Due to the importance of soil organic carbon (SOC) in supporting ecosystem services, accurate SOC assessment is vital for scientific research and decision making. However, most previous studies focused on single soil depth, leading to a poor understanding of SOC in multiple depths. To better understand the spatial distribution pattern of SOC in Northeast and North China Plain, we compared three machine learning algorithms (i.e., Cubist, Extreme Gradient Boosting (XGBoost) and Random Forest (RF)) within the digital soil mapping framework. A total of 386 sampling sites (1584 samples) following specific criteria covering all dryland districts and counties and soil types in four depths (i.e., 0\u201310, 10\u201320, 20\u201330 and 30\u201340 cm) were collected in 2017. After feature selection from 249 environmental covariates by the Genetic Algorithm, 29 variables were used to fit models. The results showed SOC increased from southern to northern regions in the spatial scale and decreased with soil depths. From the result of independent verification (validation dataset: 80 sampling sites), RF (R2: 0.58, 0.71, 0.73, 0.74 and RMSE: 3.49, 3.49, 2.95, 2.80 g kg\u22121 in four depths) performed better than Cubist (R2: 0.46, 0.63, 0.67, 0.71 and RMSE: 3.83, 3.60, 3.03, 2.72 g kg\u22121) and XGBoost (R2: 0.53, 0.67, 0.70, 0.71 and RMSE: 3.60, 3.60, 3.00, 2.83 g kg\u22121) in terms of prediction accuracy and robustness. Soil, parent material and organism were the most important covariates in SOC prediction. This study provides the up-to-date spatial distribution of dryland SOC in Northeast and North China Plain, which is of great value for evaluating dynamics of soil quality after long-term cultivation.
", "keywords": ["soil organic carbon", "2. Zero hunger", "spatial distribution", "Science", "model comparison", "Q", "controlling factor", "0211 other engineering and technologies", "0207 environmental engineering", "02 engineering and technology", "15. Life on land", "soil organic carbon; Northeast and North Plain China; model comparison; spatial distribution; controlling factor", "Northeast and North Plain China"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/10/2504/pdf"}, {"href": "https://doi.org/10.3390/rs14102504"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs14102504", "name": "item", "description": "10.3390/rs14102504", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14102504"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-23T00:00:00Z"}}, {"id": "10.1186/s40663-019-0163-5", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:20:08Z", "type": "Journal Article", "created": "2019-02-07", "title": "Spatial distribution of the potential forest biomass availability in Europe", "description": "Abstract Background European forests are considered a crucial resource for supplying biomass to a growing bio-economy in Europe. This study aimed to assess the potential availability of forest biomass from European forests and its spatial distribution. We tried to answer the questions (i) how is the potential forest biomass availability spatially distributed across Europe and (ii) where are hotspots of potential forest biomass availability located? Methods The spatial distribution of woody biomass potentials was assessed for 2020 for stemwood, residues (branches and harvest losses) and stumps for 39 European countries. Using the European Forest Information SCENario (EFISCEN) model and international forest statistics, we estimated the theoretical amount of biomass that could be available based on the current and future development of the forest age-structure, growing stock and increment and forest management regimes. We combined these estimates with a set of environmental (site productivity, soil and water protection and biodiversity protection) and technical (recovery rate, soil bearing capacity) constraints, which reduced the amount of woody biomass that could potentially be available. We mapped the potential biomass availability at the level of administrative units and at the 10\u2009km\u00a0\u00d7\u00a010\u2009km grid level to gain insight into the spatial distribution of the woody biomass potentials. Results According to our results, the total availability of forest biomass ranges between 357 and 551 Tg dry matter per year. The largest potential supply of woody biomass per unit of land can be found in northern Europe (southern Finland and Sweden, Estonia and Latvia), central Europe (Austria, Czech Republic, and southern Germany), Slovenia, southwest France and Portugal. However, large parts of these potentials are already used to produce materials and energy. The distribution of biomass potentials that are currently unused only partially coincides with regions that currently have high levels of wood production. Conclusions Our study shows how the forest biomass potentials are spatially distributed across the European continent, thereby providing insight into where policies could focus on an increase of the supply of woody biomass from forests. Future research on potential biomass availability from European forests should also consider to what extent forest owners would be willing to mobilise additional biomass from their forests and at what costs the estimated potentials could be mobilised.", "keywords": ["Europe", "2. Zero hunger", "Forest biomass", "Ecology", "13. Climate action", "Forest biomass", " EFISCEN", " Europe", " Potential supply", " Spatial distribution", "EFISCEN", "Potential supply", "Spatial distribution", "15. Life on land", "01 natural sciences", "QH540-549.5", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://link.springer.com/content/pdf/10.1186/s40663-019-0163-5.pdf"}, {"href": "https://doi.org/10.1186/s40663-019-0163-5"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Forest%20Ecosystems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1186/s40663-019-0163-5", "name": "item", "description": "10.1186/s40663-019-0163-5", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1186/s40663-019-0163-5"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-02-07T00:00:00Z"}}, {"id": "10261/373580", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:25:53Z", "type": "Dataset", "title": "Data on the profile of organic contaminants in the L'Albufera Natural Park (2019\u20132020). Target and non-target screening", "description": "Open AccessPeer reviewed", "keywords": ["Sediments", "Surface waters", "Pharmaceuticals", "Spatial distribution", "Pesticides", "Industrial compounds"], "contacts": [{"organization": "Soriano, Yolanda, Do\u00f1ate, Emilio, Asins Velis, Sabina, Andreu P\u00e9rez, V., Pic\u00f3, Yolanda,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10261/373580"}, {"rel": "self", "type": "application/geo+json", "title": "10261/373580", "name": "item", "description": "10261/373580", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/373580"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8092703", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:24:51Z", "type": "Journal Article", "created": "2022-05-24", "title": "Digital Mapping of Soil Organic Carbon with Machine Learning in Dryland of Northeast and North Plain China", "description": "Due to the importance of soil organic carbon (SOC) in supporting ecosystem services, accurate SOC assessment is vital for scientific research and decision making. However, most previous studies focused on single soil depth, leading to a poor understanding of SOC in multiple depths. To better understand the spatial distribution pattern of SOC in Northeast and North China Plain, we compared three machine learning algorithms (i.e., Cubist, Extreme Gradient Boosting (XGBoost) and Random Forest (RF)) within the digital soil mapping framework. A total of 386 sampling sites (1584 samples) following specific criteria covering all dryland districts and counties and soil types in four depths (i.e., 0\u201310, 10\u201320, 20\u201330 and 30\u201340 cm) were collected in 2017. After feature selection from 249 environmental covariates by the Genetic Algorithm, 29 variables were used to fit models. The results showed SOC increased from southern to northern regions in the spatial scale and decreased with soil depths. From the result of independent verification (validation dataset: 80 sampling sites), RF (R2: 0.58, 0.71, 0.73, 0.74 and RMSE: 3.49, 3.49, 2.95, 2.80 g kg\u22121 in four depths) performed better than Cubist (R2: 0.46, 0.63, 0.67, 0.71 and RMSE: 3.83, 3.60, 3.03, 2.72 g kg\u22121) and XGBoost (R2: 0.53, 0.67, 0.70, 0.71 and RMSE: 3.60, 3.60, 3.00, 2.83 g kg\u22121) in terms of prediction accuracy and robustness. Soil, parent material and organism were the most important covariates in SOC prediction. This study provides the up-to-date spatial distribution of dryland SOC in Northeast and North China Plain, which is of great value for evaluating dynamics of soil quality after long-term cultivation.
", "keywords": ["soil organic carbon", "2. Zero hunger", "spatial distribution", "Science", "model comparison", "Q", "controlling factor", "0211 other engineering and technologies", "0207 environmental engineering", "02 engineering and technology", "15. Life on land", "soil organic carbon; Northeast and North Plain China; model comparison; spatial distribution; controlling factor", "Northeast and North Plain China"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/10/2504/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8092703"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8092703", "name": "item", "description": "10.5281/zenodo.8092703", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8092703"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-23T00:00:00Z"}}, {"id": "10.60692/9nxrv-e7y75", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:25:23Z", "type": "Journal Article", "created": "2020-12-16", "title": "Spatial differentiation characteristics and driving factors of agricultural eco-efficiency in Chinese provinces from the perspective of ecosystem services", "description": "Farmland ecosystem service is an important output of agricultural production, but it has been incompletely reflected in current studies on eco-efficiency. In this study, the value of improved farmland ecosystem services is used as one of the expected outputs. The data envelopment method is used to evaluate the agricultural eco-efficiency (AEE) of 31 provincial administrative regions in China from 2006 to 2018. The spatial autocorrelation method is used to explore the characteristics of AEE in China. Geographical detector model (Geodetector) is adopted to detect the driving factors of AEE spatial differentiation in China. China\u2019s AEE trend from 2006 to 2018 was downward with the efficiency value decreasing from 1.023 to 0.995. China\u2019s AEE level has improved with an average of 1.004. The spatial distribution pattern represented in space is in the following order: eastern region > western region > northeast region > central region. The AEE gap among provinces in the western region is the largest, and that in the northeast region is the smallest. China\u2019s AEE spatial correlation distribution presents random distribution characteristics. During the research period, the lowehigh (LH) efficiency response area has centered on Yunnan Province. The lowelow (LL) level concentration area has centered on Inner Mongolia autonomous region and Liaoning Province. The highelow (HL) level diffusion effect agglomeration area has centered on Heilongjiang Province. Energy input, water resource input, and carbon emission are the core drivers of AEE spatial differentiation in China. Water resource input, pesticide input and labor input are the significant control factors of AEE spatial differentiation in the eastern, central, and western regions of China.", "keywords": ["Economics and Econometrics", "China", "Environmental Engineering", "Economics", "Discrete Choice Models in Economics and Health Care", "Social Sciences", "Mathematical analysis", "01 natural sciences", "Environmental science", "Data envelopment analysis", "Life Cycle Assessment and Environmental Impact Analysis", "11. Sustainability", "FOS: Mathematics", "Ecosystem services", "Spatial distribution", "Biology", "Ecosystem Services", "Ecosystem", "0105 earth and related environmental sciences", "Agricultural economics", "2. Zero hunger", "Global and Planetary Change", "Global Analysis of Ecosystem Services and Land Use", "Geography", "Ecology", "Distribution (mathematics)", "Statistics", "FOS: Environmental engineering", "Spatial analysis", "Agriculture", "Remote sensing", "15. Life on land", "Economics", " Econometrics and Finance", "Driving factors", "Archaeology", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Spatial heterogeneity", "Common spatial pattern", "Mathematics"]}, "links": [{"href": "https://doi.org/10.60692/9nxrv-e7y75"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Cleaner%20Production", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.60692/9nxrv-e7y75", "name": "item", "description": "10.60692/9nxrv-e7y75", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.60692/9nxrv-e7y75"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-01T00:00:00Z"}}, {"id": "10451/59767", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:25:58Z", "type": "Journal Article", "created": "2018-11-03", "title": "\u03b415N of lichens reflects the isotopic signature of ammonia source", "description": "Although it is generally accepted that \u03b415N in lichen reflects predominating N isotope sources in the environment, confirmation of the direct correlation between lichen \u03b415N and atmospheric \u03b415N is still missing, especially under field conditions with most confounding factors controlled. To fill this gap and investigate the response of lichens with different tolerance to atmospheric N deposition, thalli of the sensitive Evernia prunastri and the tolerant Xanthoria parietina were exposed for ten weeks to different forms and doses of N in a field manipulation experiment where confounding factors were minimized. During this period, several parameters, namely total N, \u03b415N and chlorophyll a fluorescence, were measured. Under the experimental conditions, \u03b415N in lichens quantitatively responded to the \u03b415N of released gaseous ammonia (NH3). Although a high correlation between the isotopic signatures in lichen tissue and supplied N was found both in tolerant and sensitive species, chlorophyll a fluorescence indicated that the sensitive species very soon lost its photosynthetic functionality with increasing N availability. The most damaging response to the different N chemical forms was observed with dry deposition of NH3, although wet deposition of ammonium ions had a significant observable physiological impact. Conversely, there was no significant effect of nitrate ions on chlorophyll a fluorescence, implying differential sensitivity to dry deposition versus wet deposition and to ammonium versus nitrate in wet deposition. Evernia prunastri was most sensitive to NH3, then NH4+, with lowest sensitivity to NO3-. Moreover, these results confirm that lichen \u03b415N can be used to indicate the \u03b415N of atmospheric ammonia, providing a suitable tool for the interpretation of the spatial distribution of NH3 sources in relation to their \u03b415N signal.", "keywords": ["Air Pollutants", "Nitrates", "Lichens", "Nitrogen Isotopes", "Chlorophyll A", "0211 other engineering and technologies", "02 engineering and technology", "Models", " Theoretical", "chlorophyll a fluorescence", "01 natural sciences", "nitrogen deposition", "Xanthoria parietina", "Species Specificity", "Ammonia", "13. Climate action", "source spatial distribution", "biomonitoring", "physiological response", "Photosynthesis", "Environmental Monitoring", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://repositorio.ulisboa.pt/bitstream/10451/59767/1/1-s2.0-S0048969718343560-main.pdf"}, {"href": "https://doi.org/10451/59767"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10451/59767", "name": "item", "description": "10451/59767", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10451/59767"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-02-01T00:00:00Z"}}, {"id": "1871.1/c6f52add-8521-4556-b94a-6153906d8366", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:26:26Z", "type": "Journal Article", "created": "2023-08-30", "title": "Inland Waters Increasingly Produce and Emit Nitrous Oxide", "description": "Nitrous oxide (N2O) is a long-lived greenhouse gas and currently contributes \u223c10% to global greenhouse warming. Studies have suggested that inland waters are a large and growing global N2O source, but whether, how, where, when, and why inland-water N2O emissions changed in the Anthropocene remains unclear. Here, we quantify global N2O formation, transport, and emission along the aquatic continuum and their changes using a spatially explicit, mechanistic, coupled biogeochemistry-hydrology model. The global inland-water N2O emission increased from 0.4 to 1.3 Tg N yr-1 during 1900-2010 due to (1) growing N2O inputs mainly from groundwater and (2) increased inland-water N2O production, largely in reservoirs. Inland waters currently contribute 7 (5-10)% to global total N2O emissions. The highest inland-water N2O emissions are typically in and downstream of reservoirs and areas with high population density and intensive agricultural activities in eastern and southern Asia, southeastern North America, and Europe. The expected continuing excessive use of nutrients, dam construction, and development of suboxic conditions in aging reservoirs imply persisting high inland-water N2O emissions.", "keywords": ["inland waters", "Inland waters", "Asia", " Southern", "NO cycling", "Nitrous Oxide", "Integrated process-based modeling", "Greenhouse gas emission", "greenhouse gas emission", "Environmental Chemistry", "14. Life underwater", "closed N2O budget", "integrated process-based modeling", "N2O cycling", " long-term temporal changes", "Nitrous oxide", "long-term temporal changes", "nitrous oxide", "Water", "Agriculture", "General Chemistry", "15. Life on land", "N2O cycling", "6. Clean water", "closed NO budget", "13. Climate action", "spatial distributions", "Spatial distributions"]}, "links": [{"href": "https://doi.org/1871.1/c6f52add-8521-4556-b94a-6153906d8366"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20%26amp%3B%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/c6f52add-8521-4556-b94a-6153906d8366", "name": "item", "description": "1871.1/c6f52add-8521-4556-b94a-6153906d8366", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/c6f52add-8521-4556-b94a-6153906d8366"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-30T00:00:00Z"}}, {"id": "3111070593", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:27:27Z", "type": "Journal Article", "created": "2020-12-16", "title": "Spatial differentiation characteristics and driving factors of agricultural eco-efficiency in Chinese provinces from the perspective of ecosystem services", "description": "Farmland ecosystem service is an important output of agricultural production, but it has been incompletely reflected in current studies on eco-efficiency. In this study, the value of improved farmland ecosystem services is used as one of the expected outputs. The data envelopment method is used to evaluate the agricultural eco-efficiency (AEE) of 31 provincial administrative regions in China from 2006 to 2018. The spatial autocorrelation method is used to explore the characteristics of AEE in China. Geographical detector model (Geodetector) is adopted to detect the driving factors of AEE spatial differentiation in China. China\u2019s AEE trend from 2006 to 2018 was downward with the efficiency value decreasing from 1.023 to 0.995. China\u2019s AEE level has improved with an average of 1.004. The spatial distribution pattern represented in space is in the following order: eastern region > western region > northeast region > central region. The AEE gap among provinces in the western region is the largest, and that in the northeast region is the smallest. China\u2019s AEE spatial correlation distribution presents random distribution characteristics. During the research period, the lowehigh (LH) efficiency response area has centered on Yunnan Province. The lowelow (LL) level concentration area has centered on Inner Mongolia autonomous region and Liaoning Province. The highelow (HL) level diffusion effect agglomeration area has centered on Heilongjiang Province. Energy input, water resource input, and carbon emission are the core drivers of AEE spatial differentiation in China. Water resource input, pesticide input and labor input are the significant control factors of AEE spatial differentiation in the eastern, central, and western regions of China.", "keywords": ["Economics and Econometrics", "China", "Environmental Engineering", "Economics", "Discrete Choice Models in Economics and Health Care", "Social Sciences", "Mathematical analysis", "01 natural sciences", "Environmental science", "Data envelopment analysis", "Life Cycle Assessment and Environmental Impact Analysis", "11. Sustainability", "FOS: Mathematics", "Ecosystem services", "Spatial distribution", "Biology", "Ecosystem Services", "Ecosystem", "0105 earth and related environmental sciences", "Agricultural economics", "2. Zero hunger", "Global and Planetary Change", "Global Analysis of Ecosystem Services and Land Use", "Geography", "Ecology", "Distribution (mathematics)", "Statistics", "FOS: Environmental engineering", "Spatial analysis", "Agriculture", "Remote sensing", "15. Life on land", "Economics", " Econometrics and Finance", "Driving factors", "Archaeology", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Spatial heterogeneity", "Common spatial pattern", "Mathematics"]}, "links": [{"href": "https://doi.org/3111070593"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Cleaner%20Production", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3111070593", "name": "item", "description": "3111070593", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3111070593"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-01T00:00:00Z"}}, {"id": "2c26ccc9-0ae2-4b42-bd83-7e1b68dfe6e4", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-31.29, 27.64], [-31.29, 70.08], [34.1, 70.08], [34.1, 27.64], [-31.29, 27.64]]]}, "properties": {"themes": [{"concepts": [{"id": "environment"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "EU27 (from 2020)"}, {"id": "United Kingdom"}, {"id": "EEA38 (from 2020)"}, {"id": "EEA39"}], "scheme": "Continents, countries, sea regions of the world."}, {"concepts": [{"id": "Land use"}, {"id": "Forests and forestry"}, {"id": "Biodiversity"}, {"id": "Nature protection and restoration"}], "scheme": "EEA topics"}, {"concepts": [{"id": "afforestation"}, {"id": "forestry"}, {"id": "land cover"}, {"id": "environmental pressure"}, {"id": "index"}, {"id": "spatial distribution"}, {"id": "forest deterioration"}], "scheme": "GEMET"}, {"concepts": [{"id": "Land cover"}, {"id": "Land use"}], "scheme": "http://inspire.ec.europa.eu/theme"}, {"concepts": [{"id": "Land cover"}, {"id": "Land use"}], "scheme": "http://inspire.ec.europa.eu/theme"}, {"concepts": [{"id": "Land cover"}, {"id": "Land use"}], "scheme": "http://inspire.ec.europa.eu/theme"}, {"concepts": [{"id": "European"}], "scheme": "Spatial scope"}, {"concepts": [], "scheme": "Temporal resolution"}], "updated": "2025-01-10T08:13:43.268743Z", "type": "Dataset", "language": "eng", "title": "Copernicus HRL Forests and Corine Land Cover Forest products", "description": "The High-Resolution Layers Forest consist of 3 status and change products: tree cover density, dominant leaf type and forest type product. The products are available every 3 years for the period 2012-2018.\nThe Corine Land Cover Forest products have assessed status and changes since 2000 and include 4 forest classes: 311 (broadleaved forests), 312 (conifer forests), 313 (mixed forests), and 324 (transitional woodland and scrubs). 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