lecture 22 - chapter 8 (raster analysis, part...
Post on 28-May-2018
218 Views
Preview:
TRANSCRIPT
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)
zones (raster, polygons)value raster
• 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
• example:
• for each watershed (“groups)
• get statistics (mean,min, max, etc.)
• of the elevation inside (covered)
value raster = elevation raster zone layer = watersheds
avg. elevation of blue watershed = 674.5 m
average elevation of yellow watershed = 234.6 m
• 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: numberof cells with eachwatershed
• Points and linecan also be usedas zones
Spatial Interpolation• point samples (x,y, “value”): here 7 samples, (A) irregular distribution• 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 maskMask grid or polygon
NoData
Data
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 largerSee also: Extract by Mask tool (raster clipping) or DataFrame - Clip to Shape (non-destructive, temporary visual clip!)
mask.img or mask.shp
2.
Aspect of DEM
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
•
top related