Satellite Image Based Classification Mapping For Spatially Analyzing West Virginia Corridor H Urban Development

1
Satellite Image Based Classification Mapping For
Spatially Analyzing West Virginia Corridor H
Urban Development

• Chandra Inglis-Smith

2
Introduction and Background
3
Study Area

• The study area is all of Corridor H.

Barbour Co.
Tucker Co
Grant Co
Lewis Co
Hardy Co
Upshur Co
Randolph Co
4
Study Area

• Earliest Euro-American Settlements late 18th
  century, followed patterns established by Native
  Americans.
• Transportation consisted of dirt roads and trails
  till mid 18th century with development of
  turnpikes and the railroad in the 20th century.
• Main Economic Focus
• Agriculture
• Cattle,grain corn, poultry
• Resource Extraction
• Timber, Coal and coke

5
Overview of Corridor H

• Part of the Appalachian Development Highway
  System a program run by the Appalachian Regional
  Commission (ARC).
• The main goal of Corridor H is to foster economic
  growth in the region, by improving east-west
  travel, inter-community travel, emergency
  response time, freight travel, and increasing
  access to recreational facilities in the area by
  linking the existing north-south interstates in
  the region.

6
Land-Use Land-Cover
7
Remote Sensing

• Satellite Systems collect data in multiple
  electro-magnetic spectrum bands (wavelengths).
• Ex) visible and near to mid infrared
• Resolution Types

Spectral
Temporal
Spatial
Radiometric
8
Change Detection

• Change detection is the process of identifying
  differences in the state of an object or
  phenomenon by observing it at different times.
  (Singh, 1989 in Bottomley, 22)
• Change Detection Methods
• Map Algebra, Direct Multi-date Classification,
  Post Classification Comparison
• Change detection is useful for
• land use analysis, monitoring shifting
  cultivation, assessment of deforestation,
  disaster monitoring, day/night analysis of
  thermal characteristics and for tracking urban
  and economic growth.

9
Purpose and Objective

• Purpose To look at change in the study area over
  time by mapping present day West Virginia and
  comparing it to the past through the use of
  satellite imagery and GIS data.
• Objective To perform analysis on two datasets
  obtained from the USGS of the study area from the
  years 1987 and 2005 to determine if remote
  sensing techniques, specifically change
  detection, could be utilized to measure urban
  development (economic development) and isolate
  potential locations of future economic growth
  along Corridor H.

10
Methods and Techniques
11
Data

• Landsat Satellite Imagery
• Vector Data
• NLCD Dataset

Date of Acquisition Landsat Satellite Number Imager Type Spatial Resolution (m) Sun Elevation (degrees) Sun Azimuth (degrees) Cloud Cover ()
14Oct1987 Path 17 Row 33 5 TM 25 37.80 148.19 0
05Oct2005 Path 17 Row 33 5 TM 25 40.61 145.54 0
12
(No Transcript)
13
File Conversion

• Three Software Packages ErMapper, IDRISI, ESRI
  ArcGIS
• utilized ASCII (.asc) to import and export
• .ers files loaded directly into ESRI ArcMap
• Two shapefiles were converted for use in ErMapper
  and IDRISI
• Corridor H polyline
• Corridor H boundary polygon

14
Preprocessing

• Geometric Rectification
• Registration coordinates were manually corrected.
• Polynomial Transformation using 2005 image as the
  base image.

15
PCA

• Principal Components Analysis (PCA)
• A statistical procedure of data compression. The
  1st principal component usually accounts for most
  of the variability in the data.
• Allows you to pick out patterns and relationships
  in the variables.
• Allows you to reduce the size of your dataset
  with no significant information loss.
• Isolates and defines features more thoroughly
  than conventional band combinations.

16
PCA
17
Radiometric Normalization

• Used histogram matching algorithms in ERMapper on
  the PCA datasets.
• Transformed the final output histograms of the
  red, green, blue, PCA 1987 layers to match the
  final output histograms of the 2005 layer.

18
Unsupervised Classification

• Images classified in ErMapper
• maximum number of 15 classes
• 99 iterations
• 0.1 for minimum members in a class
• all others left as default
• Classified images returned, clusters were labeled
  and identified as to land cover/use type
• Vector
• NLCD
• SAMB

19
Classification
20
Accuracy Assessment

• Non site-specific error matrix/confusion matrix
• quantitatively assessed the NLCD reference layer
  and the classified images.
• Utilized ESRI, Excel, Access

• Four accuracy assessments were derived

Omission Error (producers accuracy)
Commission Error (users accuracy)
Overall Accuracy
Kappa Analysis (Khat)
21
Image Comparison

• Done in ESRI using the Spatial Analyst procedure
  DIFF

22
Image Generalization

• Spatial Analysis in ESRI
• Majority Filter Land2 majorityfilter
  (landiff, eight, half)
• Setnull LndCvr56 setnull (Land2 lt5, Land2)
• Region Group regiongroup (LndCvr56)
• Setnull setnull (Region1.count le 10,
  lndcvr56)
• ZonalArea zonalarea (nosmlarea)
• 0.0001 , 0.000001 , 0.0002471

23
(No Transcript)
24
Vector Overlay
25
Results and Discussion
26
Preprocessing

• The results of the geometric registration
  returned a Root Mean Square error (RMS) of
  between 0.01 and 0.06 pixels.
• This results in an error of less than 55 square
  meters or 0.014 acres
• Compared to the size of the study area,
  10,414,210,255 square meters or 2,573,351 acres,
  the error is minor in comparison

27
PCA

• Bands 3, 4, 5

• Bands 2, 3, 4

• PCAs 1, 2, 3

28
Radiometric Normalization

• Before classifying the PCA images from Path 17
  1987 and 2005 were radiometrically normalized to
  standardize for effects outside of actual real
  surface change.
• The result was similar radiometric responses
  across feature types on which to base the
  classifications.

29
Classification

• Images allowed the same parameters
• 15 classes
• 99 iterations
• 0.1 for minimum members in a class
• pixels with a zero value were set to null
• all other options were left as default.
• Path 17 1987 PCA image returned 10 classes
• Path 17 2005 PCA Image returned 12 classes
• Classified Images compared to NLCD dataset and
  SAMB Aerial Photography

30
Classification

• The images were then reclassified into the exact
  same six classes.

Class Class Type Description
1 Open Water All areas of open water, generally with greater than 95 cover of water, including streams rivers, lakes and reservoirs.
2 Deciduous Includes all forested area having a predominance of trees that lose their leaves during a growing season
3 Evergreen, Transitional Includes all forested areas in which the trees are predominantly those which remain green throughout the year
4 Emergent and Woody Wetlands Dominated by both woody vegetation and herbaceous vegetation. Can include freshwater meadows, and open bogs.
5 Croplands and Pastures Characterized by high percentages of grasses of grasses, other herbaceous vegetation and crops including lands that are regularly mowed for hay and or grazed by livestock, and regularly tilled and planted cropland
6 Mixed Urban or Built up land Comprised of areas of intensive use with much of the land covered by structures. Included in this category are cities, towns, villages, Residential areas, strip developments, transportation, industrial, commercial, shopping centers, commercial enterprises, strip mines, and quarries.
31
1987
NLCD
2005
32
Accuracy Assessment
Image Date 1987 2005
Overall Accuracy 58 63

• Errors in the accuracy may be from
• geometric registration
• normalization process
• classification process
• accuracy assessment process
• or the NLCD Dataset

33
Image Comparison
34

• Weston Commercial Development as compared to 2003
  and 1991

35

• Elkins Commercial Development as compared to 2003
  and 1991

36

• Moorefield Commercial and H construction as
  compared to 2003 and 1991

37

• Moorefield Residential as compared to 2003 and
  1991

38
Image Comparison
39
Land Cover Sq M Hectares Sq Km Acres of Change
UBL in Total Study Area 149,411,952 149,412 149 36,920 1.4
CP in Total Study Area 513,855,392 513,855 514 126,974 4.9
UBL in 1 mile buffer 18,746,612 1,875 19 4,632 2.7
CP in 1 mile 32,021,191 3,202 32 7,912 4.6
UBL in 1 mile buffer around H from Weston to Elkins 10,649,236 1,065 11 2,631 4.5
CP in 1 mile buffer around H from Weston to Elkins 17,826,909 1,783 18 4,405 7.5
UBL in 5 mile buffer 53,344,840 5,334 53 13,182 1.5
CP in 5 mile buffer 145,763,807 14,576 146 36,018 4.2
UBL in 10 mile buffer 83,297,015 8,330 83 2,0583 1.2
CP in 10 mile buffer 289,096,934 28,910 289 71,436 4.1
UBL in 15 mile buffer 100,731,409 10,073 101 24,891 .9
CP in 15 mile buffer 394,467,561 39,447 394 97,473 3.6
40
1-Mile Buffer Zones
41
Conclusion
42
The Objective

• To look at change in the study area over time by
  mapping present day West Virginia and comparing
  it to the past through the use of satellite
  imagery, GIS data and Remote Sensing change
  detection techniques.
• To determine if economic development as seen
  through urban change can be measured along
  Corridor H.

43
Conclusion

• The objective was accomplished.
• Provides a baseline for future analysis.
• Can be combined with more traditional methods of
  economic development measurement.
• The use of this data can help to focus these
  economic studies, and supply spatial relevance to
  changes in rural Appalachia

44
Questions??