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Working with Raster Grids and Map Algebra

Working with Raster Grids and Map Algebra. Katherine Paybins, USGS. Basic Ingredien ts. Grid of data, such as the National Land Cover dataset, from 2006, and a point, line, or polygon feature dataset. Area of interest, such as the Coterminous US, or the boundary of WV, or drainage areas in WV

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Working with Raster Grids and Map Algebra

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  1. Working with Raster Grids and Map Algebra Katherine Paybins, USGS

  2. Basic Ingredients • Grid of data, such as the National Land Cover dataset, from 2006, and a point, line, or polygon feature dataset. • Area of interest, such as the Coterminous US, or the boundary of WV, or drainage areas in WV • Topic of interest, such as coincidence of dam locations with land cover that is related, like water bodies, wetlands, and emergent woody wetlands. • Well-defined scale of interest.

  3. Area and topic of interest, and scale • NLCD data is available for the NE, NW, SE, and SW, from 2006 imagery. • The grid cells are 30X30 meters. • The land use or cover is classified from LANDSAT satellite images • National Inventory of Dam locations is a point coverage of all types of dams, ranging from < 20 cubic feet of storage, up to >2.5 million cubic feet of storage.

  4. Step one • Increase processing speed by zeroing in on your area of interest. • Select a processing area in the options window • open the toolbox, and select raster processing-clip • Set the new extent and the name of the new grid

  5. Setting processing area

  6. Next- select the type of land cover you are interested in • In the toolbox, open the Spatial Analyst tools, and then the Extraction toolset- you want to extract by attributes of the raster

  7. Now, convert point coverage to a grid of 1 = dam, nodata = nodam • Open the conversion tools in the ArcToolbox • Open the “to Raster” directory • Choose feature to Raster • Make sure to reset the cell size to equal the cell size you wish to compare the data to, so for this example, use 30X30 meters.

  8. Now to add the grids together with Raster Math

  9. The result

  10. Some data analysis • The WV_dam_grd has 645 dam locations cells = 1 in the grid • The wv_water_dam combination results in values of 12 and 91. In other words, the addition of the two grids found no dams coincident with emergent woody wetlands • Additionally, the raster combination of water and dams has only 284 cells with data, so we can tell from this analysis that 2/3 of the dam locations are not coincident with the NLCD water or wetlands designation

  11. Other possible grid combinations for analysis • Extract urban areas from the NLCD or from USGS DRGs in raster format using the values in the raster.vat to produce an impervious surface raster • Calculate change in forest or farm areas using multiple years of NLCD data • Compare multiple years of land cover data, and animate in ArcScene the change of of land cover over time.

  12. considerations • Know your scale– a 30 meter grid is admittedly coarse for the analysis within, for instance a county. It is well-suited for state-wide or US-wide analyses. • A 30 meter grid of land cover may be your only resource other than orthoimages, though Aaron Maxwell earlier illustrated that efforts are ongoing to improve land cover resolution • Some ground-truthing will add certainty in your results, • Use the Xtools Pro toolbar function to see a photo of the area of concern (Go to Google Earth function)Slide 21 • The NLCD data are good for more than just an interesting and pretty background

  13. Questions?

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