Material stock map of CONUS - Rocky Mountains

<p>Humanity&rsquo;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 &lsquo;anthropocene&rsquo;, as humans are &lsquo;overwhelming the great forces of nature&rsquo;. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed &lsquo;manufactured capital&rsquo;, &lsquo;technomass&rsquo;, &lsquo;human-made mass&rsquo;, &lsquo;in-use stocks&rsquo;&nbsp;or &lsquo;socioeconomic material stocks&rsquo;, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14&nbsp;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 &lsquo;real&rsquo; (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called &lsquo;built structures&rsquo;) represent the overwhelming majority of all socioeconomic material stocks.</p> <p>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.</p> <p><strong>Spatial extent</strong><br> This subdataset covers the <strong>Rocky Mountains CONUS</strong>, i.e.</p> <ul> <li>CO</li> <li>ID</li> <li>MT</li> <li>UT</li> <li>WY</li> </ul> <p>For the remaining CONUS, see the <em>related identifiers</em>.</p> <p><strong>Temporal extent</strong><br> The map is representative for ca. 2018.</p> <p><strong>Data format</strong><br> The data are organized by states.&nbsp;Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.</p> <p>Within each tile, images for area, volume, and mass at 10m spatial resolution are provided.&nbsp;Units are m&sup2;, m&sup3;, and t, respectively.&nbsp;Each metric is split into buildings, other, rail and street&nbsp;(note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure).&nbsp;Each category is further split into subcategories (e.g. building types).</p> <p>Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.</p> <ul> <li>t at 10m x 10m</li> <li>kt at 100m x 100m</li> <li>Mt at 1km x 1km</li> <li>Gt at 10km x 10km</li> </ul> <p>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.&nbsp;File paths are relative, i.e. DO NOT change the file structure or file naming.&nbsp;</p> <p>Additionally, the grand total mass per state is tabulated for each county in <em>mass_grand_total_t_10m2.tif.csv</em>.&nbsp;County FIPS code and the ID in this table can be related via <em>FIPS-dictionary_ENLOCALE.csv</em>.</p> <p><strong>Material layers</strong><br> Note that material-specific layers are not included in this repository because of upload limits.&nbsp;Only the totals are provided (i.e. the sum over all materials).&nbsp;However, these can easily be derived by re-applying the material intensity factors from (see <em>related identifiers</em>):</p> <p>A. Baumgart, D. Vir&aacute;g, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), <a href="https://doi.org/10.5281/zenodo.5045337.">doi:10.5281/zenodo.5045337.</a></p> <p><strong>Further information</strong><br> For further information, please see the publication.<br> A web-visualization of this dataset is available here.<br> Visit our&nbsp;<a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a>&nbsp;to learn more about our project MAT_STOCKS -&nbsp;Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Publication</strong><br> D.&nbsp;Frantz, F.&nbsp;Schug, D.&nbsp;Wiedenhofer, A. Baumgart, D.&nbsp;Vir&aacute;g, S.&nbsp;Cooper, C.&nbsp;Gomez-Medina,&nbsp;F.&nbsp;Lehmann, T.&nbsp;Udelhoven, S.&nbsp;van der Linden, P.&nbsp;Hostert, H.&nbsp;Haberl.&nbsp;Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. <em>In prep</em></p> <p><strong>Funding</strong><br> This research was primarly funded by&nbsp;the European Research Council (ERC) under the&nbsp;European Union&rsquo;s Horizon 2020 research and innovation programme (MAT_STOCKS, grant&nbsp;agreement No 741950).&nbsp;Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)&mdash;Project-ID 414984028-SFB 1404.</p> <p><strong>Acknowledgments</strong><br> We thank the European Space Agency and the European&nbsp;Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database;&nbsp;Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on&nbsp;<a href="https://eodc.eu/">EODC</a>&nbsp;- we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.</p>