steffan-Dewenter, Ingolf ; Claßen, Alice ; Bässler, Claus ; von Oheimb, Goddert ; Fichtner, Andreas ; Thorn, Simon ; Weisser, Wolfgang
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.
Multivariate Time Series for the Analysis of Land Surface Dynamics - Evaluating Trends and Drivers of Land Surface Variables for the Indo-Gangetic River Basins (2022)
Üreyen, Soner
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.
The influence of medieval building activity on relief development within the Spessart uplands, Germany. A sedimentological, geophysical and GIS-based approach at different castle and mining sites (2022)
Trappe, Julian
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.
Mapping aquaculture ponds for the coastal zone of Asia with Sentinel-1 and Sentinel-2 time series (2021)
Ottinger, Marco ; Bachofer, Felix ; Huth, Juliane ; Kuenzer, Claudia
Asia dominates the world's aquaculture sector, generating almost 90 percent of its total annual global production. Fish, shrimp, and mollusks are mainly farmed in land-based pond aquaculture systems and serve as a primary protein source for millions of people. The total production and area occupied for pond aquaculture has expanded rapidly in coastal regions in Asia since the early 1990s. The growth of aquaculture was mainly boosted by an increasing demand for fish and seafood from a growing world population. The aquaculture sector generates income and employment, contributes to food security, and has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources, and human health. With free and open access to the rapidly growing volume of data from the Copernicus Sentinel missions as well as machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental scale. We present a multi-sensor approach that utilizes Earth observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as the onshore area up to 200 km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at a single-pond level based on temporal features derived from high-spatial-resolution SAR and optical satellite data acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth-observation-derived aquaculture dataset to investigate spatial distribution and identify production hotspots at various administrative units at regional, national, and sub-national scale.
A consumer grade UAV-based framework to estimate structural attributes of coppice and high oak forest stands in semi-arid regions (2021)
Fakhri, Seyed Arvin ; Latifi, Hooman
Semi-arid tree covers, in both high and coppice growth forms, play an essential role in protecting water and soil resources and provides multiple ecosystem services across fragile ecosystems. Thus, they require continuous inventories. Quantification of forest structure in these tree covers provides important measures for their management and biodiversity conservation. We present a framework, based on consumer-grade UAV photogrammetry, to separately estimate primary variables of tree height (H) and crown area (A) across diverse coppice and high stands dominated by Quercus brantii Lindl. along the latitudinal gradient of Zagros mountains of western Iran. Then, multivariate linear regressions were parametrized with H and A to estimate the diameter at breast height (DBH) of high trees because of its importance to accelerate the existing practical DBH inventories across Zagros Forests. The estimated variables were finally applied to a model tree aboveground biomass (AGB) for both vegetative growth forms by local allometric equations and Random Forest models. In each step, the estimated variables were evaluated against the field reference values, indicating practically high accuracies reaching root mean square error (RMSE) of 0.68 m and 4.74 cm for H and DBH, as well as relative RMSE < 10% for AGB estimates. The results generally suggest an effective framework for single tree-based attribute estimation over mountainous, semi-arid coppice, and high stands.
A training sample migration method for wetland mapping and monitoring using Sentinel data in Google Earth Engine (2021)
Fekri, Erfan ; Latifi, Hooman ; Amani, Meisam ; Zobeidinezhad, Abdolkarim
Wetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient archived images over different spatial scales. However, a lack of sufficient consistent training samples at different times is a significant limitation of multi-temporal wetland monitoring. In this study, a new training sample migration method was developed to identify unchanged training samples to be used in wetland classification and change analyses over the International Shadegan Wetland (ISW) areas of southwestern Iran. To this end, we first produced the wetland map of a reference year (2020), for which we had training samples, by combining Sentinel-1 and Sentinel-2 images and the Random Forest (RF) classifier in Google Earth Engine (GEE). The Overall Accuracy (OA) and Kappa coefficient (KC) of this reference map were 97.93% and 0.97, respectively. Then, an automatic change detection method was developed to migrate unchanged training samples from the reference year to the target years of 2018, 2019, and 2021. Within the proposed method, three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the mean Standard Deviation (SD) of the spectral bands, along with two similarity measures of the Euclidean Distance (ED) and Spectral Angle Distance (SAD), were computed for each pair of reference–target years. The optimum threshold for unchanged samples was also derived using a histogram thresholding approach, which led to selecting the samples that were most likely unchanged based on the highest OA and KC for classifying the test dataset. The proposed migration sample method resulted in high OAs of 95.89%, 96.83%, and 97.06% and KCs of 0.95, 0.96, and 0.96 for the target years of 2018, 2019, and 2021, respectively. Finally, the migrated samples were used to generate the wetland map for the target years. Overall, our proposed method showed high potential for wetland mapping and monitoring when no training samples existed for a target year.
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