Hyperspectral data dimensionality reduction and the impact of multi-seasonal hyperion EO-1 imagery on classification accuracies of tropical forest species

Photogrammetric Engineering and Remote Sensing

Volumn 80, Issue 8, 2014, Pages 773-784

  Saini, Manjit ^a   Christian, Binal ^a   Joshi, Nikita ^a   Vyas, Dhaval ^a   Marpu, Prashanth ^b   Krishnayya, N ^a  

^a M S UNIVERSITY OF BARODA   (India)

^b KHALIFA UNIVERSITY   (United Arab Emirates)

Author keywords

[No Author keywords available]

Indexed keywords

ATMOSPHERIC THERMODYNAMICS; DATA ACQUISITION; DIMENSIONALITY REDUCTION; FORESTRY; TROPICS;

CLASSIFICATION ACCURACY; COMPREHENSIVE INFORMATION; DIMENSIONALITY REDUCTION METHOD; EFFECTIVE MANAGEMENT; HYPERSPECTRAL DATA; KERNEL PRINCIPAL COMPONENT ANALYSIS; PHENOLOGICAL CHANGES; SPECTRAL SIGNATURE;

IMAGE CLASSIFICATION;

ACCURACY ASSESSMENT; HYPERION; IMAGE CLASSIFICATION; SATELLITE DATA; SEASONAL VARIATION; TROPICAL FOREST;

DATA; FORESTS; SPECIES IDENTIFICATION;

TECTONA GRANDIS;

EID: 84924243401     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.80.8.773     Document Type: Article

References (93)

1. Achard, F., H.D. Eva, P. Mayaux, H.J. Stibig, and A. Belward, 2004. Improved estimates of net carbon emissions from land cover change in the tropics for the 1990s, Global Biogeochemical Cycles, 18(2):1-11.
2. Asner, G.P., and R.E. Martin, 2008. Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels, Remote Sensing of Environment, 112(10):3958-3970.
3. Bachmann, C.M., T.L. Ainsworth, and R.A. Fusina, 2006. Improved manifold coordinate representations of large-scale hyperspectral scenes, IEEE Transactions on Geoscience and Remote Sensing, 44(10):2786-2803.
4. Bateson, C.A., and B. Curtiss, 1996. A method for manual endmember selection and spectral unmixing, Remote Sensing of Environment, 55(3):229-243.
5. Beer, C., M. Reichstein, E. Tomelleri, P. Ciais, M. Jung, N. Carvalhais, C. Rödenbeck, M.A. Arain, D. Baldocchi, G.B. Bonan, A. Bondeau, A. Cescatti, G. Lasslop, A. Lindroth, M. Lomas, S. Luyssaert, H. Margolis, K.W. Oleson, O. Roupsard, E. Veenendaal, N. Viovy, C. Williams, F.I. Woodward, and D. Papale, 2010. Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate, Science, 329(5993):834-838.
6. Benediktsson, J.A., J.R. Sveinsson, and K. Arnason, 1995. Classification and feature extraction of AVIRIS data, IEEE Transactions on Geoscience and Remote Sensing, 33(5):1194-1205.
7. Boardman, J.W., and F.A. Kruse, 1994. Automated spectral analysis: A geologic example using AVIRIS data, North Grapevine Mountains, Nevada, Proceedings of 10th Thematic Conference on Geologic Remote Sensing, Ann Arbor, Michigan, pp. 407-418.
8. Bradshaw, C.J.A., N.S. Sodhi, and B.W. Brook, 2009. Tropical turmoil: A biodiversity tragedy in progress, Frontiers in Ecology and the Environment, 7(2):79-87.
9. Borchert, R., 1994. Water storage in soil or tree stems determines phenology and distribution of tropical dry forest trees, Ecology, 75:1437-1449.
10. Buddenbaum, H., M. Schlerf, and J. Hill, 2005. Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods, International Journal of Remote Sensing, 26(24):5453-5465.
11. Burgers, K., Y. Fessehatsion, S. Rahmani, J. Seo, and T. Wittman, 2009. A comparative analysis of dimension reduction algorithms on hyperspectral data, LAMDA Research Group, 1-23.
12. Canty, M.J., and A.A. Nielsen, 2012. Linear and kernel methods for multivariate change detection, Computers and Geosciences, 38 (1):107-114.
13. Cao, L.J., K.S. Chua, W.K. Chong, H.P. Lee, and Q.M. Gu, 2003. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine, Neurocomputing, 55(1-2):321-336.
14. Carlson, K.M., G.P. Asner, R.F. Hughes, R. Ostertag, and R.E. Martin, 2007. Hyperspectral remote sensing of canopy biodiversity in Hawaiian lowland rainforests, Ecosystems, 10(4):526-549.
15. Castro-Esau, K.L., and M. Kalacska, 2008. Tropical dry forest phenology and discrimination of tropical tree species using hyperspectral data, Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests (M. Kalacska, and G.A. Sanchez-Azofeifa, editors), CRC Press: Boca Raton, Florida, pp. 1-26.
16. Castro-Esau, K.L., G.A. Sanchez-Azofeifa, and B. Rivard, 2006. Comparison of spectral indices obtained using multiple spectroradiometers, Remote Sensing of Environment, 103(3):276-288.
17. Chambers, J.Q., G.P. Asner, D.C. Morton, L.O. Anderson, S.S. Saatchi, D.B. Espirito-Santo, M. Palace, and C. Souza Jr., 2007. Regional ecosystem structure and function: Ecological insights from remote sensing of tropical forests, Trends in Ecology and Evolution, 22(8):414-423.
18. Chan, J.C.W., and D. Palinckx, 2008. Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery, Remote Sensing of Environment, 112(6):2999-3011.
19. Chang, C.I., S.S. Chiang, J.A. Smith, and I.W. Ginsberg, 2002. Linear spectral random mixture analysis for hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, 40(2):375-392.
20. Cho, M.A., P. Debba, R. Mathieu, L. Naidoo, J. van Aardt, and G.P. Asner, 2010. Improving discrimination of Savanna tree species through a Multiple-Endmember Spectral Angle Mapper approach: Canopy-Level analysis, IEEE Transactions on Geoscience and Remote Sensing, 48(11):4133-4142.
21. Cho, M.A., I. Sobhan, A.K. Skidmoreb, and J. de Leeuw, 2008. Discriminating species using hyperspectral indices at leaf and canopy scales, Proceedings of ISPRS: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37:369-376.
22. Christian, B., and N.S.R. Krishnayya, 2009. Classification of tropical trees growing in a sanctuary using Hyperion (EO-1) and SAM algorithm, Current Science, 96(12):1601-1607.
23. Clark, M. L., D.A. Roberts, and D.B. Clark, 2005. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales, Remote Sensing of Environment, 96(3-4):375-398.
24. Cochrane, M.A., 2000. Using vegetation reflectance variability for species level classification of hyperspectral data, International Journal of Remote Sensing, 21(10):2075-2087.
25. Congalton, R.G., and R.A. Mead, 1983. A quantitative method to test for consistency and correctness in photointerpretation, Photogrammetric Engineering & Remote Sensing, 49(1):69-74.
26. Datt, B., T.R. McVicar, T.G. van Niel, D.L.B. Jupp, and J.S. Pearlman, 2003. Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes, IEEE Transactions on Geoscience and Remote Sensing, 41(6):1246-1259.
27. Dennison, P.E., and D.A. Roberts, 2003. The effects of vegetation phenology on endmember selection and species mapping in southern California chaparral, Remote Sensing of Environment, 87(2-3):295-309.
28. Fauvel, M., J. Chanussot, J.A. Benediktsson, and A. Villa, 2013. Parsimonious Mahalanobis kernel for the classification of high dimensional data, Pattern Recognition, 46(3):845-854.
29. Fe’ret, J., and G.P. Asner, 2013. Tree species discrimination in tropical forests using airborne imagine spectroscopy, IEEE Transactions on Geoscience and Remote Sensing, 51(1):73-84.
30. Filippi, A.M., and J.R. Jensen, 2007. Effect of continuum removal on hyperspectral coastal vegetation classification using a fuzzy learning vector quantizer, IEEE Transactions on Geoscience and Remote Sensing, 45(6):1857-1869.
31. Fong, M., 2007. Dimension Reduction on Hyperspectral Images, UCLA Department of Mathematics.
32. Fuller, D.O., 2006. Tropical forest monitoring and remote sensing: A new era of transparency in forest governance?, Singapore Journal of Tropical Geography, 27(1):15-29.
33. Gamon, J.A., L. Serrano, and J.S. Surfus, 1997.The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels, Oecologia, 112:492-501.
34. Garbulsky, M.F., J. Peñuelas, J. Gamon, Y. Inoue, and I. Filella, 2011. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis, Remote Sensing of Environment, 115(2):281-297.
35. Gibson, L., T.M. Lee, L.P. Koh, B.W. Brook, T.A. Gardner, J. Barlow, C.A. Peres, C.J.A. Bradshaw, W.F. Laurance, T.E. Lovejoy, and N.S. Sodhi, 2011. Primary forests are irreplaceable for sustaining tropical biodiversity, Nature, 478:378-381.
36. Gillespie, T.W., G.M., Foody, D. Rocchini, A.P. Giorgi, and S. Saatchi, 2008. Measuring and modeling biodiversity from space, Progress in Physical Geography, 32(2):203-221.
37. Goodenough, D.G., A. Dyk, K.O. Niemann, J.S. Pearlman, H. Chen, T. Han, M. Murdoch, and C. West, 2003. Processing Hyperion and Ali for forest classification, IEEE Transactions on Geoscience and Remote Sensing, 41(6):1321-1331.
38. Govender, M., K. Chetty, V. Naiken, and H. Bulcock, 2008. A comparison of satellite hyperspectral and multispectral remote sensing imagery for improved classification and mapping of vegetation, Water SA, 34(2):147-154.
39. Haboudane, D., N. Tremlay, J.R. Miller, and P. Vigneaault, 2008. Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing, 46 (2):423-437.
40. Hobbs, N.T., 1989. Linking energy balance to survival in mule deer: Development and test of a simulation model, Wildlife Monographs, 101:3-39.
41. Kalacska, M., G.A. Sánchez-Azofeifa, J.C. Calvo-Alvarado, M. Quesada, B. Rivard, and D.H., Janzen, 2004. Species composition, similarity and diversity in three succesional stages of tropical dry forest, Forest Ecology and Management, 200(1-3):227-247.
42. Kaufman, Y.J., D. Tanré, L.A. Remer, E.F. Vermote, A. Chu and B.N. Holben, 1997. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer, Journal of Geophysical Research, 102(D14), 17051-17.
43. Key, T., T.A. Warner, J.B. McGraw, and M.A. Fajvan, 2001. A comparison of multispectral and multitemporal information in high spatial resolution imagery for classification of individual tree species in a temperate hardwood forest, Remote Sensing of Environment, 75(1):100-112.
44. Knight, J.R., C.K. Folland, and A.A. Scaife, 2006. Climate impacts of the Atlantic Multidecadal Oscillation, Geophysical Research Letters, 33(17):L17706.
45. Lambin, E.F., and D. Ehrlich, 1996. The surface temperature-vegetation index space for land cover and land-cover change analysis, International Journal of Remote Sensing, 17(3):463-487.
46. Landgrebe, D.A., 2001. Analysis of multispectral and hyperspectral image data, Introduction to Modern Photogrammetry (E.M. Mikhail, C. McGlone, and J.S. Bethel, editors) John Wiley & Sons, Inc., New York.
47. Lee, C., and D.A. Landgrebe, 1993. Analyzing high-dimensional multispectral data, IEEE Transactions on Geoscience and Remote Sensing, 31(4):792-800.
48. Lee, K.S., S.H. Kin, J.R. Ma, M.J. Kook, and J. Shin, 2005. Spectral variation of vegetative areas in leaf-off season observed by hyperspectral data, Proceedings of IEEE International, 6:4347-4350.
49. Lima, A.L.A., and M.J.N. Rodal, 2010. Phenology and wood density of plants growing in the semi-arid region of northeastern Brazil, Journal of Arid Environments, 74(11):1363-1373.
50. Maignan, F., F.M. Bréon, C. Bacour, J. Demarty, and A. Poirson, 2008. Interannual vegetation phenology estimates from global AVHRR measurements comparison with in situ data and applications, Remote Sensing of Environment, 112(2):496-505.
51. Marpu, P.R., M. Pedergnana, M.D. Mura, S. Peeters, J.A. Benediktsson, and L. Bruzzone, 2012. Classification of hyperspectral data using extended attribute profiles based on supervised and unsupervised feature extraction techniques, International Journal of Image and Data Fusion, 3(3):269-298.
52. Mika, S., B. Schölkopf, A.J. Smola, K. Müller, M. Scholz, and G. Rätsch, 1999. Kernel PCA and de-noising in feature spaces, Advances in Neural Information Processing Systems, 11:536-542.
53. Mitri, G., and I. Gitas, 2010. Mapping post-fire vegetation regeneration using EO-1 Hyperion, IEEE Transactions on Geoscience and Remote Sensing, 48(3):1613-1618.
54. Morris, R.J., 2010. Anthropogenic impacts on tropical forest biodiversity: Network structure and ecosystem functioning perspective, Philosophical Transaction of Royal Society - Biological Science, 365(1558):3709-3718.
55. Murali, K.S., and R. Sukumar, 1993. Leaf flushing phenology and herbivory in a tropical dry deciduous forest, southern India, Oecologia, 94(1):114-119.
56. Myneni, R.B., C.D. Keeling, C.J. Tucker, G. Asrar, and R.R. Nemani, 1997. Increased plant growth in the northern high latitudes from 1981 to 1991, Nature, 386:698-702.
57. Nagendra, H., 2001. Using remote sensing to assess biodiversity, International Journal of Remote Sensing, 22(12):2377-2400.
58. Papes, M., R. Tupayachi, P. Martínez, A.T. Peterson, G.P. Asner, and G.V.N. Powell, 2013. Seasonal variation in spectral signatures of five genera of rainforest trees, IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 6(2):339-350.
59. Peñuelas, J., M.F. Garbulsky, and I. Filella, 2011. Photochemical reflectance index (PRI) and remote sensing of plant CO2 uptake, New Phytologist, 191(3):596-599.
60. Pettorelli, N., J.O. Vik, A. Mysterud, J.M. Gaillard, C.J. Tucker, and N.C. Stenseth, 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change, Trends in Ecology and Evolution, 20(9):503-510.
61. Pignatti, S., R.M. Cavalli, V. Cuomo, L. Fusilli, S. Pascucci, M. Poscolieri, and F. Santini, 2009. Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy, Remote Sensing Environment, 113(3):622-634.
62. Plaza, A., J.A. Benediktsson, J.W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Hanussot, P. Gamba, M. Fauvel, A. Gualtieri, M. Marconcini, J.C. Yoilton, and G. Trianni, 2009. Recent advances in techniques for hyperspectral image processing, Remote Sensing of Environment, 113(1):110-122.
63. Pradeepkumar, G., 1993. Vegetational & Ecological Studies of Shoolpaneshwar Wildlife Sanctuary, Ph.D. thesis, Botany Department, The M.S. University of Baroda, India.
64. Richards, J.A., 2005. Analysis of remotely sensed data: The formative decades and the future, IEEE Transactions on Geoscience and Remote Sensing, 43(3):422-432.
65. Roberts, D.A., R.O. Green, and J.B. Adams, 1997.Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS, Remote Sensing of Environment, 62(3):223-240.
66. Rodarmel, C., and J. Shan, 2002. Principal component analysis for hyperspectral image classification, Surveying and Land Information Systems, 62(2):115-122.
67. Rodríguez, J.P., L. Brotons, J. Bustamante, and J. Seoane, 2007. The application of predictive modeling of species distribution to biodiversity conservation, Diversity and Distributions, 13(3):243-251.
68. Roy, P.S., and P.K. Joshi, 2002. Tropical forest cover assessment in North East India - Potentials of temporal wide swath satellite data (IRS 1C-WiFS), International Journal of Remote Sensing, 23(22):4881-4896.
69. Sabnis, S.D., and J.C. Amin, 1992. Eco-environmental Studies of Sardar Sarovar Environs, The M.S. University of Baroda, Vadodara.
70. Sánchez-Azofeifa, G.A., K.L. Castro, B. Rivard, and M.R. Kalascka, 2003. Remote sensing research priorities in tropical dry forest environments, Biotropica, 35(2):134-142.
71. Schölkopf, B., S. Mika, C.J.C. Burges, P. Knirsch, K. Müller, G. Rätsch, and A.J. Smola, 1999. Input space versus feature space in kernel-based methods, IEEE Transactions on Neural Networks, 10(5):1000-1016.
72. Schölkopf, B., A.J. Smola, and K. Müller, 1998. Nonlinear component analysis as a kernel eigenvalue problem, Neural Computation, 10(5):1299-1399.
73. Shafri, H.Z.M., A. Suhaili, and S. Mansor, 2007. The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis, Journal of Computer Science, 3(6):419-423.
74. Silva, F.B., Y.E. Shimabukuro, L.E.O.C. Aragão, L.O. Anderson, G. Pereira, F. Cardozo, and E. Arai, 2013. Large-scale heterogeneity of Amazonian phenology revealed from 26-year long AVHRR/NDVI time-series, Environmental Research Letters, 8(2):024011.
75. Skoupý, O., L. Zejdová, and J. Hanuš, 2011. The use of hyperspectral remote sensing for mapping the age composition of forest stands, Journal of Forest Science, 57(6):287-297.
76. Thenkabail, P.S., G.J. Lyon, and A. Huete, 2011. Hyperspectral Remote sensing of vegetation and agricultural crops: Knowledge gain and knowledge gap after 40 years of research, Hyperspectral Remote Sensing of Vegetation, Boca Raton, London, New York: CRC Press/Taylor and Francis Group, pp.663-668.
77. Thenkabail, P.S., I. Mariotto, M.K. Gumma, E.M. Middleton, D.R. Landis, and K.F. Huemmrich, 2013. Selection of Hyperspectral Narrowbands (HNBs) and composition of hyperspectral two band vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2):427-439.
78. Thenkabail, P.S., E.A. Enclona, M.S. Ashton, and B.V.D. Meer, 2004. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications, Remote Sensing of Environment, 91(3-4):354-376.
79. Townsend, A.R., G.P. Asner, and C.C. Cleveland, 2008. The biogeochemical heterogeneity of tropical forests, Trends in Ecology and Evolution, 23(8):424-431.
80. Tsai, F., E.K. Lin, and K. Yoshino, 2007. Spectrally segmented principal component analysis of hyperspectral imagery for mapping invasive plant species, International Journal of Remote Sensing, 28(5):1023-39.
81. Ustin, S.L., D.A. Roberts, J.A. Gamon, G.P. Asner, and R.O. Green, 2004. Using imaging spectroscopy to study ecosystem properties and processes, Bioscience, 54(6):523-534.
82. Vaiphasa, C., S. Ongsomwang, T. Vaiphasa, and A.K. Skidmore, 2005. Tropical mangrove species discrimination using hyperspectral data: a laboratory study, Estuarine, Coastal and Shelf Science, 65(1-2):371-379.
83. Valdez-Hernández, M., J.L. Andrade, P.C. Jackson, and M. Rebolledo-Vieyra, 2010. Phenology of five tree species of a tropical dry forest in Yucatan, Mexico: Effects of environmental and physiological factors, Plant Soil, 329(1-2):155-171
84. van Schaik, C.P., J.W. Terborgh, and S.J. Wright, 1993. Phenology of tropical forest: Adaptive significance and consequences for primary consumers, Annual Review of Ecology and Systematics, 24:353-377.
85. Villa, A., J. Chanussot, C. Jutten, J.A. Benediktsson, and S. Mossaoui, 2009. On the use of ICA for hyperspectral image analysis, Proceedings of IEEE IGARSS 4, IV-97-IV-100.
86. Vina, A., A.A. Gitelson, D.C. Rundquist, G.P. Keydan, B. Leavitt, and J. Schepers, 2004. Monitoring maize (Zea mays L.) phenology with remote sensing, Agronomy Journal, 96(4):1139-147.
87. Vyas, D., B. Christian, and N.S.R. Krishnayya, 2013. Canopy level estimations of chlorophyll and LAI for two tropical species (teak and bamboo) from Hyperion (EO1) data, International Journal of Remote Sensing, 34(5):1676-1690.
88. Vyas, D., N. Mehta, J. Dinakaran, and N.S.R. Krishnayya. 2010. Allometric equations for estimating leaf area index (LAI) of two important tropical species (Tectona grandis and Dendrocalamus strictus), Journal of Forestry Research, 21:197-200.
89. Vyas, D., N.S.R. Krishnayya, K.R. Manjunath, S.S. Ray, and S. Panigrahy, 2011. Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation, International Journal of Applied Earth Observation and Geoinformation, 13(2):228-235.
90. Wang, J., and C.I. Chang, 2006. Independent component analysis based dimensionality reduction with applications in hyperspectral image analysis, IEEE Transactions on Geoscience and Remote Sensing, 44(6):1586-1600.
91. Wu, C., L. Wang, Z. Niu, S. Gao, and M. Wu, 2010. Nondestructive estimation of canopy chlorophyll content using Hyperion and Landsat/TM images, International Journal of Remote Sensing, 31(8):2159-2167.
92. Zagajewski, B., A. Kozlowska, M. Krowczynska, M. Sobczak, and M. Wrzesien, 2005. Mapping high mountain vegetation using hyperspectral data, EARSeL eProceedings, 4:70-78.
93. Zhang, J., B. Rivard, G.A. Sanchez-Azofeifa, and K.L. Castro-Esau, 2006. Intra-and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery, Remote Sensing of Environment, 105(2):129-141.