ALBERT

Description: The most comprehensive database of in situ destructive sampling measurements of forest biomass in Eurasia have been compiled from a combination of experiments undertaken by the authors and from scientific publications. Biomass is reported as five components: live trees (stem, bark, branches, foliage, roots); understory (above- and below ground); green forest floor (above- and below ground); and coarse woody debris (snags, logs, dead branches of living trees and dead roots), consisting of ca 10300 unique records of sample plots and ca 9600 sample trees from ca 1200 experiments for the period 1930-2014. Some components are better represented than others, e.g. stem wood compared to roots. The database also contains other forest stand parameters such as tree species composition, average age, tree height, growing stock volume, etc., when available. Such a database can be used for the development of models of biomass structure, biomass extension factors, the calibration of remotely sensed data, change detection in biomass structure, and the assessment of carbon pool and its dynamics, among many others.

Description: The concept of homogenous response units (HRU) was designed as a general concept for the delineation of basic spatial units. Only those characteristics of landscape, which are relatively stable over time (even under climate change) and largely unsusceptible to anthropogenic influence, were selected. The HRU can be seen as a basic spatial framework for the implementation of climate change and land management alternative scenarios into global modeling and therefore is a basic input for delineation of landscape units. HRUs are defined based on classifications of altitude (five classes: 1 (0 - 300m), 2 (300 - 600m), 3 (600 - 1100m), 4 (1100 - 2500m), 5 (〉 2500m)), slope (seven classes(degrees): 1 (0 - 3), 2 (3 - 6), 3 (6 - 10), 4 (10 - 15), 5 (15 - 30), 6 (30 - 50), 7 (〉 50)) and soil composition (five classes: 1 (sandy), 2 (loamy), 3 (clay), 4 (stony), 5 (peat)). e.g. HRU111 refers to Altitude class 1: 0-300m; Slope class 1: 0-3 degrees; and Soil class 1: sandy. Areas of non-soil are assigned 88. HRUs have a spatial resolution of approximately 10 km**2.

Description: Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation

Description: Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general.

Description: The dataset contains a cropland percent coverage map for Africa created through the combination of five existing land cover products: GLC-2000, MODIS Land Cover, GlobCover, MODIS Crop Likelihood and AfriCover. A synergy map was created in which the products are ranked by experts, which reflects the likelihood or probability that a given pixel is cropland. The cropland map was calibrated with national and sub-national crop statistics using a novel approach. Preliminary validation of the map was undertaken. The resulting cropland map has an accuracy of 83%, which is higher than the accuracy of any of the individual maps. The cropland percent coverage map for Africa is available for overlay on Google Earth or for download at http://agriculture.geo-wiki.org.

Description: This data set provides spatially explicit estimates of the area directly used for surface mining on a global scale. It contains more than 21,000 polygons of activities related to mining, mainly of coal and metal ores. Several data sources were compiled to identify the approximate location of mines active at any time between the years 2000 to 2017. This data set does not cover all existing mining locations across the globe. The polygons were delineated by experts using Sentinel-2 cloudless (https://s2maps.eu by EOX IT Services GmbH (contains modified Copernicus Sentinel data 2017 & 2018)) and very high-resolution satellite images available from Google Satellite and Bing Imagery. The derived polygons cover the direct land used by mining activities, including open cuts, tailing dams, waste rock dumps, water ponds, and processing infrastructure. The main data set consists of a GeoPackage (GPKG) file, including the following variables: ISO3_CODE〈string〉, COUNTRY_NAME〈string〉, AREA〈double〉 in squared kilometres, FID〈integer〉 with the feature ID, and geom〈polygon〉 in geographical coordinates WGS84. The summary of the mining area per country is available in comma-separated values (CSV) file, including the following variables: ISO3_CODE〈string〉, COUNTRY_NAME〈string〉, AREA〈double〉 in squared kilometers, and N_FEATURES〈integer〉 number of mapped features. Grid data sets with the mining area per cell were derived from the polygons. The grid data is available at 30 arc-second resolution (approximately 1x1 km at the equator), 5 arc-minute (approximately 10x10 km at the equator), and 30 arc-minute resolution (approximately 55x55 km at the equator). We performed an independent validation of the mining data set using control points. For that, we draw a 1,000 random samples stratified between two classes: mine and no-mine. The control points are also available as a GPKG file, including the variables: MAPPED〈string〉, REFERENCE〈string〉, FID〈integer〉 with the feature ID, and geom〈point〉 in geographical coordinates WGS84. The overall accuracy calculated from the control points was 88.4%, other accuracy metrics are shown below. Confusion Matrix and Statistics Reference Prediction Mine No-mine Mine 394 106 No-mine 10 490 Accuracy : 0.884 95% CI : (0.8625, 0.9032) No Information Rate : 0.596 P-Value [Acc 〉 NIR] : 〈 2.2e-16 Kappa : 0.768 Mcnemar's Test P-Value : 〈 2.2e-16 Sensitivity : 0.9752 Specificity : 0.8221 Pos Pred Value : 0.7880 Neg Pred Value : 0.9800 Precision : 0.7880 Recall : 0.9752 F1 : 0.8717 Prevalence : 0.4040 Detection Rate : 0.3940 Detection Prevalence : 0.5000 Balanced Accuracy : 0.8987 This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme grant number 725525 FINEPRINT project (https://www.fineprint.global/).

Description: This dataset updates the global-scale mining polygons (Version 1) available from https://doi.org/10.1594/PANGAEA.910894. It contains 44,929 polygon features, covering 101,583 km² of land used by the global mining industry, including large-scale and artisanal and small-scale mining. The polygons cover all ground features related to mining, .e.g open cuts, tailing dams, waste rock dumps, water ponds, processing infrastructure, and other land cover types related to the mining activities. The data was derived using a similar methodology as the first version by visual interpretation of satellite images. The study area was limited to a 10 km buffer around the 34,820 mining coordinates reported in the S&P metals and mining database. We digitalized the mining areas using the 2019 Sentinel-2 cloudless mosaic with 10 m spatial resolution (https://s2maps.eu by EOX IT Services GmbH - Contains modified Copernicus Sentinel data 2019). We also consulted Google Satellite and Microsoft Bing Imagery, but only as additional information to help identify land cover types linked to the mining activities. The main data set consists of a GeoPackage (GPKG) file, including the following variables: ISO3_CODE〈string〉, COUNTRY_NAME〈string〉, AREA〈double〉 in squared kilometres, FID〈integer〉 with the feature ID, and geom〈polygon〉 in geographical coordinates WGS84. The summary of the mining area per country is available in comma-separated values (CSV) file, including the following variables: ISO3_CODE〈string〉, COUNTRY_NAME〈string〉, AREA〈double〉 in squared kilometres, and N_FEATURES〈integer〉 number of mapped features. Grid data sets with the mining area per cell were derived from the polygons. The grid data is available at 30 arc-second resolution (approximately 1x1 km at the equator), 5 arc-minute (approximately 10x10 km at the equator), and 30 arc-minute resolution (approximately 55x55 km at the equator). We performed an independent validation of the mining data set using control points. For that, we draw 1,000 random samples stratified between two classes: mine and no-mine. The control points are also available as a GPKG file, including the variables: MAPPED〈string〉, REFERENCE〈string〉, FID〈integer〉 with the feature ID, and geom〈point〉 in geographical coordinates WGS84. The overall accuracy calculated from the control points was 88.3%, Kappa 0.77, F1 score 0.87, producer's accuracy of class mine 78.9 % and user's accuracy of class mine 97.2 %.