This file provides the pan-tropical biomass map published by Avitabile et al. (2016) "An integrated pan-tropical biomass map using multiple reference datasets". The data shows the aboveground biomass in Mg per ha in the tropic region at approximately 1 km resolution. For a proper use and description of this dataset, please refer to the mentioned article. Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N. J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J., Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry, S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R., Verbeeck, H., Wijaya, A. and Willcock, S. (2016), An integrated pan-tropical biomass map using multiple reference datasets. Glob Change Biol, 22: 1406–1420. doi:10.1111/gcb.13139

The GHSL Landsat is a spatial raster dataset that is mapping human settlements globally based on the Landsat satellite data collection. The GHSL Landsat uses the Global Land Survey (GLS) collection of Landsat imagery, which is a carefully coordinated collection of high resolution imagery for global modelling and is produced by the Global Land Cover Facility (www.landcover.org). This allows the mapping of settlements back in time until the year 1975. In addition, Landsat GHSL uses recent Landsat-8 from 2013/2014 for the latest coverage. GHS BUILT-UP GRID These data contain a multitemporal information layer on built-up presence as derived from Landsat image collections (GLS1975, GLS1990, GLS2000, and ad-hoc Landsat 8 collection 2013/2014). The data have been produced by means of Global Human Settlement Layer methodology in 2015. 250m of resolution - World Mollweide (EPSG:54009)

The GHSL Landsat is a spatial raster dataset that is mapping human settlements globally based on the Landsat satellite data collection. The GHSL Landsat uses the Global Land Survey (GLS) collection of Landsat imagery, which is a carefully coordinated collection of high resolution imagery for global modelling and is produced by the Global Land Cover Facility (www.landcover.org). This allows the mapping of settlements back in time until the year 1975. In addition, Landsat GHSL uses recent Landsat-8 from 2013/2014 for the latest coverage. GHS BUILT-UP GRID These data contain a multitemporal information layer on built-up presence as derived from Landsat image collections (GLS1975, GLS1990, GLS2000, and ad-hoc Landsat 8 collection 2013/2014). The data have been produced by means of Global Human Settlement Layer methodology in 2015. 250m of resolution - World Mollweide (EPSG:54009)

A global map of built-up presence derived from backscattered information of Sentinel1 images. Both the GHS BUILT-UP GRID (LDS) as derived from Landsat image collections and the GlobeLand30 (GLC30) were used for training of the Symbolic Machine Learning (SML) classifier. 20m of resolution - Spherical Mercator (EPSG:3857) Dataset name (size): GHS_BUILT_S12016NODSM_GLOBE_R2016A_3857_20 Legend: 0 = no built-up 1 = built-up

The GHSL Landsat is a spatial raster dataset that is mapping human settlements globally based on the Landsat satellite data collection. The GHSL Landsat uses the Global Land Survey (GLS) collection of Landsat imagery, which is a carefully coordinated collection of high resolution imagery for global modelling and is produced by the Global Land Cover Facility (www.landcover.org). This allows the mapping of settlements back in time until the year 1975. In addition, Landsat GHSL uses recent Landsat-8 from 2013/2014 for the latest coverage. The aggregated set has the coordinate Reference System: Spherical Mercator (EPSG:3857) and the spatial resolution of 38 m. This data is provided as single GEOTIFF file.

This file provides the global biomass map produced with the EU FP7 GEOCARBON project (www.geocarbon.net) and presented by Avitabile et al. (2014) at the Global Vegetation Monitoring and Modeling, 3-7 February 2014, Avignon (France). The map is obtained by combining and harmonizing the pan-tropical biomass map by Avitabile et al. (2016) with the boreal forest biomass map by Santoro et al. (2015). The map covers only forest areas, where forest are defined as areas with dominance of tree cover in the GLC2000 map (Bartholomé and Belward, 2005). For a proper use and description of this dataset, please refer to the mentioned articles. Source: Avitabile, V., Herold, M., Lewis, S.L., Phillips, O.L., Aguilar-Amuchastegui, N., Asner, G. P., Brienen, R.J.W., DeVries, B., Cazzolla Gatti, R., Feldpausch, T.R., Girardin, C., de Jong, B., Kearsley, E., Klop, E., Lin, X., Lindsell, J., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A.,  Mitchard, E., Pandey, D., Piao, S., Ryan, C., Sales, M., Santoro, M., Vaglio Laurin, G., Valentini, R., Verbeeck, H., Wijaya, A., Willcock, S., 2014. Comparative analysis and fusion for improved global biomass mapping.  Global Vegetation Monitoring and Modeling, 3 – 7 February 2014, Avignon (France) (https://colloque.inra.fr/gv2m) Based on data from: Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N. J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J., Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry, S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R., Verbeeck, H., Wijaya, A. and Willcock, S. (2016), An integrated pan-tropical biomass map using multiple reference datasets. Glob Change Biol, 22: 1406–1420. doi:10.1111/gcb.13139 Santoro, M., Beaudoin, A., Beer, C., Cartus, O., Fransson, J.E.S., Hall, R.J., Pathe, C., Schmullius, C., Schepaschenko, D., Shvidenko, A., Thurner, M. and Wegmüller, U. (2015). Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR. Remote Sensing of Environment, Vol. 168, pag. 316-334 Source: Avitabile V, Herold M, Heuvelink G, Lewis SL, Phillips OL, Asner GP et al. (2016). An integrated pan-tropical biomass maps using multiple reference datasets. Global Change Biology, 22: 1406–1420. doi:10.1111/gcb.13139.