layer_ID,layer_name,layer_paituli,dataset_name,dataset_description,default_interpolation,dataset_unit,dataset_authors,dataset_year,dataset_rest,dataset_intext,dataset_url
1_1,MINING_AREA,paituli__hy_spectre_1_1_mining_area,Renewable energy production will exacerbate mining threats to biodiversity,"Mining density based on the number of known mining properties (pre-operational, operational, and closed) in a 50-cell radius (1x1 km cells).",nn,[NA],"Sonter, L. J., Dade, M. C., Watson, J. E., & Valenta, R. K.",2020,"Renewable energy production will exacerbate mining threats to biodiversity. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-17928-5",Sonter et al.,https://doi.org/10.1038/s41467-020-17928-5
1_2,HAZARD_POTENTIAL,paituli__hy_spectre_1_2_hazard_potential,Natural Disaster Hotspots,"Number of significant hazards (earthquakes, volcanoes, landslides, floods, drought, cyclones) potentially affecting cells based on hazard frequency data. ",nn,[NA],SEDAC,2005,"Global Multihazard Frequency and Distribution, v1: Natural Disaster Hotspots. NASA Socioeconomic Data and Applications Center. https://doi.org/10.7927/H45718Z5",SEDAC,https://doi.org/10.7927/H45718Z5
1_3,HUMAN_DENSITY,paituli__hy_spectre_1_3_human_density,Global Human Settlement Layer,Continuous metric of population density.,bilinear,[persons/km^2],"Schiavina, M., Freire, S., & MacManus, K.",2019,"GHS-pop R2019A - GHS population grid multitemporal (1975-1990-2000-2015). European Commission, Joint Research Centre (JRC). https://doi.org/10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218",Schiavina et al.,https://doi.org/10.2905/0C6B9751-A71F-4062-830B-43C9F432370F
1_4,BUILT_AREA,paituli__hy_spectre_1_4_built_area,Global Human Settlement Layer,Percentage metric indicating the built-up presence.,bilinear,[%],"Corbane, C., Florczyk, A., Pesaresi, M., Politis, P. & Syrris, V.",2018,"GHS built-up grid, derived from Landsat, multitemporal (1975-1990-2000-2014). European Commission, Joint Research Centre (JRC). https://doi.org/10.2905/jrc-ghsl-10007",Corbane et al.,https://doi.org/10.2905/jrc-ghsl-10007
1_5,ROAD_DENSITY,paituli__hy_spectre_1_5_road_density,"GRIP, Global Roads Inventory Project (Global patterns of current and future road infrastructure)",Continuous metric of road density.,bilinear,[m/km^2],"Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. and Schipper, A. M.",2018,"Global patterns of current and future road infrastructure. Environmental Research Letters, 13(6), p.064006. https://doi.org/10.1088/1748-9326/aabd42",Meijer et al.,https://doi.org/10.1088/1748-9326/aabd42
1_6,FOOTPRINT_PERC,paituli__hy_spectre_1_6_footprint_perc,Global Human Footprint,Percentage metric indicating anthropogenic impacts on the environment.,bilinear,[%],SEDAC,2005,"Global Human Footprint (Geographic), v2 (1995-2004): Last of the Wild. NASA Socioeconomic Data and Applications Center. https://doi.org/10.7927/H4M61H5F",SEDAC,https://doi.org/10.7927/H4M61H5F
1_7,IMPACT_AREA,paituli__hy_spectre_1_7_impact_area,Low Impact Area,"Classification of land into very low impact areas (1), low impact areas (2) and non-low impact areas (3) based on global data on human impacts including human population, livestock density, forest change, land cover and nighttime lights. ",nn,[NA],"Jacobson, A. P., Riggio, J., Tait, A. M. & Baillie, J. E. M.",2019,"Global areas of low human impact (""Low Impact Areas"") and fragmentation of the natural world. Dryad. https://doi.org/10.5061/dryad.z612jm67g",Jacobson et al.,https://doi.org/10.5061/dryad.z612jm67g
1_8,MODIF_AREA,paituli__hy_spectre_1_8_modif_area,Managing the middle: A shift in conservation priorities based on the global human modification gradient,"A continuous metric between 0 and 1 that reflects the proportion of a landscape that has been modified by collating remote sensing imagery and ground inventory data from 1980 to 2016 (with a median year of 2016) on a group of 5 anthropogenic stressors: 1) human settlement (population density & built‐up areas), 2) agriculture (cropland & livestock), 3) transportation (major roads, minor roads, two tracks (a kind of two direction dirt road created through pedestrian and vehicle passage) & railroads), 4) mining and energy production (mining, oil wells & wind turbines) and 5) electrical infrastructure (powerlines, nighttime lights).",bilinear,[NA],"Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S., & Kiesecker, J.",2019,"Managing the middle: A shift in conservation priorities based on the global human modification gradient. Global Change Biology, 25(3), 811-826. https://doi.org/10.1111/gcb.14549",Kennedy et al.,https://doi.org/10.1111/gcb.14549
1_9,HUMAN_BIOMES,paituli__hy_spectre_1_9_human_biomes,Putting people in the map: anthropogenic biomes of the world,"Classification of land cover into different anthropogenic biomes of differing pressure (0 to 5), wildlands (0), forested (1), rangeland (2), croplands (3), villages (4) and dense settlements (5).",nn,[NA],"Ellis, E.C. & Ramankutty, N.",2008,"Putting people in the map:  anthropogenic biomes of the world.  Frontiers in Ecology and the Environment, 6(8): 439-447. DOI: https://doi.org/10.1890/070062",Ellis & Ramankutty,https://doi.org/10.1890/070062
1_10,FIRE_OCCUR,paituli__hy_spectre_1_10_fire_occur,Global fire atlas,Continuous metric of mean fire occurrence during the years of 2006 and 2016.,bilinear,[NA],"Andela, N., Morton, D. C., Giglio, L., Paugam, R., Chen, Y., Hantson, S., van der Werf, G. R., & Randerson, J. T.",2019,"The global fire atlas of individual fire size, duration, speed and direction. Earth System Science Data, 11(2), 529-552. https://doi.org/10.5194/essd-11-529-2019",Andela et al.,https://doi.org/10.5194/essd-11-529-2019
2_1,CROP_PERC_UNI,paituli__hy_spectre_1_11_crop_perc_uni,A Unified Cropland Layer at 250 m for Global Agriculture Monitoring ,Percentage metric indicating the proportion of cropland in each cell.,bilinear,[%],"Waldner, F., Fritz, S., Di Gregorio, A., Plotnikov, D., Bartalev, S., Kussul, N., Gong, P., Thenkabail, P., Hazeu, G., Klein, I., Low, F., Miettinen, J., Dadhwal, V. K., Lamarche, C., Bontemps, S., & Defourny, P.",2016,"A unified cropland layer at 250 m for Global Agriculture Monitoring. Data, 1(1), 3. https://doi.org/10.3390/data1010003",Waldner et al.,https://doi.org/10.3390/data1010003
2_2,CROP_PERC_IIASA,paituli__hy_spectre_1_12_crop_perc_iiasa,IIASA-IFPRI Cropland (Mapping global cropland and field size),Percentage metric indicating the proportion of cropland in each cell.,bilinear,[%],"Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E., Duerauer, M., Albrecht, F., Schill, C., Perger, C., Havlik, P., Mosnier, A., Thornton, P., Wood?Sichra, U., Herrero, M., Becker?Reshef, I., Justice, C., Hansen, M., Gong, P., Abdel Aziz, S., Cipriani, A., Cumani, R., Cecchi, G., Conchedda, G., Ferreira, S., Gomez, A., Haffani, M., Kayitakire, F., Malanding, J., Mueller, R., Newby, T., Nonguierma, A., Olusegun, A., Ortner, S., Rajak, D. R., Rocha, J., Schepaschenko, D., Schepaschenko, M., Terekhov, A., Tiangwa, A., Vancutsem, C., Vintrou, E., Wenbin, W., van der Velde, M., Dunwoody, A., Kraxner, F. & Obersteiner, M.",2015,"Mapping global cropland and field size. Global Change Biology, 21(5), pp.1980-1992. DOI: https://doi.org/10.1111/gcb.12838",Fritz et al.,https://doi.org/10.1111/gcb.12838
2_3,LIVESTOCK_MASS,paituli__hy_spectre_1_13_livestock_mass,Gridded Livestock of the World (Mapping the Global Distribution of Livestock),Estimated total amount of livestock wet biomass based on global livestock head counts.,bilinear,[kg],"Robinson, T. P., Wint, G. R. W., Conchedda, G., Van Boeckel, T. P., Ercoli, V., Palamara, E., Cinardi, G., D'Aietti, L., Hay, S. I., & Gilbert, M.",2014,"Mapping the global distribution of Livestock. PLoS ONE, 9(5). https://doi.org/10.1371/journal.pone.0096084",Robinson et al.,https://doi.org/10.1371/journal.pone.0096084
2_4,FOREST_LOSS_PERC,paituli__hy_spectre_2_1_forest_loss_perc,New global forest/non-forest maps from ALOS PALSAR data (2007-2010),Continuous -100 to 100 metric of forest tree cover loss between 2007 and 2017.,bilinear,[proportion of tree cover],"Shimada, M., Itoh, T., Motooka, T., Watanabe, M., Shiraishi, T., Thapa, R., & Lucas, R.",2014,"New Global Forest/non-forest maps from Alos Palsar data (2007-2010). Remote Sensing of Environment, 155, 13-31. https://doi.org/10.1016/j.rse.2014.04.014",Shimada et al.,https://doi.org/10.1016/j.rse.2014.04.014
2_5,FOREST_TREND,paituli__hy_spectre_2_2_forest_trend,Global Forest Watch,"Classification metric of 0 (no loss) or a discrete value from 1 to a potential maximum of 19, representing loss (a stand-replacement disturbance or change from a forest to non-forest state) detected primarily in the year 2001–2019, respectively.",nn,[NA],Global Forest Watch,2014,World Resources Institute.,Global Forest Watch,http://www.globalforestwatch.org/
3_1,LIGHT_MCDM2,paituli__hy_spectre_3_1_light_mcdm2,Atlas of Artificial Night Sky Brightness,Continuous simulated zenith radiance data.,bilinear,[mcd/m^2],"Falchi, F., Cinzano, P., Duriscoe, D., Kyba, C. C. M., Elvidge, C. D., Baugh, K., Portnov, B. A., Rybnikova, N. A. & Furgoni, R.",2016,"The new world atlas of artificial night sky brightness. Science Advances, 2(6). https://doi.org/10.1126/sciadv.1600377",Falchi et al.,https://doi.org/10.1126/sciadv.1600377
5_1,TEMP_TRENDS,paituli__hy_spectre_5_1_temp_trends,MODIS Land Surface Temperature,"Continuous metric of temperature trends, based on the linear regression coefficients of mean monthly temperature for the years of 1950 to 2019.",bilinear,[NA],"Wan, Z., Hook, S., Hulley, G.",2015,MOD11C3 MODIS/Terra Land Surface Temperature/Emissivity Monthly L3 Global 0.05Deg CMG V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD11C3.006,Wan et al.,https://doi.org/10.5067/MODIS/MOD11C3.006
5_2,TEMP_SIGNIF,paituli__hy_spectre_5_2_temp_signif,MODIS Land Surface Temperature,"Continuous metric of temperature trend significance, the temperature trends divided by its standard error.",bilinear,[NA],"Wan, Z., Hook, S., Hulley, G.",2015,MOD11C3 MODIS/Terra Land Surface Temperature/Emissivity Monthly L3 Global 0.05Deg CMG V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD11C3.006,Wan et al.,https://doi.org/10.5067/MODIS/MOD11C3.006
5_3,CLIM_EXTREME,paituli__hy_spectre_5_3_clim_extreme,TerraClimate,Continuous metric calculated as whatever is the largest of the absolute of the trend coefficients of the months with the lowest or highest mean temperatures.,bilinear,[NA],"Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. and Hegewisch, K. C.",2018,"TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific Data, 5. https://doi.org/10.1038/sdata.2017.191",Abatzoglou et al.,https://doi.org/10.1038/sdata.2017.191
5_4,CLIM_VELOCITY,paituli__hy_spectre_5_4_clim_velocity,MODIS Land Surface Temperature,"Continuous metric of the velocity of climate change, the ratio between TEMP_TRENDS and a local spatial gradient in mean temperature calculated as the slope of a plane fitted to the values of a 3x3 cell neighbourhood centered on each pixel.",bilinear,[NA],"Wan, Z., Hook, S., Hulley, G.",2015,MOD11C3 MODIS/Terra Land Surface Temperature/Emissivity Monthly L3 Global 0.05Deg CMG V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD11C3.006,Wan et al.,https://doi.org/10.5067/MODIS/MOD11C3.006
5_5,ARIDITY_TREND,paituli__hy_spectre_5_5_aridity_trend,TerraClimate,"Continuous metric of aridity trends, based on the linear regression coefficients of aridity for the years of 1990 to 2019, i.e: MPET/(MPRE+1).",bilinear,[NA],"Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. and Hegewisch, K. C.",2018,"TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific Data, 5. https://doi.org/10.1038/sdata.2017.191",Abatzoglou et al.,https://doi.org/10.1038/sdata.2017.191
