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A Crowdsourced Global Data Set for Validating Built-up Surface Layers
No full textThis collection contains data that were collected during a crowdsourcing campaign using Geo-Wiki (https://www.geo-wiki.org/). The campaign involved visual interpretation of a sample that is designed for validating any existing global built-up surface product. A zipped shapefile (ValidationGrids.zip) contains the random stratified sample of 50K locations, which consist of 80x80m grids further sub-divided into 10m cells so there are 64 cells per grid. These locations were provided to the crowd, who used very high-resolution satellite images to label the grids as built-up (i.e., containing a building), non-built-up or unsure. The file (Geo-WikiBuilt-upCentroidsAll.csv) contains the data collected in the campaign summarized by the centroid (or central point of each 80m grid location). The data collected for all 64 cells per grid can be found in Geo-WikiBuilt-upCellsAll.csv. The Geo-Wiki campaign uses visually interpreted grid locations called control points as part of the scoring mechanism of Geo-Wiki for quality control. These control points are provided by centroid (Geo-WikiBuilt-upCentroidsControls.csv) and for all cells in the 80m grid (Geo-WikiBuilt-upCellsControls.csv). Finally, the file Strata.csv contains the mapping between the grid location and the sampling stratum used in the design of the sample
Crowdsourcing LUCAS: Data set from the 2018 FotoQuest Go Europe campaign
No full textThe following data set shows the results of the 2018 FotoQuest Go Europe crowdsourcing campaign. This land cover and land use change monitoring citizen science campaign ran during the summer of 2018 and asked participants across Europe to use the FotoQuest mobile application and visit specific locations across the continent. The locations were taken from the 2015 LUCAS campaign, a 3-year land use and land cover survey undertaken by EUROSTAT. The participants were awarded between 1 and 3 euros for every location visited and up to 30 euros in special challenge locations. There was also a feedback mechanism that allowed IIASA researchers to review the each submitted quest, approve or reject it, and send feedback to the participants. The campaign and results are described in detail in the Land journal paper “Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App” (Laso Bayas et al 2020) freely accessible here: https://doi.org/10.3390/land9110446 . The word file accompanying the data describes all variables contained in the FotoQuest Go Europe 2018 data set
Crowdsourcing deforestation in the tropics during the last decade: Data sets from the “Driver of Tropical Forest Loss” Geo-Wiki campaign
No full textThe data set is the result of the Drivers of Tropical Forest Loss crowdsourcing campaign. The campaign took place in December 2020. A total of 58 participants contributed validations of almost 120k locations worldwide. The locations were selected randomly from the Global Forest Watch tree loss layer (Hansen et al 2013), version 1.7. At each location the participants were asked to look at satellite imagery time series using a customized Geo-Wiki user interface and identify drivers of tropical forest loss during the years 2008 to 2019 following 3 steps: Step 1) Select the predominant driver of forest loss visible on a 1 km square (delimited by a blue bounding box); Step 2) Select any additional driver(s) of forest loss and; Step 3) Select if any roads, trails or buildings were visible in the 1 km bounding box. The Geo-Wiki campaign aims, rules and prizes offered to the participants in return for their work can be seen here: https://application.geo-wiki.org/Application/modules/drivers_forest_change/drivers_forest_change.html . The record contains 3 files: One “.csv” file with all the data collected by the participants during the crowdsourcing campaign (1158021 records); a second “.csv” file with the controls prepared by the experts at IIASA, used for scoring the participants (2001 unique locations, 6157 records) and a ”.docx” file describing all variables included in the two other files. A data descriptor paper explaining the mechanics of the campaign and describing in detail how the data was generated will be made available soon
Supporting information for: Climate warming from managed grasslands cancels the cooling effect of carbon sinks in sparsely grazed and natural grasslands
No full textGrasslands absorb and release carbon dioxide (CO2), emit methane (CH4) from grazing livestock and emit nitrous oxide (N2O) from soils. Little is known about how the fluxes of these three greenhouse gases, from managed and natural grasslands worldwide, have contributed to past climate change, or the roles of managed pastures versus natural grasslands. Here, global trends and regional patterns of the full greenhouse gas balance of grasslands are estimated for the period 1750 to 2012. A new spatially explicit land surface model is applied, to separate the direct effects of human activities from land management and the indirect effects from climate change, increasing CO2 and regional changes in nitrogen deposition. Direct human management activities are simulated to have caused grasslands to switch from a sink to a source of GHG, because of increased livestock numbers and accelerated conversion of natural lands to pasture. However, climate change drivers contributed a net carbon sink in soil organic matter, mainly from the increased productivity of grasslands due to increased CO2 and nitrogen deposition. The net radiative forcing of all grasslands is currently close to neutral, but has been increasing since the 1960s. Here, we show that the net global climate warming caused by managed grassland cancels the net climate cooling from carbon sinks in sparsely grazed and natural grasslands. In the face of future climate change and increased demand for livestock products, these findings highlight the need to use sustainable management to preserve and enhance soil carbon storage in grasslands and to reduce GHG emissions from managed grasslands. A full description of the files is available in the README of the dataset
Dataset for "Household contributions to and impacts from air pollution in India”
No full textThis Excel workbook contains the data used in the plotting of the figures in the manuscript titled "Household contributions to and impacts from air pollution in India” (to be) published in Nature Sustainability in 2021. Note that the final figure counter in the paper were shifted by one, thus the published Fig. 2 corresponds to the Fig 1 tab in the Excel sheets
Supplementary Material to Quantifying Memory and Persistence in the Atmosphere–Land/Ocean Carbon System
Full text linkThe Supplementary Material download gives access to
1. the Supplementary Information (SI) file; and
2. the Supplementary Data (SD) file
supporting the manuscript Quantifying memory and persistence in the atmosphere–land/ocean carbon system, submitted for publication as a research article in Earth Systems Dynamics (manuscript no. esd-2021-27).
The SI file is a PDF document. It combines ten sections which provide full explanations of the mathematics used in the manuscript or other useful information allowing to keep the manuscript short and readable. The ten sections are referred to as Supplementary Information 1 to Supplementary Information 10 in the manuscript.
The SD file is an Excel document. It consists of 16 worksheets which are referred to as Supplementary Data 1 to Supplementary Data 16 in the manuscript. The introductory worksheet (Supplementary Data Guide) provides a list of contents to guide a user through the 16 worksheets
Reconciling regional nitrogen boundaries with global food security
No full textThe dataset provides data supporting the above publication. It includes global and regional (10 broad regions) major agricultural consumption/production/nitrogen flows for the year 2000 to 2050 at regional level simulated by GLOBIOM under various scenarios described in the paper. They are 1) dietary energy and protein availability; 2) population at risk of hunger; 3) price index; 4) crop and animal production and consumption; 5) fertilizer demand, surplus and agricultural non-CO2 GHG emissions; 6) global trade of crop and animal products
A Crowdsourced Global Data Set for Validating Built-up Surface Layers V.2
No full textThis collection contains data that were collected during a crowdsourcing campaign using Geo-Wiki (https://www.geo-wiki.org/). The campaign involved visual interpretation of a sample that is designed for validating any existing global built-up surface product. A zipped shapefile (ValidationGrids.zip) contains the random stratified sample of 50K locations, which consist of 80x80m grids further sub-divided into 10m cells so there are 64 cells per grid. These locations were provided to the crowd, who used very high-resolution satellite images to label the grids as built-up (i.e., containing a building), non-built-up or unsure. The file (Geo-WikiBuilt-upCentroidsAll.csv) contains the data collected in the campaign summarized by the centroid (or central point of each 80m grid location). It also contains fields for quality control, one that indicates if the change information matches the control points (see below) or the majority answer from the crowd, and another that indicates whether the presence/absence of built-up matches the control points (see below) or the majority answer from the crowd. The data collected for all 64 cells per grid can be found in Geo-WikiBuilt-upCellsAll.csv. The Geo-Wiki campaign uses visually interpreted grid locations called control points as part of the scoring mechanism of Geo-Wiki for quality control. These control points are provided by centroid (Geo-WikiBuilt-upCentroidsControls.csv) and for all cells in the 80m grid (Geo-WikiBuilt-upCellsControls.csv). In addition to the raw data, two additional quality-controlled files have been produced. The first file (Geo-WikiBuilt-upCentroidsChangeQualityControlled.csv) provides a single record for each location on change in built-up (if built-up is present) that lists either the control point answer or the majority answer from the crowd. The second file (Geo-WikiBuilt-upCellsQualityControlled.csv) contains a single record for each of the 64 cells in each grid, listing either the control point answer or the majority answer from the crowd. Finally, the file Strata.csv contains the mapping between the grid location and the sampling stratum used in the design of the sample
Global cropland nitrogen flows for the year 2000 and 2010 simulated by GLOBIOM
No full textThe dataset provides data on global cropland nitrogen flows for the year 2000 and 2010 at country level simulated by GLOBIOM. The nitrogen flows include N fertilizer, N manure, N fixation, N deposition, and harvested N. GLOBIOM (Global Biosphere Management Model) is a global partial equilibrium model allocating land-based activities, i.e. management of cropland, livestock systems and forestry, under land availability constraints, to maximize the sum of producer and consumer surpluses. In a recent version, the N cycle in global agricultural systems, including cropland, pasture and livestock systems, and in related human food systems was implemented in GLOBIOM. All biomass flows in GLOBIOM were transformed into N flows, and further accounted for additional N flows, including crop residues, biological nitrogen fixation (BNF), manure and fertilizer application, atmospheric deposition and N losses through leaching and gaseous of NH3, NOx, N2O, and N2. A description of the variables and associated data sources is available in the spreadsheet named “BasicInfo"
The environmental challenge and trade implications of China’s future food demand
No full textThis Excel file contains the data used in the plotting of the figures in the manuscript titled "The environmental challenge and trade implications of China’s future food demand" to be published in Nature Sustainability in 2021. In the data file you can find 1) bilateral trade quantity, 2) domestic environmental impact, 3), virtual environmental trade flows, 4) sensitivity results of environmental impacts in 2050.