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PLAN 281 Final > Lecture 8 - Raster Analysis > Flashcards

Lecture 8 - Raster Analysis Flashcards

1
Q

What are the different raster analysis methods (map algebra functions)?

A
  • local operations
  • focal (neighbourhood) operations
  • zonal (regional) operations
  • global (extended neighbourhood) operations
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2
Q

What is a basic display (type of values) ?

A

The simplest type of values to display are integers.

  • each integer value can be assigned a unique colour
  • legend: explains the meaning of each colour
  • recoding layer before display may be required if there are too many values for the number of categories/colours available
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3
Q

What is map algebra?

A

An informal language for manipulating representations of continuous variables defined over a common domain

  • provides conventions and a syntax for how operators are combined and the order they are used to create new raster layers
  • ex. newlayer = input1 + input2
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4
Q

What are local operations?

A
  • operate on cell-by-cell basis
  • arithmetic operations only make sense if the values have appropriate scales of measurement
  • ex. cannot find “average” of soil types 3 and 5, nor is soil 5 greater than soil 3 b/c they are ordinal values
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5
Q

What are the 3 local operations?

A

a. Reclassification/recoding
b. Overlay
c. Scalar

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6
Q

Explain reclassification/recoding

A
  1. assign new value to each unique value on input layer (useful when number of unique input values i small)
  2. assign new values by assigning cells to classes or ranges based on their old values (useful when old layer has diff values in each cell - ex. elevation)
  3. sort unique values found on input layer and replace by rank of value
  4. assign new values based on mathematical operations
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7
Q

Explain raster overlay

A
  • create new data by combining map layers of diff features or conditions based on spatial coincidence of individual cells in input layer
  • grids should share certain characteristics: geographic extent; projection; cell resolution; sampling interval
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8
Q

What are the three groups of mathematical operators for map algebra?

A
  • arithmetic operators
  • boolean operators
  • relational operators
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9
Q

What are arithmetic operators?

A
  • / - +

- allows for addition, subtraction, multiplication and division of 2 raster maps or numbers

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10
Q

What are boolean operators?

A

AND, OR, NOT, XOR

  • uses boolean logic (TRUE or FALSE) on input values
  • output values of TRUE are written as 1 and FALSE as 0
  • each attribute can be viewed as a set
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11
Q

What are relational operators?

A

< <= <> = > >=

  • evaluates specific relational conditions (min/max)
  • if condition is TRUE, output is 1
  • if condition is FALSE, output is 0
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12
Q

Explain what each boolean operator means

A 
AND = intersection of sets (multiply)
OR = union of sets (add, reclass anything greater than 1 to 1)
XOR = records that belong to one set or the other, but not both (add, reclass anything greater than 1 to zero b/c can't have both)
NOT = difference b/w sets (reclass to reverse - 0 to 1 and 1 to 0)
  • REMEMBER, can only have binary (only 0 or 1!!), so if you add and its more than 1, you must reclass to make it 0 or 1
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13
Q

What are some other uses of overlay?

A
  • cut and fill
  • change detection
  • many layer updates
  • multivariate classification
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14
Q

What are some other uses of overlay?

A
  • cut and fill
  • change detection
  • many layer updates
  • multivariate classification
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15
Q

What are focal operations?

A
  • a cell’s value in a new layer is determined by the values of the cells surrounding it on the input layer
  • define neighbourhood around cell: output = f(neighbourhood values)
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16
Q

What are the 2 local neighbourhood (focal) operations?

A

a. filtering

b. slope and aspect derivation

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17
Q

What is filtering?

A

A filter operates by moving a window or kernel across the entire raster

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18
Q

What are the steps to filtering?

A
  1. windows (kernels) are often 3x3 cell blocks (also 5x5 and 7x7)
  2. compute a new value for the cell in the centre of the window based on the weighted values of the cells in the window
  3. changing weights (which sum to 1) can have several impacts
    - smoothing (low pass filter reduces local detail)
    - edge enhancement (high pass filter exaggerates local detail)
    - changing distributions
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19
Q

What do low pass filters do? What are the four types of low pass filters?

A

Smooths, reduces local detail, and removes extreme values

  1. mean filter
  2. weighted average
  3. majority filter
  4. median filter
20
Q

What is a weighted average filter?

A

Low pass filter.

  • different weights are assigned to cells, so the value of the central cell in the window is elevated in importance
  • gives the cells old value 12 times the weight of its neighbouring values
  • slightly smooths the layer
21
Q

What is a weighted average filter?

A

Low pass filter.

  • different weights are assigned to cells, so the value of the central cell in the window is elevated in importance
  • gives the cells old value 12 times the weight of its neighbouring values
  • slightly smooths the layer
22
Q

What is a majority filter?

A

Low pass filter

  • replaces central value with most frequently occurring value in kernel
  • used to generalize nominal images
  • fills gaps after vector to raster conversion
23
Q

What is median filter?

A

Low pass filter

  • replaces central value with median value in kernel
  • good for noise removal in poor quality images
24
Q

What do high pass filters do?

A

Exaggerate local detail for edge enhancement

  • useful for finding road edges or polygon edges, especially in remote sensing and aerial photos
  • enhances local detail by giving neighbours negative weights if condition is different (derive boundary pixels to infer presence of discrete spatial objects/entities in an image)
25
Q

What is slope and aspect derivation?

A

We can compute steepness of slope from a layer of elevation measurements by a moving window method similar to filtering (ex. look at difference b/w cell’s elevation value and those of its adjacent neighbours

26
Q

Define slope

A

Steepness: rate of rise or fall of a quantity against horizontal distance (ratio, decimal, fraction, percentage)

27
Q

Define aspect

A

Direction: compass (horizontal) direction in which a slope faces (expressed in degrees clockwise from north)

28
Q

What are slope and aspect useful in analyzing?

A
  • vegetation patterns
  • computing energy balances
  • modelling erosion or runoff (aspect determines direction of runoff)
29
Q

What are zonal operations?

A

Operations on groups of cells (zones)

  • value of each cell in an output grid = f(all input grid cell values that share the same zone)
  • calculate aggregate statistics for a zone (mean, sum, min, range, etc.)
30
Q

What are the applications of zonal operations?

A
  • identifying zones
  • areas of zones
  • perimeters of zones
31
Q

Explain identifying zones

A

By comparing adjacent cells, identify all patches or zones having the same value

  • give each such patch or zone a unique number
  • set each cell’s value to the number of its or zone
  • creates nominal land use (or other) class areas
32
Q

Explain areas of zones

A
  • measure the area of each zone and assign this value to each cell instead of the zones number (alternatively, output may be in form of summary table sent to printer or file)
33
Q

What is the area calculation of contiguous region?

A

(# cells) x (area of each cell)

34
Q

What is the uncertainty with area zones?

A
  • boundary locations are approximated or fuzzy in raster model
  • cell resolution and complexity of shape are important
35
Q

Explain perimeter of zones

A

Measure the perimeter of each zone and assign this value to each cell instead of the zone’s number

  • sum the number of exterior cell edges in each zone
  • alternatively, output may be in form of summary table sent to printer/file
36
Q

What is the perimeter estimation formula?

A

(# exterior cell edges) x (resolution of cell)

37
Q

What is the uncertainty with perimeter of zones?

A
  • accuracy of calculation declines with lower resolution and more complex polygon edges
  • grid orientation effect: calculated values are highly dependent upon the orientation of objects (zones) with respect to orientation of grid
38
Q

What are the global (extended neighbourhood) operations?

A

a. distances
b. buffer zones
c. viewsheds
d. least cost pathways

39
Q

Explain the global operation of distance

A
  • calculate distance of each cell from a cell or nearest of several cells (each cells value in the new layer is its distance from given cells)
  • calculated using Euclidean distances in a raster grid
  • buffers and pathways rely on cell distances
40
Q

Explain the global operation of buffer zones

A
  • buffers around objects and features are very useful GIS capabilities (Ex. noise buffer around roads)
  • buffer operations can be visualized as spreading the object spatially by a given cell-based distance (each cell has its own value + the cell width to its neighbouring cells)
  • raster buffering conceptualized as distance measurement operations on the whole grid and reclassifying cells
41
Q

What are the steps for buffer zone analyses?

A
  1. calculate distance across grid

2. reclassify based on buffer value

42
Q

Explain the global operation of viewsheds

A
  • the visible area aka viewshed
  • given a layer of elevations, and one or more viewpoints, compute the area visible from at least one viewpoint (1 = visible, 0 = not visible)
43
Q

What are viewsheds useful for planning the locations of?

A
  • unsightly facilities (smoke stacks)
  • surveillance facilities (fire towers)
  • transmission facilities
44
Q

Explain the global operation of least cost pathways

A
  • process of finding a route/path between points (origin to destination), which has lowest traversal cost
  • uses friction surface or cost surface to determine path of least resistance (can be specified by slope, physical barriers, land prices, etc.)
  • used to model paths for pipelines, habitat corridors, etc.
45
Q

What are the steps of analysis for least cost pathways?

A
  1. generate friction surface (represents how difficult it is to travel through each cell - barriers)
    - absolute barriers: cannot travel through cell (fence, lakes)
    - relative barriers: can pass but with travel costs (ex. caribou crossing river)
  2. create an accumulated cost surface from points of origin to destination cells
    - track cell-by-cell movement throughout grid to accumulate incremental cost
    - test accumulated cost value against other cell-by-cell travel paths and assign lowest total cost from all paths to the cell
  3. create a least cost path across accumulated cost surface from origin to destination
    - start at origin, iterative search through accumulated cost surface to determine least cost path to destination

PLAN 281 Final (10 decks)

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