Crowdsourcing LUCAS: Data set from the 2018 FotoQuest Go Europe campaign

The 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

Supporting information for: Climate warming from managed grasslands cancels the cooling effect of carbon sinks in sparsely grazed and natural grasslands

Grasslands 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

Crowdsourcing deforestation in the tropics during the last decade: Data sets from the “Driver of Tropical Forest Loss” Geo-Wiki campaign

The 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

Gridded Soil Surface Nitrogen Surplus on Grazing and Agricultural Land: Impact of Land Use Maps

Excess N application on agricultural land greatly impacts the environment in multiple ways, driven by population growth and improving quality of human diets. Therefore, it is essential to quantify the sources of the emissions of N compounds and their determinants (e.g. biological N fixation (BNF), mineral fertilizer, manure N and N deposition) to develop adequate mitigation measures. Here aim at comprehensively mapping and quantifying N fluxes on agricultural land to analyze these sources on different scales. As underlying grazing land maps used for such calculations fairly different in terms of methodology and definition and thus spatial extent and pattern, we investigate how this diversity in grazing land maps affects quantification of N indicators. We compared three different global grazing land maps and analyzed the propagation of differences to discrepancies in N indicators calculated from them. We discovered that (i) area differences propagated to high discrepancies in N surplus mostly in Asia, and to a minor extent also in Europe and Northern Africa. (ii) A more inclusive definition of grazing land results in overall less N surplus given the larger areas included but allows to provide a more comprehensive estimate of the influence of human activity on the N cycle. (iii) BNF was identified as constituting an important translator for differences on grazing land to N indicators, while also being a source of further uncertainty, which warrants further scrutiny. This study is the first to provide an in-depth analysis of the effect of grazing land and agricultural land area differences on various N budget terms and N indicator calculation, highlighting opportunities for further research, and the importance of a comprehensive accounting of N surplus when using an inclusive definition of grazing land

Dataset for "Household contributions to and impacts from air pollution in India”

This 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

A Crowdsourced Global Data Set for Validating Built-up Surface Layers

This 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

Data for: A map of the extent and year of detection of oil palm plantations in Indonesia, Malaysia and Thailand

The dataset consists of nine tiles of 16-bit GeoTIFF at a resolution of 30 m with a single attribute value, i.e., the year in which the oil palm plantation was first detected. At this point the plantation is 2 to 3 year of age. The data values range from 0 to 37 where 0 is no data and subsequent numbers run through until 2017, the last year of our analysis. A value of 4 corresponds to the year 1984, the first year oil palm was detected and 37 corresponds to 2017. Values 1 to 3 are not present

Supplementary data for: Global Gridded Nitrogen Indicators: Influence of Crop Maps

Displaying Nitrogen (N) indicators on a global grid poses unique opportunities to quantify environmental impacts from N application in different world regions under a variety of conditions. Such calculations require the use of maps showing the geo-spatial distribution of crop production. Although there are several crop maps in the scientific literature to choose from, the consequences of this choice for the calculation of N indicators still need to be evaluated. In this study we analyze the differences in results for NUE and N surplus calculated on the global scale using two different crop maps (SPAM and M3). For our calculations we used publicly available statistical and literature data combined with each crop map and carefully traced the origins of the differences in the results. Our results showed that the regions most affected by discrepancies caused by differences in crop maps (yields and physical area) are Central Asia and the Russian Federation, Australia and Oceania, and North Africa. However, we also found that the inclusion or exclusion of grass crops influences the results, as does the aggregation of crops to categories. Considering all these differences, we note that M3 seems to provide the more plausible results for the calculation of N indicators. Our analysis not only highlights the importance of determining the critical parameters for N indicator calculation, but also allows key parameters connected with N use and overuse to be identified on the global scale. This dataset contains: All gridded N input and output data (provided in netCDF format and includes a description of the calculation) Gridded results of N surplus based on SPAM and M3 (provided in netCDF format and includes a description of the calculation) NUE per country (comparison of data by Leip et al., 2009, Lassaletta et al., 2014 and variations of our SPAM and M3 based calculations) N content per crop and their sources for all crops in M3 and SPAM Maps of N surplus for the SPAM based calculation and the M3 based calculation Supplementary Informatio

Soil respiration database

Soil respiration (Rs) in situ measurements that were reported in peer-reviewed publications were collected into a database. The database contains annual Rs flux, share of autotrophic Rs, climate parameters, type of soil and vegetation. 3881 records on Rs fluxes around the globe were collected from 944 studies, spanning the measurement years 1961-2019. The largest portion of data was taken from the global database by Bond-Lamberty and Thomson (2014, 2018). We have taken from this database only the records where annual Rs flux or mean seasonal rate of Rs or root contribution to the Rs were reported. Data from additional 302 sources were collected on the same basis, from the northern hemisphere, with a special focus on Russia. The regions most frequently represented are Northern America (n=1835), Europe (n=1171) and Asia (n=872). Data from temperate ecosystems dominate in the database (n=1833), boreal zone is represented by 958 records, subtropical and tropical biomes are represented by 462 and 628 records accordingly. The most data collected in forests (n=2510), grasslands (n=520) or arable land (n=519)

Global population and human capital projections for Shared Socioeconomic Pathways – 2015 to 2100, Revision-2018

The data contains five-sets of Shared Socioeconomic Pathway scenarios results. It consists of population projection by educational attainment and mean years of schooling by age and sex in 201 countries of the world for the period of 2015-2100. This is an update of earlier SSP population projection published in 2013 (SSP Database) and 2014 (IIASA-WIC Data explorer)