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Comparing Landsat ETM+ imagery with LiDAR data

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Results from remote sensing research conducted in Spring 2010.

Results from remote sensing research conducted in Spring 2010.


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  • 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

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