20253 research outputs found
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High-resolution canopy fuel maps based on GEDI: a foundation for wildfire modeling in Germany
No full textForest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data. We propose a two-step approach where we first use measurements from NASA’s Global Ecosystem Dynamics Investigation (GEDI) instrument to estimate canopy fuel variables at the footprint level, before predicting high-resolution raster maps. Instead of using field measurements, we generate (GEDI-) footprint-level estimates for canopy (Base) height (CH, CBH), cover (CC), bulk density (CBD), and fuel load (CFL) by segmenting airborne Light Detection and Ranging point clouds and processing tree-level metrics with allometric crown biomass models. To predict footprint-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k -fold nearest neighbor distance matching. Predictions at >1.6 M GEDI footprints and biophysical raster covariates are combined with a universal Kriging method to produce countrywide maps at 20 m resolution. Agreement ( RMSE / R 2 ) with validation data (from the same population) was strong for footprint-level predictions and moderate for map predictions. A validation with estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH) rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation of model prediction standard errors. Contributing factors may be simplification through allometric equations and spatial and temporal mismatch of data inputs. The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable
Projecting Labour Market Imbalances and Skill Mismatch Under Demographic Change in the EU
No full textWe assess long-term labour mismatches in the European Union (EU27) by projecting the occupational distribution of workers and skill-specific labour demand up to 2060. Using a dynamic microsimulation approach ( Link4Skills-Mic ), we jointly model demographic, educational, and labour force dynamics at the individual level and combine country-specific projections of labour supply with projections of occupational demand. The analysis highlights growing imbalances: although the supply of highly educated workers continues to rise, shifts in demand are not evenly distributed across skill levels. Consequently, underutilization of high-skilled workers is projected to coexist with persistent vacancies in medium- and low-skilled occupations. Rather than indicating widespread labour shortages, these trends point to structural mismatches driven by the misalignment of worker qualifications, job characteristics, and hiring practices. To explore potential responses, we examine a series of policy scenarios such as expanded immigration, education reform, mid-career retraining, delayed retirement, and employer-led automation and upskilling. The findings show that, while certain policies can reduce specific mismatches, no single intervention closes all the gaps that emerge. Notably, automation reduces vacancies but increases underutilization, whereas human capital strategies shift mismatches across skill levels. These results suggest that addressing future labour mismatches will require coordinated, comprehensive and varied strategies that integrate demographic realities with evolving job demands in Europe’s ageing and increasingly digitalized and knowledge-based economies
Aligning differentiated mitigation capacity with the Paris agreement goals
No full textRegional disparities in mitigation capacity and the slow deployment of certain novel technologies pose significant challenges to achieving ambitious climate goals. We explore how accounting for technological and mitigation capacity considerations alters the regional distribution of mitigation efforts, and how these shifts relate to fairness considerations, all while staying within the scenario space aligned with the Paris Agreement’s goal of holding the increase in the global average temperature to well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels. To do so, we use a new set of scenarios generated using eight global integrated assessment models (IAMs). These scenarios shift near-term mitigation efforts to regions with greater mitigation capacity by implementing differentiated carbon pricing and emission caps, deviating from the default assumption of a uniform carbon price in global IAMs. We examine the scale of regional emissions reductions and energy system transformations needed, highlighting the implications in the near term. Our findings from the most ambitious scenario, highlight that Organisation for Economic Co-operation and Development (OECD) countries could reduce total CO 2 emissions as reported in the models by approximately 85% (range: 81%–114%) by 2040 relative to 2020 levels and achieve net-zero CO 2 emissions around 2045—well beyond the 58% reduction (range: 33%–71%) projected under default 2 °C pathways with a globally uniform carbon price. Similarly, China could reduce CO 2 emissions by 78% (range: 55%–83%) by 2040 and reach net-zero by 2050, compared to a 50% reduction (range: 47%–72%) in default scenarios. In this ambitious scenario, other regions could aim to reach net-zero CO 2 by 2070. This redistribution of mitigation efforts involves an accelerated phase-out of fossil fuels—coal, oil, and gas—primarily within the OECD region and, to a certain extent, in China. It also includes an early—but, in line with our feasibility considerations, limited—scale-up of carbon capture and storage capacity, along with significant reductions in final energy demand that go beyond current pledges and ambition levels. Beyond feasibility considerations, the new scenarios assume more mitigation efforts in regions with higher mitigation capacity proxied through institutional capacity, consistent with a capacity-based conception of regional fairness. Integrating certain considerations of feasibility and fairness into scenario assessments enables the development of alternative pathways that are, in some respects, more policy-relevant and help expand the scenario space—thereby responding to some of the recent critiques of global IAMs
Rising income inequality across half of global population and socioecological implications
No full textIncome inequality is one of the most important measures to indicate socioeconomic welfare and quality of life, and has implications for the environment. Yet, especially at the subnational level, comprehensive global data on income distribution are widely missing. Such data are essential for assessing patterns of inequality within countries and their development over time. Here we created seamless global subnational Gini coefficient and gross national income purchasing power parity per capita datasets for the period 1990–2023 and used these to assess the status and trends of income inequality and income, as well as their interplay. We show that while gross national income has increased for most people globally (94%), inequality has also increased for around 46–59% (depending on the national dataset used) of the global population, while it has decreased for 31–36% and has not shown a significant trend for 10–18%. We illustrate heterogeneities in inequality trends between and within countries, analyse plausible confounding factors related to inequality, and highlight the broad utility of the datasets through a case study that investigates correlations with terrestrial ecological diversity. Our dataset and analyses provide valuable insights for relevant stakeholders to direct future research and make informed decisions at the global, national and subnational levels, addressing societal, economic and environmental challenges caused by inequality
Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024) V2-beta
No full textSub-dataset: Dominant grassland class, 2018-2020
Description
Global annual grassland class and extent for 2000—2024 produced by Parente et al. (2024) within the scope of the Global Pasture Watch initiative. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a:
maximum of 50% tree canopy cover (greater than 5 meters),
maximum of 70% of other woody vegetation (scrubs and open shrubland), and
maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.
The grassland extent is classified into two classes:
Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.
Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.
Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.
The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including:
Probabilities of cultivated grassland (values range from 0–100),
Probabilities of natural/semi-natural grassland (values range from 0–100), and
Probabilities of open shrubland (values range from 0–100), and
Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).
All raster files are in unsigned 8-bit integer format and use 255 as no-data value (pixels ignored by prediction), following an specific naming convention:
Project name: Global Pasture Watch (gpw)
Class name: cultivated grassland (cultiv.grassland), natural/semi-natural grassland (nat.semi.grassland), open shrubland (open.shrubland) and dominant grassland (grassland)
Procedure combination: Random Forest (rf), median filter (med.filt) and balanced threshold (bthr).
Variable type: probability (p) and factor class (c)
Spatial resolution: 30m
Begin of time reference: date of first Landsat composite used by the modeling (20240101)
End of time reference: date of last Landsat composite used by the modeling (20241231)
Spatial extent: global (go)
Coordinate system: World Geodetic System 1984, used in GPS (epsg.4326)
Version: v2
Related resources
Maps of dominant grassland:
2000-2002 2003-2005 2006-2008 2009-2011 2012-2014 2015-2017 2018-2020 2021-2023 2024
Probability maps of cultivated grassland:
2000-2024 (All URLs)
Probability maps of natural/semi-natural grassland:
2000-2024 (All URLs)
Grassland reference samples based on VHR imagery (2000–2024):
GeoPackage files
Global machine learning models (Random Forest):
Parquet and joblib python files
Reference sampling design derived by FSCV:
GeoPackage and raster files
Harmonized reference samples based on existing LULC dataset:
GeoPackage and raster files
Source code for reproducibility:
GitHub release
Mapping feedback tool:
GeoWiki
Data catalogues:
OpenLandMap STAC Google Earth Engine
Support
For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in https://github.com/wri/global-pasture-watc
Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024) V2-beta
No full textSub-dataset: Dominant grassland class, 2000-2002
Description
Global annual grassland class and extent for 2000—2024 produced by Parente et al. (2024) within the scope of the Global Pasture Watch initiative. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a:
maximum of 50% tree canopy cover (greater than 5 meters),
maximum of 70% of other woody vegetation (scrubs and open shrubland), and
maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.
The grassland extent is classified into two classes:
Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.
Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.
Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.
The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including:
Probabilities of cultivated grassland (values range from 0–100),
Probabilities of natural/semi-natural grassland (values range from 0–100), and
Probabilities of open shrubland (values range from 0–100), and
Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).
All raster files are in unsigned 8-bit integer format and use 255 as no-data value (pixels ignored by prediction), following an specific naming convention:
Project name: Global Pasture Watch (gpw)
Class name: cultivated grassland (cultiv.grassland), natural/semi-natural grassland (nat.semi.grassland), open shrubland (open.shrubland) and dominant grassland (grassland)
Procedure combination: Random Forest (rf), median filter (med.filt) and balanced threshold (bthr).
Variable type: probability (p) and factor class (c)
Spatial resolution: 30m
Begin of time reference: date of first Landsat composite used by the modeling (20240101)
End of time reference: date of last Landsat composite used by the modeling (20241231)
Spatial extent: global (go)
Coordinate system: World Geodetic System 1984, used in GPS (epsg.4326)
Version: v2
Related resources
Maps of dominant grassland:
2000-2002 2003-2005 2006-2008 2009-2011 2012-2014 2015-2017 2018-2020 2021-2023 2024
Probability maps of cultivated grassland:
2000-2024 (All URLs)
Probability maps of natural/semi-natural grassland:
2000-2024 (All URLs)
Grassland reference samples based on VHR imagery (2000–2024):
GeoPackage files
Global machine learning models (Random Forest):
Parquet and joblib python files
Reference sampling design derived by FSCV:
GeoPackage and raster files
Harmonized reference samples based on existing LULC dataset:
GeoPackage and raster files
Source code for reproducibility:
GitHub release
Mapping feedback tool:
GeoWiki
Data catalogues:
OpenLandMap STAC Google Earth Engine
Support
For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in https://github.com/wri/global-pasture-watc
Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2022) V2-beta
No full textSub-dataset: Dominant grassland class, 2006-2008
Global annual grassland class and extent for 2000—2022 produced by Parente et al. (2024) within the scope of the Global Pasture Wath initiative. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a:
maximum of 50% tree canopy cover (greater than 5 meters),
maximum of 70% of other woody vegetation (scrubs and open shrubland), and
maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.
The grassland extent is classified into two classes:
Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.
Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.
The dataset is organized in 69 global mosaics (23 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including:
Probabilities of cultivated grassland (values range from 0–100),
Probabilities of natural/semi-natural grassland (values range from 0–100), and
Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland.
All raster files are in unsigned 8-bit integer format and use 255 as no-data value (pixels ignored by prediction), following an specific naming convention:
Project name: Global Pasture Watch (gpw)
Class name: cultivated grassland (cultiv.grassland), natural/semi-natural grassland (nat.semi.grassland) and dominant grassland (grassland)
Procedure combination: Random Forest (rf), Savitzky-golay (savgol), balanced threshold (bthr) and mean absolute difference (madi).
Variable type: probability (p)
Spatial resolution: 30m
Begin of time reference: date of first Landsat composite used by the modeling (20220101)
End of time reference: date of last Landsat composite used by the modeling (20221231)
Spatial extent: global (go)
Coordinate system: World Geodetic System 1984, used in GPS (epsg.4326)
Version: v1
Related resources
Maps of dominant grassland:
2000-2002 2003-2005 2006-2008 2009-2011 2012-2014 2015-2017 2018-2020 2021-2022
Probability maps of cultivated grassland:
2000-2022 (All URLs)
Probability maps of natural/semi-natural grassland:
2000-2022 (All URLs)
Grassland reference samples based on VHR imagery (2000–2022):
GeoPackage files
Global machine learning models (Random Forest):
Parquet and joblib python files
Reference sampling design derived by FSCV:
GeoPackage and raster files
Harmonized reference samples based on existing LULC dataset:
GeoPackage and raster files
Source code for reproducibility:
GitHub release
Mapping feedback tool:
GeoWiki
Data catalogues:
OpenLandMap STAC Google Earth Engine
Support
For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in https://github.com/wri/global-pasture-watc
Data underpinning the EuropaBON roadmap for a unified, transnational biodiversity observation system in Europe
No full textThis repository provides the datasets underpinning the EuropaBON roadmap for a transnational biodiversity observation system in Europe. The data summarize 84 Essential Biodiversity Variables (EBVs), EBV-policy links, EU monitoring coverage of selected terrestrial, freshwater and marine examples, primary and supplementary monitoring techniques for EBVs and their relative importance, examples of monitoring sample sizes in Europe, and priorities for EBV workflow development.
The files include:
EBV metadata and characteristics: Overview and summary characteristics of 84 Essential Biodiversity Variables (EBVs), their entity types, spatiotemporal resolutions, and associated taxonomic and ecosystem groups.
EBV-policy links: The direct or indirect, partial or complete links between a specific EBV and a specific EU policy.
Monitoring coverage and effort: Sample size examples from EU-wide monitoring schemes, counts of freshwater monitoring sites under the Water Framework Directive, and shapefiles describing transect density, river and lake monitoring coverage, and number of marine monitoring programmes across Europe.
Monitoring methods and priorities: Survey-based assessments of the relative importance of monitoring approaches (DNA-based, in-situ, citizen science, remote sensing, digital sensors), identification of primary and supplementary methods per EBV, and expert-elicited priorities for workflow development across terrestrial, freshwater, and marine domains.
Together, these datasets provide a unique evidence base for:
Designing an optimised spatial and methodological framework for biodiversity monitoring in Europe;
Assessing current monitoring strengths, gaps, and redundancies across taxa, ecosystems, and regions;
Supporting workflow development for EBV integration, modelling, and policy uptake;
Informing the establishment of the proposed European Biodiversity Observation Coordination Centre (EBOCC).
By making these datasets openly available, this repository provides transparacy for the design and implementation of a coordinated, policy-relevant, and technology-enabled biodiversity monitoring network in Europe. It serves as a direct companion to an associated scientific paper and as a reusable resource for researchers, policymakers, and practitioners working to strengthen biodiversity monitoring and conservation action in Europe and beyond
LAMASUS NUTS-level agricultural land rents
No full textThis dataset provides harmonised estimates of agricultural land rents across the European Union, derived from the Farm Accountancy Data Network (FADN) and aggregated to the NUTS 0 (national) and NUTS 2 (regional) levels. The data are expressed in EUR per hectare and represent average land rental values for agricultural holdings reporting rental payments under the FADN survey framework
Data underpinning the EuropaBON roadmap for a unified, transnational biodiversity observation system in Europe
No full textThis repository provides the datasets underpinning the EuropaBON roadmap for a transnational biodiversity observation system in Europe. The data summarize 84 Essential Biodiversity Variables (EBVs), EBV-policy links, EU monitoring coverage of selected terrestrial, freshwater and marine examples, primary and supplementary monitoring techniques for EBVs and their relative importance, examples of monitoring sample sizes in Europe, and priorities for EBV workflow development.
The files include:
EBV metadata and characteristics: Overview and summary characteristics of 84 Essential Biodiversity Variables (EBVs), their entity types, spatiotemporal resolutions, and associated taxonomic and ecosystem groups.
EBV-policy links: The direct or indirect, partial or complete links between a specific EBV and a specific EU policy.
Monitoring coverage and effort: Sample size examples from EU-wide monitoring schemes, counts of freshwater monitoring sites under the Water Framework Directive, and shapefiles describing transect density, river and lake monitoring coverage, and number of marine monitoring programmes across Europe.
Monitoring methods and priorities: Survey-based assessments of the relative importance of monitoring approaches (DNA-based, in-situ, citizen science, remote sensing, digital sensors), identification of primary and supplementary methods per EBV, and expert-elicited priorities for workflow development across terrestrial, freshwater, and marine domains.
Together, these datasets provide a unique evidence base for:
Designing an optimised spatial and methodological framework for biodiversity monitoring in Europe;
Assessing current monitoring strengths, gaps, and redundancies across taxa, ecosystems, and regions;
Supporting workflow development for EBV integration, modelling, and policy uptake;
Informing the establishment of the proposed European Biodiversity Observation Coordination Centre (EBOCC).
By making these datasets openly available, this repository provides transparacy for the design and implementation of a coordinated, policy-relevant, and technology-enabled biodiversity monitoring network in Europe. It serves as a direct companion to an associated scientific paper and as a reusable resource for researchers, policymakers, and practitioners working to strengthen biodiversity monitoring and conservation action in Europe and beyond