Identifying santa barbara's urban tree species from AVIRIS imagery using canonical discriminant analysis

Remote Sensing Letters

Volumn 4, Issue 5, 2013, Pages 513-521

  Alonzo, Mike ^a   Roth, Keely ^a   Roberts, Dar ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGGREGATION TECHNIQUES; AIRBORNE VISIBLE/INFRARED IMAGING SPECTROMETERS; CALIFORNIA; CANONICAL DISCRIMINANT ANALYSIS; CLASSIFICATION ACCURACY; CLASSIFICATION RESULTS; FORWARD FEATURE SELECTIONS; NORMALIZED DIFFERENCE VEGETATION INDEX; SANTA BARBARA; SPECTRAL INFORMATION; SPECTRAL RANGE; TRAINING SAMPLE; URBAN TREES;

DISCRIMINANT ANALYSIS; INFRARED SPECTROMETERS;

FORESTRY;

ACCURACY ASSESSMENT; AVIRIS; COHORT ANALYSIS; DISCRIMINANT ANALYSIS; IMAGE CLASSIFICATION; SATELLITE IMAGERY;

CALIFORNIA; DISCRIMINANT ANALYSIS; FORESTRY; INFRARED SPECTROSCOPY;

CALIFORNIA; SANTA BARBARA [CALIFORNIA]; UNITED STATES;

EID: 84873619133     PISSN: 2150704X     EISSN: 21507058     Source Type: Journal    
DOI: 10.1080/2150704X.2013.764027     Document Type: Article

References (23)

1. ASNER, G. and MARTIN, R., 2008, Spectral and chemical analysis of tropical forests: scaling from leaf to canopy levels. Remote Sensing of Environment, 112, pp. 3958-3970.
2. BENZ, U.C., HOFMANN, P., WILLHAUCK, G., LINGENFELDER, I. and HEYNEN, M., 2004, Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58, pp. 239-258.
3. CITY OF SANTA BARBARA, 2010, Publically managed trees database. Available online at: http://www.santabarbaraca.gov/government/maps/(accessed October 2010).
4. CLARK, M., ROBERTS, D. and CLARK, D., 2005, Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sensing of Environment, 96, pp. 375-398.
5. COCHRANE, M., 2000, Using vegetation reflectance variability for species level classification of hyperspectral data. International Journal of Remote Sensing, 21, pp. 2075-2087.
6. FISHER, R., 1936, The use of multiple measures in taxonomic problems. Annuals of Eugenics, 7, pp. 179-188.
7. GONG, P., PU, R. and YU, B., 1997, Conifer species recognition: an exploratory analysis of in situ hyperspectral data. Remote Sensing of Environment, 62, pp. 189-200.
8. GREEN, R., EASTWOOD, M., SARTURE, C., CHRIEN, T., ARONSSON, M., CHIPPENDALE, B., FAUST, J., PAVRI, B., CHOVIT, C., SOLIS, M. and OLAH, M., 1998, Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sensing of Environment, 65, pp. 227-248.
9. JING, L., HU, B., LI, J. and NOLAND, T., 2012, Automated delineation of individual tree crowns from lidar data by multi-scale analysis and segmentation. Photogrammetric Engineering and Remote Sensing, 78, pp. 1275-1284.
10. KLECKA, W., 1980, Discriminant Analysis (Beverly Hills, CA: Sage Publications).
11. LAĆAN, I. and MCBRIDE, J.R., 2008, Pest Vulnerability Matrix (PVM): a graphic model for assessing the interaction between tree species diversity and urban forest susceptibility to insects and diseases. Urban Forestry & Urban Greening, 7, pp. 291-300.
12. MYINT, S.W., GOBER, P., BRAZEL, A., GROSSMAN-CLARKE, S. and WENG, Q., 2011, Perpixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115, pp. 1145-1161.
13. NOWAK, D., CRANE, D., STEVENS, J., HOEHN, R., WALTON, J. and BOND, J., 2008, A groundbased method of assessing urban forest structure and ecosystem services. Arboriculture and Urban Forestry, 34, pp. 347-358.
14. PU, R. and LIU, D., 2011, Segmented canonical discriminant analysis of in situ hyperspectral data for identifying 13 urban tree species. International Journal of Remote Sensing, 32, pp. 2207-2226.
15. RICHTER, R. and SCHLÄPFER, D., 2002, Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: atmospheric/topographic correction. International Journal of Remote Sensing, 23, pp. 37-41.
16. ROUSE, J.W., HAAS, R.S., SCHELL, J.A. and DEERING, D.W., 1973, Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings, 3rd ERTS Symposium, NASA SP-351, vol. 1, pp. 48-62.
17. SANTAMOUR JR, F.S., 1990, Trees for urban planting: diversity, uniformity, and common sense. In Proceedings of the Seventh Conference of the Metropolitan Tree Improvement Alliance (METRIA), vol. 7, pp. 57-65.
18. VAN AARDT, J.A. and WYNNE, R.H., 2001, Spectral separability among six southern tree Species. Photogrammetric Engineering & Remote Sensing, 67, pp. 1367-1375.
19. WHITE, R.H. and ZIPPERER, W.C., 2010, Testing and classification of individual plants for fire behaviour: plant selection for the wildland-urban interface. International Journal of Wildland Fire, 19, pp. 213-227.
20. XIAO, Q., USTIN, S.L. and MCPHERSON, E.G., 2004, Using AVIRIS data and multiple-masking techniques to map urban forest tree species. International Journal of Remote Sensing, 25, pp. 5637-5654.
21. YU, B., OSTLAND, M., GONG, P. and PU, R., 1999, Penalized discriminant analysis of in situ hyperspectral data for conifer species recognition. IEEE Transactions on Geoscience and Remote Sensing, 37, pp. 2569-2577.
22. ZHANG, C. and QIU, F., 2012, Mapping individual tree species in an urban forest using airborne lidar data and hyperspectral imagery. Photogrammetric Engineering & Remote Sensing, 78, pp. 1079-1087.
23. ZHAO, G. and MACLEAN, A., 2000, A comparison of canonical discriminant analysis and principal component analysis for spectral transformation. Photogrammetric Engineering & Remote Sensing, 7, pp. 841-847.