comparing landsat etm+ imagery with lidar data
DESCRIPTION
Results from remote sensing research conducted in Spring 2010.TRANSCRIPT
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Lesley Bross,June 7, 2010
Geography 582
Comparing Landsat ETM+ imagery with LiDAR data when classifying suburban
areas
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Research Question
Can LiDAR elevation/intensity data be used to generate landcover maps comparable to those sourced from Landsat spectral
data?
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Study Area
Approximately 296 ha includes portions of Beaverton and unincorporated Washington county
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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
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Landsat ETM+ Data
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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
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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
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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
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Landsat classification
Level 2 Kappa: 0.48Overall accuracy: 55%
Level 1 Kappa: 0.62Overall accuracy: 77%
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LiDAR classification
Level 2 Kappa: 0.40Overall accuracy: 50%
Level 1 Kappa: 0.57Overall accuracy: 76%
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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.
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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?
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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/.
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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.
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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.
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Questions ?
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Land-Use codes
LU_CODE Land Use Descriptions
1 Urban or Built-up Land
11211112141617
High Density Residential (multi-family DU)Low Density Residential (single-family DU)Commercial and ServicesTransportation/Communications/Utilities (impervious)Mixed Urban or Built Up LandUrban/Recreation (park, lawn)
3 Rangeland
31 Herbaceous (Pasture/grass/bushes)
4 Forest Land
414243
Deciduous ForestEvergreen ForestMixed forest
5 Water
51 Streams and Canals
52 Lakes and Ponds
6 Wetland
6162
ForestedNon-forested
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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
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Landsat accuracy report
Level 2Level 1Producer's User'sAccuracy Accuracy
Class 1 33.3% 25.0%Class 2 14.3% 33.3%Class 3 50.0% 71.4%Class 4 69.2% 62.1%Class 5 0.0% 0.0%Class 6 62.5% 100.0%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%
Producer's User'sAccuracy Accuracy
Class 1 87.3% 87.3%Class 2 66.7% 76.9%Class 3 78.3% 72.0%Class 4 14.3% 14.3%
Overall Accuracy: 77.0%KAPPA: 0.6264
Overall Accuracy: 55.0%KAPPA: 0.4783
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LiDAR accuracy report
Level 2Level 1Producer's User'sAccuracy Accuracy
Class 1 0.0% 0.0%Class 2 0.0% 0.0%Class 3 30.0% 100.0%Class 4 96.2% 59.5%Class 5 0.0% 0.0%Class 6 62.5% 50.0%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
Producer's User'sAccuracy Accuracy
Class 1 94.5% 74.3%Class 2 33.3% 50.0%Class 3 82.6% 95.0%Class 4 0.0% 0.0%
Overall Accuracy: 76.0%KAPPA: 0.5668