Georeferencing/spatial analysis Flashcards
what is georeferencing
the process of aligning an unreferenced dataset to one that has a spatial reference system
what are examples of data NOT georeferenced
satellite and aerial images
what are control points
locations that are identifiable and have known coordinates
why are control points important for georeferencing
used to TIE unreferenced data to the dataset with known coordinates
what are good control points
- road intersections
- corners of budlings
- boulders
- mountain peaks
what are bad control points
- tops of buildings
- trees
- center of field
- shoelines
what transformation can be done with 1 control point
shifts the map, NO change in scale or rotation
what transformation can be done with 3 control point
can shift and scale and rotate the map
what transformation can be done with 6 control point
can bend the image
what transformation can be done with 10 control point
twist the image
what is the shift called with 1 control point
zero order shift
what is the shift called with 6 control point
second order shift
what is the shift called with 3 control point
first order affine
what is the shift called with 10 control point
third order shift
what is calculated when a transformation is done
residual error
what is a residual error
difference between where the georeferenced point is and the specific location
what is the RMSE
root mean squared error (square root of the mean value of all squared residual)
do you want a high or low residual error
LOW
what is the MINIMUM control points to calculate the RMSE
FOUR
what is the amount of residual error based on
the quality of the control points
what is high RMSE caused by
poor control points
what are the three residuals
- forward
- inverse
- forward-inverse
forward residual
shows the error in the SAME units as the data frame
inverse residual
shows you the error in pixel units
forward inverse residual
measure of overall accuracy measured by pixels in the image
what is resampling during a transformation
each cell is given a new value based on its location
three types of resampling
- nearest neighbour
- bilinear interpolation
- cubic convolution
what is nearest neighbour resampling
does not alter the original values but adopts the value of the nearest pixel
what are issues with nearest neighbour resampling
- may result in blocky images
- may duplicate or lose data
what kind of data is nearest neighbour resampling good for
discrete data (zoning, roads)
what is bilinear interpolation
weighted average of four pixels in the original grid nearest the new pixel
what is bilinear interpolation not suitable for
discrete data
what is bilinear interpolation used for
continuous data
how does cubic convolution differ from bilinear interpolation
calculates a distanced weighed of 16 pixels NOT 4 pixels from the original grid that surrounds the new output
what is cubic convolution good for
continuous data
what is cubic convolution NOT good for
discrete data
what types of resampling produce SHARPER images
bilinear interpolation
cubic convolution
what type of resampling is generally avoided for GIS
nearest neighbour
what is spatial analysis
how features are spatially related to one another
what are some uses of spatial analysis
- cause and effect
- suitability assessment
- predict outcomes
what are four types of spatial analysis
- constraints
- proximity
- networks
- clustering
what is constraints spatial analysis
selections and queries to identify features that meet criteria
what is proximity spatial analysis
how close one feature is to another feature
what is networks spatial analysis
- what is the shortest route to a location
what is clustering spatial analysis
are nearby features similar to one another
what is an example of proximity
Thiessen polygons
Thiessen polygons
a set of polygons that define areas of influence around points in a space
what are buffers
spatial proximity built around a point, line or polygon
what kind of data do buffers use and what is the issue
Euclidean distance (straight line that doesn’t account for restrictions in topography; rivers)
how are networks measured
Manhattan distance
what is the manhattan distance
distance between two points on a grid
what is near
measures the distance between input features and near features
what does kernel density calculate
the density of point features around each output raster cell
what is kernel density an example of
clustering
what kind of features can be used in kernel density
point and line features
what are some uses for kernel density
- house density
- crime reports
- wildlife habitats
can coordinates have a z coordinate
yes
what is the z coordinate
the vertical datum
what does the vertical datum measure
baseline for measuring elevation
what is elevation represented by ion topographic maps
contour lines
what is photogrammetry
stereo pairs used to calculate elevation
what is LIDAR
emits a laser pulse to the earth’s surface and measures the return
what is the accuracy of LIDAR
ranges from 3 to 30 cm
what is digital elevation model
representation of the surface of the earth
does DEM include features on earth’s surface
NO - bare earth model
what is TIN
vector based approach to creating digital elevation models
contrast TIN and DEM
TIN is vector based models and DEM is raster based models
how are points represented in TIN
connected by lines to create a network of non overlapping triangles
is this DEM or TIN
DEM
is this DEM or TIN
TIN
what does TIN stand for
triangulated irregular network
advantages of DEM and TIN
DEM
- accepts data directly from matrix cell
- less complex and faster to process
TIN
- randomly sample data
- displays linear features
- accepts point features
disadvantages of DEM and TIN
DEM
- must be resampled if irregular data
- may miss complex topography
- includes redundant data in low relief areas
TIN
- data is intense and takes longer to process
- each vertex stores x, y, z data
what is DSM
measurements of ground elevation height as well as objects on the ground
How des DSM differ from DEM
DSM measures earth with the features while DEM measures bare earth elevation
what are some applications of spatial analysis
- slope
- aspect
- hill shade
- contour lines
- viewshed
- surface drape
is it DEM or DSM that analyzed watersheds
DEM
