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  • Raster Analysis

    Raster cells store data (nominal, ordinal, interval/ratio)

    •Complex constructs built from raster data

    Connected cells can be formed in to networks

    Related cells can be grouped into neighborhoods or regions

    Examples:

    Predict fate of pollutants in the atmosphere

    The spread of disease

    Animal migrations

    Crop yields

    EPA - hazard analysis of urban superfund sites

    Local to global scale forest growth analysis

  • Raster

    operations

    require a

    special set

    of tools

  • Raster Analysis

    Map algebra Concept introduced and developed by by Dana Tomlin and

    Joseph Berry (1970’s)

    Cell by Cell combination of raster data layers

    Each number represents a value at a raster cell

    location

    Simple operations can be applied to each number

    Raster layers may be combined through operations

    Addition, subtraction and multiplication

  • Scope: Local operations

  • Scope: Neighborhood operations

  • Scope: Global operation

  • Many Local

    Functions

    (page412 of book)

  • Logical Operations

    AND Non-zero values are “true”, zero values are “false”

    N = null values

    Pg 412 of book

  • Logical Operations

    OR Non-zero values are “true”, zero values are “false”

    N = null values

    Pg 412 of book

  • Logical Operations

    NOT

  • More Local Functions – logical comparisons

    (pg 414 of book)

  • Conditional

    Function

  • Nested

    Functions

    no yes

    Output= Con(ISNULL(LayerA), LayerC, LayerB)

  • Neighborhood

    Operations

    Moving Windows (Windows can be any size;

    often odd to provide a center)

  • Neighborhood

    Operations

  • Neighborhood Operations: Mean Function

    What about the edges?

  • Neighborhood Operations: Separate edge kernals can be used

  • Example:Identifying

    spatial differences in

    a raster layer

  • Raster Analysis

    Moving windows and kernals can be used with a mean

    kernal to reduce the difference between a cell and

    surrounding cells. (done by average across a group of cells)

    Raster data may also contain “noise”; values that are large

    or small relative to their spatial context. (Noise often requiring correction or smooth(ing))

    Know as “high-pass” filters

    The identified spikes or pits can then be corrected or

    removed by editing

  • Raster Analysis

    High pass filters

    Return:

    •Small values when smoothly changing values.

    •Large positive values when centered on a spike

    •Large negative values when centered on a pit

  • 35.7

  • Mean filter

    applied

    Note edge erosion

  • Moving windows: Consider the overlap in cell calculations

    Neighborhood operations often

    Increase spatial covariance

  • Overlay in Raster

    Union and Clip

    Cell by Cell Addition or Multiplication

    Attribute combinations corresponding to

    unique cell combinations

  • Raster Clip or “Mask” (used in Lab 10)

    What if you

    only want

    certain cells?

  • Raster Clip or “Mask” (used in Lab 10)

    Note: removed cell output values could be

    0 or N depending of the GIS software

    used.

  • Raster zonal function

  • Issues in Raster Addition

  • A Problem with Raster

    Analysis

    • Too many cells

    • Typically, one-to-one relationship between

    spatial object and attribute table

    • Rasters have multiple cells per feature

    • Attribute tables grow to be unwieldy

  • Vector Raster

  • Raster overlay as addition

  • Output layer DOES NOT

    have unique records Raster Overlay

  • What to do? First multiply Layer A by 10

  • Cost Surface

    The minimum cost of reaching cells in a layer from

    one or more sources cells

    “travel costs” Time to school; hospital;

    Chance of noxious foreign weed spreading out from an introduction point

    •Units can be money, time, etc.

    •Distance measure is combined with a fixed cost per unit

    distance to calculate travel cost

    •If multiple source cells, the lowest cost is typically placed in

    the output cell

  • Friction Surface (version of a Cost Surface)

    The cell values of a friction surface represent the cost per unit

    travel distance for crossing each cell – varies from cell to cell

    Used to represent areas with variable travel cost.

    Notes:

    •Barriers can be added.

    •Multiple paths are often not allowed

    •Cost and Friction Surfaces are always related to a source

    cell(s); “from something”

    •The center of a cell is always used the distance calculations

  • Digital Elevation Models &

    Terrain Analysis

  • Terrain determines or influences:

    - natural availability and location of surface water,

    and hence soil moisture and drainage.

    - water quality through control of sediment

    entrainment/transport, slope steepness.

    - direction which defines flood zones, watershed

    boundaries and hydrologic networks.

    - location and nature of transportation networks or

    the cost(methods) of house(road) construction.

  • Digital Elevation Models

    •Used for: hydrology, conservation, site planning, other

    infrastructure development.

    •Watershed boundaries, flowpaths and direction, erosion

    modeling, and viewshed determination all use slope and/or

    aspect data as input.

    •Slope is defined as the change is elevation (a rise) with a

    change in horizontal position (a run).

    •Slope is often reported in degrees (0° is flat, 90° is vertical)

  • Formats - Contour Elevation Data

    • Source Independent

    • USGS topo maps

    • Contour shows a

    line of constant

    elevation

    • Generally used

    more as a

    cartographic

    representation

    From Sean Vaughn, MNDNR

  • DEM’s consist of an array representing

    elevation values at regularly spaced intervals

    commonly known as cells.

    ELEVATION

    VALUES (ft)

    Formats - Digital Elevation Models

    X

    Y

    Z

    From Sean Vaughn, MNDNR

  • DEM = Raster = Grid

    Digital Elevation Models

    Raster (Format)

    DEM = Grid vs. Vector data format

    From Sean Vaughn, MNDNR

  • DEM Structure • Each cell usually

    stores the average

    elevation of grid cell.

    • Typically they store

    the value at the

    center of the grid cell.

    • Elevations are

    presented graphically

    in shades or colors.

    67 56 49

    53 44 37

    58 55 22

    D ig

    it a l

    G ra

    p h ic

    a l

    Digital Elevation Models

    From Sean Vaughn, MNDNR

  • DEMs are a common way of representing elevation where every

    grid cell is given an elevation value. This allows for very rapid

    processing and supports a wide-array of analyses.

    Digital Elevation Models

    From Sean Vaughn, MNDNR

  •  Resolution

     30 Meter

     USGS produced from Quad Hypsography.

     DNR published format in MN.

     Course resolution

     10 Meter

     Interpolated

     Resampled

    52

    Previously Published National DEMs

    From Sean Vaughn, MNDNR

  •  Resolution

     1 Meter

     3 Meter

     Most common published format

    in MN.

     Storage requirements & faster

    drawing speeds. 53

    Previously Published National DEMs

  • Resolution Tradeoff

    • Lower resolution = Faster processing

    • Higher resolution = Maintain small features

    1-meter DEM claims 9-

    times more process

    resources and storage

    than a 3-meter DEM

    From Sean Vaughn, MNDNR

  • Viewshed

    The viewshed for a point is the collection

    of areas visible from that point.

    Views from any non-flat location are blocked by

    terrain.

    Elevations will hide a point if they are higher than the

    viewing point, or higher than the line of site between

    the viewing point and target point

  • not

  • Shaded Relief Surfaces

  •  The azimuth is the

    angular direction of the

    sun.

     Measured from north in

    clockwise degrees from 0

    to 360.

     The altitude is the slope or

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