{"type": "FeatureCollection", "features": [{"id": "10.1016/j.jenvman.2016.07.002", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-21T16:15:56Z", "type": "Journal Article", "created": "2016-08-05", "title": "The PRECOS framework: Measuring the impacts of the global changes on soils, water, agriculture on territories to better anticipate the future", "description": "In a context of increased land and natural resources scarcity, the possibilities for local authorities and stakeholders of anticipating evolutions or testing the impact of envisaged developments through scenario simulation are new challenges. PRECOS's approach integrates data pertaining to the fields of water and soil resources, agronomy, urbanization, land use and infrastructure etc. It is complemented by a socio-economic and regulatory analysis of the territory illustrating its constraints and stakes. A modular architecture articulates modeling software and spatial and temporal representations tools. It produces indicators in three core domains: soil degradation, water and soil resources and agricultural production. As a territory representative of numerous situations of the Mediterranean Basin (urban pressures, overconsumption of spaces, degradation of the milieus), a demonstration in the Crau's area (Southeast of France) has allowed to validate a prototype of the approach and to test its feasibility in a real life situation. Results on the Crau area have shown that, since the beginning of the 16th century, irrigated grasslands are the cornerstones of the anthropic-system, illustrating how successfully men's multi-secular efforts have maintained a balance between environment and local development. But today the ecosystem services are jeopardized firstly by urban sprawl and secondly by climate change. Pre-diagnosis in regions of Emilia-Romagna (Italy) and Valencia (Spain) show that local end-users and policy-makers are interested by this approach. The modularity of indicator calculations and the availability of geo-databases indicate that PRECOS may be up scaled in other socio-economic contexts.", "keywords": ["DYNAMICS", "Conservation of Natural Resources", "550", "330", "[SDE.MCG]Environmental Sciences/Global Changes", "CITY", "Climate Change", "0211 other engineering and technologies", "Environmental Sciences & Ecology", "CELLULAR-AUTOMATA", "02 engineering and technology", "URBAN", "01 natural sciences", "CROP MODEL", "Urban sprawl", "SUSTAINABILITY", "Soil", "Theoretical", "Models", "Water Supply", "MD Multidisciplinary", "11. Sustainability", "Climate change", "Humans", "Territory", "0105 earth and related environmental sciences", "2. Zero hunger", "Science & Technology", "STICS", "Water", "Agriculture", "REGIONAL-SCALE", "Models", " Theoretical", "15. 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We assess the potential for 40 practices to address these land challenges and find that: Nine options deliver medium to large benefits for all four land challenges. A further two options have no global estimates for adaptation, but have medium to large benefits for all other land challenges. Five options have large mitigation potential (&gt;3\uffc2\uffa0Gt CO2eq/year) without adverse impacts on the other land challenges. Five options have moderate mitigation potential, with no adverse impacts on the other land challenges. Sixteen practices have large adaptation potential (&gt;25 million people benefit), without adverse side effects on other land challenges. Most practices can be applied without competing for available land. However, seven options could result in competition for land. A large number of practices do not require dedicated land, including several land management options, all value chain options, and all risk management options. Four options could greatly increase competition for land if applied at a large scale, though the impact is scale and context specific, highlighting the need for safeguards to ensure that expansion of land for mitigation does not impact natural systems and food security. 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They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). 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A series of high resolution imperviousness datasets (for the 2006, 2009, 2012 and 2015 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI).\nThis series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (100% - +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 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Copyright holder: European Environment Agency (EEA).", "updated": "2025-10-09T10:52:58.736431Z", "type": "Dataset", "created": "2019-01-28", "language": "eng", "title": "High Resolution Layer: Imperviousness Density 2009 (raster 100m) accounting layer, Jan. 2019", "description": "The current dataset was derived from original Copernicus High Resolution Layer Imperviousness Density 2009 product product for the purposes of imperviousness indicator (LSI002) calculations within the EEA LEAC CUBE environment. The dataset is also input to the imperviousness indicator itself. \n\nThe high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012 and 2015 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (100% - +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. The latest version (published: 15/01/2019) of 100m resolution imperviousness status layers represents a time series consistent with 100m resolution imperviousness change layers - the cell by cell difference of the degree of imperviousness status corresponds to the degree of imperviousness change. 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