Results from remote sensing research conducted in Spring 2010.

1. Comparing Landsat ETM+ imagery with
LiDAR data when classifying suburban
areas
Lesley Bross,
June 7, 2010
Geography 582

2. Research Question
Can LiDAR elevation/intensity data be used to generate landcover
maps comparable to those sourced from Landsat spectral
data?

3. Study Area
Approximately 296 ha includes portions of Beaverton and
unincorporated Washington county

4. Landsat ETM+ Data
• Landsat 7 images acquired from April 6, 2007 and
May 8, 2007
• P46R28
• SLC failure 
• Data processing
1. Histogram match (b/w and color)
2. Model maker interleaves bands (b/w and color)
3. Resolution merge (pan-sharpen) color from
panchromatic data (15m)
4. Subset to match LiDAR tiled area

5. Landsat ETM+ Data

6. LiDAR Data
• Portland LiDAR Consortium
• Acquired March 16 - April
15, 2007
• Ground Pulse Density: 1.28
points per sq meter
• LiDAR tiles 45122D7103 and
45122D7104

7. LiDAR Data
• ESRI tools for processing .LAS
files
• Point Information
• LAS to Multipoint
• Point to Raster (15m cell size)
• VBA script copies i-values to z-
values so they are accessible
• ESRI-to-ERDAS gotchas
• No nullData values -> raster
calculator with con statement
• Recalculate statistics in IMAGINE

8. Unsupervised classification
• Landsat data
• 6 color bands + NDVI band
• PCA (output 3 PCA bands)
• LiDAR data
• Standard deviation of first returns
• Mean feature height (first returns – last returns)
• Mean intensity of all returns
• Generate 50 spectral clusters with ISODATA algorithm
• Accuracy assessment
• 100 random stratified points shared between scenes
• Ground-truth data: 4 ft infrared photo, tax lots, THPRD map

9. Landsat classification
Level 2
Kappa: 0.48
Overall accuracy: 55%
Level 1
Kappa: 0.62
Overall accuracy: 77%

10. LiDAR classification
Level 2
Kappa: 0.40
Overall accuracy: 50%
Level 1
Kappa: 0.57
Overall accuracy: 76%

11. Conclusions
• LiDAR did not generate maps comparable to Landsat
• Missed water and wetlands classes
• Could not distinguish between built-up level 2 classes
• Some technologies better for some land covers
• LiDAR detected isolated tree stands
• Higher accuracy for roads; Higher overall %?
• Accuracy of ArcMap LiDAR toolset?
• LiDAR i-values should be normalized and filtered (Song
et all)
• LiDAR more susceptible to ‘mixels’? Data at smaller
grain.

12. Conclusions
LiDAR picks out two specific buildings at St. Mary’s school in
two of fifty spectral clusters. Perhaps better for smaller areas
or identifying distinct features? Segmentation?

13. Data sources
• Metro RLIS. (2007). Bare earth DEM. Retrieved May 18, 2010, from PSU
I:/resources/Students/Data/GIS/RLIS/RLIS_Extra_DEM.
• Metro RLIS. (2006). NIR aerial photo. Retrieved May 1, 2010, from PSU
I:/resources/Students/Data/GIS/RLIS/Photo_2006/Color_Infrared/4ft.
• Metro RLIS. (2009 November). Taxlot shapefiles. Retrieved May 21, 2010,
from PSU
I:/resources/Students/Data/GIS/RLIS/2009_Nov/ESRISHAPEFILES/TAXLOTS.
• Portland LiDAR Consortium (2007). LAS files received from Geoffrey Duh.
• Tualatin Hills Park and Recreation District(2010). Nature Park Trail Map.
Retrieved May 5, 2010 from http://www.thprd.org/pdfs/document49.pdf .
• USGS (2007). EarthExplorer. Landsat 7 imagery. Retrieved April 27, 2010 from
http://edcsns17.cr.usgs.gov/EarthExplorer/.

14. References
• Duh, Geoffrey, Associate Professor, Geography Department, Portland
State University. Contributed expert opinion and technical assistance.
• ERDAS. September 2008. ERDAS IMAGINE Professional Tour Guides. p.
149-155
• Jensen, J. R. 2005. Introductory Digital Image Processing (3rd edition).
Prentice Hall. p. 343-344.
• Martin, Kevin S, Adjunct Instructor, Geography Department,
Portland State University. Contributed expert opinion and technical
assistance.
• McCauley, S. and Goetz, S.J. 2004. Mapping residential density patterns
using multi-temporal Landsat data and a decision-tree classifier.
International Journal of Remote Sensing. 25(6): 1077-1094.

15. References
• Shackelford and Davis. 2003. A hierarchical fuzzy classification approach
for high-resolution multispectral data over urban areas. IEEE
Transactions on geosciences and remote sensing, 41(9): 1920 – 1932.
• Short Sr., Nicholas M.. 2009. Last accessed May 5, 2010. Vegetation
Applications – Agriculture, Forestry, and Ecology. The Remote Sensing
Tutorial, Last accessed May 5, 2010 at
http://rst.gsfc.nasa.gov/Sect3/Sect3_5.html.
• Song, J.H., Han, S.H., Yu, K., Kim, Y. 2002. Assessing the possibility of
land-cover classification using LiDAR intensity data, IAPRS, 9-13
September, Graz, vol. 34: 1-4. Last accessed May 27, 2010 at
http://www.isprs.org/proceedings/XXXIV/part3/papers/paper128.pdf.

16. Questions ?

17. Land-Use codes
LU_CODE Land Use Descriptions
1 Urban or Built-up Land
112 High Density Residential (multi-family DU)
111 Low Density Residential (single-family DU)
12 Commercial and Services
14 Transportation/Communications/Utilities (impervious)
16 Mixed Urban or Built Up Land
17 Urban/Recreation (park, lawn)
3 Rangeland
31 Herbaceous (Pasture/grass/bushes)
4 Forest Land
41 Deciduous Forest
42 Evergreen Forest
43 Mixed forest
5 Water
51 Streams and Canals
52 Lakes and Ponds
6 Wetland
61 Forested
62 Non-forested

18. Erdas recode
L1_CODE L2_CODE LU_CODE Description
1 1 111 Low density residential
1 2 112 High density residential
1 3 12 Commercial
1 4 14 Transportation
1 5 16 Mixed urban
1 6 17 Recreation
2 7 31 Herbaceous
3 8 41 Deciduous
3 9 42 Evergreen
3 10 43 Mixed forest
4 11 61 Forested wetland
4 12 62 Non-forested wetland

19. Landsat accuracy report
Level 1 Level 2
Producer's User's Producer's User's
Accuracy Accuracy Accuracy Accuracy
Class 1 87.3% 87.3% Class 1 33.3% 25.0%
Class 2 66.7% 76.9% Class 2 14.3% 33.3%
Class 3 78.3% 72.0% Class 3 50.0% 71.4%
Class 4 14.3% 14.3% Class 4 69.2% 62.1%
Class 5 0.0% 0.0%
Overall Accuracy: 55.0% Class 6 62.5% 100.0%
KAPPA: 0.4783 Class 7 66.7% 76.9%
Class 8 58.3% 46.7%
Class 9 57.1% 57.1%
Class 10 75.0% 100.0%
Class 11 0.0% 0.0%
Class 12 25.0% 14.3%
Overall Accuracy: 77.0%
KAPPA: 0.6264

20. LiDAR accuracy report
Level 1 Level 2
Producer's User's Producer's User's
Accuracy Accuracy Accuracy Accuracy
Class 1 94.5% 74.3% Class 1 0.0% 0.0%
Class 2 33.3% 50.0% Class 2 0.0% 0.0%
Class 3 82.6% 95.0% Class 3 30.0% 100.0%
Class 4 0.0% 0.0% Class 4 96.2% 59.5%
Class 5 0.0% 0.0%
Overall Accuracy: 76.0% Class 6 62.5% 50.0%
KAPPA: 0.5668 Class 7 33.3% 50.0%
Class 8 73.3% 61.1%
Class 9 16.7% 50.0%
Class 10 0.0% 0.0%
Class 11 0.0% 0.0%
Class 12 0.0% 0.0%
Overall Accuracy: 50.0%
KAPPA: 0.4008