Topic 10 Flashcards
what is isarithmic mapping
deals with continuous fields
elevation mainly
impression of depth
based on the concept of continuity of phenomena
rate of change maps
isometric
isopleth
change of elevation over space = ______
slope
isometric
by far more common
location of points is real
true point data
isopleth
conceptual point data
phenomena we have is continuous, but we measure at a location. measured at the centre of a polygon
where do points come from?
lidar
TIN (triangulated networks) (sketchup angled surfaces)
isolines
rasters (pixels and cells)
Sfm
surveyed points
satellite measurements
how to model continuous surfaces
raster
TIN
isolines
what is thiessen polygon
used for socio-economic data
polygons represent the spacing of the dots themselves
irregular tesallation
what is TIN
triangulated networks
estimating between known points
describe global mapping methods
raster
regression model that produces a surface to then extract elevations
trend surface
shows general trends of the data
extreme values along the edges (nothing to control it if there no points there)
describe local mapping methods
look at the same amount of your point observations to estimate values
Inverse distance weighting
geospatial - kriging
more hyper local/defined
describe inverse distance weighting
estimating values of a point based on nearest neighbours
the closer another point the more influence it has
creates little tragets in your data (data is most likely not dense enough)
geospatial - kriging
often the “best” interpolation method
look at the spatial distribution of points and their attribures and then how it sets up the inverse distance weighting
data distribution controls the search parameters
how to symbolize isarithmic maps
isolines
shading betweeen isolines
continuous tone
fishnet or 3d perspective
augmentation
hillshade
slope, azimuth, curvature
classed values vs. colour ramp
what works better for your map?
hypsometric curve
use of colour and size allocation can be problematic
elevation changes on earths surface
contouring characteristics
contours usually relate to elevation
estimating values
draw line of equal value between data points
inverse weighting distancing
isolines : show lines of equal value
create vector representation of ‘breaks’
can only be ratio or interval measurement level
what are slope and aspect measurements and the two components
slope and aspect are key measurements that can be performed on terrain models
two components
slope (vertical)
aspect (horizontal)
0 = north
90 = east
180 = south
270 = west
slope computations
raster DEM
computation is the ratio of two components
(vertical and window distance)
convert to % by multiplying by 100
can be extended to account for more than 4 neighbours (extended to 8)
complex and used inside ArcGIS
aspect computations
raster DEM
aspect is expressed using angles on a unit circle (circular data)
sign and magnitude of differences reveals the “tilt”
does representation of slope appear less or more noisy (blurry) with larger pixels?
less noisy
small pixels = _____
more noise
usually not as good for slope and aspect maps
precision of slope and aspect maps
derivation of slope and aspect maps from terrain models are very sensitive to precision/accuracy of the input DEM on TIN
questionable
precision is much lower with larger pixels
best is 2x the original data pixel size
deriving slope curvature
measured in slope direction or aspect
spatial change of slope or aspect
spatial derivative
input map
first order produce (slope angle, aspect)
second order products (slope profile, plan curvature, flat, convex, concave)
profile curvature
going down or to 0 (skate ramp) = concave up (+)
becomes steeper (bows out) = convex up (-)
planform curvature
diverges away from middle = convex (+)
converges to middle = concave (-)
what is relative radiance?q
simulate relative amount of light being reflected by a surface
best ways to visualize terrain
contours and hillshade are the best for elevation
DEMS and TIN are very useful
early techniques involved artists shading the map, now there is automation within the software to “project light”
describe unidirectional vs multidirectional
uni has light source from one direction
multi is coming from various angles but makes the image appear more washed out but also has more detail
Eyton (1990) colour sterescopic effect
added detail and countours can sometimes make your map harder to interpret or understand quickly
what is a low pass filter
removes all the high frequency information (noise) and shows general trends in the data
what is a high pass filter
removes all the low frequency information and shows the high frequency information
takes out general trends
what does a convolution filter do
widely applied operations in a variety of raster application
used to smooth things out
extract things
remove things
DEM (slope and aspect)
there is always a ________ when running filters on images
trade-off
low pass filter in photoshop example
the girl photo
remove noise and “scratch marks”
high pass filter photoshop example
owl photo
sharpens edges
extract high frequency and then add it back in
for aesthetics
convolution coefficients
the moving window (kernal) is a matrix of convolution coefficients (weights) commonly 3x3, 5x5, 7x7 pixels in size
moving window (kernal)
3x3, 5x5, 7x7 pixels in size
1/9
low pass
smoothing
only interval and ratio data
bigger the window the smoother it will be
reduces difference between pixels
edge detector (laplacian) filters
shows edges
high pass
enhance or sharpen
exaggerates difference between pixels
nominal = ________ filter
modal
sobel filters
horizontal edge detector
vertical edge detector
laplacian filters
edge detector
edges = 0
shows you where the edges are
look at nearest neighbour
if you put a 9 in the middle it becomes and edge enhancement
statistical filters
median (remove noise)
modal (reduce noise)
minimum ( erosion)
maximum (expansion)
