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Geog 480 > Topic 10 > Flashcards

Topic 10 Flashcards

1
Q

what is isarithmic mapping

A

deals with continuous fields
elevation mainly
impression of depth
based on the concept of continuity of phenomena

rate of change maps

isometric
isopleth

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2
Q

change of elevation over space = ______

A

slope

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3
Q

isometric

A

by far more common
location of points is real
true point data

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4
Q

isopleth

A

conceptual point data
phenomena we have is continuous, but we measure at a location. measured at the centre of a polygon

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5
Q

where do points come from?

A

lidar
TIN (triangulated networks) (sketchup angled surfaces)
isolines
rasters (pixels and cells)
Sfm
surveyed points
satellite measurements

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6
Q

how to model continuous surfaces

A

raster
TIN
isolines

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7
Q

what is thiessen polygon

A

used for socio-economic data
polygons represent the spacing of the dots themselves
irregular tesallation

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8
Q

what is TIN

A

triangulated networks
estimating between known points

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9
Q

describe global mapping methods

A

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)

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10
Q

describe local mapping methods

A

look at the same amount of your point observations to estimate values

Inverse distance weighting

geospatial - kriging

more hyper local/defined

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11
Q

describe inverse distance weighting

A

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)

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12
Q

geospatial - kriging

A

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

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13
Q

how to symbolize isarithmic maps

A

isolines
shading betweeen isolines
continuous tone

fishnet or 3d perspective

augmentation
hillshade
slope, azimuth, curvature

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14
Q

classed values vs. colour ramp

A

what works better for your map?

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15
Q

hypsometric curve

A

use of colour and size allocation can be problematic

elevation changes on earths surface

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16
Q

contouring characteristics

A

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

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17
Q

what are slope and aspect measurements and the two components

A

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

18
Q

slope computations

A

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

19
Q

aspect computations

A

raster DEM
aspect is expressed using angles on a unit circle (circular data)

sign and magnitude of differences reveals the “tilt”

20
Q

does representation of slope appear less or more noisy (blurry) with larger pixels?

A

less noisy

21
Q

small pixels = _____

A

more noise

usually not as good for slope and aspect maps

22
Q

precision of slope and aspect maps

A

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

23
Q

deriving slope curvature

A

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)

24
Q

profile curvature

A

going down or to 0 (skate ramp) = concave up (+)
becomes steeper (bows out) = convex up (-)

25
Q

planform curvature

A

diverges away from middle = convex (+)
converges to middle = concave (-)

26
Q

what is relative radiance?q

A

simulate relative amount of light being reflected by a surface

27
Q

best ways to visualize terrain

A

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”

28
Q

describe unidirectional vs multidirectional

A

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

29
Q

Eyton (1990) colour sterescopic effect

A

added detail and countours can sometimes make your map harder to interpret or understand quickly

30
Q

what is a low pass filter

A

removes all the high frequency information (noise) and shows general trends in the data

31
Q

what is a high pass filter

A

removes all the low frequency information and shows the high frequency information
takes out general trends

32
Q

what does a convolution filter do

A

widely applied operations in a variety of raster application

used to smooth things out
extract things
remove things
DEM (slope and aspect)

33
Q

there is always a ________ when running filters on images

A

trade-off

34
Q

low pass filter in photoshop example

A

the girl photo
remove noise and “scratch marks”

35
Q

high pass filter photoshop example

A

owl photo
sharpens edges
extract high frequency and then add it back in
for aesthetics

36
Q

convolution coefficients

A

the moving window (kernal) is a matrix of convolution coefficients (weights) commonly 3x3, 5x5, 7x7 pixels in size

37
Q

moving window (kernal)

A

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

38
Q

edge detector (laplacian) filters

A

shows edges
high pass
enhance or sharpen
exaggerates difference between pixels

39
Q

nominal = ________ filter

A

modal

40
Q

sobel filters

A

horizontal edge detector
vertical edge detector

41
Q

laplacian filters

A

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

42
Q

statistical filters

A

median (remove noise)

modal (reduce noise)

minimum ( erosion)

maximum (expansion)

Geog 480 (14 decks)

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