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These data were collected as part of a case study for the ipaast project. The aim of the survey was to produce datasets interoperable for applications in archaeology and precision agriculture. The OptRx® Crop Sensors (AgLeader Technology, Ames, IO, USA) measure the reflectance in the 630–685 nm (red), 695–750 nm (RE red edge) and 760–850 nm (NIR—Near InfraRed) wavebands. Using those wavebands, NDVI and NDRE indexes are calculated. NDVI and NDRE are vegetative indexes obtained from the red, red-edge and NIR wavebands with formulas 1 and 2: NDVI = NIR−REDNIR+RED ; NDRE= NIR−RENIR+RE The two index values range from -1 (bare ground or water) to 1 (highly vigorous vegetation). To collect data, the sensor was mounted on a ground vehicle, a Kubota B2420 tractor. The sensor was paired with a GNNS receiver, GPS 6500 from AgLeader Technology (Ames, IO, USA). The instrumentation was coupled with the hardware and the rough book (Panasonic ToughPad FG-Z1, Panasonic Core. It was possible to install the sensor facing the ground using a metal bracket positioned on the front of the tractor. The sensor was positioned 1.15 m from the ground, emitting a rectangular footprint of 1.14 m in length and 20cm in width. The data were collected every 30 cm in alternate rows. 12 rows in total were analysed, covering a surface of 1.07 ha. Data were processed on QGIS. First, the data was interpolated with the Inverse Distance Weighting (IDW) function. The function was set up with a distance coefficient P of 4, with 40 rows and 98 columns. A Gaussian filter with a standard deviation value of 2 and a range of research of 3 was subsequently applied to create a representative raster.

Humanity's role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the 'anthropocene', as humans are 'overwhelming the great forces of nature'. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed 'manufactured capital', 'technomass', 'human-made mass', 'in-use stocks' or 'socioeconomic material stocks', they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with 'real' (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called 'built structures') represent the overwhelming majority of all socioeconomic material stocks. This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Spatial extentThis subdataset covers the South CONUS, i.e. AL AR FL GA KY LA MS NC SC TN VA WV For the remaining CONUS, see the related identifiers. Temporal extentThe map is representative for ca. 2018. Data formatThe data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. t at 10m x 10m kt at 100m x 100m Mt at 1km x 1km Gt at 10km x 10km For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layersNote that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers): A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337. Further informationFor further information, please see the publication.A web-visualization of this dataset is available here.Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. PublicationD. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gómez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, and H. Haberl (2023): Unveiling patterns in human dominated landscapes through mapping the mass of US built structures. Nature Communications 14, 8014. https://doi.org/10.1038/s41467-023-43755-5 FundingThis research was primarly funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404. AcknowledgmentsWe thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

A sublittoral transect (P3) in the North of Helgoland that had been investigated ~40 years earlier by Lüning (1970) was traversed again. Scuba dives were carried out between 24.06.2005 and 19.08.2005. For each sampling point, time, date and coordinates were given as well as the corrected depth in m mean low water spring tide. Along the transect, substrate and topography were recorded. Relative frequency and cover of 32 species grown attached to the seafloor and cover of 12 epiphytic species were obtained.

Live woody vegetation is the largest reservoir of biomass carbon with its restoration considered one of the most effective natural climate solutions. However, carbon fluxes associated with terrestrial ecosystems still remain the largest source of uncertainty of the global carbon balance. Here, we develop spatially explicit estimates of global carbon stock changes of live woody biomass from 2000 to 2019 using measurements from ground, air, and space. We show live biomass has removed 4.9-5.5 PgC yr-1 from the atmosphere in this century, offsetting 4.6±0.1 PgC yr-1 of gross emissions from land-use and environmental disturbances and adding substantially (0.23-0.88 PgC yr-1) to the global carbon stocks. Gross emissions and removals in the tropics were four times larger than temperate and boreal ecosystems combined. Although live biomass is responsible for more than 80% of gross terrestrial fluxes, soil, dead organic matter, and lateral transport may play important roles in terrestrial carbon sink.

A collection of field data from four agricultural sites in the Rur catchment in Western Germany collected in the frame of the Transregional Collaborative Research Centre 32 “Patterns in Soil-Vegetation-Atmosphere-Systems: Monitoring, Modelling and Data Assimilation” (TR32). The dataset includes data on vegetation (states and fluxes), weather, soil, and agricultural management. Vegetation-related data comprises fresh and dry biomass (green and brown, predominantly per organ), plant height, green and brown leaf area index, phenological development state, nitrogen and carbon content, and carbon-, energy- and water-fluxes for a variety of agricultural plants. In addition, masses of harvest residues and regrowth of vegetation after harvest or before planting of the main crop are included. Data on agricultural management includes sowing and harvest dates, and information on cultivation, fertilization and agrochemicals. The dataset also includes gap-filled weather data and soil parameters (particle size distributions, carbon and nitrogen contents). This data can be useful for development and validation of remote sensing products. A detailed description of the dataset can be found in Reichenau et al. (2020).

We present in depth data of the sea urchin population in the Arctic Porsangerfjord. Sea urchins were collected by hand from randomly chosen square meters of two present habitats: a sea urchin barren, characterized by stony ground without major algae growth, and the neighbouring kelp forest with a healthy macroalgae community. Both habitats are located near the shoreline of a bay in the Porsangerfjord, called the Holmfjord, and sampling took place in less than 1 m depth at low tide. Sampling was conducted in two consecutive years, summer 2022 and summer 2023. Data collected from the sea urchins includes the test-diameter, freshmass, species and morphotype.

Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. This dataset features a detailed map of material stocks for the whole of Germany on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Temporal extent The map is representative for ca. 2018. Data format Per federal state, the data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. The dataset features area and mass for different street types area and mass for different rail types area and mass for other infrastructure area, volume and mass for different building types Masses are reported as total values, and per material category. Units area in m² height in m volume in m³ mass in t for infrastructure and buildings Further information For further information, please see the publication or contact Helmut Haberl (helmut.haberl@boku.ac.at). A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Publication Haberl, H., Wiedenhofer, D., Schug, F., Frantz, D., Virág, D., Plutzar, C., Gruhler, K., Lederer, J., Schiller, G. , Fishman, T., Lanau, M., Gattringer, A., Kemper, T., Liu, G., Tanikawa, H., van der Linden, S., Hostert, P. (accepted): High-resolution maps of material stocks in buildings and infrastructures in Austria and Germany. Environmental Science & Technology Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). ML and GL acknowledge funding by the Independent Research Fund Denmark (CityWeight, 6111-00555B), ML thanks the Engineering and Physical Sciences Research Council (EPSRC; project Multi-Scale, Circular Economic Potential of Non-Residential Building Scale, EP/S029273/1), JL acknowledges funding by the Vienna Science and Technology Fund (WWTF), project ESR17-067, TF acknowledges the Israel Science Foundation grant no. 2706/19.

With respect to the ecological relevance of endpoints, biomass as an endpoint might be promising regarding ecotoxicological assessments of benthic communities. In a freshwater microcosm study the effect of two cadmium (Cd) concentrations (50 and 400 mg Cd kg(-1) dw) on biomass and abundance of a benthic community were investigated over a period of seven months. Specifically, the sensitivity of both endpoints in distinguishing differential effects was compared. While bacteria were found to be unaffected by Cd, abundance and biomass of protozoans and metazoans decreased. In a short-term comparison, differences between control and Cd treatments were, overall, more pronounced for flagellate biomass and for metazoan abundance with strong differences between the taxonomic groups; furthermore, over the long-term, the differences among organisms and endpoints changed. Based on toxicant sensitivity, the reasonably low variance of the data and the workload involved, biomass can provide a useful additional endpoint in microcosm studies.