Citation:

Impact/Purpose:

The primary objectives of this research are to:

Develop methodologies so that landscape indicator values generated from different sensors on different dates (but in the same areas) are comparable; differences in metric values result from landscape changes and not differences in the sensors;

Quantify relationships between landscape metrics generated from wall-to-wall spatial data and (1) specific parameters related to water resource conditions in different environmental settings across the US, including but not limited to nutrients, sediment, and benthic communities, and (2) multi-species habitat suitability;

Develop and validate multivariate models based on quantification studies;

Develop GIS/model assessment protocols and tools to characterize risk of nutrient and sediment TMDL exceedence;

Complete an initial draft (potentially web based) of a national landscape condition assessment.

This research directly supports long-term goals established in ORDs multiyear plans related to GPRA Goal 2 (Water) and GPRA Goal 4 (Healthy Communities and Ecosystems), although funding for this task comes from Goal 4. Relative to the GRPA Goal 2 multiyear plan, this research is intended to "provide tools to assess and diagnose impairment in aquatic systems and the sources of associated stressors." Relative to the Goal 4 Multiyear Plan this research is intended to (1) provide states and tribes with an ability to assess the condition of waterbodies in a scientifically defensible and representative way, while allowing for aggregation and assessment of trends at multiple scales, (2) assist Federal, State and Local managers in diagnosing the probable cause and forecasting future conditions in a scientifically defensible manner to protect and restore ecosystems, and (3) provide Federal, State and Local managers with a scientifically defensible way to assess current and future ecological conditions, and probable causes of impairments, and a way to evaluate alternative future management scenarios.

Description:

This map was created from National Land Cover Data (NLCD) using a 9 x 9 moving window to generate forest edge measurements. The NLCD data was reclassified into three categories: forest other natural (e.g. grassland wetland~ etc.) and anthropogenic use (e.g. agriculture, urban, etc.). Water, ice and bare rock/sand were ignored in calculations. Within each window, the edges of all forest pixels were examined to determine what type of land-cover shared each edge. Three new grids were created, one for each edge type (forest-forest forest-natural, and forest-anthropogenic). The values in these grids were calculated as the number of edges with the appropriate type in the window divided by the total number of forest edges regardless of neighbor. These grids represented forest connectivity (forest-forest edges), naturally caused forest fragmentation (forest-natural edges) and human caused forest fragmentation (forest-anthropogenic edges). In the map, forest connectivity is displayed in green natural fragmentation in blue, and human fragmentation in red. Pure green identifies areas of highly connected forest where most or all forest edges are shared by another forest pixel. Blue areas show where most or all forest edges are shared with another natural land-cover type and red areas are where forest edges are largely shared with a human land-use. Different mixes of the three edge types can produce any color. Two common examples in the map are yellow and cyan. Yellow identifies areas with roughly equal amounts of forest connectivity and anthropogenic fragmentation. Cyan is where forest connectivity and natural fragmentation are fairly equal. Black represents areas with no forest in the 9 x 9 window and ignored areas, mostly water, as well as state boundaries.

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