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Original Articles

Influence of tree species complexity on discrimination performance of vegetation Indices

, &
Pages 15-37 | Received 22 Apr 2015, Accepted 09 Jul 2015, Published online: 17 Feb 2017

References

  • Alonzo M., Bookhagen B., Roberts D.A. (2014)—Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sensing of Environment, 148: 70–83. doi: http://dx.doi.org/10.1016/j.rse.2014.03.018.
  • Ballantine J.-A.C., Okin G.S., Prentiss D.E., Roberts D.A. (2005)—Mapping North African landforms using continental scale unmixing of MODIS imagery. Remote Sensing of Environment, 97: 470–483. doi: http://dx.doi.org/10.1016/j.rse.2005.04.023.
  • Carter G.A. (1994)—Ratios of leaf reflectances in narrow wavebands as indicators ofplant stress. International Journal of Remote Sensing, 15: 697–703. doi: http://dx.doi.org/10.1080/01431169408954109.
  • Cho M.A. (2007)—Hyperspectral remote sensing of biochemical and biophysical parameters: The derivative red-edge “double-peak feature”, a nuisance or an opportunity? In: Wageningen University, The Netherlands.
  • Cho M.A., Skidmore A.K. (2006)—A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sensing of Environment, 101: 181–193. doi: http://dx.doi.org/10.1016/j.rse.2005.12.011.
  • Cho M.A., Sobhan I., Skidmore A.K., Leeuw J.D. (2008)—Discriminating species using hyperspectral indices at leaf and canopy scales. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing, pp. 369–376.
  • Clark M.L., Roberts D.A., Clark D.B. (2005)—Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sensing of Environment, 96: 375–398. doi: http://dx.doi.org/10.1016/j.rse.2005.03.009.
  • Clevers J.G.P.W., De Jong S.M., Epema G.F., Van Der Meer F., Bakker W.H., Skidmore A.K., Addink E.A. (2001)—MERIS and the red-edge position. International Journal of Applied Earth Observation and Geoinformation, 3(4): 313–320. doi: http://dx.doi.org/10.1016/S0303-2434(01)85038-8.
  • Croft H., Chen J.M., Noland T.L. (2014a)—Stand age effects on Boreal forest physiology using a long time-series of satellite data. Forest Ecology and Management, 328: 202–208. doi: http://dx.doi.org/10.1016/jioreco.2014.05.023.
  • Croft H., Chen J.M., Zhang Y. (2014b)—The applicability of empirical vegetation indices for determining leafchlorophyll content over different leaf and canopy structures. Ecological Complexity, 17: 119–110. doi: http://dx.doi.org/10.1016/j.ecocom.2013.11.005.
  • Dawson T.P., Curran P.J. (1998)—A new technique for interpolating the reflectance red edge position. International Journal of Remote Sensing, 19: 2133–2139. doi: http://dx.doi.org/10.1080/014311698214910.
  • Dennison P.E., Roberts D.A. (2003)—Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE. Remote Sensing of Environment, 87: 123–135. doi: http://dx.doi.org/10.1016/S0034-4257(03)00135-4.
  • Franke J., Roberts D.A., Halligan K., Menz G. (2009)—Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sensing of Environment, 113: 1712–1723. doi: http://dx.doi.org/10.1016/j.rse.2009.03.018.
  • Gamon J.A., Peñuelas J., Field C.B. (1992)—A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41: 35–44. doi: http://dx.doi.org/10.1016/0034-4257(92)90059-S.
  • Gamon J.A., Serrano L., Surfus J.S. (1997)—The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia, 112: 492–501. doi: http://dx.doi.org/10.1007/s004420050337.
  • Gao B.C. (1996)—NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58: 257–266. doi: http://dx.doi.org/10.1016/S0034-4257(96)00067-3.
  • Ghiyamat A., Shafri H.Z.M., Mahdiraji G.A., Shariff A.R.M., Mansor S. (2013)—Hyperspectral Discrimination of Tree Species with Different Classifications using Single- and Multiple-End-Member. International Journal of Applied Earth Observation and Geoinformation, 23: 177–191. doi: http://dx.doi.org/10.1016/j.jag.2013.01.004.
  • Ghiyamat A., Shafri H.Z.M., Mahdiraji G.A., Ashurov R., Shariff A.R.M., Mansour S. (2015)—Airborne hyperspectral discrimination of tree species with different ages using discrete wavelet transform. International Journal of Remote Sensing, 36: 318–342. doi: http://dx.doi.org/10.1080/01431161.2014.995272.
  • Ghosh A., Fassnacht F.E., Joshi P.K., Koch B. (2014)—A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales. International Journal of Applied Earth Observation and Geoinformation, 26: 49–63. doi: http://dx.doi.org/10.1016/jjag.2013.05.017.
  • Gitelson A.A., Merzlyak M.N. (1997)—Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18: 2691–2697. doi: http://dx.doi.org/10.1080/014311697217558.
  • Gitelson A.A., Zur Y., Chivkunova O.B., Merzlyak M.N. (2002)—Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology, 75: 272–281. doi: http://dx.doi.org/10.1562/0031-8655(2002)075<0272:ACCIPL>2.0.CO;2.
  • Guyot G., Baret F. (1988)—Utilisation de la haute resolution spectrale pour suivre I'etat des couverts vegetaux. In 4th International Colloquium “Spectral Signatures of Objects in Remote Sensing”, Aussois, 18–22 January 1988, Paris, ESA Publication SP-287, pp. 279–286.
  • Guyot G., Baret F., Jacquemoud S. (1992)—Imaging spectroscopy for vegetation studies. Imaging Spectroscopy: Fundamentals and Prospective Application, 145–165.
  • Hashemi S.A., Chai M.M.F., Bayat S. (2013)—An analysis of vegetation indices in relation to tree species diversity using by satellite data in the northern forests of Iran. Arabian Journal of Geosciences, 6: 3363–3369. doi: http://dx.doi.org/10.1007/s12517-012-0576-8.
  • Heiskanen J., Rautiainen M., Stenberg P., Mottus M., Vesanto V.-H. (2013)—Sensitivity of narrowband vegetation indices to boreal forest LAI, reflectance seasonality and species composition. ISPRS Journal of Photogrammetry and Remote Sensing, 78: 1–14. doi: http://dx.doi.org/10.1016/j.isprsjprs.2013.01.001.
  • Hunt Jr E.R., Rock B.N. (1989)—Detection of changes in leaf water content using Near- and Middle-Infrared reflectances. Remote Sensing of Environment, 30: 43–54. doi: http://dx.doi.org/10.1016/0034-4257(89)90046-1.
  • Jensen J.R. (1983)—Biophysical Remote Sensing. Annals of the Association of American Geographers, 73: 111–132. doi: http://dx.doi.org/10.1111/j.1467-8306.1983.tb01399.x.
  • Jensen R.R., Hardin P.J., Hardin A.J. (2012)—Classificaiton of urban tree species using hyperspectral imagery. Geocarto International, 27: 443–458. doi: http://dx.doi.org/10.1080/10106049.2011.638989.
  • Kerr J.T., Ostrovsky M. (2003)—From space to species: ecological applications for remote sensing. Trends in Ecology & Evolution, 18: 299–305. doi: http://dx.doi.org/10.1016/S0169-5347(03)00071-5.
  • Lee K.-S., Cohen W.B., Kennedy R.E., Maiersperger T.K., Gower S.T. (2004)—Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sensing of Environment, 91: 508–520. doi: http://dx.doi.org/10.1016/j.rse.2004.04.010.
  • Lee T.-M., Yeh, H.-C. (2009)—Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities, Taiwan. Ecological Engineering, 35: 487–496. doi: http://dx.doi.org/10.1016/j.ecoleng.2008.01.007.
  • Machala M., Zejdová L. (2014)—Forest Mapping Through Object-based Image Analysis of Multispectral and LiDAR Aerial Data. European Journal of Remote Sensing, 47: 15. doi: http://dx.doi.org/10.5721/eujrs20144708.
  • Mutanga O., Skidmore A.K. (2004)—Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 25: 3999–4014. doi: http://dx.doi.org/10.1080/01431160310001654923.
  • Nagendra H. (2001)—Using remote sensing to assess biodiversity. International Journal of Remote Sensing, 22: 2377–2400. doi: http://dx.doi.org/10.1080/01431160117096.
  • Pettorelli N., Vik J.O., Mysterud A., Gaillard J.M., Tucker C.J., Stenseth N.C. (2005)—Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology and Evolution, 20: 503–510. doi: http://dx.doi.org/10.1016/j.tree.2005.05.011.
  • Powell R.L., Roberts D.A., Dennison P.E., Hess L.L. (2007)—Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil. Remote Sensing of Environment, 106: 253–267. doi: http://dx.doi.org/10.1016/j.rse.2006.09.005.
  • Pu R. (2009)—Broadleaf species recognition with in situ hyperspectral data. International Journal of Remote Sensing, 30: 2759–2779. doi: http://dx.doi.org/10.1080/01431160802555820.
  • Pu R., Gong P., Biging G., Larrieu M.R. (2003)—Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index. IEEE Transactions on Geoscience and Remote Sensing, 41: 916–921. doi: http://dx.doi.org/10.1109/TGRS.2003.813555.
  • Qi Y., Li F., Liu Z., Lin G. (2014)—Impact of understorey on overstorey leaf area index estimation from optical remote sensing in five forest types in northeastern China. Agricultural and Forest Meteorology, 198–199: 72–80. doi: http://dx.doi.org/10.1016/j.agrformet.2014.08.001.
  • Roberts D.A., Gardner M., Church R., Ustin S., Scheer G., Green R.O. (1998)—Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models. Remote Sensing of Environment, 65: 267–279. doi: http://dx.doi.org/10.1016/S0034-4257(98)00037-6.
  • Rouse J.W., Haas R.H., Schell J.A., Deering D.W. (1973)—Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third ERTS Symposium, Washington DC., Nasa SP-351, pp. 309–317.
  • Sims D.A., Gamon J.A. (2002)—Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81: 337–354. doi: http://dx.doi.org/10.1016/S0034-4257(02)00010-X.
  • Stagakis S., Gonzalez-Dugo V., Cid P., Guillen-Climent M.L., Zarco-Tejada P.J. (2012)—Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band strcutural and physiological remote sensing indices. ISPRS Journal of Photogrammetry and Remote Sensing, 71: 47–61. doi: http://dx.doi.org/10.1016/j.isprsjprs.2012.05.003.
  • Thorp K.R., French A.N., Rango A. (2013)—Effect of image spatial and spectral characteristics on mapping semi-arid rangeland vegetation using multiple endmember spectral mixture analysis (MESMA). Remote Sensing of Environment, 132: 120–130. doi: http://dx.doi.org/10.1016/j.rse.2013.01.008.
  • Vogelmann J.E., Rock B.N., Moss D.M. (1993)—Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14: 1563–1575. doi: http://dx.doi.org/10.1080/01431169308953986.
  • Youngentob K.N., Roberts D.A., Held A.A., Dennison P.E., Jia X., Lindenmayer D.B. (2011)—Mapping two Eucalyptus subgenera using multiple endmember spectral mixture analysis and continuum-removed imaging spectrometry data. Remote Sensing of Environment, 115: 1115–1128. doi: http://dx.doi.org/10.1016/j.rse.2010.12.012.