The following Power Point presentation shows GIS methodology that
Download
1 / 19

Landscape and Urban Planning Volume 79, Issue 1 , 15 January 2007, Pages 110-123 - PowerPoint PPT Presentation


  • 110 Views
  • Uploaded on

The following Power Point presentation shows GIS methodology that I used to to contribute to a research project that was published in Landscape and Urban Planning, January 2007. Landscape and Urban Planning Volume 79, Issue 1 , 15 January 2007, Pages 110-123

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Landscape and Urban Planning Volume 79, Issue 1 , 15 January 2007, Pages 110-123' - ardelle-york


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

The following Power Point presentation shows GIS methodology that

I used to to contribute to a research project that was published in

Landscape and Urban Planning, January 2007

Landscape and Urban PlanningVolume 79, Issue 1,

15 January 2007, Pages 110-123

Biological integrity in urban streams: Toward resolving multiple dimensions of urbanization

B. Michael Waltona, , , Mark Sallingb, 1, , James Wylesb, 2, and Julie Wolina, 3,

aDepartment of Biological, Geological, and Environmental Sciences, Cleveland State University, Cleveland, OH 44115, United States

bNortheast Ohio Data and Information Service, Levin College of Urban Affairs, Cleveland State University, Cleveland, OH 44115, United States

Abstract

Most studies of urban streams have relied on single variables to characterize the degree of urbanization, which may not reflect interactions among features of urban landscapes adequately. We report on an approach to the characterization of urbanization effects on streams that used principal components analysis and multiple regression to explore the combined, interactive effects of land use/land cover, human population demography, and stream habitat quality on an index of biological integrity (IBI) of fish communities. Applied to a substantially urbanized region in northeast OH, USA, the analysis demonstrated the interactive nature of urbanization effects. Urban land use and stream habitat quality were significant predictors of IBI, but were no better than and, in some cases, poorer predictors than other gradients and interactions among gradients. High integrity sites were characterized by low forest cover and high grassland cover at sub-catchment scale, but high forest cover within a 500 m radius local zone of the sample point, conditions often found in protected parklands in the region. The analysis also indicated that variability in stream habitat quality was

unrelated to landscape or demographic features, a result we attribute to the interaction between the the geological and urbanization histories of the region.


GIS that

Methodology

By James C. Wyles



  • Watershed Selection Criteria based on: that

  • Catchment area size (range- 52 to 130 sq. kilometers)

  • Catchment portion intersects urbanized area

  • Sites of “special interest” to regional water management areas

  • Amount or coverage of available biological data- EPA samples


Project based on Sample Point Catchment Area: that

Area that drains to a single point until reaching the next upstream

sample point catchment or adjoiningsample point catchment

  • Each catchment polygon represents the drainage area for its

    corresponding EPA sample site.

  • Catchment polygons populated with data values that describe land use and selected census variables


  • Data required to determine catchment areas: that

    • Ohio EPA sample points

    • Vector stream file- Valley Segment Type rivers (VST) from

    • National Hydrography Dataset (NHD) of USGS & US EPA

    • Digital Elevation Model (DEM)- 10 & 30 m cell size from USGS

    • Flow direction grid

    • DEM derived streams

    • Geometric network linking points and streams

INPUT

OUTPUT


  • Digital Elevation Model (DEM) Processing that

  • ArcHydro Terrain Processing functions in ESRI ArcGIS

    • Adjust or enhance DEM using Valley Stream Segment (AGREE)

    • Fill Sinks

    • Determine flow direction

    • Determine flow accumulation

    • Define stream definition & segmentation

    • Transform into vector stream drainage

Before enhancement


  • Digital Elevation Model (DEM) Processing that

  • ArcHydro Terrain Processing functions in ESRI ArcGIS

    • Adjust or enhance DEM using Valley Stream Segment (AGREE)

    • Fill Sinks

    • Determine flow direction

    • Determine flow accumulation

    • Define stream definition & segmentation

    • Transform into vector stream drainage

After enhancement


Terrain Processing: that Edit DEM Elevation Values AGREE


Terrain Processing: that Create Flow Direction Grid


Terrain Processing: that Flow Accumulation Grid


Terrain Processing: that Stream Definition/Segmentation


Terrain Processing: that Drainage Line Processing

Vector stream created

(DEM Derived Stream)


Terrain Processing: that

Create catchments

  • The geometric network consists of 3 data layers- network

  • junctions, EPA sample points, and the DEM stream.

  • The flow direction grid and geometric network are used to

  • delineate the sample point catchment polygon.


Populate Catchments with Data that

  • Land Use (1994 from ODNR)

    • Clip land use polygons at catchment boundary

    • Recalculate area in square meters for each land use

      (Urban, Agricultural/Open Urban Areas, Shrub/Scrub,

      Wooded, Open Water, Non Forested Wetlands, Barren)

  • Census (2000 from US Census Bureau)

    • Clip Census data polygons at catchment boundary

    • Area proportion field values according to new polygon size

    • Fields- Year structure built, population,households, &

      housing units


  • Create 500 meter Buffer Polygons that

  • within Sample Catchments

  • why? To determine ifvariations observed in the IBI/ICI data could be

  • explained by smaller areas of influence

    • Center of buffer is sample point

    • Areas outside the 500 m radius or catchment are deleted (clipped)

    • Recalculate area in square meters for each land use

    • Area proportion census field values according to new polygon


  • Create Riparian Buffer Polygons of that

  • 15, 30, 60, 90, 120, &150 meters

  • from Streams within Sample Catchments

  • why? To determine ifvariations observed in the IBI/ICI data could be

  • explained by stream buffers at various distances of influence

    • Center of buffer is Stream within catchment

    • Areas outside the buffer zone or catchment are deleted (clipped)

    • Recalculate area in square meters for each land use

    • Area proportion Census field values according to new polygon

120 meter Riparian Buffer Example


Determine Downstream Sample Point & Catchment that

Calculate Downstream Distance Between Sample Points


Create Catchment Aggregation that

of Urbanization Data by Magnitude

  • Network trace created to identify all upstream points within the water subshed.

  • Catchment magnitudes are determined.

  • Catchment magnitude = 0 means that the data is only aggregated for one specific catchment.

  • Magnitude = 1 means the data of original catchment and one catchment level upstream

  • of the current catchment are aggregated.

  • Catchment magnitude is determined until proceeding upstream to the headwaters.

  • Land use and Census data are aggregated by the magnitude of the catchment.

  • River length, the average and sum of stream distances are calculated per magnitude.


ad