Comparing Landsat ETM+ imagery with
LiDAR data when classifying suburban
June 7, 2010
Can LiDAR elevation/intensity data be used to generate landcover
maps comparable to those sourced from Landsat spectral
Approximately 296 ha includes portions of Beaverton and
unincorporated Washington county
Landsat ETM+ Data
• Landsat 7 images acquired from April 6, 2007 and
May 8, 2007
• 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
• Portland LiDAR Consortium
• Acquired March 16 - April
• Ground Pulse Density: 1.28
points per sq meter
• LiDAR tiles 45122D7103 and
• ESRI tools for processing .LAS
• 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
• 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
• 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
• LiDAR more susceptible to ‘mixels’? Data at smaller
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?
• Metro RLIS. (2007). Bare earth DEM. Retrieved May 18, 2010, from PSU
• Metro RLIS. (2006). NIR aerial photo. Retrieved May 1, 2010, from PSU
• Metro RLIS. (2009 November). Taxlot shapefiles. Retrieved May 21, 2010,
• 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
• Duh, Geoffrey, Associate Professor, Geography Department, Portland
State University. Contributed expert opinion and technical assistance.
• ERDAS. September 2008. ERDAS IMAGINE Professional Tour Guides. p.
• 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
• 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.
• 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
• 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
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)
31 Herbaceous (Pasture/grass/bushes)
4 Forest Land
41 Deciduous Forest
42 Evergreen Forest
43 Mixed forest
51 Streams and Canals
52 Lakes and Ponds