1. Remote Sensing Applications Supporting Regional Transportation Database Development CLEM 2001 August 6, 2001 Santa Barbara, CA Chris Chiesa, Chris.Chiesa@Veridian.com (520) 326-7005 ext. 106

2. Remote Sensing Application Supporting Regional Database for Transportation Planning In Partnership with:

3. Presentation Overview Project Summary Project Objective Approach Benefits Technical Discussion Land Cover Change Detection and Mapping Road Feature Characterization and Extraction

4. Project Objective Develop tools and methods to facilitate regional transportation road network database development and maintenance Utilize commercial remote sensing sources to identify and map changes in land use and transportation infrastructure Automate procedure for extracting and attributing road vectors Develop procedures within COTS software environment (ERDAS IMAGINE / CAFÉ) Promote awareness of tools and processes through outreach activities Training / Workshops Web-based Interactive Tutorial

5. Commercial Remote Sensing Sources LANDSAT Thematic Mapper High-resolution IKONOS

6. Approach 1. Use multi-date Landsat Thematic Mapper imagery to identify areas within a large region where intensive urban development (hot spots) has occurred. May 26, 1984 June 15, 2000 Urban development between 1984 and 2000

7. Approach 2. Acquire high-resolution (IKONOS) imagery over hot spots and enhance road network with one or more spectral features developed for the types of roads present and the geographic environment. IKONOS panchromatic band (1-meter) IKONOS false color composite (4-meter) Road feature derived from linear combination of IKONOS multi-spectral bands (4-meter)

8. Approach 3. Extract road locations in newly developed regions and store as vector coverage’s using Veridian’s Lines of Communication (LOC) extraction software.

9. Approach 4. Assign attributes (e.g. surface type, width) to vector coverages. 2-lane roads 3-lane roads

10. Benefits The LANDSAT program provides an inexpensive means of identifying landcover change over a large area. Landsat Coverage IKONOS Coverage

11. Benefits Automated (i.e., user-assisted) road extraction using road spectral features and/or LOC toolkit can be faster, less tedious and less error prone than traditional processing of hand digitizing from aerial photography or satellite imagery. Panchromatic Aerial PhotographRoad feature derived from Multispectral Imagery

12. Change Detection and Feature Extraction Process Change detection over a large area Radiometric normalization Categorize both dates Categorical change Radiometric change Hybrid change Feature extraction and attribution Identify regions of intensive development Generate road features. Extract road network Attribute road network

13. Date 1 Geo-coded Date 2 Geo-coded Radiometric Normalization Process Date 2 Geo- coded and normalized to Date 1 Categorical Process Date 1 Categorized Image Date 2 Categorized Image Categorical Change Process Categorical Change Detection Image Radiometric Change Detection Process Change Magnitude and Change Direction Hybrid Change Detection Process Hybrid Change Product Procedure Overview Hybrid Change Detection Radiometric Correction Radiometric Change Detection Categorical Change Detection Categorical Processing

14. Acquire Data … Acquire 2 dates of LANDSAT data Summer season Cloud free Same time of year Mid-Michigan on June 8, 1986 Mid-Michigan on June 6, 2000

15. Categorize Both Dates… Label resultant clusters into water, vegetation, bare ground, and urban areas, as appropriate. Water Vegetation Urban Bare ground Unsupervised clustering of Landsat Thematic Mapper image over portion of Michigan on June 8, 1986. Unsupervised clustering of Landsat Thematic Mapper image over portion of Michigan on June 6, 2000

16. Categorical Change … Recode categorized files to urban/non-urban. Water Vegetation UrbanBare ground Urban No data

17. Categorical Change… Combine binary files from both dates to determine where urban changes have occurred. Date1 Date2 Urban on date 1, not on date 2 Urban on both dates Urban on date 2, not on date 1 No data

18. Increasing difference between pixel values from date 1 to date 2 input images. This change magnitude channel shows differences in two dates of Landsat imagery for a region in Michigan. Brighter areas indicate higher magnitudes of change. Often a threshold from this channel is established so that only changes above a certain magnitude will be considered when extracting changes of interest. Radiometric Change Detection

19. The sector code channel provides information on the “direction” or nature of change. Each color corresponds to a sector code. Each sector code relates to a specific combination of changes observed in image bands as shown in the table above. For example, sector code 6, shown in orange in the image to the left, shows areas that have increased spectral reflectance in bands 2 and 3, and decreased spectral reflectance in band 4. 0 7 6 5 4 3 2 1 ColorSector Code Band 2Band 3Band 4 BlueNear IR RedGreen

20. Radiometric Change Detection… Create a change image composition (CIC) and determine sector codes that best represent urban areas.

21. Hybrid Change Advantages are: 1. Labels from categorization 2. Reduction in false categorical change from CVA Hybrid urban change product of Delta Township in Michigan. Changed areas are annotated in yellow over a Landsat Thematic Mapper False color composite

22. Feature Extraction and Attribution Identify geographic locations of localized regions in LANDSAT change product where intensive development has occurred Generate road features Extract road network Attribute road network

23. Identify Geographic Locations Identify areas in the Landsat hybrid change product where urban change has occurred and order IKONOS data

24. Order and Receive Data Acquire IKONOS data over area of interest IKONOS false color composite with green band displayed in blue, red band displayed in green, and near infrared band displayed in red. IKONOS panchromatic band IKONOS natural color composite with blue band displayed in blue, green band displayed in green, and red band displayed in red.

25. Generate Road Features… This scatterplot illustrates how different landcover materials can be separated in 2-dimensional space (2 spectral bands). The arrow shows a direction that can be described as a linear combination of these two bands. The dashed line indicates that both concrete and asphalt can be separated from the other materials with this 2-dimensional feature. Often features are created by using multiple bands ( > than 2 dimensions)

26. Generate Road Features… This plot illustrates how well a specific 4-band spectral feature will work in isolating certain landcover material from other materials in the image. Natural materials are projected towards a categorical value of 1, while man made materials are projected towards a categorical value of 2. The vertical dashed line between these two categories illustrates that this equation will work in separating these 2 categories. In the feature created, man made materials will appear as the brightest objects and natural materials will appear as darker objects. Level slicing the feature at around 150 will separate the two.

27. Generate Road Features… Apply coefficients of spectral feature to data and produce road feature. [(Band 1 * -.0256) + (Band 2 *.0915) + (Band 3 *.1346) + (Band 4 * -.2241)] + 148 Weighted average of satellite raw bands Adjusts data values into 0-255 range for unsigned 8-bit output

28. Generate Road Features… False color composite of IKONOS data displayed with green band in blue, red band in green, and near infrared band in red IKONOS road feature derived from a linear combination of the raw bands

29. Extract Road Network Use road feature as input to LOC toolkit and semi-automatically extract roads. Convert to vector coverage.

30. Extract Road Network…

32. Attribute Road Network 2-lane roads 3-lane roads 2-lane roads

33. Process Summary Landsat imagery provides broad spatial and temporal coverage over which to observe land changes Hybrid change detection offers advantages over traditional post-classification change detection in that it also incorporates important radiometric change information and allows “thresholding” of changes IKONOS imagery provides high spatial resolution to identify the specific transportation features that constitute the changes observed in Landsat imagery Using a “Road Feature” helps maximize the differentiability of roads and background classes in the imagery Semi-automated extraction and labeling tools facilitate the process of developing GIS database layers from these remote sensing sources

34. Questions? Please contact: Chris Chiesa Veridian Systems 4400 East Broadway, Suite 116 Tucson, AZ 85711 (520)326-7005 ext. 106 chris.chiesa@veridian.com www.veridian.com