Evaluation of simulated bands in airborne optical sensors for tree species identification

Remote Sensing of Environment

Volumn 138, Issue , 2013, Pages 27-37

  Pant, Paras ^a   Heikkinen, Ville ^a   Hovi, Aarne ^b   Korpela, Ilkka ^b   Hauta Kasari, Markku ^a   Tokola, Timo ^a  

^a UNIVERSITY OF EASTERN FINLAND   (Finland)

^b UNIVERSITY OF HELSINKI   (Finland)

Author keywords

Airborne multispectral sensors; Feature extraction; Pattern classification; Sensor sensitivity; Tree classification

Indexed keywords

AUTOMATIC IDENTIFICATION; CLASSIFICATION PERFORMANCE; DIGITAL MAPPING CAMERAS; MULTISPECTRAL REMOTE SENSING; MULTISPECTRAL SENSORS; SENSOR SENSITIVITY; SUPPORT VECTOR MACHINE CLASSIFIERS; TREE SPECIES IDENTIFICATIONS;

AUTOMATION; COMPUTER SIMULATION; DISCRIMINANT ANALYSIS; FEATURE EXTRACTION; PATTERN RECOGNITION; REMOTE SENSING; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

FORESTRY;

ACCURACY ASSESSMENT; AIRBORNE SENSOR; AUTOMATION; CLASSIFICATION; OPTICAL INSTRUMENT; SIMULATION; SPECTRAL ANALYSIS; TREE; WAVELENGTH;

FORESTRY; REMOTE SENSING; SENSORS; SPECTROSCOPY;

EID: 84882605980     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2013.07.016     Document Type: Article

References (47)

1. Anger C.D., Mah S., Babey S. Technological enhancements to the compact airborne spectrographic imager (CASI). First International Airborne Remote Sensing Conference and Exhibition 1994, 205-213. Strasbourg, France.
2. Becker B.L., Lusch D.P., Qi J. A classification-based assessment of the optimal spectral and spatial resolutions for great lakes coastal wetland imagery. Remote Sensing of Environment 2007, 108:111-120.
3. Beisl U. Absolute spectroradiometric calibration of the ads40 sensor. Proc. Congrés ISPRS Commission Technique I, Symposium 2006, 14-18. Marne-la-Vallée, FRANCE.
4. Bishop C.M. Pattern recognition and machine learning (information science and statistics) 2006, Springer-Verlag New York, Inc., Secaucus, NJ, USA.
5. Bunting P., Lucas R. The delineation of tree crowns in Australian mixed species forests using hyperspectral compact airborne spectrographic imager (CASI) data. Remote Sensing of Environment 2006, 101:230-248.
6. Chang C.I. Hyperspectral imaging: Techniques for spectral detection and classification 2003, Plenum Publishing Co.
7. Chang C.C., Lin C.J. LIBSVM: A library for support vector machines. ACM transactions on intelligent systems and technology (2 27:1-27:27. Software available at). http://www.csie.ntu.edu.tw/cjlin/libsvm.
8. Clark M.L., Roberts D.A., Clark D.B. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sensing of Environment 2005, 96:375-398.
9. Cocks T., Jenssen R., Stewart A., Wilson I., Shields T. The HyMap airborne hyperspectral sensor: the system, calibration and performance. First EARSeL workshop on imaging spectroscopy 1998, 37-42. Remote Sensing Laboratories, University of Zurich, Zurich, Switzerland.
10. Cohen J. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 1960, 20:37-46.
11. Cramer M. Geometry perfect - Radiometry unknown?. Proceedings of the photogrammetric week 2011, 65-77. Stuttgart, Germany.
12. Crippen R. Calculating the vegetation index faster. Remote Sensing of Environment 1990, 34:71-73.
13. Dalponte M., Bruzzone L., Gianelle D. Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas. Geoscience and Remote Sensing, IEEE Transactions on 2008, 46:1416-1427.
14. Dalponte M., Bruzzone L., Gianelle D. Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sensing of Environment 2012, 123:258-270.
15. Dalponte M., Bruzzone L., Vescovo L., Gianelle D. The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas. Remote Sensing of Environment 2009, 113:2345-2355.
16. Filella I., Penuela J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing 1994, 15:1459-1470.
17. Green R.O., Eastwood M.L., Sarture C.M., Chrien T.G., Aronsson M., Chippendale B.J., et al. Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sensing of Environment 1998, 65:227-248.
18. Green K., Tukman M., Finkbeiner M. Comparison of DMC, UltraCam, and ADS40 imagery for benthic habitat and propeller scar mapping. Photogrammetric Engineering and Remote Sensing 2011, 77:589-599.
19. Hamada Y., Stow D.A., Coulter L.L., Jafolla J.C., Hendricks L.W. Detecting tamarisk species (Tamarix spp.) in riparian habitats of southern California using high spatial resolution hyperspectral imagery. Remote Sensing of Environment 2007, 109:237-248.
20. Heikkinen V., Korpela I., Tokola T., Honkavaara E., Parkkinen J. An SVM classification of tree species radiometric signatures based on the Leica ADS40 sensor. Geoscience and Remote Sensing, IEEE Transactions on 2011, 49:4539-4551.
21. Heikkinen V., Tokola T., Parkkinen J., Korpela I., Jääskelainen T. Simulated multispectral imagery for tree species classification using support vector machines. Geoscience and Remote Sensing, IEEE Transactions on 2010, 48:1355-1364.
22. Hill R.A., Wilson A., George M., Hinsley S. Mapping tree species in temperate deciduous woodland using time-series multi-spectral data. Applied Vegetation Science 2010, 13:86-99.
23. Holmgren J., Persson A., Söderman U. Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images. International Journal of Remote Sensing 2008, 29:1537-1552.
24. Honkavaara E., Arbiol R., Markelin L., Martínez L., Bovet S., Bredif M., Chandelier L., Heikkinen V., Korpela I., Lelegard L., Pérez F., Schläpfer D., Tokola T. Radiometric aspects of digital photogrammetric images - Results of empirical phase 2011, ISPRS IPI WS, Hannover, Germany.
25. Honkavaara E., Markelin L. Radiometric performance of digital image data collection - A comparison of ADS40/DMC/UltraCam and EmergeDSS. Photogrammetric week 2007 2007, 117-129. D. Fritsch (Ed.).
26. Jääskelainen T., Silvennoinen R., Hiltunen J., Parkkinen J. Classification of the reflectance spectra of pine, spruce, and birch. Applied Optics 1994, 33:2356-2362.
27. Korpela I. Individual tree measurements by means of digital aerial photogrammetry of Silva Fennica. Monographs 2004, Vol. 3. Finnish Society of Forest Science, Helsinki, Finland.
28. Korpela I., Heikkinen V., Honkavaara E., Rohrbach F., Tokola T. Variation and directional anisotropy of reflectance at the crown scale - Implications for tree species classification in digital aerial images. Remote Sensing of Environment 2011, 115:2062-2074.
29. Korpela I., Ørka H.O., Maltamo M., Tokola T., Hyyppä J. Tree species classification using airborne LiDAR - Effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type. Silva Fennica 2010, 44:319-339.
30. Korpela I., Rohrbach F. Variation and anisotropy of reflectance of forest trees in radiometrically calibrated airborne line sensor images - Implications to species classification. Proc. ISPRS TC VII Symp. - 100years ISPRS 2010, 342-347. (B.S.), Vienna, Austria. W. Wagner (Ed.).
31. Kropfl M., Gruber M. UltraCam calibration geometry and radiometry analysis. Technical Report. Microsoft Vexcel. Graz Austria (Last date accessed 22nd May 2012). http://www.gtbi.net/export/sites/default/GTBiWeb/productos/descargas/VexcelUltracamD-Calibracion-en.pdf.
32. Latifi H., Fassnacht F., Koch B. Forest structure modeling with combined airborne hyperspectral and LiDAR data. Remote Sensing of Environment 2012, 121:10-25.
33. Lucas R., Bunting P., Paterson M., Chisholm L. Classification of Australian forest communities using aerial photography, CASI and HyMap data. Remote Sensing of Environment 2008, 112:2088-2103.
34. Makisara K., Meinander M., Rantasuo M., Okkonen J., Aikio M., Sipola K. Airborne imaging spectrometer for applications (AISA). Better Understanding of Earth Environment, International 1993, vol.2:479-481.
35. Markelin L., Honkavaara E., Beisl U., Korpela I. Validation of the radiometric processing chain of the Leica ADS40 airborne photogrammetric sensor. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2010, 38:145-150.
36. Packalén P., Suvanto A., Maltamo M. A two stage method to estimate species-specific growing stock. Photogrammetric Engineering and Remote Sensing 2009, 75:1451-1460.
37. Rayn R., Pagnutti M. Enhance absolute and relative radiometric calibration for digital aerial cameras. Proceedings of the photogrammetric week 2009, 81-90. Stuttgart, Germany.
38. Richardson A.D., Duigan S.P., Berlyn G.P. An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytologist 2002, 153:185-194.
39. Schläpfer D., Schaepman M.E., Itten K.I. PARGE: Parametric geocoding based on GCP-calibrated auxiliary data. Imaging spectrometry IV 1998, 334-344. SPIE. M.R. Descour, S.S. Shen, M.R. Descour, S.S. Shen, M.R. Descour, S.S. Shen (Eds.).
40. Scholkopf B., Smola A.J. Learning with kernels: Support vector machines, regularization, optimization, and beyond 2001, MIT Press, Cambridge, MA, USA.
41. Schowengerdt R.A. Remote sensing: Models and methods for image processing 2007, The Netherlands: Elsevier, Amsterdam. 3rd ed.
42. Sims D., Gamon J. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment 2002, 81:337-354.
43. SPECIM AisaEAGLE hyperspectral system. Technical Report. Spectral Imaging Ltd. Teknologiantie 18 A FIN-90571 Oulu, Finland (Last date accessed 05 September 2012). http://www.specim.fi/index.php/products/airborne/aisaeagle/.
44. SPECIM SPECIM CaliGeo brochures.Technical Report. Spectral Imaging Ltd. Teknologiantie 18 A FIN-90571 Oulu, Finland (Last date accessed 05 September 2012). http://www.spectralcameras.com/files/Brochures/CaliGeo-ver5-08.pdf.
45. Tsai F., Philpot W. A derivative-aided hyperspectral image analysis system for land-cover classification. Geoscience and Remote Sensing. IEEE Transactions on 2002, 40:416-425.
46. Waser L.T., Klonus S., Ehlers M., Küchler M., Jung A. Potential of digital sensors for land cover and tree species classifications a case study in the framework of the DGPF-project. PFG Photogrammetrie, Fernerkundung, Geoinformation 2010, 2010:141-156.
47. Yuan D., Elvidge C.D. Comparison of relative radiometric normalization techniques. ISPRS Journal of Photogrammetry and Remote Sensing 1996, 51:117-126.