GEOL 452/552 - GIS for Geoscientists I Lecture 22 - Chapter 8 (Raster Analysis, part 3) Today: Zonal Analysis (statistics) for polygons, lines, points, interpolation (IDW), Effects Toolbar, analysis masks USGS Seamless raster data server Ch 8 Tut 36 - end HW 13: Ch 8 challenge questions (+ extra, see WebCT) Class exercise data in follow_along/ch8c_class_ex folder, copy and start ArcMap with Ch8C_class_ex.mxd
Zonal functions (statistics) Needs 2 inputs: a zone layer: polygons, lines or points; or integer raster a value raster Zones in a zone raster don t need to be continuous! (see: regions) value raster zones (raster, polygons)
value raster = elevation raster zone layer = watersheds for each zone do: get stat for value raster cells that are inside the zone Creates many types of stats per zone id creates a standalone table and a chart Table can be joined to zone layer (via OID as key) think: Summary but with spatial grouping instead of group average elevation of yellow watershed = 234.6 m avg. elevation of blue watershed = 674.5 m example: for each watershed ( groups) get statistics (mean,min, max, etc.) of the elevation inside (covered)
Which watershed (or type of outcrop) has the lowest average (mean) elevation of any? Spatial Analyst - Zonal Statistics zone layer: watershed_zones (or geol_zones.img) value raster: dem2 (elevation) Join output to zone: Yes, Save as elev_per_watershed.shp All Stats are about elevation Sort by MEAN COUNT: number of cells with each watershed Points and line can also be used as zones
Spatial Interpolation point samples (x,y, value ): here 7 samples, (A) irregular distributio for a raster (B) fill each cell with a value Principle: a cell value should be similar to the cell s closest samples different spatial interpolation algorithms (IDE, spline, kriging) distance to sample and sample value matter A) B)
Interpolation Need: point samples (here: elevation, think GPS points) Go over all cells of an empty raster, for each cell: Grab all the closest point samples (here: within a radius around the cell) Do some spatial math with these point samples IDW: faster, Spline: smoother result: cell s elevation
True vs interpolated elevation: Usually: loss of detail, smoother results Limit: number (density) of input point samples Spatial Analyst - Interpolate to Raster - Inverse Distance Weighting (IWD) Calculate a 50 m raster (IDE_elev.img) from elevation_point_samples Us default parameters for IDE algorithm (ELEV, power 2, 12 closest points) Symbolize as stretched, import colors from dem2 True elevation Interpolated
blurry results from IDE interpolation(compared to dem2) same raster resolution (50 m) but far less samples were used than when making the real DEM (probably used points from contour lines) (re-visit next lecture: DEM from Lidar)
The Effects toolbar How can we visually compare the two elevation rasters? make sure both rasters have the same high/low color values (1965 and 872, same color ramp, stretched) Move IDE elevation on top of dem2 Activate the Effects Toolbar Set Layer to IDE elevation layer press swipe button and drag left mouse or: press flicker (500 ms) button
IDW: search radius variable search radius: grab n (12) closest points (... up to a max. distance, 0=no distance limit) fixed search radius: grab ALL points within a distance of X units (... but use at least n points, 0=use all)
Setting an Analysis mask 2. Mask grid or polygon mask.img or mask.shp Data Aspect of DEM NoData Applies a clip with mask to ALL output grids Masks may be grids, polygon shapefiles or feature classes. But: rasters are still rectangles, outside is simply filled with NoData (usually transparent) Example: Analysis always within a county but your input rasters are larger See also: Extract by Mask tool (raster clipping) or DataFrame - Clip to Shape (nondestructive, temporary visual clip!)
ortho.gis.iastate.edu raster data Home > Map type (here: Lidar Hillshade) > Status map (shown here) Recenter vs. Zoom in, may need to press Refresh Map Change width/height (e.g. to 2000 x 2000 pixels), will keep center but may be too big to view) When viewing the right area, press Download map
10 Mb (10000K) download limit Get Geotiff (interanally georeferenced to UTM 15 NAD83) Or: get JPEG version AND ArcView Header (.jgw worldfile, coordinates of corners) put 1234.jpg and 1234.jpg in same folder, if rename: keep same base name!
Seamless.usgs.gov Web based raster download (interface can be very clunky!) Display = preview in Browser (not all types of data are available everywhere!) Request download area: interactive rectangle or extent (lat/long) Download data or modify Data request (wait for refresh) If possible get geotifs, warning: data can be large, don t download multiple files!
Wrap up HW 12, ex 5 gotcha: use zonal analysis (zones = points), new due data Nov. 17 (check WebCT) HW 13, challenge problem + in addition to AND'ing (or multiplying) the 6 binary maps (for "where are ALL criteria true?"), also ADD (+) the 6 binary maps together ( Visualize the resulting 0-6 values in a separate map Explain the meaning of the added binary maps Next lecture: Lidar Data, ArcScene