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Wind energy is an effective solution for achieving the carbon-neutrality target and mitigating climate change. The expansion of onshore wind energy evokes extensive attention to environmental impact in the locality. The landscape visual impact has become the critical reason for the local protest. This paper proposed a landscape visual impact evaluation (LVIE) model that combines the theoretical framework and practical solutions and optimizes the onshore wind farm planning procedures. Based on the theoretical research on landscape connotation, the evaluation principles, criteria, and a quantitative indicator set is constructed for LVIE model with three dimensions: landscape sensitivity, the visual impact of WTs, and viewer exposure. The practicality of this evaluation model is conducted through multi-criteria GIS analysis by the case study of Friedrich-Wilhelm Raiffeisen Wind Farm in Germany. The evaluation results illustrate detailed, visualized outcomes of landscape visual impact that are deeply combined with planning procedures. The innovation of this paper is to refine the form of evaluation results, optimize the procedures of wind farm planning, and enable cooperation between different planning departments and stakeholders with definite, visible, user-friendly evaluation results. This research provides precise comparison opportunities for different projects or the same project at different periods to obtain quantitative conclusions and feedback information. This paper enhances the accurate processing of multiple information and standardization process in wind energy visual impact evaluation. [ABSTRACT FROM AUTHOR]

This work shows the original projection of a 1:57,600 scale map of southern Germany at the end of the 18th century, produced under the direction of Karl-Heinrich von Schmitt (1743–1805). The sections of the map were scanned and georeferenced as part of the MAPIRE project, and the results are publicly available. In the present work, we use contemporary documents, in particular the books of César-Francois Cassini de Thury and manuscript sketches of the map found in the Military Archive of Vienna, to show that the overall projection of the map is identical to that used in Cassini's survey of France (first half of the 18th century). In the archive, we managed to find the overview sheet on which—in addition to the Paris Cassini coordinate system—the section grid of the Schmitt map was also constructed. This sketch served as the basis for the compilation and copying work, wherein the existing map works and survey sketches were inserted into 197 sections of the Schmitt map. Thus, the map coordinate system can be modeled in GIS systems using the Cassini (or Cassini-Soldner) projection, with the Paris Observatory as the projection origin. The georeferencing accuracy of using the pure Cassini projection is around 1–1.3 km (at the extremes, around 5 km), which is much more inaccurate than the one used in later topographic surveys. It is considered a combined result of the compilation of the different maps, presumably surveyed by graphic triangulation with measuring tables. [ABSTRACT FROM AUTHOR]

Due to financial or administrative constraints, access to official spatial base data is currently limited to a small subset of all potential users in the field of spatial planning and research. This increases the usefulness of Volunteered Geographic Information (VGI), in particular OpenStreetMap (OSM), as supplementary datasets or, in some cases, alternative sources of primary data. In contrast to the OSM street network, which has already been thoroughly investigated and found to be practically complete in many areas, the degree of completeness of OSM data on buildings is still unclear. In this paper we describe methods to analyze building completeness and apply these to various test areas in Germany. Official data from national mapping and cadastral agencies is used as a basis for comparison. The results show that unit-based completeness measurements (e.g., total number or area of buildings) are highly sensitive to disparities in modeling between official data and VGI. Therefore, we recommend object-based methods to study the completeness of OSM building footprint data. An analysis from November 2011 in Germany indicated a completeness of 25% in the federal states of North Rhine-Westphalia and 15% in Saxony. Although further analyses from 2012 confirm that data completeness in Saxony has risen to 23%, the rate of new data input was slowing in the year 2012. OPEN ACCESS [ABSTRACT FROM AUTHOR]

This paper describes a methodological approach that is able to analyse socio-demographic and -economic data in large-scale spatial detail. Based on the two variables, population density and annual income, one investigates the spatial relationship of these variables to identify locations of imbalance or disparities assisted by bivariate choropleth maps. The aim is to gain a deeper insight into spatial components of socioeconomic nexuses, such as the relationships between the two variables, especially for high-resolution spatial units. The used methodology is able to assist political decision-making, target group advertising in the field of geo-marketing and for the site searches of new shop locations, as well as further socioeconomic research and urban planning. The developed methodology was tested in a national case study in Germany and is easily transferrable to other countries with comparable datasets. The analysis was carried out utilising data about population density and average annual income linked to spatially referenced polygons of postal codes. These were disaggregated initially via a readapted three-class dasymetric mapping approach and allocated to large-scale city block polygons. Univariate and bivariate choropleth maps generated from the resulting datasets were then used to identify and compare spatial economic disparities for a study area in North Rhine-Westphalia (NRW), Germany. Subsequently, based on these variables, a multivariate clustering approach was conducted for a demonstration area in Dortmund. In the result, it was obvious that the spatially disaggregated data allow more detailed insight into spatial patterns of socioeconomic attributes than the coarser data related to postal code polygons. [ABSTRACT FROM AUTHOR]

Humans' activities in urban areas put a strain on local water resources. This paper introduces a method to accurately simulate the stress urban water demand in Germany puts on local resources on a single-building level, and scalable to regional levels without loss of detail. The method integrates building geometry, building physics, census, socio-economy and meteorological information to provide a general approach to assessing water demands that also overcome obstacles on data aggregation and processing imposed by data privacy guidelines. Three German counties were used as validation cases to prove the feasibility of the presented approach: on average, per capita water demand and aggregated water demand deviates by less than 7% from real demand data. Scenarios applied to a case region Ludwigsburg in Germany, which takes the increment of water price, aging of the population and the climate change into account, show that the residential water demand has the change of −2%, +7% and −0.4% respectively. The industrial water demand increases by 46% due to the development of economy indicated by GDP per capita. The rise of precipitation and temperature raise the water demand in non-residential buildings (excluding industry) of 1%. [ABSTRACT FROM AUTHOR]

Agenda 2030 pursues a universal approach and identifies countries in the Global South and in the Global North that are in need of transformation toward sustainability. Therefore, countries of the Global North such as Germany have signed the commitment to implement the Sustainable Development Goals (SDGs). However, the SDGs need to be "translated" to the specific national context. Existing sustainability indicators and monitoring and reporting systems need to be adjusted as well. Our paper evaluates how three different initiatives translated SDG 11 ("Make cities and human settlements inclusive, safe, resilient, and sustainable") to the German context, given the specific role of cities in contributing to sustainable development. These initiatives included the official 'National Sustainable Development Strategy' of the German Government, a scientific initiative led by the 'German Institute for Urban Affairs', and a project carried out by the 'Open Knowledge Foundation', a non-governmental organization (NGO). This article aims to analyze how global goals addressing urban developments are contextualized on a national level. Our findings demonstrate that only a few of the original targets and indicators for SDG 11 are used in the German context; thus, major adjustments have been made according to the main sustainability challenges identified for Germany. Furthermore, our results show that the current contextualization of SDG 11 and sustainable urban development in Germany are still ongoing, and more changes and commitments need to be made. [ABSTRACT FROM AUTHOR]

In the context of national-level strategies, the importance of tourism in national parks is on the rise. The objective of this study is to investigate the relationship between the number of visitors to national parks and five variables: area, number of employees, budget, average employee salary and number of researchers in 12 national parks in the Czech Republic, Germany and Austria. Analysis of factors influencing the number of visitors to national parks uses the method of retrospective analysis of the data contained in internal documents and questionnaires among managers of national parks. The number of candidate predictors is relatively high when compared with the number of observations. Due to this fact, the Gilmour method for statistical analysis is used. Statistical results represented by the parameter β2 for number of employees is -33,016 (95% CI, -50,592--15,441) and by the parameter β3 for budget is 0.586 (95% CI, 0.295-0.878), showing that the number of visitors increases with budget, while it decreases with the number of employees. The results of this study are a useful starting point for managers in their efforts to focus on developing key areas in an appropriate way. In conclusion, results show that increasing the economic benefits accruing from national parks regional policy could aim at a qualitative upgrading of tourist services, increased marketing of the unique national park label and the promotion of a diverse regional supply base. [ABSTRACT FROM AUTHOR]

Volunteered Geographic Information (VGI) such as data derived from the OpenStreetMap (OSM) project is a popular data source for freely available geographic data. Normally, untrained contributors gather these data. This fact is frequently a cause of concern regarding the quality and usability of such data. In this study, the quality of OSM land use and land cover (LULC) data is investigated for an area in southern Germany. Two spatial data quality elements, thematic accuracy and completeness are addressed by comparing the OSM data with an authoritative German reference dataset. The results show that the kappa value indicates a substantial agreement between the OSM and the authoritative dataset. Nonetheless, for our study region, there are clear variations between the LULC classes. Forest covers a large area and shows both a high OSM completeness (97.6%) and correctness (95.1%). In contrast, farmland also covers a large area, but for this class OSM shows a low completeness value (45.9%) due to unmapped areas. Additionally, the results indicate that a high population density, as present in urbanized areas, seems to denote a higher strength of agreement between OSM and the DLM (Digital Landscape Model). However, a low population density does not necessarily imply a low strength of agreement. [ABSTRACT FROM AUTHOR]

Shortly after the Fukushima Daiichi nuclear disaster in 2011, the Federal Government of Germany decided to change the structure of the country's energy supply system by ending nuclear energy conversion and strongly promoting the development of renewable energies. In order to politically set the course for sustainable energy supply in this time of transition, it is important to analyze the factors influencing the future development of renewable energies. This work contributes to this purpose in the field of onshore wind electricity generation by displaying the temporal development of areas suitable for wind energy use. The availability of such areas is crucial to the extension of sites for wind energy plants. In our approach, the current potential area is determined by excluding areas unsuitable for this kind of electricity generation. For the determination of potential areas of the future, assumptions are made based on the expansion of settlement and traffic areas, and the occupation of protection areas. According to various scenarios, a decline of potential areas between 3% and 8% between 2011 and 2030 is indicated. [ABSTRACT FROM AUTHOR]

In recent years, electricity production from wind turbines and photovoltaic systems has grown significantly in Germany. To determine the multiple impacts of rising variable renewable energies on an increasingly decentralized power supply, spatially and temporally resolved data on the power generation are necessary or, at least, very helpful. Because of extensive data protection regulations in Germany, especially for smaller operators of renewable power plants, such detailed data are not freely accessible. In order to fill this information gap, simulation models employing publicly available plant and weather data can be used. The numerical simulations are performed for the year 2016 and consider an ensemble of almost 1.64 million variable renewable power plants in Germany. The obtained time series achieve a high agreement with measured feed-in patterns over the investigated year. Such disaggregated power generation data are very advantageous to analyze the energy transition in Germany on a spatiotemporally resolved scale. In addition, this study also derives meaningful key figures for such an analysis and presents the generated results as detailed maps at county level. To the best of our knowledge, such highly resolved electricity data of variable renewables for the entire German region have never been shown before. [ABSTRACT FROM AUTHOR]

Wind power has risen continuously over the last 20 years and covered almost 25% of the total German power provision in 2019. To investigate the effects and challenges of increasing wind power on energy systems, spatiotemporally disaggregated data on the electricity production from wind turbines are often required. The lack of freely accessible feed-in time series from onshore turbines, e.g., due to data protection regulations, makes it necessary to determine the power generation for a certain region and period with the help of numerical simulations using publicly available plant and weather data. For this, a new approach is used for the wind power model which utilizes a sixth-order polynomial for the specific power curve of a turbine. After model validation with measured data from a single wind turbine, the simulations are carried out for an ensemble of 25,835 onshore turbines to determine the German wind power production for 2016. The resulting hourly resolved data are aggregated into a time series with daily resolution and compared with measured feed-in data of entire Germany which show a high degree of agreement. Such electricity generation data from onshore turbines can be applied to optimize and monitor renewable power systems on various spatiotemporal scales. [ABSTRACT FROM AUTHOR]

The electrification of the transport sector together with an increasing share of renewable energies has the potential to reduce CO2 emissions significantly. This transformation requires the rollout of charging infrastructure, which has an impact on power grids. For grid planning and dimensioning purposes, it is crucial to assess this rapidly growing impact. We present an approach using socio-economic data such as income levels together with a model for demographic changes to estimate where electric mobility is likely to be concentrated, especially during the transformation phase. We present a total-cost-of-ownership approach for the ramp-up of electric mobility, considering an increased penetration of renewable energies. With the city of Wiesbaden in Germany as an example for an application area, the possible expansion of vehicle ownership and charging points is modeled on the level of individual buildings. Compared to a simpler approach, the detailed model results in more consistent charging point allocations, higher line/transformer loadings and lower bus voltages for the investigated grids. Predicting future distributions of charging points with such a level of detail in terms of ramp-up and spatial resolution proves potentially beneficial for grid analysis and planning purposes, especially in urban areas, where infrastructure changes are expensive and time-consuming. [ABSTRACT FROM AUTHOR]

Photovoltaics, as one of the most important renewable energies in Germany, have increased significantly in recent years and cover up to 50% of the German power provision on sunny days. To investigate the manifold effects of increasing renewables, spatiotemporally disaggregated data on the power generation from photovoltaic (PV) systems are often mandatory. Due to strict data protection regulations, such information is not freely available for Germany. To close this gap, numerical simulations using publicly accessible plant and weather data can be applied to determine the required spatiotemporal electricity generation. For this, the sunlight-to-power conversion is modeled with the help of the open-access web tool of the Photovoltaic Geographical Information System (PVGIS). The presented simulations are carried out for the year 2016 and consider nearly 1.612 million PV systems in Germany, which have been aggregated into municipal areas before performing the calculations. The resulting hourly resolved time series of the entire plant ensemble are converted into a time series with daily resolution and compared with measured feed-in data to validate the numerical simulations that show a high degree of agreement. Such power production data can be used to monitor and optimize renewable energy systems on different spatiotemporal scales. [ABSTRACT FROM AUTHOR]

This article proposes a method for registration of two different point clouds with different point densities and noise recorded by airborne sensors in rural areas. In particular, multi-sensor point clouds with different point densities are considered. The proposed method is marker-less and uses segmented ground areas for registration.Therefore, the proposed approach offers the possibility to fuse point clouds of different sensors in rural areas within an accuracy of fine registration. In general, such registration is solved with extensive use of control points. The source point cloud is used to calculate a DEM of the ground which is further used to calculate point to raster distances of all points of the target point cloud. Furthermore, each cell of the raster DEM gets a height variance, further addressed as reconstruction accuracy, by calculating the grid. An outlier removal based on a dynamic threshold of distances is used to gain more robustness against noise and small geometry variations. The transformation parameters are calculated with an iterative least-squares optimization of the distances weighted with respect to the reconstruction accuracies of the grid. Evaluations consider two flight campaigns of the Mangfall area inBavaria, Germany, taken with different airborne LiDAR sensors with different point density. The accuracy of the proposed approach is evaluated on the whole flight strip of approximately eight square kilometers as well as on selected scenes in a closer look. For all scenes, it obtained an accuracy of rotation parameters below one tenth degrees and accuracy of translation parameters below the point spacing and chosen cell size of the raster. Furthermore, the possibility of registration of airborne LiDAR and photogrammetric point clouds from UAV taken images is shown with a similar result. The evaluation also shows the robustness of the approach in scenes where a classical iterative closest point (ICP) fails. [ABSTRACT FROM AUTHOR]

Wildlife–vehicle collisions (WVCs) cause significant road mortality of wildlife and have led to the installation of protective measures along streets. Until now, it has been difficult to determine the impact of roadside infrastructure that might act as a barrier for animals. The main deficits are the lack of geodata for roadside infrastructure and georeferenced accidents recorded for a larger area. We analyzed 113 km of road network of the district Freyung-Grafenau, Germany, and 1571 WVCs, examining correlations between the appearance of WVCs, the presence or absence of roadside infrastructure, particularly crash barriers and fences, and the relevance of the blocking effect for individual species. To receive infrastructure data on a larger scale, we analyzed 5596 road inspection images with a neural network for barrier recognition and a GIS for a complete spatial inventory. This data was combined with the data of WVCs in GIS to evaluate the infrastructure's impact on accidents. The results show that crash barriers have an effect on WVCs, as collisions are lower on roads with crash barriers. In particular, smaller animals have a lower collision share. The risk reduction at fenced sections could not be proven as fenced sections are only available at 3% of the analyzed roads. Thus, especially the fence dataset must be validated by a larger sample number. However, these preliminary results indicate that the combination of artificial intelligence and GIS may be used to analyze and better allocate protective barriers or to apply it in alternative measures, such as dynamic WVC risk-warning. [ABSTRACT FROM AUTHOR]