Lesley Bross,June 7, 2010

Geography 582

Comparing Landsat ETM+ imagery with LiDAR data when classifying suburban

areas

Research Question

Can LiDAR elevation/intensity data be used to generate landcover maps comparable to those sourced from Landsat spectral

data?

Study Area

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

• 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

Landsat ETM+ Data

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

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

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

Landsat classification

Level 2 Kappa: 0.48Overall accuracy: 55%

Level 1 Kappa: 0.62Overall accuracy: 77%

LiDAR classification

Level 2 Kappa: 0.40Overall accuracy: 50%

Level 1 Kappa: 0.57Overall accuracy: 76%

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.

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?

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/.

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.

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.

Questions ?

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

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

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

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