Transcript
Page 1: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Raster Analysis and Terrain Analysis

Chapter 10 & 11Raster Analysis

Page 2: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Raster Data Model

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

Forest 1,…9,10 259.63

•Excellent for terrain and hydrological modeling•Complex constructs built from raster data

-Connected cells can be formed in to networks -Related cells can be grouped into neighborhoods or regions

Page 3: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Examples of Raster Analysis

•Predict fate of pollutants in the atmosphere•The spread of disease•Animal migrations•Crop yields•EPA - hazard analysis of urban superfund sites•Market analysis•Watershed analysis•Terrain analysis

Page 4: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis
Page 5: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Map algebra•Concept introduced and developed 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 such as addition, subtraction and multiplication

Page 6: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis
Page 7: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis
Page 8: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Scope: Local operations

Page 9: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Many Local Functions

(page 412 of text)

Page 10: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Logical OperationsAND

Non-zero values are “true”, zero values are “false”N = null values

Page 11: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Logical OperationsOR

Non-zero values are “true”, zero values are “false”N = null values

Page 12: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Logical OperationsNOT

Page 13: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

More Local Functions – logical comparisons

Page 14: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

An Example of a Logical Operation

Page 15: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Reclassification

Page 16: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

ConditionalFunction

Page 17: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Nested Functions

Page 18: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Overlay

Page 19: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Raster Clip Operation

Page 20: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Raster Addition

Page 21: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Be Careful of Ambiguity

Page 22: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Be Careful of Ambiguity

21 31 51

23 74 13

22 11 3

Page 23: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Raster Overlay in Idrisi

Page 24: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

1 0 1

1 0 1

1 0 1

1 1 1

0 0 1

0 0 1

1 0 1

0 0 1

0 0 1

First * Second

0*0=0

0*1=0

1*1=1

Page 25: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

1 0 1

1 0 1

1 0 1

1 1 1

0 0 1

0 0 1

2 1 2

1 0 2

1 0 2

First + Second

0+0=0

0+1=1

1+1=2

Page 26: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis
Page 27: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Scope: Neighborhood operations

Page 28: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Neighborhood Operations

Moving Windows(Windows can be any size; often odd to provide a center)

Page 29: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Kernels vs. Moving Window

Page 30: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Neighborhood Operations: Separate edge kernals can be used

Page 31: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Neighborhood Operations

Page 32: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Example:Identifying spatial differences in a raster layer

Page 33: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

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

Page 34: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Zonal Functions

7.211

30

11

22341131247

Page 35: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Scope: Global operation

Page 36: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

ArcMap’s Raster Calculator

                                                                  

        

Raster Calculator tool dialog box exampleFrom ArcGIS Help Files

Page 37: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

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

Page 38: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis
Page 39: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

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

Page 40: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Friction Surface

6.54

4.22

4.225001020 22

Page 41: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Terrain Analysis

Page 42: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Digital Elevation Models (DEM)/Terrain Analysis

Terrain determines the natural availability and location of surface water, and hence soil moisture and drainage.

Water quality through control of sediment entrainment and transport, slope steepness and direction defines flood zones, watershed boundaries and hydrologic networks.

Terrain also strongly influences location and nature of transportation networks or the cost and methods of house and road construction.

Page 43: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis
Page 44: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Digital Elevation Models

Terrain AnalysisSlope and Aspect

•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)

Page 45: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Slope (continued)

Page 46: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Slope (continued)Measured in the steepest direction of elevation change

Often does not fall parallel to the raster rows or columns

Which cells to use?

Several different methods:

•Four nearest cells•3rd Order Finite Difference

Page 47: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Slope (continued)

Elevation is Z•Using a 3 by 3 (or 5 by 5) moving window•Each cell is assigned a subscript and the elevation value at that location is referred to by a subscripted Z value

The most common formula:

Page 48: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Slope (continued)

for Zo

ΔZ/Δx = (49 – 40)/20 = 0.45

ΔZ/Δy = (45 – 48)/20 = -0.15

Page 49: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Slope (continued)

•Slope calculation base on cells adjacent to the center cell•The distance is from cell center to cell center

for Zo

ΔZ/Δx = (49 – 40)/20 = 0.45

ΔZ/Δy = (45 – 48)/20 = -0.15

Generalized formula for

ΔZ/Δx and ΔZ/Δy

ΔZ/Δx = (Z5 – Z4)2*

ΔZ/Δy = (Z2 – Z7)2*

Using the four nearest cells

* = times cell width

Page 50: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Slope (continued)

ΔZ/Δx = (49 – 40)/20 = 0.45 ΔZ/Δy = (45 – 48)/20 = -0.15

Kernal for ΔZ/Δx Kernal for ΔZ/Δy

Multiply (kernal, cell by cell)Add (results)Divide by #cells x cell widthUse slope formula

Page 51: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Multiply (kernal, cell by cell)Add (results)Divide by #cells x cell widthUse slope formula

Page 52: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Slope in ArcGIS 10

From ArcGIS 10 Help

Page 53: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Aspect

Page 54: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Aspect

The orientation (in compass angles) of a slope

Calculation:

Aspect = tan-1[ -(ΔZ/Δy)/(ΔZ/Δx)]

As with slope, estimated aspect varies with the methods used to determine ΔZ/Δx and ΔZ/Δy

Aspect calculations also use the four nearest cell or the 3rd-order finite difference methods

Page 55: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Aspect in ArcGIS 10

From ArcGIS 10 Help

Page 56: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Curvature

Page 57: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Viewshed

The viewshed for a point is the collectionof areas visible from that point.

Views from any non-flat location are blocked byterrain.

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

Page 58: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis
Page 59: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis
Page 60: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Shaded Relief Surfaces

Page 61: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

From ArcGIS 10 Desktop Help

Flow Direction

Page 62: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

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

Page 63: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

High Pass Filter

• Raster data may also contain “noise”; values that are large or small relative to their spatial context.

• A mean kernal is used to reduce the difference between a cell and surrounding cells. (done by average across a group of cells)

• The identified spikes or pits can then be corrected or removed by editing

Page 64: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

35.7

Page 65: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis
Page 66: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Watershed

•An area that contributes flow to a point on the landscapeWater falling anywhere in the upstream area of a watershed will pass through that point.

•Many be small or large

•Identified from a flow direction surface

Drainage network

•A set of cells through which surface water flows

•Based on the flow direction surface

Page 67: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis
Page 68: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Routing and Allocation•Routing

–Finding the shortest path between any nodes in a network–Optimal path

•Each link in the net can also be assigned an impedance value •Using an accumulated distance and the impedance factor

–Most efficient route can be found, rather than just the shortest

–Nodes can also be coded with stops and barriers•Preventing movement and forcing traffic along another path

–Although routing can be done in raster, it is much easier if performed in a vector system

Page 69: Raster Analysis and Terrain Analysis Chapter 10 & 11 Raster Analysis

Routing and Allocation

•Allocation –Process used to define the areal extent of services areas

–Service areas are defined around a site•Region is formed that includes a defined area

–Location/allocation model (optimizes network efficiency)•Technique for the evaluation of multiple facility locations

–Determining the configuration of facilities (location)–Assigning demand for the facilities (allocation)


Top Related