Tracking progress towards the green energy transition: Nationwide mapping of roof-top photovoltaic installations

The decarbonization of the global energy system through the transition to renewable energy sources is one of the main pillars of mitigation measures addressing climate change. A key determinant in the realization of a successful energy transition is the rapid installation of renewable energy infrastructure, which, for the most part, is of very heterogenous nature and spatially decentralized [1]. This is particularly true for roof-top photovoltaic systems (PVs), which are often small-scale, privately owned, differ widely in their capacity and are non-randomly distributed in space, with even the occasional emergence of patterns of socio-economic and political boundary conditions. In order to foster the rapid expansion of PV installations, which is needed for reaching aims and commitments with respect to the energy transition, an automatically updatable monitoring of the evolution of such PV installations over time can be a vital asset for political decision makers, both on the communal and national level, supporting the efficient allocation of resources. Here, we demonstrate a system for nationwide mapping of rooftop PV installations based on multiple timesteps of aerial imagery for all of Germany. We demonstrate our system with an exemplary wall-to-wall analysis, where we exploited publicly available registry data as well as manually labelled samples for training data collection. Based on this curated training data set of around 350.000 samples, we trained ensembles of supervised, state-of-the-art deep neural networks (ResNets, ResNests, EfficientNets, ConvNexts and VisionTransformers) for predicting the presence of PV installations for each building in Germany. Following a rigorous validation exercise based on over 20.000 samples, we report a very high predictive performance of 0.96% F1-score overall, with a regional variability of +/- 0.03. Going beyond single date classifications, this system enables us to track the growth of PV installations over time throughout Germany. Add-on analyses of installed PVs in combination with spatially explicit solar potential models [2] allow us now to identify and suggest priority areas for high-return-on-investment policy stimulation for fostering the much-needed growth of decentralized PV installations. References: [1] M. Victoria, K. Zhu, T. Brown, G. B. Andresen, and M. Greiner, 'Early decarbonisation of the european energy system pays off' Nature Communications, vol. 11, no. 1, 2020. [2] S. Joshi, S. Mittal, P. Holloway, P. R. Shukla, B. Ó. Gallachóir, and J. Glynn, 'High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation' Nature Communications, vol. 12, no. 1, 2021

Habitat use during spring migration: Remote sensing meets movement ecology

Forage availability during spring migration is crucial for the survival and successful reproduction of many migratory species. With careful timing in relation to spring growth and small-scale selection of suitable food sites, large avian herbivory migrants are known to maximise foraging rate during spring. However, especially for Arctic breeders, the recent levels of climate and habitat change alter the conditions that they meet at their spring stopover and breeding sites. In the EO-MOVE project we examine the habitat use of greater white-fronted geese (Anser albifrons) along their spring migration route between central Europe and northern Russia. This species is known to be sensitive to land-use intensity, phenology and landscape configuration, which calls for the exploitation of high resolution tracking and remote sensing technologies. To characterise the movement of geese within their spring stopovers, we use over 150 highly resolved GPS tracks of individual adult geese from the years 2006-2017. Since 2014 we have additionally collected acceleration data to classify the animals' behaviour and energy expenditure. We select within-stopover GPS positions that are classified as flight or feeding and overlay the movements connecting different small-scale feeding sites with optical and SAR time series data (20Ã-20m) from the Sentinel 1 and 2 satellite missions using step selection functions. Habitat preference outcomes are then set into context with vegetation indices and compared between individuals, years and stopover sites. First results indicate that white-fronted geese generally select for highly green, low and young vegetation, but also that there are large differences between stopovers. We expect to reveal in detail how the birds select for suitable feeding sites in relation to availability and recent levels of habitat change, potentially allowing for site selection prediction, an important prerequisite for spatially or temporally targeted conservation schemes

World Settlement Footprint 3D - A first three-dimensional survey of the global building stock

Settlements, and in particular cities, are at the center of key future challenges related to global change and sustainable development. Widely used indicators to assess the efficiency and sustainability of settlement development are the compactness and density of the built-up area. However, at global scale, a temporally consistent and spatially detailed survey of the distribution and concentration of the building stock – meaning the total area and volume of buildings within a defined spatial unit or settlement, commonly referred to as building density – does not yet exist. To fill this data and knowledge gap, an approach was developed to map key characteristics of the world’s building stock in a so far unprecedented level of spatial detail for every single settlement on our planet. The resulting World Settlement Footprint 3D dataset quantifies the fraction, total area, average height, and total volume of buildings for a measuring grid with 90 m cell size. The World Settlement Footprint 3D is generated using a modified version of the World Settlement Footprint human settlements mask derived from Sentinel-1 and Sentinel-2 satellite imagery at 10 m spatial resolution, in combination with 12 m digital elevation data and radar imagery collected by the TanDEM-X mission. The underlying, automated processing framework includes three basic workflows: one estimating the mean building height based on an analysis of height differences along potential building edges, a second module determining the building fraction and total building area within each 90 m cell, and a third part combining the height information and building area in order to determine the average height and total built-up volume at 90 m gridding. Optionally, a simple 3D building model (level of detail 1) can be generated for regions where data on the building footprints is available. A comprehensive validation campaign based on 3D building models obtained for 19 regions (~86,000 km2) and street-view samples indicating the number of floors for >130,000 individual buildings in 15 additional cities documents that the novel World Settlement Footprint 3D data provides valuable and, for the first time, globally consistent information on key characteristics of the building stock in both, large urban agglomerations as well as small-scale rural settlements. Thus, the new dataset represents a promising baseline dataset for a wide range of previously impossible environmental, socioeconomic, and climatological studies worldwide

Large-scale 3D Modelling of the Built Environment - Joint Analysis of TanDEM-X, Sentinel-2 and Open Street Map Data

Continental to global scale mapping of the human settlement extent based on earth observation satellite data has made considerable progress. Nevertheless, the current approaches only provide a two-dimensional representation of the built environment. Therewith, a full characterization is restricted in terms of the urban morphology and built-up density, which can only be gained by a detailed examination of the vertical settlement extent. This paper introduces a methodology for the extraction of three-dimensional (3D) information on human settlements by analyzing the digital elevation and radar intensity data collected by the German TanDEM-X satellite mission in combination with multispectral Sentinel-2 imagery and data from the Open Street Map initiative and the Global Urban Footprint human settlement mask. The first module of the underlying processor generates a normalized digital surface model from the TanDEM-X digital elevation model for all regions marked as a built-up area by the Global Urban Footprint. The second module generates a building mask based on a joint processing of Open Street Map, TanDEM-X/TerraSAR-X radar images, the calculated normalized digital surface model and Sentinel-2 imagery. Finally, a third module allocates the local relative heights of the normalized digital surface model to the building structures provided by the building mask. The outcome of the procedure is a 3D map of the built environment showing the estimated local height for all identified vertical building structures at 12 m spatial resolution. The results of a first validation campaign based on reference data collected for the seven cities of Amsterdam (NL), Indianapolis (US), Kigali (RW), Munich (DE), New York (US), Vienna (AT), and Washington (US) indicate the potential of the proposed methodology to accurately estimate the distribution of building heights within the built-up area

Potentiale der Fernerkundung für die Forschung zu Biodiversitätsfragestellungen

Jeder Punkt der Erde wird regelmäßig von verschiedensten, satellitenbasierten Systemen erfasst, um räumliche Muster, Zustand und Entwicklung der Landoberfläche objektiv zu quantifizieren. Über Disziplinen hinweg etabliert sich die fernerkundliche Erdbeobachtung damit zusehends als eine der wichtigsten Informationsquellen für die Erforschung raum-zeitlicher Fragestellungen, aber auch für das Monitoring anthropogener und natürlicher Prozesse. Insbesondere in der Ökologie, einer inhärent räumlichen Disziplin, wie auch im angewandten Naturschutz sind Nutzen und Potential der Fernerkundung nicht mehr zu übersehen. Im Folgenden wird ein Überblick über die Entwicklung und Status Quo fernerkundlicher Systeme, Methodik und Produkte gegeben. Besonderes Augenmerk wird dabei auf fernerkundliche Anwendungen und Anwendungspotentiale für ökologische Fragestellungen geleg

Modelling Forest α-Diversity and Floristic Composition — On the Added Value of LiDAR plus Hyperspectral Remote Sensing

The decline of biodiversity is one of the major current global issues. Still, there is a widespread lack of information about the spatial distribution of individual species and biodiversity as a whole. Remote sensing techniques are increasingly used for biodiversity monitoring and especially the combination of LiDAR and hyperspectral data is expected to deliver valuable information. In this study spatial patterns of vascular plant community composition and α-diversity of a temperate montane forest in Germany were analysed for different forest strata. The predictive power of LiDAR (LiD) and hyperspectral (MNF) datasets alone and combined (MNF+LiD) was compared using random forest regression in a ten-fold cross-validation scheme that included feature selection and model tuning. The final models were used for spatial predictions. Species richness could be predicted with varying accuracy (R2 = 0.26 to 0.55) depending on the forest layer. In contrast, community composition of the different layers, obtained by multivariate ordination, could in part be modelled with high accuracies for the first ordination axis (R2 = 0.39 to 0.78), but poor accuracies for the second axis (R2 ≤ 0.3). LiDAR variables were the best predictors for total species richness across all forest layers (R2 LiD = 0.3, R2 MNF = 0.08, R2 MNF+LiD = 0.2), while for community composition across all forest layers both hyperspectral and LiDAR predictors achieved similar performances (R2 LiD = 0.75, R2 MNF = 0.76, R2 MNF+LiD = 0.78). The improvement in R2 was small (≤0.07)—if any—when using both LiDAR and hyperspectral data as compared to using only the best single predictor set. This study shows the high potential of LiDAR and hyperspectral data for plant biodiversity modelling, but also calls for a critical evaluation of the added value of combining both with respect to acquisition costs