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NatureMap_HumanPressures (ImageServer)

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In order to describe the human pressures on the biodiversity component of nature, we used the 2009 Human Footprint (HF) map. The HF is a widely recognised dataset describing the global, cumulative human pressure on the environment in 2009, at a spatial resolution of ~1 km (Venter et al., 2016). The human pressure is measured using eight variables including built-up environments, population density, electric power infrastructure, crop lands, pasture lands, roads, railways, and navigable waterways.

For water pressure a human footprint on water quality (% contamination) was modelled using the Waterworld version 2 policy support system.

The pressure on carbon storing ecosystems are similar in nature to those on biodiversity as captured by the human footprint (HF) map. Nevertheless, we recognised specific drivers of change in below ground carbon sinks such as wetland drainage and soil disturbance and erosion which are not necessarily linked to biodiversity loss or described by the HF. However, due to a paucity of globally consistent spatial data on these drivers, we omitted them from this analysis. We used fires and climate change risk as the two major pressures on carbon. We used the number and intensity of active fives for a 5 year period (from January 2015 - December 2019) from MODIS active fire data as the fire risk layer. Climate risk was estimated based on the difference between average annual future temperature for the reference year 2050 and mean annual historic temperature (1970 – present) (Karger et al. 2017).

Finally, these three layers were combined into one single layer to illustrate broad scale patterns of cumulative human pressures in order to ascertain where the intrinsic values of nature are most at risk.



Name: NatureMap_HumanPressures

Description:

In order to describe the human pressures on the biodiversity component of nature, we used the 2009 Human Footprint (HF) map. The HF is a widely recognised dataset describing the global, cumulative human pressure on the environment in 2009, at a spatial resolution of ~1 km (Venter et al., 2016). The human pressure is measured using eight variables including built-up environments, population density, electric power infrastructure, crop lands, pasture lands, roads, railways, and navigable waterways.

For water pressure a human footprint on water quality (% contamination) was modelled using the Waterworld version 2 policy support system.

The pressure on carbon storing ecosystems are similar in nature to those on biodiversity as captured by the human footprint (HF) map. Nevertheless, we recognised specific drivers of change in below ground carbon sinks such as wetland drainage and soil disturbance and erosion which are not necessarily linked to biodiversity loss or described by the HF. However, due to a paucity of globally consistent spatial data on these drivers, we omitted them from this analysis. We used fires and climate change risk as the two major pressures on carbon. We used the number and intensity of active fives for a 5 year period (from January 2015 - December 2019) from MODIS active fire data as the fire risk layer. Climate risk was estimated based on the difference between average annual future temperature for the reference year 2050 and mean annual historic temperature (1970 – present) (Karger et al. 2017).

Finally, these three layers were combined into one single layer to illustrate broad scale patterns of cumulative human pressures in order to ascertain where the intrinsic values of nature are most at risk.



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Copyright Text: Citation: UNEP-WCMC (2020). Human pressures on biodiversity, water and carbon. Cambridge, UK.References: Karger, D.N. et al. (2017) Climatologies at high resolution for the earth's land surface areas. Scientific Data 4, 170122. doi.org/10.1038/sdata.2017.122. Mulligan, M. (2019) Human footprint on water quality (% contamination). Model results from the Waterworld version 2 policy support system (non commercial-use). http://www.policysupport.org/waterworld [prepared by user mark.mulligan_kcl.ac.uk]Mulligan, M. (2013). WaterWorld: a self-parameterising, physically based model for application in data-poor but problem-rich environments globally. Hydrology research 44, 5; 748-769.Venter, O., Sanderson, E. W., Magrach, A., et al. (2016). Global terrestrial Human Footprint maps for 1993 and 2009. Scientific Data, 3, 160067. https://doi.org/10.1038/sdata.2016.67

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Standard Deviation Values: 8.536391029795352

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Child Resources:   Info   Histograms   Statistics   Key Properties   Legend   Raster Function Infos

Supported Operations:   Export Image   Identify   Measure   Compute Histograms   Compute Statistics Histograms   Get Samples   Compute Class Statistics   Query Boundary   Compute Pixel Location   Compute Angles   Validate   Project