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Earth Science Informatics

, Volume 11, Issue 4, pp 487–524 | Cite as

Hyperspectral Remote Sensing of Forests: Technological advancements, Opportunities and Challenges

  • Vipin Upadhyay
  • Amit Kumar
Review Article
  • 129 Downloads

Abstract

In real world what we are able to see is just because of light or energy reflected or emitted from the viewing object is falling upon retina of human eye. The variations in intensity of light reflected back from any object in different wavelengths are sensed and provide ability of discriminating different objects having similar size and shape. In the same way, in spectroscopy we sense the reflected light through artificial sensors and record as image (in airborne and satellite spectroscopy) or as spectrum (in field spectroscopy). In remote sensing discrimination of different object mainly depends on difference in reflection of energy in different wavelength region of light. Considering this behaviour of light, in hyperspectral remote sensing the reflected light coming from object is split into multiple continuous and small-small wavelength bands and are sensed in each wave band separately. Therefore we are having reflection response of object in multiple and narrow wavelength regions, which can be used in discrimination of different objects that are not separable in multispectral remote sensing due to less number of broad range wave bands. Collection of data is one aspect of the technology but as soon as these data are collected, a question arises how to and where to use this data? To answer where to use, a list of applications like discrimination, mapping and monitoring of different features and process of landforms in ecosystem have been reported, and forestry is one of them. And question of how to use these data in each applications involve converting the raw data into useful information using a multistep process of atmospheric, radiometric and geometric correction, removal of bad data and data redundancy, transformation and extraction of most useful data, data segmentation and extraction of useful information. For this purpose variety of data processing techniques, algorithms, concepts and schemes have been reported from time to time. In this review article we have summarized the available technical developments in hyperspectral remote sensing during the last three decades and tried to discuss the opportunities and challenges in hyperspectral remote sensing applications in the forestry sector.

Key words

Atmospheric correction Data dimensionality Feature extraction Forestry Hyperspectral Image classification Remote sensing Noise reduction 

Notes

Acknowledgements

Authors acknowledge the grant received from Interdisciplinary Cyber Physical Systems Division, Department of Science & Technology, Ministry of Science & Technology for Network Programme on Imaging Spectroscopy and Applications (NISA) under GAP-0201. We are also thankful to Dr. Sanjay Kumar, Director, CSIR-IHBT, Palampur, Himachal Pradesh, India for providing support and facilities (MLP-0205). This is CSIR-IHBT communication number 4175.

References

  1. Abe BT, Olugbara OO, Marwala T (2014) Experimental comparison of support vector machines with random forests for hyperspectral image land cover classification. J Earth Syst Sci 123(4):779–790Google Scholar
  2. Alsuwaidi A, Veys C, Hussey M (2016) Hyperspectral Selection Based Algorithm for Plant Classification. IEEE Int Conf Imaging Syst Techniques:395–400Google Scholar
  3. Adam E, Mutanga O, Abdel REM (2014) Estimating standing biomass in papyrus (Cyperus papyrus ) swamp: exploratory of in situ hyperspectral indices and random forest regression. Int J Remote Sens 35(2):693–714Google Scholar
  4. Artigas FJ, Yang JS (2005) Hyperspectral remote sensing of marsh species and plant vigour gradient in the New Jersey Meadowlands. Int J Remote Sens 26(23):5209–5220Google Scholar
  5. Atzberger C, Jarmer T, Schlerf M, Kotz B, Werner D (2003) Spectro-radiometric determination of wheat bio-physical variables: comparison of different empirical-statistical approaches. Remote Sens Trans:463–470Google Scholar
  6. Binaghi E, Gallo I, Boschetti M (2004) A neural adaptive model for hyperspectral data classification under minimal training conditions. Proceedings of the society of photo-optical instrumentation engineers 5573: 173-181Google Scholar
  7. Bakos KL, Gamba P (2011) Hierarchical hybrid decision tree fusion of multiple hyperspectral data processing chains. IEEE Trans Geosci Remote Sens 49(1):388–394Google Scholar
  8. Barnsley MJ, Lewis P, O'Dwyer S, Disney MI, Hobson P, Cutter M, Lobb D (2000) On the potential of CHRIS/PROBA for estimating vegetation canopy properties from space. Remote Sens Rev 19(1-4):171–189Google Scholar
  9. Barry KM, Stone C, Mohammed CL (2008) Crown-scale evaluation of spectral indices for defoliated and discoloured eucalypts. Int J Remote Sens 29(1):47–69Google Scholar
  10. Blackburn GA (2007) Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation. Int J Remote Sens 28(12):2831–2855Google Scholar
  11. Blackburn GA, Ferwerda JG (2008) Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. Remote Sens Environ 112(4):1614–1632Google Scholar
  12. Behmann J, Steinruecken J, Pluemer L (2014) Detection of early plant stress responses in hyperspectral images. ISPRS 93:98–111Google Scholar
  13. Ben I, Mohamed M, Bchir O (2014) Survey on number of endmembers estimation techniques for hyperspectral data unmixing. International Conference on Audio. Language And Image Processing 1-2: 651-655Google Scholar
  14. Berk A, Anderson GP, Acharya PK, Chetwynd JH, Bernstein LS, Shettle EP, Matthew MW and Adler-Golden S M, (2000) MODTRAN4 user’s manual Hanscom AFB: Air Force Research Laboratory. Space Vehicles Directorate, Air Force Materiel Command, MA, 97Google Scholar
  15. Bernard K, Tarabalka Y, Angulo J (2011) A stochastic minimum spanning forest approach for spectral-spatial classification of hyperspectral images. IEEE Int Conf Image Process:1265–1268Google Scholar
  16. Ball G, Hall D (1965) ISODATA, a novel method of data analysis and classification CA, USA. Technical report, AD-699616, Stanford University, StanfordGoogle Scholar
  17. Boschetti M, Boschetti L, Oliveri S, Casati L, Canova I (2007) Tree species mapping with Airborne hyper-spectral MIVIS data. Int J Remote Sens 28(6)Google Scholar
  18. Bostater CR (2006) Optimal band selection for hyperspectral remote sensing of aquatic benthic features - a wavelet filter window approach. Proceedings of The Society of Photo-Optical Instrumentation Engineers 6360: U185-U194Google Scholar
  19. Brackx M, Van WS, Verhelst J (2017) Hyperspectral leaf reflectance of Carpinus betulus L saplings for urban air quality estimation. Environ Pollut 220(A:159–167Google Scholar
  20. Breiman L (2001) Random forest. Mach Learn 45:5–32Google Scholar
  21. Brelsford C, Shepherd D (2014) Using mixture-tuned match filtering to measure changes in subpixel vegetation area in Las Vegas, Nevada. J Appl Remote Sens 8(1):083660Google Scholar
  22. Brunn A, Dittmann C, Fischer C (2001) Atmospheric correction of 2000 HyMap (TM) data in the framework of the EU-Project MINEO. Proceedings Of The Society Of Photo-Optical Instrumentation Engineers 4541: 382-392Google Scholar
  23. Bulcock HH, Jewitt GPW (2010) Spatial mapping of leaf area index using hyperspectral remote sensing for hydrological applications with a particular focus on canopy interception. Hydrol Earth Syst Sci 14(2):383–392Google Scholar
  24. Burai P, Deak B, Valko O (2015) Classification of herbaceous vegetation using airborne hyperspectral imagery. Remote Sens 7(2):2046–2066Google Scholar
  25. Cachorro VE, Vergaz R, De Frutos AM (1999) A model for atmospheric correction of DAIS hyperspectral imager sensor based on experimental optical measurements, remote sensing in the 21st century. Economic And Environmental Applications 541-547Google Scholar
  26. Carter GA (1994) Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Int J Remote Sens 15:697–703Google Scholar
  27. Carter GA (1998) Reflectance bands and indices for remote estimation of photosynthesis and stomatal conductance in pine canopies. Remote Sens Environ 63:61–72Google Scholar
  28. Carvalho OA, Menezes PR, (2000) Spectral Correlation Mapper (SCM): an improving Spectral Angle Mapper (SAM). Proceedings of the Nincth JPL Airborne Earth Science Workshop 18: 65-74Google Scholar
  29. Carvalho OA, De Carvalho APF, Guimaraes RF (2003) Classification of hyperspectral image using SCM methods for geobotanical analysis in the Brazilian Savanna region. IEEE Int Symp Geosci Remote Sens:3754–3756Google Scholar
  30. Chabrillat S, Kaufmann H, Palacios OA (2004) Development of land degradation spectral indices in a semiarid Mediterranean ecosystem. Proceedings of the society of photo-optical instrumentation engineers (SPIE) 5574: 235-243Google Scholar
  31. Chaichoke V, Suwit O, Tanasak V, Andrew KS (2005) Tropical mangrove species discrimination using hyperspectral data: A laboratory study. Estuar Coast Shelf Sci 65(1–2):371–379Google Scholar
  32. Chan JC, Paelinckx D (2008) Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sens Environ 112(6):2999–3011Google Scholar
  33. Cheng T, Rivard B, Sanchez-Azofeifa GA (2010) Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation. Remote Sens Environ 114(4):899–910Google Scholar
  34. Cho MA, Skidmore AK (2009) Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella National Park, Italy. Int J Remote Sens 30(2):499–515Google Scholar
  35. Cho MA, Skidmore AK, Sobhan I (2009) Mapping beech (Fagus sylvatica L) forest structure with airborne hyperspectral imagery. Int J Appl Earth Obs Geoinf 11(3):201–211Google Scholar
  36. Cho MA, Debba P, Mathieu R (2010) Improving discrimination of savanna tree species through a multiple-endmember spectral angle mapper approach: Canopy-level analysis. IEEE Trans Geosci Remote Sens 48(11):4133–4142Google Scholar
  37. Cho MA, Debba P, Mutanga O (2012) Potential utility of the spectral red-edge region of SumbandilaSat imagery for assessing indigenous forest structure and health. Int J Appl Earth Obs Geoinf 16:85–93Google Scholar
  38. Christian B, Krishnayya NSR (2009) Classification of tropical trees growing in a sanctuary using Hyperion (EO-1) and SAM algorithm. Curr Sci 96(12):1601–1607Google Scholar
  39. Ciraolo G, Cox E, La Loggia G (2003) The classification of submerged vegetation using hyperspectral MIVIS data Conference on Airborne Remote Sensing for Geophysical and Environmental Application. Annals of. Geophysics 49(1):287–294Google Scholar
  40. Cocks T, Jenssen R, Stewart A, Wilson I, and Shields T (1998) The Hymap airborne hyperspectral sensor: The system, calibration and performance. EARSEL Workshop on Imaging SpectroscopyGoogle Scholar
  41. Conese C, Maselli F (1993) Selection of optimal bands from TM scenes through mutual information analysis. ISPRS J Photogramm Remote Sens 48(3):2–11Google Scholar
  42. Cooley JW, Tukey OW (1965) An algorithm for the machine calculation of complex fourier series. Math Comput 19:297–301Google Scholar
  43. Cord M, Cunningham P (2008) Machine learning techniques for multimedia: Case studies on organization and retrieval. Springer Science & Business Media 1-29Google Scholar
  44. Craig R, Jie S (2002) Principal component analysis for hyperspectral image classification. Surveying and Land. Inf Syst 62(2):115–000Google Scholar
  45. Clark ML, Kilham NE (2016) Mapping of land cover in northern California with simulated hyperspectral satellite imagery. ISPRS J Photogramm Remote Sens 119:228–245Google Scholar
  46. Clark ML, Roberts DA (2012) Species-Level Differences in hyperspectral Metrics among Tropical Rainforest Trees as Determined by a Tree-Based Classifier. Remote Sens 4(6):1820–1855Google Scholar
  47. Croft H, Chen JM, Zhang Y (2013) Modelling leaf chlorophyll content in broadleaf and needle leaf canopies from ground CASI Landsat TM 5 and MERIS reflectance data. Remote Sens Environ 133:128–140Google Scholar
  48. Calderon R, Navas CJA, Lucena C (2013) High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sens Environ 139:231–245Google Scholar
  49. Clasen A, Somers B, Pipkins K (2015) Spectral unmixing of forest crown components at close range Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale. Remote Sens 7(11):15361–15387Google Scholar
  50. Cui LL, Fan WY, Shi J (2004) Some key pre-processing techniques on airborne imaging spectrometer data for quantitative analysis. Proceedings of The Society of Photo-Optical Instrumentation Engineers 5548: 398-408Google Scholar
  51. Cui M, Prasad S, Bruce LM (2012) Robust spatial-spectral hyperspectral image classification for vegetation stress detection. IEEE International Symposium on Geoscience and Remote Sensing:5486–5489Google Scholar
  52. De Backer S, Kempeneers P, Debruyn W (2005) A band selection technique for spectral classification. IEEE Geosci Remote Sens Lett 2(3):319–323Google Scholar
  53. Deng C, Wu C (2013) A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution. Remote Sens Environ:13362–13370Google Scholar
  54. Dennison PE, Roberts DA (2003) The effects of vegetation phenology on endmember selection and species mapping in southern California chaparral. Remote Sens Environ 87(2-3):295–309Google Scholar
  55. Dennison PE, Halligan KQ, Roberts DA (2004) A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper. Remote Sens Environ 93(3):359–367Google Scholar
  56. Deventer VH, Cho MA Mutanga O (2015) Capability of models to predict leaf N and P across four seasons for six sub-tropical forest evergreen trees. ISPRS J Photogramm Remote Sens 101:209–220Google Scholar
  57. Dian Y, Li Z, Pang Y (2013) Forest tree species classification based on airborne hyperspectral imagery. Proceedings of SPIE 8921: UNSP 892107Google Scholar
  58. Dian Y, Fang S, Yuan L (2014) Comparison of the different classifiers in vegetation species discrimination using hyperspectral reflectance data. J Indian Soc Remote Sens 42(1):61–72Google Scholar
  59. Dian Y, Li Z, Pang Y (2015) Spectral and texture features combined for forest tree species classification with airborne hyperspectral imagery. J Indian Soc of Remote Sens 43(1):101–107Google Scholar
  60. Dian Y, Le Y, Fang S (2016) Influence of Spectral Bandwidth and Position on Chlorophyll Content Retrieval at Leaf and Canopy Levels. J Indian Soc Remote Sens 44(4):583–593Google Scholar
  61. Delalieux S, Somers B, Haest B (2012) Heathland conservation status mapping through integration of hyperspectral mixture analysis and decision tree classifiers. Remote Sens Environ 126:222–223Google Scholar
  62. Dalponte M, Bruzzone L, Vescovo L (2009) The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas. Remote Sens Environ 113(11):2345–2355Google Scholar
  63. Du H, Chang CI, Ren HD, Amico FM, Jensen JO (2004) New hyperspectral discrimination measure for spectral characterization. Opt Eng 43(8):1777–1786Google Scholar
  64. Dudley KL, Dennison PE, Roth KL (2015) A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients. Remote Sens Environ 167:121–134Google Scholar
  65. Dyk A, Goodenough DG, Thompson S (2003) Compressed hyperspectral imagery for forestry. IEEE Int Symp Geosci Remote Sens:294–296Google Scholar
  66. Ebadi L, Shafri HZM (2015) A stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum. Earth Sci Inf 8(2):411–425Google Scholar
  67. Ebadi L, Shafri HZM, Mansor SB (2013) A review of applying second-generation wavelets for noise removal from remote sensing data. Environ Earth Sci 70(6):2679–2690Google Scholar
  68. Everitt JH, Yang C, Summy K (2013) Using hyperspectral reflectance data to assess biocontrol damage of giant salvinia. Geocarto Int 28(6):502–516Google Scholar
  69. Emengini EJ, Blackburn GA, Theobald JC (2013) Discrimination of plant stress caused by oil pollution and waterlogging using hyperspectral and thermal remote sensing. J Appl Remote Sens 7:073476Google Scholar
  70. Elatawneh A, Kalaitzidis C, Petropoulos GP (2014) Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data. Int J Digital Earth 7(3):194–216Google Scholar
  71. Fahsi A, Tsegaye T, Rajbhandari N (1999) Effect of vegetation density and vegetation conditions on the spectral backscattering in the visible and the near infrared. Proceedings of The Society of Photo-Optical Instrumentation Engineers (Spie) 3868: 132-140Google Scholar
  72. Fan F, Deng Y (2014) Enhancing endmember selection in multiple endmember spectral mixture analysis for urban impervious surface area mapping using spectral angle and spectral distance parameters. Int J Appl Earth Obs Geoinf 33:290–301Google Scholar
  73. Fan W, Li M, Yu Y (2011) Quantitative retrieving of vegetation factors for desertification area. Adv Mater Res 183-185:376–380Google Scholar
  74. Fassnacht FE, Neumann C, Foerster M (2014) Comparison of feature reduction algorithms for classifying tree species with hyperspectral data on three central european test sites. IEEE J Sel Top Appl Earth Obs Remote Sens 7(6):2547–2561Google Scholar
  75. Feilhauer H, Asner GP, Martin RE (2010a) Brightness-normalized partial least squares regression for hyperspectral data. J Quant Spectrosc Radiat Transf 111(12-13):1947–1957Google Scholar
  76. Feilhauer H, Oerke EC, Schmidtlein S (2010b) Quantifying empirical relations between planted species mixtures and canopy reflectance with PROTEST. Remote Sens Environ 114(7):1513–1521Google Scholar
  77. Feng J, Jiao L, Sun T (2016) Multiple kernel learning based on discriminative kernel clustering for hyperspectral band selection. IEEE Trans Geosci Remote Sens 54(11):6516–6530Google Scholar
  78. Feret JB, Asner GP (2011) Spectroscopic classification of tropical forest species using radiative transfer modeling. Remote Sens Environ 115(9):2415–2422Google Scholar
  79. Feret JB, Francois C, Gitelson A (2011) Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sens Environ 115(10):2742–2750Google Scholar
  80. Fevotte C, Dobigeon N (2015) Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization. IEEE Trans Image Process 24(12):4810–4819Google Scholar
  81. Filippi AM Jensen JR (2006) Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sens Environ 100(4):512–530Google Scholar
  82. Filippi AM, Jensen JR (2007) Effect of continuum removal on hyperspectral coastal vegetation classification using a fuzzy learning vector quantizer. IEEE Trans Geosci Remote Sens 45(6):1857–1869Google Scholar
  83. Foody GM, Curran PJ, Honzak M (1997) Non-linear mixture modelling without endmembers using an artificial neural network. Int J Remote Sens 18(4):937–953Google Scholar
  84. Forzieri G, Moser G, Catani F (2012) Assessment of hyperspectral MIVIS sensor capability for heterogeneous landscape classification. ISPRS J Photogramm Remote Sens 74:175–184Google Scholar
  85. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. Proceedings of the 13th International Conference on Machine Learning Bari Italy 148–156Google Scholar
  86. Gao Y, Li D (2015) Assessing leaf senescence in tall fescue (Festuca arundinacea Schreb) under salinity stress using leaf spectrum. Eur J Hortic Sci 80(4):170–176Google Scholar
  87. Ge S, Carruthers RI, Kramer M (2011) Multiple-level defoliation assessment with hyperspectral data: integration of continuum-removed absorptions and red edges. Int J Remote Sens 32(21):6407–6422Google Scholar
  88. Gholizadeh A, Misurec J, Kopackova V (2016) Assessment of red-edge position extraction techniques: a case study for norway spruce forests using Hymap and simulated sentinel-2 data. Forests 7(10):226Google Scholar
  89. Gomez CMT, Lopez GF, Pena-Barragan Jose M (2007) Assessing nitrogen and potassium deficiencies in olive orchards through discriminant analysis of hyperspectral data. J Am Soc Hortic Sci 132(5):611–618Google Scholar
  90. Gomez JA, Zarco-Tejada PJ, Garcia-Morillo J (2011) Determining Biophysical Parameters for Olive Trees Using CASI-Airborne and Quickbird-Satellite Imagery. Agron J 103(3):644–654Google Scholar
  91. Gong P, Pu R, Heald RC (2002) Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia. Int J Remote Sens 23(9):1827–1850Google Scholar
  92. Goodenough DG, Dyk A, Niemann O (2003) Processing Hyperion and ALI for forest classification. IEEE Trans Geosci Remote Sens 41(6):1321–1331Google Scholar
  93. Goodenough DG, Han T, Pearlman JS (2004) Forest chemistry mapping with hyperspectral data. IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data 395-398Google Scholar
  94. Grace J, Nichol C, Disney M (2007) Can we measure terrestrial photosynthesis from space directly using spectral reflectance and fluorescence. Glob Chang Biol 13(7):1484–1497Google Scholar
  95. Green AA, Berman M, Switzer P, Craig MD (1988) A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans Geosci Remote Sens 26:65–74Google Scholar
  96. Green RO, Eastwood ML, Sarture CM, Chrien TG, Aronsson M, Chippendale BJ, Faust JA, Pavri BE, Chovit CJ, Solis M, Olah MR, Williams O (1998) Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sens Environ 65:227–248Google Scholar
  97. Gruninger J, Fox M, Lee J (2002) Use of the Vis-SWIR to aid atmospheric correction of multispectral and hyperspectral thermal infrared (TIR) imagery: The TIR model. Proceedings of The Society of Photo-Optical Instrumentation Engineers 4816: 80-92Google Scholar
  98. Guo Y, Zeng F (2012) Atmospheric Correction Comparison Of Spot-5 Image Based On Model FLAASH And Model QUAC. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences XXXIX-B7Google Scholar
  99. Guo B, Gunn SR, Damper RI (2006) Band selection for hyperspectral image classification using mutual information. IEEE Geosci Remote Sens Lett 3(4):522–526Google Scholar
  100. Harsanyi JC, Chang CI (1994) Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection. IEEE Trans Geosci Remote Sens 32(4):779–785Google Scholar
  101. Hernandez CR, Navarro CRM, Suarez L (2011) Assessing structural effects on PRI for stress detection in conifer forests. Remote Sens Environ 115(9):2360–2375Google Scholar
  102. Heylen R, Parente M, Gader P (2014) A Review of nonlinear hyperspectral unmixing methods. IEEE J Sel Top Appl Earth Obs Remote Sens 7(6):1844–1868Google Scholar
  103. Hsu PHT, Seng YH, Gong P (2002) Dimension reduction of hyperspectral images. Geographic. Inf Sci 8:1–8Google Scholar
  104. Hu B, Li Q (2007) Vegetation classification using hyperspectral remote sensing and singular spectrum analysis. Proceedings of The Society Of Photo-Optical Instrumentation Engineers 6696(1-2): N6960-N6960Google Scholar
  105. Huete A, Miura T, Gao X (2002) Land cover conversion and degradation analyses through coupled soil-plant biophysical parameters derived from hyperspecral EO-1 Hyperion. IEEE Int Symp Geosci Remote Sens:799–801Google Scholar
  106. Huete AR, Miura T, Gao X (2003) Land cover conversion and degradation analyses through coupled soil-plant biophysical parameters derived from hyperspectral EO-1 Hyperion. IEEE Trans Geosci Remote Sens 41(6):1268–1276Google Scholar
  107. Hui F (2013) Land-cover mapping in the Nujiang Grand Canyon: integrating spectral textural and topographic data in a random forest classifier. Int J Remote Sens 34(21):7545–7567Google Scholar
  108. Jin H, Li P, Cheng T (2012) Land cover classification using CHRIS/PROBA images and multi-temporal texture. Int J Remote Sens 33(1):101–119Google Scholar
  109. Jin J, Jiang H, Zhang X (2013) Using multivariate analysis to detect the hyperspectral response of Chinese fir to acid stress. Int J Remote Sens 34(11):3775–3786Google Scholar
  110. Jacquemoud S, Baret J (1990) PROSPECT: A model of leaf optical properties spectra. Remote Sens Environ 34:75–91Google Scholar
  111. Jengo C M, LaVeigne J (2004) Sensor performance comparison of HyperSpecTIR instruments 1 and 2. IEEE Aerospace Conference Proceedings 3: 1805Google Scholar
  112. Jiantao FQ, Gong J (2016) Land-cover classification of the yellow river delta wetland based on multiple end-member spectral mixture analysis and a random forest classifier. Int J Remote Sens 37(8):1845–1867Google Scholar
  113. Ju Y, Pan J, Wang X (2014) Detection of Bursaphelenchus xylophilus infection in Pinus massoniana from hyperspectral data. Nematology 16:1197–1207Google Scholar
  114. Kopackova V, Misurec J, Lhotakova Z (2014) Using multi-date high spectral resolution data to assess the physiological status of macroscopically undamaged foliage on a regional scale. Int J Appl Earth Obs Geoinf 27:169–186Google Scholar
  115. Kalacska M, Lalonde M, Moore TR (2015) Estimation of foliar chlorophyll and nitrogen content in an ombrotrophic bog from hyperspectral data: Scaling from leaf to image. Remote Sens Environ 169:270–279Google Scholar
  116. Karathanassi V, Andreou C, Andronis V (2014) Effects of band selection on endmember extraction for forestry applications. Proceedings of SPIE 9245: UNSP 92451OGoogle Scholar
  117. Kefauver SC, Penuelas JUS (2013) Using topographic and remotely sensed variables to assess ozone injury to conifers in the Sierra Nevada (USA) and Catalonia (Spain). Remote Sens Environ 139:138–148Google Scholar
  118. Kempeneers P, De Backer SB, Debruyn W (2004) Wavelet based feature extraction for hyperspectral vegetation monitoring. Proceedings of The Society of Photo-Optical Instrumentation Engineers 5238: 297-305Google Scholar
  119. Kempeneers P, Deronde B, Bertels L (2004) Classifying hyperspectral airborne imagery for vegetation survey along coastlines. IEEE Int Symp Geosci Remote Sens:1475–1478Google Scholar
  120. Kempeneers P, Zarco-Tejada PJ, North PRJ (2008) Model inversion for chlorophyll estimation in open canopies from hyperspectral imagery. Int J Remote Sens 29(17-18):5093–5111Google Scholar
  121. Khurshid KS, Staenz K, Sun L (2005) Preprocessing of EO-1 hyperion data. Can J Remote Sens 32(2):84–97Google Scholar
  122. Kim Y, Glenn DM, Park J (2011) Hyperspectral image analysis for water stress detection of apple trees. Comput Electron Agric 77(2):155–160Google Scholar
  123. Kira O, Linker R, Gitelson A (2015) Non-destructive estimation of foliar chlorophyll and carotenoid contents: Focus on informative spectral bands. Int J Appl Earth Obs Geoinf 38:251–260Google Scholar
  124. Kolluru P, Pandey K, Padalia H (2014) A Unified framework for dimensionality reduction and classification of hyperspectral data. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences 40(8): 447-453Google Scholar
  125. Kovacs JM, Liu Y, Zhang C (2011) A field based statistical approach for validating a remotely sensed mangrove forest classification scheme. Wetl Ecol Manag 19(5):409–421Google Scholar
  126. Kruse FA (2004) Comparison of ATREM ACORN and FLAASH atmospheric corrections using low-altitude AVIRIS data of Boulder. Jet Propulsion Laboratory PublicationGoogle Scholar
  127. Kruse FA, Richardson LL, Ambrosia VG (1997) Techniques developed for geologic analysis of hyperspectral data applied to near-shore hyperspectral ocean data. Proceedings of Fourth International Conferenceon Remote Sensing for Marine and Coastal Environments Orlando FloridaGoogle Scholar
  128. Kumar V, Ghosh JK (2017) Camouflage Detection Using MWIR Hyperspectral Images. J Indian Soc Remote Sens 45:139Google Scholar
  129. Kumar A, Manjunath KR, Meenakshi (2013) Field hyperspectral data analysis for discriminating spectral behavior of tea plantations under various management practices. Int J Appl Earth Obs Geoinf 23:352–359Google Scholar
  130. Lee CM, Morgan LC, Hook SJ, Green RO, Susan LU, Daniel JM, Elizabeth MM (2015) An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities. Remote Sens Environ 167:6–19Google Scholar
  131. Lee J, Cai X, Lellmann J (2016) Individual Tree Species Classification From Airborne Multisensor Imagery Using Robust PCA. IEEE Sel Top Appl Earth Obs Remote Sens 9(6):2554–2567Google Scholar
  132. Lei Q, Bodechtel J (1999) Application of MAIS (Modular Airborne Imaging Spectrometer) data for mineral prospection in Gansu Province China. Geoscience and Remote Sensing SymposiumGoogle Scholar
  133. Levesque J, Staenz K (2004) A method for monitoring mine tailings re-vegetation using hyperspectral remote sensing. IEEE Int Symp Geosci Remote Sens:575–578Google Scholar
  134. Lewis M (2000) Discrimination of arid vegetation composition with high resolution CASI imagery. Rangel J 22(1):141–167Google Scholar
  135. Lhotakova Z, Brodsky L, Kupkova L (2013) Detection of multiple stresses in Scots pine growing at post-mining sites using visible to near-infrared spectroscopy. Environ Sci.:Processes Impacts 15(11):2004–2015Google Scholar
  136. Li L, Ustin SL, Lay M (2005) Application of AVIRIS data in detection of oil-induced vegetation stress and cover change at Jornada New Mexico. Remote Sens Environ 94(1):1–16Google Scholar
  137. Li N, Lue J, Altermann W (2010) Hyperspectral remote sensing in monitoring the vegetation heavy metal pollution. Spectrosc Spectr Anal 30(9):2508–2511Google Scholar
  138. Li X, Jia X, Wang L (2015) On spectral unmixing resolution using extended support vector machines. IEEE Trans Geosci Remote Sens 53(9):4985–4996Google Scholar
  139. Li J, Xi T, Huang W (2016a) Application of Long-Wave Near Infrared hyperspectral Imaging for Measurement of Soluble Solid Content (SSC) in Pear. Food Anal Methods 9(11):3087–3098Google Scholar
  140. Li SP, Wu ZF, Zhao YS (2016b) Hyperspectr`al and red-edge characteristics of typical hardwoods leaf coloring date in Mudan Valley Changbai. Mt J Infrared Millimeter Waves 35(5):584–591Google Scholar
  141. Lillesand TM., Kiefer RW, Chipman JW (2008) Remote Sensing and Image Interpretation. 6th Edition John Wiley & Sons, Hoboken.Google Scholar
  142. Liu S, Jiao L, Yang S (2016) Hierarchical sparse learning with spectral-spatial information for hyperspectral imagery denoising. Sensors 16(10):1718Google Scholar
  143. Lorente D, Aleixos N, Gomez-Sanchis J (2013) Selection of optimal wavelength features for decay detection in citrus fruit using the roc curve and neural networks. Food Bioprocess Technol 6(2):530–541Google Scholar
  144. Lu X, Hu Z, Guo S (2009) The Quantitative Estimation of Periurban vegetation ecology Using hyperspectral Remote Sensing Joint Urban. Remote Sensing Event 1-3:13–18Google Scholar
  145. Lu D, Song K, Wang Z (2010) Application of wavelet transform (wt) on canopy hyperspectral data for soybean leaf area index (lai) estimation in the Songnen Plain China. Proceedings of SPIE-The International Society for Optical Engineering 7807(1): 78070VGoogle Scholar
  146. Ma J, Zheng Z, Tong Q, Zheng L, Zhang B (2001) Hyperspectral image band selection based on genetic algorithms. SPIE 4548:195–198Google Scholar
  147. Manjunath KR, Kumar T, Kundu N (2013) Discrimination of mangrove species and mudflat classes using in situ hyperspectral data: A case study of Indian Sundarbans. GIScience Remote Sens 50(4):400–417Google Scholar
  148. Mannel S, Price M (2012) Comparing classification results of multi-seasonal TM against AVIRIS imagery - seasonality more important than number of bands. Photogrammetrie Fernerkundung Geoinformation (5):603–612Google Scholar
  149. Markelin L, Honkavaara E, Schlaepfer D (2012) Assessment of Radiometric Correction Methods for ADS40. Imagery. Photogrammetrie Fernerkundung Geoinformation (3):251–266Google Scholar
  150. Maselli F, Conese C, Petkov L, Resti R (1992) Inclusion of prior probabilities derived from a nonparametric process into the maximum likelihood classifier. Photogramm Eng Remote Sens 58:201–207Google Scholar
  151. McGwire K, Minor T, Fenstermaker L (2000) Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments. Remote Sens Environ 72(3):360–374Google Scholar
  152. Male EJ, Pickles WL, Silver EA (2010) Using hyperspectral plant signatures for CO2 leak detection during the 2008 ZERT CO2 sequestration field experiment in Bozeman Montana. Environ Earth Sci 6(2):251–261Google Scholar
  153. Meggio F, Zarco-Tejada PJ, Nunez LC (2010) Grape quality assessment in vineyards affected by iron deficiency chlorosis using narrow-band physiological remote sensing indices. Remote Sens Environ 114(9):1968–1986Google Scholar
  154. Meng R, Dennison PE (2015) Spectroscopic Analysis of Green Desiccated and Dead Tamarisk Canopies. Photogramm Eng Remote Sens 81(3):199–207Google Scholar
  155. Malenovsky Z, Homolova L, Cudlin P (2007) Physically-based retrievals of Norway spruce canopy variables from very high spatial resolution hyperspectral data. IEEE Int Symp Geosci Remote Sens:4057–4060Google Scholar
  156. Metternicht G, Zinck JA, Blanco PD (2010) Remote sensing of land degradation: experiences from latin america and the caribbean. J Environ Qual 39(1):42–61Google Scholar
  157. Miao L, Qi H, Szu H (2007) A maximum entropy approach to unsupervised mixed-pixel decomposition. IEEE Trans Image Process 16(4):1008–1021Google Scholar
  158. Miao X, Patil R, Heaton JS (2011) Detection and classification of invasive saltcedar through high spatial resolution airborne hyperspectral imagery. Int J Remote Sens 32(8):2131–2150Google Scholar
  159. Miglani A, Ray SS, Vashishta DP (2011) Comparison of Two Data Smoothing Techniques for vegetation Spectra Derived From EO-1 Hyperion. J Indian Soc Remote Sens 39(4):443–453Google Scholar
  160. Mirik M, Steddom K, Michels GJ (2006) Estimating biophysical characteristics of musk thistle (Carduus nutans) with three remote sensing instruments. Rangel Ecol Manag 59(1):44–54Google Scholar
  161. Mishra A, Karimi D, Ehsani R (2011) Evaluation of an active optical sensor for detection of Huanglongbing (HLB) disease. Biosyst Eng 110(3):302–309Google Scholar
  162. Mitchell PA (1995) Hyperspectral digital imagery collection experiment (HYDICE). Proc SPIE 2587:70Google Scholar
  163. Mitchell JJ, Glenn NF (2009) Leafy Spurge (Euphorbia esula) Classification Performance Using hyperspectral and Multispectral Sensors. Rangel Ecol Manag 62(1):16–27Google Scholar
  164. Mitchell JJ, Glenn NF, Sankey TT (2012) Remote sensing of sagebrush canopy nitrogen. Remote Sens Environ 124:217–223Google Scholar
  165. Moroni M, Lupo E, Cenedese A (2013) Hyperspectral Proximal Sensing of Salix Alba Trees in the Sacco River Valley (Latium Italy). Sensors 13(11):14633–14649Google Scholar
  166. Moustakidis S, Mallinis G, Koutsias N (2012) SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images. IEEE Trans Geosci Remote Sens 50(1):149–169Google Scholar
  167. Mukherjee K, Ghosh JK, Mittal RC (2013) Variogram fractal dimension based features for hyperspectral data dimensionality reduction. J Indian Soc Remote Sens 41(2):249–258Google Scholar
  168. Mueller R, Cerra D, Reinartz P (2013) Synergetics Framework For hyperspectral Image Classification. International Archives of the Photogrammetry. Remote Sens Spat Inf Sci 40(W-1):257–262Google Scholar
  169. Murphy RJ, Underwood AJ, Tolhurst TJ (2008) Field-based remote-sensing for experimental intertidal ecology: Case studies using hyperspatial and Hyperspectral data for New South Wales (Australia). Remote Sens Environ 112(8):3353–3365Google Scholar
  170. Nascimento JMP, Bioucas-Dias JM (2005) Vertex component analysis: A fast algorithm to unmix hyperspectral data. IEEE Trans Geosci Remote Sens 43(4):898–910Google Scholar
  171. Nawar S, Buddenbaum H and Hill (2015) Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopy: A case study from Egypt. Arab J Geosci 8: 5127Google Scholar
  172. Nikonorov A, Bibikov S, Myasnikov V (2016) Correcting color and hyperspectral images with identification of distortion model. Pattern Recogn Lett 83(2):178–187Google Scholar
  173. O'Connell JL, Kristin BB, Kelly M (2014) Remotely-Sensed Indicators of N-Related Biomass Allocation in Schoenoplectus acutus. PLoS One 9(3):e90870Google Scholar
  174. Okujeni A, Sebastian VL, Laurent T (2013) Support vector regression and synthetically mixed training data for quantifying urban land cover. Remote Sens Environ 137:184–197Google Scholar
  175. Oumar Z, Mutanga O, Ismail R (2013) Predicting Thaumastocoris peregrinus damage using narrow band normalized indices and hyperspectral indices using field spectra resampled to the Hyperion sensor. Int J Appl Earth Obs Geoinf 21:113–121Google Scholar
  176. Panigada C, Rossini M, Busetto L (2010) Chlorophyll concentration mapping with MIVIS data to assess crown discoloration in the Ticino Park oak forest. Int J Remote Sens 31(12):3307–3332Google Scholar
  177. Parshakov I, Coburn C, Staenz K (2014) Automated Class Labeling Of Classified Landsat TM Imagery Using a Hyperion-Generated hyperspectral Library. Photogramm Eng Remote Sens 80(8):797–805Google Scholar
  178. Pena MA, Brenning A, Sagredo A (2012) Constructing satellite-derived hyperspectral indices sensitive to canopy structure variables of a Cordilleran Cypress (Austrocedrus chilensis) forest. ISPRS J Photogramm Remote Sens 74:1–10Google Scholar
  179. Perumal K, Bhaskaran R (2010) Supervised classification performance of multispectral images. J Comput 2(2):124–129Google Scholar
  180. Petropoulos GP, Kalivas DP, Georgopoulou IA (2015) Urban vegetation cover extraction from hyperspectral imagery and geographic information system spatial analysis techniques: case of Athens Greece. J Appl Remote Sens 9:096088Google Scholar
  181. Phillips RD, Watson LT, Wynne RH (2012) Continuous iterative guided spectral class rejection classification algorithm. IEEE Trans Geosci Remote Sens 50(6):2303–2317Google Scholar
  182. Pipkins K, Foerster M, Clasen A (2014) A Comparison of Feature Selection Methods for Multitemporal Tree Species Classification. Proc SPIE 9245:92450VGoogle Scholar
  183. Plourde LC, Ollinger SV, Smith ML (2007) Estimating species abundance in a northern temperate forest using spectral mixture analysis. Photogramm Eng Remote Sens 73(7):829–840Google Scholar
  184. Prasad KA, Gnanappazham L (2013) Spectral Separability among mangrove species of rhizophoraceae family using field spectroscopy. Ocean. Electronics:213–220Google Scholar
  185. Prasad ST, Eden AE, Mark SA, Bauke VDM (2004) Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sens Environ 91:354–376Google Scholar
  186. Pu R, Gong P (2004) Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping. Remote Sens Environ 91:212–224Google Scholar
  187. Pinzon JE, Ustin SL, Castaneda CM (1998) Robust spatial and spectral feature extraction for multispectral and hyperspectral imagery. Proceedings Of The Society Of Photo-Optical Instrumentation Engineers 3372: 199-210Google Scholar
  188. Qiu F (2008) Neuro-fuzzy based analysis of hyperspectral imagery. Photogramm Eng Remote Sens 74(10):1235–1247Google Scholar
  189. Qiu HL, Gamon JA, Roberts DA (1998) Monitoring post fire succession in the Santa Monica Mountains using hyperspectral imagery. Proceedings of the society of photo-optical instrumentation engineers 3502: 201-208Google Scholar
  190. Qu Y, Jiao S, Liu S (2015) Retrieval of copper pollution information from hyperspectral satellite data in a vegetation cover mining area. Spectrosc Spectr Anal 35(11):3176–3181Google Scholar
  191. van der Meer FD, Jia X (2012) Collinearity and orthogonality of end members in linear spectral unmixing. Int J Appl Earth Obs Geoinf 18:491–503Google Scholar
  192. Raksuntorn N, Du Q (2010) Nonlinear spectral mixture analysis for hyperspectral imagery in an unknown environment. IEEE Geosci Remote Sens Lett 7(4):836–840Google Scholar
  193. Rasel SMM, Chang HC, Ralph T (2015) Endmember identification from EO-1 Hyperion L1_R hyperspectral data to build saltmarsh spectral library in Hunter Wetland NSW Australia. Proc SPIE 9637:96371OGoogle Scholar
  194. Rautiainen M, Lang M, Mottus M (2008) Multi-angular reflectance properties of a hemiboreal forest: An analysis using CHRIS PROBA data. Remote Sens Environ 112(5):2627–2642Google Scholar
  195. Raychaudhuri B (2012) Synthesis of mixed pixel hyperspectral signatures. Int J Remote Sens 33(6):1954–1966Google Scholar
  196. Rodriguez GVF, Chica OM, Abarca HF (2012) Random Forest classification of mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sens Environ 121:93–107Google Scholar
  197. Roth KL, Dennison PE, Roberts DA (2012) Comparing endmember selection techniques for accurate mapping of plant species and land cover using imaging spectrometer data. Remote Sens Environ 127:139–152Google Scholar
  198. Roth KL, Roberts DA, Dennison PE (2015) The impact of spatial resolution on the classification of plant species and functional types within imaging spectrometer data. Remote Sens Environ 171:45–57Google Scholar
  199. Rubeena V, Tiwari KC (2016) Multisensor multiresolution data fusion for improvement in classification. Proc SPIE 9880:98800XGoogle Scholar
  200. Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674Google Scholar
  201. Sandmeier S, Deering DW (1999) Structure analysis and classification of boreal forests using airborne hyperspectral BRDF data from ASAS. Remote Sens Environ 69(3):281–295Google Scholar
  202. Sandor LS (1999) A subspace projection approach to characterization and classification of TRWIS III data. Proceedings Of The Society Of Photo-Optical Instrumentation Engineers (3753): 318-326Google Scholar
  203. Santiago FF, Kovacs JM, Jinfei W (2016) Examining the influence of seasonality, condition, and species composition on mangrove leaf pigment contents and laboratory based spectroscopy data. Remote Sens 8(3):1–20Google Scholar
  204. Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627–1639Google Scholar
  205. Schmid T, Koch M, Gumuzzio J (2004) A spectral library for a semi-arid wetland and its application to studies of wetland degradation using hyperspectral and multispectral data. Int J Remote Sens 25(13):2485–2496Google Scholar
  206. Schmidt KS, Skidmore AK (2004) Smoothing vegetation spectra with wavelets. Int J Remote Sens 25(6):1167–1184Google Scholar
  207. Schlerf M, Atzberger C (2006) Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data. Remote Sens Environ 100(3):281–294Google Scholar
  208. Schlerf M, Atzberger C, Hill J (2005) Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sens Environ 95(2):177–194Google Scholar
  209. Serrano L, Gonzalez-Flor C, Gorchs G (2012) Assessment of grape yield and composition using the reflectance based water index in mediterranean rainfed vineyards. Remote sensing of environment 118: 249–258Google Scholar
  210. Settle JJ, Drake NA (1993) Linear mixing and the estimation of ground cover proportions. Int J Remote Sens 14:1159–1177Google Scholar
  211. Singh S, Dutta D, Singh U (2014) Hydat-A hyperspectral Data Processing Tool For Field Spectroradiometer Data. International Archives of the Photogrammetry. Remote Sens Spat Inf Sci 40(8):481–484Google Scholar
  212. Shackelford K, Davis CH (2003) A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas. IEEE Trans Geosci Remote Sens 41(9):1920–1932Google Scholar
  213. Shafri HZM, Yusof MRM (2009) Determination of optimal wavelet denoising parameters for red edge feature extraction from hyperspectral data. J Appl Remote Sens 3(1):033533Google Scholar
  214. Shafri HZM, Anuar MI, Saripan MI (2009) Modified vegetation indices for Ganoderma disease detection in oil palm from field spectroradiometer data. J Appl Remote Sens 3:033556Google Scholar
  215. Shang X, Chisholm LA (2014) Classification of australian native forest species using hyperspectral remote sensing and machine-learning classification algorithms. IEEE J Sel Top Appl Earth Obs Remote Sens 7(6):2481–2489Google Scholar
  216. Shang J, Neville R, Staenz K (2008) Comparison of fully constrained and weakly constrained unmixing through mine-tailing composition mapping. Can J Remote Sens 34(1):S92–S109Google Scholar
  217. Shang K, Zhang X, Zhang L (2011) Evaluation of hyperspectral classification methods based on FISS data Proceedings of SPIE 8002(1): 80020LGoogle Scholar
  218. Sheikh ZG, Thakare VM (2016) Wavelet based feature extraction technique for face recognition and retrieval: A review. IOSR J Comput Eng:49–54Google Scholar
  219. Sibanda M, Mutanga O, Rouget M (2016) Comparing the spectral settings of the new generation broad and narrow band sensors in estimating biomass of native grasses grown under different management practices. Giscience Remote Sens 53(5):614–633Google Scholar
  220. Silvestri S, Marani M, Settle J (2002) Salt marsh vegetation radiometry - Data analysis and scaling. Remote Sens Environ 80(3):473–482Google Scholar
  221. Simental E, Bosch EH, Rand RS (2004) Wavelet-based feature indices as a data mining tool for hyperspectral imagery exploitation. Proceedings of the society of photo-optical instrumentation engineers 5558(1): 169-180Google Scholar
  222. Sluiter R, Pebesma EJ (2010) Comparing techniques for vegetation classification using multi- and hyperspectral images and ancillary environmental data. Int J Remote Sens 31(23):6143–6161Google Scholar
  223. Smith KL, Steven MD, Colls JJ (2004) Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote Sens Environ 92(2):207–217Google Scholar
  224. Soares GL, Jorge PF, Veraldo L (2009) Possibilities of discriminating tropical secondary succession in Amazonia using hyperspectral and multiangular CHRIS/PROBA data. Int J Appl Earth Obs Geoinf 11(1):8–14Google Scholar
  225. Somers B, Cools K, Delalieux S (2009) Nonlinear hyperspectral Mixture Analysis for tree cover estimates in orchards. Remote Sens Environ 113(6):1183–1193Google Scholar
  226. Somers B, Delalieux S, Verstraeten WW, Van Aardt JAN, Albrigo G, Coppin P (2010) An automated waveband selection technique for optimized hyperspectral mixture analysis. Int J Remote Sens 31:5549–5568Google Scholar
  227. Somers B, Zortea M, Plaza A, Asner GP (2012) Automated extraction of image-based endmember bundles for improved spectral unmixing. J Sel Top Appl Earth Obs Remote Sens 5(2):396–408Google Scholar
  228. Sommer S, Mehl W, Leone AP (1997) Application of MIVIS airborne imaging spectrometer data to the assessment of land degradation risk in the Southern Apennines (Fortore Beneventano Italy). Remote sensing '96: integrated applications for risk assessment and disaster prevention for the mediterraneanGoogle Scholar
  229. Song X, Jiang H, Yu S (2008) Detection of acid rain stress effect on plant using hyperspectral data in Three Gorges region China. Chin Geogr Sci 18(3):249–254Google Scholar
  230. Staenz K, Szeredi T, Schwarz J (1998) ISDAS–A System for Processing/Analyzing Hyperspectral Data. Can J Remote Sens 24(2):99–113Google Scholar
  231. Stagakis S, Markos N, Sykioti O (2010) Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite Hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations. Remote Sens Environ 114(5):977–994Google Scholar
  232. Stagakis S, Markos N, Sykioti O (2014) Tracking seasonal changes of leaf and canopy light use efficiency in a Phlomis fruticosa Mediterranean ecosystem using field measurements and multi-angular satellite hyperspectral imagery. ISPRS 97:138–151Google Scholar
  233. Stagakis S, Vanikiotis T, Sykioti O (2016) Estimating forest species abundance through linear unmixing of CHRIS/PROBA imagery. ISPRS J Photogramm Remote Sens 119:79–89Google Scholar
  234. Stamnes K, Tsay SC, Wiscombe W, Jayaweera K (1988) Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media. Appl Opt 27(12):2505–2509Google Scholar
  235. Stavrakoudis DG, Galidaki GN, Gitas IZ (2012) A genetic fuzzy -rule-based classifier for land cover classification from hyperspectral imagery. IEEE Trans Geosci Remote Sens 50(1):130–148Google Scholar
  236. Strahler AN (1980) Systems theory in physical geography. Phys Geogr 1:1–27Google Scholar
  237. Suarez L, Zarco-Tejada PJ, Sepulcre-Canto G (2008) Assessing canopy PRI for water stress detection with diurnal airborne imagery. Remote Sens Environ 112(2):560–575Google Scholar
  238. Sun T, Zhao Y, Zhu F (2013) An Analysis of the marginal value of hyperspectral features of the mixed pixel of lotus leaf and water body. Indian Soc Remote Sens 41(4):757–762Google Scholar
  239. Sun C, Liu Y, Zhao S (2016) Classification mapping and species identification of salt marshes based on a short-time interval NDVI time-series from HJ-1 optical imagery. Int J Appl Earth Obs Geoinf 45:27–41Google Scholar
  240. Sweet JN (2008) Dominant component suppression with applications to spectral analysis. IEEE Applied Imagery Pattern Recognition Workshop 198-204Google Scholar
  241. Tejada PJ, Miller JR, Mohammed GH (2002) Vegetation stress detection through chlorophyll a+b estimation and fluorescence effects on Hyperspectral imagery. J Environ Qual 31(5):1433–1441Google Scholar
  242. Tejada PJ, Berjon A, Lopez-Lozano R (2005) Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens Environ 99(3):271–287Google Scholar
  243. Tirelli C, Curci G, Manzo C, Tuccella P, Bassani C (2015) Effect of the aerosol model assumption on the atmospheric correction over land: case studies with CHRIS/PROBA hyperspectral images over Benelux. Remote Sens 7:8391–8415Google Scholar
  244. Thenkabail PS (2002) Optimal hyperspectral narrowbands for discriminating agricultural crops. Remote Sens Rev 20(4):257–291Google Scholar
  245. Thomas M, Jonas D, Honor PC (2014) Classification of grassland successional stages using airborne hyperspectral imagery. Remote Sens 6(8):7732–7761Google Scholar
  246. Thompson DR, Gao BC, Green RO (2015) Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign. Remote Sens Environ 167(SI):64–77Google Scholar
  247. Thoonen G, Hufkens K, Vanden BJ (2012) Accuracy assessment of contextual classification results for vegetation mapping. Int J Appl Earth Obs Geoinf 15(1):7–15Google Scholar
  248. Tits L, Delabastita W, Somers B (2012) First results of quantifying nonlinear mixing effects in heterogeneous forests: a modeling approach. IEEE Int Symp Geosci Remote Sens :7185–7188Google Scholar
  249. Tong Q, Zheng L, Wang J (1997) Vegetation spectral identification and biomass mapping from hyperspectral imagery. Phys Meas Signatures Remote Sens 1-2:801–807Google Scholar
  250. Tu TN, Chen CH, Wu JL, Chang CI (1998) A fast two-stage classification method for high-dimensional remote sensing data. IEEE Trans Geosci Remote Sens 36:182–191Google Scholar
  251. Ullah S, Groen TA, Schlerf M (2012) Using a genetic algorithm as an optimal band selector in the mid and thermal infrared to discriminate vegetation species. Sensors 12(7):8755–8769Google Scholar
  252. Udelhoven T, Hill J, Schutt B (1998) A neural network approach for the identification of the organic carbon content of soils in a degraded semiarid ecosystem (Guadalentin SE Spain) based on hyperspectral data from the DAIS-7915 sensor Earsel. workshop on imaging spectroscopyGoogle Scholar
  253. Vahtmaee E, Kutser T (2013) Classifying the baltic sea shallow water habitats using image-based and spectral library methods. Remote Sens 5(5):2451–2474Google Scholar
  254. Vaiphasa C (2006) Consideration of smoothing techniques for hyperspectral remote sensing. ISPRS J Photogramm Remote Sens 60(2):91–99Google Scholar
  255. Van WS, Alonso L, Verrelst J (2013) Upward and downward solar-induced chlorophyll fluorescence yield indices of four tree species as indicators of traffic pollution in Valencia. Environ Pollut 173:29–37Google Scholar
  256. Verhoef W (1984) Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL Model. Remote Sens Environ 16:125–141Google Scholar
  257. Verhoef W, Bach H (2003) Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models. Remote Sens Environ 87(1):23–41Google Scholar
  258. Verrelst J, Riveraa JP, Gitelsonc A, Delegidoa J, Morenoa J, Gustau CV (2016) Spectral band selection for vegetation properties retrieval using gaussian processes regression. Int J Appl Earth Obs Geoinf 52:554–567Google Scholar
  259. Vicent J, Sabater N, Tenjo C (2016) FLEX end-to-end mission performance simulator. IEEE Trans Geosci Remote Sens 54(7):4215–4223Google Scholar
  260. Vyas D, Krishnayya NSR, Manjunath KR (2011) Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation. Int J Appl Earth Obs Geoinf 13(2):228–235Google Scholar
  261. Wang L, Jia X (2009) Integration of soft and hard classifications using extended support vector machines. IEEE Geosci Remote Sens Lett 6(3):543–547Google Scholar
  262. Wang L, Sousa WP (2009) Distinguishing mangrove species with laboratory measurements of hyperspectral leaf reflectance. Int J Remote Sens 30(5):1267–1281Google Scholar
  263. Wang JN, Zhang LF, Tong QX (1998) The derivative spectral matching for wetland vegetation identification and classification by hyperspectral data. Proc SPIE 3502:280–288Google Scholar
  264. Wang H, Wang K, Xie Y (2009a) Application of hyperspectral Remote Sensing in Research on Ecological Boundary in North Farming-Pasturing. Transition in China. Spectrosc Spectr Anal 29(6):1636–1639Google Scholar
  265. Wang ZH, Hu GD, Zhou YZ (2009b) A Classification model of hyperion image base on SAM combined decision tree. Proc SPIE 7146:71461WGoogle Scholar
  266. Wang L, Ji HB, Shi Y (2011a) Face recognition using maximum local fisher discriminant analysis. 18th IEEE International Conference on Image Processing 1737–40Google Scholar
  267. Wang Q, Zhang J, Chen J (2011b) An improved spectral reflectance and derivative feature fusion for hyperspectral image classification. IEEE Int Symp Geosci Remote Sens:1696–1699Google Scholar
  268. Wang L, Liu D, Zhao L (2012a) Exploring support vector machine in spectral unmixing. Workshop on Hyperspectral Image and Signal ProcessingGoogle Scholar
  269. Wang P, Xing Z, Feng Y (2012b) Comparison of evaluation based on different atmospheric correction methods for HJ-1A hyperspectral imaging data. Appl Mech Mater 108:224–229Google Scholar
  270. Wang X, Zhang J, Ren G (2014) Yellow river estuary typical wetlands classification based on hyperspectral derivative transformation. Proc SPIE 9142:91421OGoogle Scholar
  271. Wang J, Shi T, Liu H (2016a) Successive projections algorithm-based three-band vegetation index for foliar phosphorus estimation. Ecol Indic 67:12–20Google Scholar
  272. Wang W, Li Y (2009) Bayesian denoising for remote sensing image based on undecimated discrete wavelet transform. International conference on information engineering and computer science 1-4Google Scholar
  273. Wang Y, Cui S (2014) Hyperspectral image feature classification using stationary wavelet transform. International Conference on Wavelet Analysis and Pattern Recognition 104-108Google Scholar
  274. Wang Z, Wang T, Darvishzadeh R (2016b) Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest. Remote Sens 8(6):491Google Scholar
  275. Wen X, Yang X (2008) An unsupervised classification method for hyperspectral image using spectra clustering. IEEE International Symposium on Knowledge Acquisition and Modeling Workshop Proceedings 1-2: 1117-1120Google Scholar
  276. White JC, Gomez C, Wulder MA (2010) Characterizing temperate forest structural and spectral diversity with Hyperion EO-1 data. Remote Sens Environ 114(7):1576–1589Google Scholar
  277. Wolf N (2013) Object features for pixel-based classification of urban areas comparing different machine learning algorithms. Photogrammetrie Fernerkundung Geoinformation 3:149–116Google Scholar
  278. Winter ME (1999) N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data. Proceedings of SPIE Imaging Spectrometry 266–275Google Scholar
  279. Wu J, Liu Y, Wang J (2010) Application of Hyperion data to land degradation mapping in the Hengshan region of China. Int J Remote Sens 31(19):5145–5161Google Scholar
  280. Wu J, Gao Z, Li Z (2014) Estimation for Sparse vegetation information in Desertification Region Based on Tiangong-Hyperspectral Image. Spectrosc Spectr Anal 34(3):751–756Google Scholar
  281. Xiao GZ, Wu XL, Teng K (2016) Hyperspectral Analysis and Electrolyte Leakage Inversion of Creeping Bentgrass under Salt Stress. Spectrosc Spectr Anal 36(11):3630–3636Google Scholar
  282. Yan L, Liu SH, Liu HL (2014) Two inverse processes: spectral reconstruction and pixel unmixing. International Workshop on Earth Observation and Remote Sensing ApplicationsGoogle Scholar
  283. Yang KM, Li H (2008) Feasibility analysis to extract hyperspectral image features based on the best basis of wavelet packet decompositions. Proceedings of information technology and environmental system. Science 3:488–493Google Scholar
  284. Yao W, Van LM, Romaczyk P (2015) Assessing the impact of sub-pixel vegetation structure on imaging spectroscopy via simulation. Proc SPIE 9472:94721KGoogle Scholar
  285. Younan NH, King RL, Bennett HH (2000) Hyperspectral data analysis using wavelet-based classifiers. IEEE Int Symp Geosci Remote Sens:390–392Google Scholar
  286. Youngentob KN, Roberts DA, Held AA (2011) Mapping two Eucalyptus subgenera using multiple endmember spectral mixture analysis and continuum-removed imaging spectrometry data. Remote Sens Environ 115(5):1115–1128Google Scholar
  287. Yu H, Wang Q, Liu L (2016) Research Process on hyperspectral Imaging Detection Technology for the Quality and Safety of Grain and Oils. Spectrosc Spectr Anal 36(11)Google Scholar
  288. Zhang C, Xie Z (2013) Object-based vegetation mapping in the kissimmee river watershed using hymap data and machine learning techniques. Wetlands 33(2):233–244Google Scholar
  289. Zhang B, Wang XG, Liu JG (2000) Hyperspectral image processing and analysis system (HIPAS) and its applications. Photogramm Eng Remote Sens 66(5):605–609Google Scholar
  290. Zhang Y, Chen JM, Miller JR (2008) Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery. Remote Sens Environ 112(7):3234–3247Google Scholar
  291. Zhao J, Ouyang Q, Chen Q (2010) Detection of bruise on pear by hyperspectral imaging sensor with different classification algorithms. Sens Lett 8(4):570–576Google Scholar
  292. Zhao K, Valle D, Popescu S (2013) Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection. Remote Sens Environ 132:102–119Google Scholar
  293. Zhou HJ, Mao ZH, Wang DF (2005) Classification of coastal areas by airborne hyperspectral image. Proceedings Of The Society Of Photo-Optical Instrumentation Engineers 5832: 471-476Google Scholar
  294. Zhou D, Wang QJ, Tian QJ (2009) Wavelet analysis and its application in denoising the spectrum of hyperspectral image. Spectrosc Spectr Anal 29(7):1941–1945Google Scholar
  295. Zhou M, Shu J, Chen Z (2010) Classification of hyperspectral remote sensing image based on genetic algorithm and SVM. Proceedings of SPIE-The International Society for Optical Engineering 7809: 78090AGoogle Scholar
  296. Zhou M, Shu J, Chen Z (2012) Classification of urban vegetation patterns from hyperspectral imagery: hybrid algorithm based on genetic algorithm tuned fuzzy support vector machine. Opt Eng 51(11):111709Google Scholar
  297. Zhu L, Zhao X, Lai L (2013) Soil TPH Concentration Estimation Using vegetation Indices in an Oil Polluted Area of Eastern China. PLoS One 8(1):e54028Google Scholar
  298. Zinnert JC, Via SM, Young DR (2013) Distinguishing natural from anthropogenic stress in plants: physiology fluorescence and Hyperspectral reflectance. Plant Soil 366(1-2):133–141Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.RS-GIS Laboratory, High Altitude Biology DivisionCSIR-Institute of Himalayan Bioresource TechnologyPalampurIndia

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