• Rural Digital Europe
• Austrian Science Fund (FWF) close
• ZENODO close

In the era of the Internet of Things (IoT), efficient localization is essential for emerging mass-market services and applications. IoT devices are heterogeneous in signaling, sensing, and mobility, and their resources for computation and communication are typically limited. Therefore, to enable location awareness in large-scale IoT networks, there is a need for efficient, scalable, and distributed multisensor fusion algorithms. This article presents a framework for designing network localization and navigation (NLN) for the IoT. Multisensor localization and operation algorithms developed within NLN can exploit spatiotemporal cooperation, are suitable for arbitrary, large network sizes, and only rely on an information exchange among neighboring devices. The advantages of NLN are evaluated in a large-scale IoT network with 500 agents. In particular, because of multisensor fusion and cooperation, the presented network localization and operation algorithms can provide attractive localization performance and reduce communication overhead and energy consumption.

Assessing changes in the extent and management intensity of land use is crucial to understanding land-system dynamics and their environmental and social outcomes. Yet, changes in the spatial patterns of land management intensity, and thus how they might relate to changes in the extent of land uses, remains unclear for many world regions.Wecompiled and analyzed high-resolution, spatiallyexplicit land-use change indicators capturing changes in both the extent and management intensity of cropland, grazing land, forests, and urban areas for all of Europe for the period 1990–2006. Based on these indicators, we identified hotspots of change and explored the spatial concordance of area versus intensity changes.Wefound a clear East–West divide with regard to agriculture, with stronger cropland declines and lower management intensity in the East compared to the West. Yet, these patterns were not uniform and diverging patterns of intensification in areas highly suitable for farming, and disintensification and cropland contraction in more marginal areas emerged. Despite the moderate overall rates of change, many regions in Europe fell into at least one land-use change hotspot during 1990–2006, often related to a spatial reorganization of land use (i.e., co-occurring area decline and intensification or co-occurring area increase and disintensification). Our analyses highlighted the diverse spatial patterns and heterogeneity of land-use changes in Europe, and the importance of jointly considering changes in the extent and management intensity of land use, as well as feedbacks among land-use sectors. Given this spatial differentiation of land-use change, and thus its environmental impacts, spatially-explicit assessments of land-use dynamics are important for context-specific, regionalized land-use policy making. Die Zweitveröffentlichung der Publikation wurde durch Studierende des Projektseminars "Open Access Publizieren an der HU" im Sommersemester 2017 betreut. Nachgenutzt gemäß den CC-Bestimmungen des Lizenzgebers bzw. einer im Dokument selbst enthaltenen CC-Lizenz. Peer Reviewed

Satellite‐derived surface soil moisture data are available for the Arctic, but detailed validation is still lacking. Previous studies have shown low correlations between in situ and modeled data. It is hypothesized that soil temperature variations after soil thaw impact MetOp ASCAT satellite‐derived surface soil moisture (SSM) measurements in wet tundra environments, as C band backscatter is sensitive to changes in dielectric properties. We compare in situ measurements of water content within the active layer at four sites across the Arctic in Alaska (Barrow, Sagwon, Toolik) and Siberia (Tiksi), taken in the spring after thawing and in autumn prior to freezing. In addition to the long‐term measurement fields, where sensors are installed deeper in the ground, we designed a monitoring setup for measuring moisture very close to the surface in the Lena River Delta, Siberia. The volumetric water content (VWC) and soil temperature sensors were placed in the moss organic layer in order to account for the limited penetration depth of the radar signal. ASCAT SSM variations are generally very small, in line with the low variability of in situ VWC. Short‐term changes after complete thawing of the upper organic layer, however, seem to be mostly influenced by soil temperature. Correlations between SSM and in situ VWC are generally very low, or even negative. Mean standard deviation matching results in a comparably high root‐mean‐square error (on average 11%) for predictions of VWC. Further investigations and measurement networks are needed to clarify factors causing temporal variation of C band backscatter in tundra regions. Key Points Organic layer temperature variations explain temporal behavior of soil moisture derived from C band radar in unfrozen wet tundraTemporal variability of volumetric water content from in situ point measurements is representative over distances of several kilometers in tundraNear‐surface volumetric water content predicted from matched ASCAT data has an average RMSE of 11% across five sites in Alaska and Siberia

The survival of insect larvae often depends on the mother's choice of oviposition substrate, and thus, this choice is an essential part of an insect species' ecology. Especially species with narrow substrate preferences may suffer from changes in substrate availability triggered by, for example, climate change. Recent climate warming is affecting species directly (e.g., physiology) but also indirectly (e.g., biological interactions) leading to mismatching phenologies and distributions. However, the preferred oviposition substrate is still unknown for many drosophilid species, especially for those at higher elevations. In this study, we investigated the oviposition-substrate preference of the montane-alpine fly Drosophila nigrosparsa in rearing and multiple-choice experiments using natural substrates in the laboratory. Insect emergence from field-collected substrates was tested. More than 650 insects were reared from natural substrates, among them 152 drosophilids but no individual of D. nigrosparsa. In the multiple-choice experiments, D. nigrosparsa preferred ovipositing on mushrooms (> 93% of eggs); additionally, a few eggs were laid on berries but none on other substrates such as cow faeces, rotten plant material, and soil. The flies laid 24 times more eggs per day when mushrooms were included in the substrates than when they were excluded. We infer that D. nigrosparsa is a mushroom breeder with some variation in oviposition choice. The flies favoured some mushrooms over others, but they were not specialised on a single fungal taxon. Although it is unclear if and how climate change will affect D. nigrosparsa, our results indicate that this species will not be threatened by oviposition-substrate limitations in the near future because of the broad altitudinal distribution of the mushrooms considered here, even if the flies will have to shift upwards to withstand increasing temperatures.

Abstract Synthetic aperture radar (SAR) applications often require normalization to a common incidence angle. Angular signatures of radar backscatter depend on surface roughness and vegetation cover, and thus differ, from location to location. Comprehensive reference datasets are therefore required in heterogeneous landscapes. Multiple acquisitions from overlapping orbits with sufficient incidence angle range are processed in order to obtain parameters of the location specific normalization function. We propose a simpler method for C-band data, using single scenes only. It requires stable dielectric properties (no variations of liquid water content). This method is therefore applicable for frozen conditions. Winter C-band data have been shown of high value for a number of applications in high latitudes before. In this paper we explore the relationship of incidence angle and Sentinel-1 backscatter across the tundra to boreal transition zone. A linear relationship (coefficient of determination R 2 = 0.64) can be found between backscatter and incidence angle dependence (slope of normalization function) as determined by multiple acquisitions on a pixel by pixel basis for typical land cover classes in these regions. This allows a simplified normalization and thus reduced processing effort for applications over larger areas.

Multiregional input–output (MRIO) databases are used to analyze the impact of resource use and environmental impacts along global supply chains. To accurately account for pressures and impacts that are highly concentrated in specifc sectors or regions of the world, such as agricultural and land-use-related impacts, MRIO databases are being fueled by increasingly more detailed data. To date no MRIO database exists which couples a high level of harmonized sector detail with high country resolution. Currently available databases either aggregate minor countries into rest-of-the-world (WIOD and EXIOBASE 3), or the high country resolution is achieved at the cost of nonharmonized or lower sectoral detail (Eora, OECD-ICIO or the GTAP-MRIO). This aggrega‑ tion can cause potentially signifcant diferences in environmental and socioeconomic impact calculations. In this paper, we describe the development of an EXIOBASE 3 variant that expands regional coverage from 49 regions to 214 countries, while keeping the high and harmonized sectoral detail. We show the relevance of disaggregation for land-use accounting. Previous rest-of-the-world regions supply one-third of global land, which is used to produce a large range of diferent products under very diferent levels of productivity. We fnd that the aggregation of regions leads to a diference in the balance of land embodied in trade of up to 6% and a diference of land embodied in imports of up to 68% for individual countries and up to 600% for land-use-relevant sectors. Whilst the database can still be considered experimental, it is expected to increase the accuracy of estimates for environmental footprint studies of the original EXIOBASE countries, and provides the frst estimates for the countries in the previous rest-of-the world. © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativeco mmons.org/licenses/by/4.0/

Global food systems contribute to climate change, the transgression of planetary boundaries and deforestation. An improved understanding of the environmental impacts of different food system futures is crucial for forging strategies to sustainably nourish a growing world population. We here quantify the greenhouse gas (GHG) emissions of global food system scenarios within a biophysically feasible “option space” in 2050 comprising all scenarios in which biomass supply – calculated as function of agricultural area and yields – is sufficient to cover biomass demand – derived from human diets and the feed demand of livestock. We assessed the biophysical feasibility of 520 scenarios in a hypothetical no-deforestation world. For all feasible scenarios, we calculate (in) direct GHG emissions related to agriculture. We also include (possibly negative) GHG emissions from land-use change, including changes in soil organic carbon (SOC) and carbon sinks from vegetation regrowth on land spared from food production. We identify 313 of 520 scenarios as feasible. Agricultural GHG emissions (excluding land use change) of feasible scenarios range from 1.7 to 12.5 Gt CO2e yr−1. When including changes in SOC and vegetation regrowth on spare land, the range is between −10.7 and 12.5 Gt CO2e yr−1. Our results show that diets are the main determinant of GHG emissions, with highest GHG emissions found for scenarios including high meat demand, especially if focused on ruminant meat and milk, and lowest emissions for scenarios with vegan diets. Contrary to frequent claims, our results indicate that diets and the composition and quantity of livestock feed, not crop yields, are the strongest determinants of GHG emissions from food-systems when existing forests are to be protected.