This dataset assesses the exposure of a system or a community to cope with or adjust to the adverse effects of climate change and associated hazards, considering the available information on the following indicators: - Areas where crop, grass and shrub cover is above 30%; - Fishery-cover, as sreas where total fish biomass is above the 50th percentile; - Forest-cover, as areas where forest cover is above 30%; - Biodiversity areas with presence of mangroves; - Physical areas located between 10 meters below and above sea level.

Digital elevation model cover: areas located between 10 meters below and above sea level. ETOPO5 was generated from a digital data base of land and sea- floor elevations on a 5-minute latitude/longitude grid.

The Dynamic Land Cover map at 100 m resolution (CGLS-LC100) is a new product in the portfolio of the CGLS and delivers a global land cover map at 100 m spatial resolution. The CGLS Land Cover product provides a primary land cover scheme. Next to these discrete classes, the product also includes continuous field layers for all basic land cover classes that provide proportional estimates for vegetation/ground cover for the land cover types. This continuous classification scheme may depict areas of heterogeneous land cover better than the standard classification scheme and, as such, can be tailored for application use (e.g. forest monitoring, crop monitoring, biodiversity and conservation, monitoring environment and security in Africa, climate modelling, etc.). These consistent Land Cover maps (v3.0.1) are provided for the period 2015-2019 over the entire Globe, derived from the PROBA-V 100 m time-series, a database of high quality land cover training sites and several ancillary datasets, reaching an accuracy of 80% at Level1 over al years. It is planned to provide yearly updates from 2020 through the use of a Sentinel time-series.

The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a consistent set of climate impact data across sectors and scales. It also provides a unique opportunity for considering interactions between climate change impacts across sectors through consistent scenarios. In this case, fixed-year direct human influences (e.g. land use, nitrogen deposition and fertilizer input, fishing effort).

The Dynamic Land Cover map at 100 m resolution (CGLS-LC100) is a new product in the portfolio of the CGLS and delivers a global land cover map at 100 m spatial resolution. The CGLS Land Cover product provides a primary land cover scheme. Next to these discrete classes, the product also includes continuous field layers for all basic land cover classes that provide proportional estimates for vegetation/ground cover for the land cover types. This continuous classification scheme may depict areas of heterogeneous land cover better than the standard classification scheme and, as such, can be tailored for application use (e.g. forest monitoring, crop monitoring, biodiversity and conservation, monitoring environment and security in Africa, climate modelling, etc.). These consistent Land Cover maps (v3.0.1) are provided for the period 2015-2019 over the entire Globe, derived from the PROBA-V 100 m time-series, a database of high quality land cover training sites and several ancillary datasets, reaching an accuracy of 80% at Level1 over al years. It is planned to provide yearly updates from 2020 through the use of a Sentinel time-series.

The Dynamic Land Cover map at 100 m resolution (CGLS-LC100) is a new product in the portfolio of the CGLS and delivers a global land cover map at 100 m spatial resolution. The CGLS Land Cover product provides a primary land cover scheme. Next to these discrete classes, the product also includes continuous field layers for all basic land cover classes that provide proportional estimates for vegetation/ground cover for the land cover types. This continuous classification scheme may depict areas of heterogeneous land cover better than the standard classification scheme and, as such, can be tailored for application use (e.g. forest monitoring, crop monitoring, biodiversity and conservation, monitoring environment and security in Africa, climate modelling, etc.). These consistent Land Cover maps (v3.0.1) are provided for the period 2015-2019 over the entire Globe, derived from the PROBA-V 100 m time-series, a database of high quality land cover training sites and several ancillary datasets, reaching an accuracy of 80% at Level1 over al years. It is planned to provide yearly updates from 2020 through the use of a Sentinel time-series.

Tentative abstract: This dataset assesses the exposure of the project area to the hazards based on the available information related to the richness of species or ecosystems. Source:

The Dynamic Land Cover map at 100 m resolution (CGLS-LC100) is a new product in the portfolio of the CGLS and delivers a global land cover map at 100 m spatial resolution. The CGLS Land Cover product provides a primary land cover scheme. Next to these discrete classes, the product also includes continuous field layers for all basic land cover classes that provide proportional estimates for vegetation/ground cover for the land cover types. This continuous classification scheme may depict areas of heterogeneous land cover better than the standard classification scheme and, as such, can be tailored for application use (e.g. forest monitoring, crop monitoring, biodiversity and conservation, monitoring environment and security in Africa, climate modelling, etc.). These consistent Land Cover maps (v3.0.1) are provided for the period 2015-2019 over the entire Globe, derived from the PROBA-V 100 m time-series, a database of high quality land cover training sites and several ancillary datasets, reaching an accuracy of 80% at Level1 over al years. It is planned to provide yearly updates from 2020 through the use of a Sentinel time-series.

This dataset shows the global distribution of mangrove forests, derived from earth observation satellite imagery. The dataset was created using Global Land Survey (GLS) data and the Landsat archive. Approximately 1,000 Landsat scenes were interpreted using hybrid supervised and unsupervised digital image classification techniques. See Giri et al. (2011) for full details.