GEOG 4047 Final Exam Lecture 11
Terms in this set (33)
Raster Data Model
- Pixel and cell
- Binary data, bit and bite, bite expressed in base 2
- Pixel depth, how integer and float points are stored
- Raster data can be used to store both discrete and continuous data
- Bands, resolution vs. cell size, cell size units
Pixel and Cell
• Each pixel contains one numeric value
• Dimension of a pixel varies (resolution)
• Value represents some property of that pixel area, e.g. elevation or rainfall
• Values may be integers or floating point numbers
- Unlike a polygon, each cell has only ONE attribute: its value. Storing multiple values means storing multiple rasters.
• Most raster formats use binary storage
1 10 11 100 101 110 111 1000 1001 1010 1011
• Numbers are stored as a series of 0's representing numbers in base 2
• Binary values are grouped by eight
• The number of bytes used for each pixel - More bytes = larger numbers = more space
• Integer values
- 8-bit pixel (one byte) stores 0 - 255
- 16-bit pixel (two bytes) stores 0 - 65,565
- 24-bit pixel (three bytes) stores 0 -16.7 million
• Floating point values
- Required for decimal number storage - 32-bit pixel (four bytes)
A single raster may include multiple arrays
• Most often used to store color images and satellite images
• Measured by cell size dimensions
• Storage space increases as the square of the resolution
Cell size units
Cell x-y resolution units are based on the raster's coordinate system definition
- Decimal degrees*
*Because distances and areas are fundamental to raster analysis, it is best to use projected coordinate systems for rasters.
Three resampling methods
- Nearest neighbor resampling
- Bilinear resampling
- Cubic convolution resampling
If input grids do not match, then one must be resampled to match the other. Resampling can degrade the accuracy of a raster even if the difference in cell size and location is small.
The new cell grid is determined, and the old cell values must be fit into the new structure somehow.
Several methods are used for resampling.
Nearest neighbor resampling
grabs the value from the old cell that falls at the center of the new cell. It preserves the original value and should always be used with categorical data, or when the original data values need to be preserved. It is the fastest method.
calculates a new value from the four cells that fall closest to the center of the new cell. It uses a distance-weighted algorithm based on the old cell centers. It is best used with continuous data such as elevation.
Cubic convolution resampling
calculates a new value from the sixteen cells that fall closest to the center of the new cell. It uses a distance-weighted algorithm based on the old cell centers. It is best used with continuous data such as elevation. It is the most time-consuming method.
Raster analysis techniques
- Map algebra: logical and Boolean operators
- Surface analysis: DEM, slope, aspect, hillshade, contouring - Viewshed analysis, watershed
- Distance functions
- Lowest cost path
- Interpolation (IDW, Kriging, trend, spline)
- Focal vs. block function
- Zonal statistics
• Rasters are essentially arrays of numbers
• Can be added, subtracted, etc
• Line up matching cells vertically
produce either TRUE (1) or FALSE (0) values in the output grid, based on whether a cell meets the condition.
represent maps of True/False states for a particular condition
Convert one set of grid values to another
Manual or classify
Calculates slope of the surface based on surrounding cells. Can be expressed in degrees or percent.
Calculates direction of steepest slope, e.g. which way the slope "faces". Value represents direction from 0-360 where 0/360 is North. Flat areas are assigned a -1 value.
Calculates the brightness or illumination of a surface from a specified light source.
Applications include terrain display and modeling satellite reflectance.
Azimuth is direction of illumination source (315 by default)
Altitude is the angle of the source above the horizon (45 deg)
Calculate areas visible from a set of observation points
Euclidean distance, euclidean direction
• Starts from a set of features (points, lines, polygons).
• Creates a grid where each cell represents distance to the closest of the features.
• Distances are given in coordinate system map units
• Often created in association with a distance function
• Indicates direction of travel to the closest road.
Logical expressions easily create Boolean rasters representing buffers from distance rasters.
Lowest cost path
1. Create start/stop shapefiles
2. Create cost grid
3. Calculate cost distance grid and cost direction grid
4. Find lowest cost path
• Interpolation is the prediction of values in between measured points.
• Sampling of points may be uniform, random, or based on a sampling scheme.
• Numerous methods are used which have different mathematical models and make different assumptions about the data.
• Best application of interpolation relies on substantial study of models and assumptions. If you use it a lot—learn more!
• Appear similar to interpolation, but are calculated differently
- Interpolation predicts values between points using a variety of mathematical methods
- Density functions count occurrences within a given radius and divide by the area
Focal vs. Block function
look at lecture
• Zones defined by the zone layer (watersheds)
• Generates statistics for each zone from the value grid (slope)
• Output is either a raster, or a table
Zonal statistics of lines
Use Feature-ID for zone designation so that each stream is a unique zone.
Zonal statistics of points
• Use name or FID for each point so each is unique zone.
• Calculate stats for each point.
• All statistics are the same—the elevation of the point.