Filters: Tags: human modification (X)5 results (57ms)
For the Green River Basin Landscape Conservation Design (GRB LCD) assessment, we mapped the vulnerability of riparian habitat for terrestrial species and process. Using a vulnerability framework, we defined Sensitivity (S) as the percent riparian vegetation within the valley bottom and Exposure (E) as the amount of human modification within the valley bottom. For each 12-digit hydrologic unit code within the GRB LCD we summarized the riparian sensitivity and exposure to human modification. We also computed Potential Impact (PI), and Adaptive Capacity (AC) metrics at the HUC12 level. PI is the square root transformed product of human modification exposure and riparian sensitivity. AC for riparian exposure to human...
Integrates models that represent (a) the flow fragmentation by dams and roads, (b) degree of human modification in the valley bottoms, and (c) upland soil loss for the surrounding watershed.
Quantifying landscape dynamics is a central goal of landscape ecology, and numerous metrics have been developed to measure the influence of human activities on natural landscapes. Composite scores that characterize human modifications to landscapes have gained widespread use. A parsimonious alternative is to estimate the proportion of a cover type (i.e. natural) within a spatial neighborhood to characterize both compositional and structural aspects of natural landscapes. Here I extend this approach into a multi-scale, integrated metric and apply it to national datasets on land cover, housing density, road existence, and highway traffic volume to measure the dynamics of natural landscapes in the conterminous US....
This dataset is an extract of the resistance surface created for the Pacific Northwest Duke Climate Resilience Project. It incorporates data on various human activities from the following data sources: National Landcover Dataset, Energy Infrastructure data from the Ventyx Corporation, National Wetlands Inventory, TIGER 2010 Road Data, active railroads and Dave Theobald's housing density dataset developed from 2010 census data. More details on the creation of this surface can be found in the document "Creation of Resistance Surfaces for the Resistant Kernel Pacific Northwest Duke Landscape Resilience Project".
The Human Modification (HM) model is designed to provide a comprehensive, but parsimonious approach, that uses several stressor/threats datasets to estimate level of human modification. There are three important elements that define the HM approach: (a) the human modification stressors and their data sources (b) the measurement unit used for each stressor, and (c) the method used to combine the effects of multiple stressors into an overall score of human modification. The way in which these various data layers are combined into a single index is quite important. We use a method that minimizes bias associated with non-independence among several stressor/threats layers (Theobald 2013). The HM model assumes the contribution...