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  • This service visualizes land system archetypes data. Land use is a key driver of global environmental change. Unless major shifts in consumptive behaviours occur, land-based production will have to increase drastically to meet future demands for food and other commodities. To better understand the drivers and impacts of agricultural intensification, identifying global, archetypical patterns of land systems is needed. However, current approaches focus on broad-scale representations of dominant land cover with limited consideration of land-use intensity. In this study, we derived a new global representation of land systems based on more than 30 high-resolution datasets on land-use intensity, environmental conditions and socioeconomic indicators. Using a self-organizing map algorithm, we identified and mapped twelve archetypes of land systems for the year 2005. Our analysis reveals unexpected similarities in land systems across the globe but the diverse pattern at sub-national scales implies that there are no one-size-fits-all solutions to sustainable land management. Our results help to identify generic patterns of land pressures and environmental threats and provide means to target regionalized strategies to cope with the challenges of global change. Mapping global archetypes of land systems represents a first step towards better understanding the driving forces and environmental and social outcomes of land system dynamics.

  • THIS METADATA-RECORD IS CURRENTLY UNDER DEVELOPMENT

  • Based on Sentinel-2A imageries from 2016, the map shows a detailed classification of Germany's arable land, distinguishing 19 land-use classes. "Forests", "Waters", "Urban" and "Other Vegetation" are predefined classes and were not classified in this study. The map is the result of a highly automated adaptable approach for pixel-based compositing and classification, called APiC. The novel APiC concept is designed for the use of high-resolution spatio-temporal space-borne data. Within the scope of extensive land-use mapping we define composite periods purely data-driven based on cloud-free training pixels. Since these adapted periods (APs) are not static nor dependent on expert knowledge, they support the creation of training pixel composites (TPCs) in regions with different weather conditions, species composition and phenological behaviour. We aimed to maximize the AP number within the classification year in order to best represent land-use phenology. To account for the cloud cover distribution of all pixels to be classified, over 10,000 predictive models were needed to create this map. Multiple classification models allow to contrast the classified land-use with the associated model performance at pixel level. Due to different climatic and geomorphological gradients in Germany that have an impact on plant phenology, six landscape regions have been independently classified. The individual classification results have been combined for the map shown.

  • This service provides cartographic visualization for global pollination benefits. Global pollination benefits for the year 2000. This dataset sums total and crop values for different plants. Temporal extent and resolution: 2000-01-01 to 2000-12-31, annual.

  • Visualization of data for Figure 2 from Dittrich, Andreas, Ralf Seppelt, Tomáš Václavík, and Anna F. Cord. 2017. “Integrating Ecosystem Service Bundles and Socio-Environmental Conditions – A National Scale Analysis from Germany.” Ecosystem Services 28: 273–82. doi.org/10.1016/j.ecoser.2017.08.007

  • This service provides cartographic visualization for Agricultural Land Evaluation / Start of the (first) growing cycle. Start of the growing cycle at a spatial resolution of 30 arc seconds, considering rain-fed conditions and irrigation on currently irrigated areas. In case of multiple cropping, the start of the first growing cycle is shown. The service contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100). Unit: Calendar Week. Temporal resolution: 30 year period.

  • This service provides cartographic visualization for Agricultural Land Evaluation / Agricultural Suitability. Agricultural suitability at a spatial resolution of 30 arc seconds, considering rain-fed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of the considered 16 plants. The service contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100). Unit: Suitability. Temporal resolution: 30 year period.

  • This service provides cartographic visualization for Agricultural Land Evaluation / Multiple Cropping All plants. Potential number of suitable crop cycles at a spatial resolution of 30 arc seconds, considering rain-fed conditions and irrigation on currently irrigated areas. The service contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100). Unit: Crop Cycles / Year. Temporal resolution: 30 year period.

  • This service provides cartographic visualization for Agricultural Land Evaluation / Agricultural Suitability. Agricultural suitability at a spatial resolution of 30 arc seconds, considering rain-fed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of the considered 16 plants. The service contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100). Unit: Suitability. Temporal resolution: 30 year period.

  • This service provides cartographic visualization for Agricultural Land Evaluation / Agricultural Suitability Change between 1981-2010 and 2071-2100. Change in agricultural suitability between 1981-2010 and 2071-2100 at a spatial resolution of 30 arc seconds, considering rain-fed conditions and irrigation on currently irrigated areas. Unit: Suitability Change. Temporal resolution: 30 year period.