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LEAST COST PATH BETWEEN TWO POINTS 09/17/2016 1 Mohamed Magdy Least Cost Path Analysis Overview: The purpose for this project is find the shortest path between two points (A to B) through western desert of Egypt, but while the vehicle used I this project is military vehicle which need some condition to move smoothly as follows: 1- Slope Not greater than 10 degrees 2- Land cover must not be Water or Agriculture Data Provided: The data provided as follows: 1- DEM raster for Study Area 2- Landsat imagery for Study Area 3- Polygon Feature Class for the Area of Interest 4- Two Point Feature Classes for the Start Point and End Point DEM Layer Landsat Layer Analysis Procedure: The Analysis procedure can be divider to four main process as follows: 1- Land Use Land Cover (Outside Model): a. Show the “Image Classification ” toolbar in ArcMap b. Create 10 Training sample polygons for each class as show

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Page 1: Least Cost Path Analysis - Amazon S3 · Mohamed Magdy 1 Least Cost Path Analysis Overview: The purpose for this project is find the shortest path between two points (A to B) through

LEAST COST PATH BETWEEN TWO POINTS 09/17/2016

1 Mohamed Magdy

Least Cost Path Analysis

Overview: The purpose for this project is find the shortest path between two points (A to B) through

western desert of Egypt, but while the vehicle used I this project is military vehicle which need some condition to move smoothly as follows:

1- Slope Not greater than 10 degrees

2- Land cover must not be Water or Agriculture

Data Provided: The data provided as follows:

1- DEM raster for Study Area

2- Landsat imagery for Study Area

3- Polygon Feature Class for the Area of Interest

4- Two Point Feature Classes for the Start Point and End Point

DEM Layer Landsat Layer

Analysis Procedure: The Analysis procedure can be divider to four main process as follows:

1- Land Use Land Cover (Outside Model):

a. Show the “Image Classification” toolbar in ArcMap

b. Create 10 Training sample polygons for each class as show

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c. The output classes as (Water, Agriculture, Urban and Vacant)

d. After finish training step in “Training Sample Manager” window click “Create

Signature file”

e. From “Image Classification” “Classification” menu choose “Maximum

Likelihood Classification” and set:

i. Input raster bands: Landsat Image

ii. Input signature file: Save file from step “d”

iii. Output classified raster: path to output classified raster (Model Input)

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f. Now in Model Insert the GP Tool called “Reclassify” and set:

i. Input raster: Land Cover Raster

ii. Reclass field: Value

iii. Reclassification:

Old Values New Values 61 NoData

71 NoData

81 3

91 1

NoData NoData

iv. Output raster: Path to output reclassified raster

v. Change missing values to NoData: Checked

g. The output Land Cover reclassified raster as shown below

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2- Slope (Inside Model):

a. Insert GP toll called “Slope” in Model builder and set:

i. Input raster: DEM

ii. Output raster: DEM_Slope (path to the output Slope raster)

iii. Output measurement: DEGREE

b. The output Slope raster shown as below

c. Insert GP toll called “Reclassify” in Model builder and set:

i. Input raster: DEM_Slope

ii. Reclass field: Value

iii. Reclassification:

Old values New values

0 – 1.5 1

1.5 – 3 2

3 – 5 3 5 – 7 4

7 – 10 5

NoData NoData

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iv. Output raster: DEM_Slope_reclass (path to output raster)

v. Change missing values to NoData: Checked

d. The reclassified slope as follow

3- Overlay (Inside Model):

a. Insert GP Tool called “Weighted Overlay” in Model Builder and set:

i. Weighted overlay table:

ii. Evaluation scale: 1 to 5 by 1

iii. Output raster: Cost_Raster (path of output raster)

b. The output raster as below

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4- Cost Path (Inside Model):

a. Insert GP Tool called “ Cost Distance” in Model Builder and set:

i. Input raster or feature source data: Point_From (FC for Start Point)

ii. Input cost raster: Cost_Raster

iii. Output distance raster: Cost_Distance

iv. Output backlink raster: Cost_Backlink

b. Insert GP Tool called “Cost Path” in Model Builder and set:

i. Input raster or feature destination data: Point_To (FC for End Point)

ii. Input cost distance raster: Cost_Distance

iii. Input cost backlink raster: Cost_Backlink

iv. Output raster: CostPath

c. Insert GP Tool called “Raster To Polyline” in Model Builder and set:

i. Input raster: CostPath

ii. Field: Value

iii. Output polyline features: LeastCostPath

iv. Simplify polylines: Checked

Results: The final Model shown as below

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Conclusion:

The Path length is 125073.115192 meter (125 Km) from Point A (Southeast corner) Point

B (Northwest corner) not path through Water or Agriculture land with slope less than 10

degrees.