Estimación de biomasa en herbáceas a partir de datos hiperespectrales, regresión PLS y la transformación continuum removal

  1. Marabel-García, M. 1
  2. Álvarez-Taboada, F. 1
  1. 1 GEOINCA-202. Universidad de León (Ponferrada)
Journal:
Revista de teledetección: Revista de la Asociación Española de Teledetección

ISSN: 1133-0953

Year of publication: 2014

Issue: 42

Pages: 49-60

Type: Article

DOI: 10.4995/RAET.2014.2286 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista de teledetección: Revista de la Asociación Española de Teledetección

Abstract

The aim of this research work was to compare the results of two methods to estimate aboveground biomass by using field spectrometer data: (i) Partial least squares regression (PLSR), and (ii) linear regression applied to the Maximum Band Depth (MBD) and Area Over the Minimum (AOM) indices. In both cases different regions of the spectrum were transformed by Continuum Removal (CR). Since the results using PLSR (R2=0.920, RMSE=3.622 g/m2) were similar to the results achieved by the indices (R2=0.915, RMSE=3.615 g/m2 for AOM), using the indices derived from CR is recommended, since their interpretation is easier than the PLS output.

Bibliographic References

  • Adjorlolo, C., Cho, M.A., Mutanga, O., Ismail, R. classification of C3 and C4 grass species, using wavelengths of known absorption features. Journal of Applied Remote Sensing, 6(1), 1-15. http://dx.doi.org/10.1117/1.JRS.6.063560
  • Atzberger, C., Guérif, M., Baret, F., Werner, W. 2010. Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat. Computers and Electronics in Agriculture, 73(2), 165-173. http://dx.doi.org/10.1016/j.compag.2010.05.006
  • Axelsson, C., Skidmore, A.K., Schlerf, M., Fauzi, A., Verhoef, W. 2013. Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression. International Journal of Remote Sensing, 34(5), 1724-1743. http://dx.doi.org/10.1080/01431161.2012.725958
  • Barrio, A.M., Balboa, M.M.A., Castedo, D.F., Diéguez, A.U., Álvarez, G.J.A. 2006. An ecoregional model for estimating volume, biomass and carbon pools in maritime pine stands in Galicia (northwestern Spain). Forest Ecology and Management, 223(1-3), 24-34. http://dx.doi.org/10.1016/j.foreco.2005.10.073
  • Cho, M.A., Skidmore, A.K., Corsi, F., Van Wieren, S.E., Sobhan, I. 2007. Estimation of green grass - herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. International Journal of Applied Earth Observation and Geoinformation, 9(4), 414-424. http://dx.doi.org/10.1016/j.jag.2007.02.001
  • Chuvieco, E., Huete, A. 2010. Fundamentals of satellite remote sensing. Boca Raton (FL), CRC Press. Boca Raton (USA), 302-310.
  • Clevers, J.G.P.W., Kooistra, L., Schaepman, M.E. 2008. Using spectral information from the NIR water absorption features for the retrieval of canopy water content. International Journal of Applied Earth Observation and Geoinformation, 10(3), 388-397. http://dx.doi.org/10.1016/j.jag.2008.03.003
  • Curran, P.J., Dungan, J.L., Peterson, D.L. 2001. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry testing the Kokaly and Clark methodologies. Remote Sensing of Environment, 76(3), 349-359. http://dx.doi.org/10.1016/S0034-4257(01)00182-1
  • De Jong, S.M. 1994. Applications of Reflective Remote Sensing for Land Degradation Studies in a Mediterranean, Environment. (Netherlands Geographical Studies (NGS)). Ph.D. Dissertation, Utrecht University, Utrecht, The Netherlands.
  • Dunn, B.W., Beecher, H.G., Batten, G.D., Ciavarella, S. 2002. The potential of near-infrared reflectance spectroscopy for soil analysis, a case study from the Riverine Plain of south-eastern Australia. Australian Journal of Experimental Agriculture, 42(5), 607-614. http://dx.doi.org/10.1071/EA01172
  • Gao, X., Huete, A.R., Ni, W., Miura, T. 2000. Optical biophysical relationships of vegetation spectra without background contamination. Remote Sensing of Environment, 74(3), 609-620. http://dx.doi.org/10.1016/S0034-4257(00)00150-4
  • Geladi, P., Kowalski, B.R. 1986. Partial leastsquares regression: a tutorial. Analytica Chimica Acta, 185, 1-17. http://dx.doi.org/10.1016/0003-2670(86)80028-9
  • Grossman, Y.L. Ustin, S.L., Jacquemoud, S., Sanderson, E.W., Schmuck, G.; Verdebout, J., 1996. Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data. Remote Sensing of Environment, 56(3), 182-193. http://dx.doi.org/10.1016/0034-4257(95)00235-9
  • Huang, Z., Turner, B.J., Dury, S.J., Wallis, I.R., Foley, W.J. 2004. Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sensing of Environment, 93(1-2), 18-29. http://dx.doi.org/10.1016/j.rse.2004.06.008
  • Kokaly, R.F., Clark, R.N. 1999. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67(3), 267-287. http://dx.doi.org/10.1016/S0034-4257(98)00084-4
  • Kooistra, L., Wanders, J., Epema, G.F., Leuven, R.S.E.W., Wehrens, R., Buydens, L.M.C. 2003. The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Analytica Chimica Acta, 484(2), 189-200. http://dx.doi.org/10.1016/S0003-2670(03)00331-3
  • Kooistra, L., Suárez Barranco, M.D., van Dobben, H., Schaepman, M.E. 2006. Regional Scale Monitoring of Vegetation Biomass in river floodplains using Imaging Spectroscopy and Ecological Modeling. En: Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Denver, CO, USA, 31 July-4 August 2006, 124-127.
  • Marabel, M., Álvarez, F. 2013. Spectroscopic Determination of Aboveground Biomass in Grasslands Using Spectral Transformations, Support Vector Machine and Partial Least Squares Regression. Sensors, 13(8), 10027-10051. http://dx.doi.org/10.3390/s130810027
  • Mutanga, O., Skidmore, A.K. 2004. Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 25(19), 3999-4014. http://dx.doi.org/10.1080/01431160310001654923
  • Mutanga, O., Ismail R. 2010. Variation in foliar water content and hyperspectral reflectance of Pinus patula trees infested by Sirex noctilio. Southern Forests, 72 (1), 1-7. http://dx.doi.org/10.2989/2070 2620.2010.481073
  • Nitsch, B.B., Meyer, G.E., Mortensen, D.A. 1991. Visible near-infrared plant, soil and crop residue reflectivity for weed sensor design. ASAE, Paper No. 91-3006. ASAE, St. Joseph, MI, USA.
  • Pordesimo, L.O., Edens, W.C., Sokhansanj, S. 2004. Distribution of aboveground biomass in corn stover. Biomass and Bioenergy, 26(4), 337-343. http://dx.doi.org/10.1016/S0961-9534(03)00124-7
  • Pu, R., Ge, S., Kelly, N.M., Gong, P. 2003. Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia) leaves. International Journal of Remote Sensing, 24(9), 1799-1810.
  • http://dx.doi.org/10.1080/01431160210155965
  • Schlerf, M., Atzberger, C., Hill, J. 2005. Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sensing of Environment, 95(2), 177-194. http://dx.doi.org/10.1016/j.rse.2004.12.016
  • Smith, G.M., Currran, P.J. 1996. The signal-to-noise ratio (SNR) required for the estimation of foliar biochemical concentrations. International Journal of Remote Sensing, 17(5), 1031-1058. http://dx.doi.org/10.1080/01431169608949062
  • Vasques, G.M., Grunwald, S., Sickman, J.O. 2008. Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma, 146(1-2), 14-25. http://dx.doi.org/10.1016/j.geoderma.2008.04.007
  • Williams, P.C., Norris, K.H. 1987. Near-Infrared Technology in the Agricultural and Food Industries. St. Paul, MN, USA: American Association of Cereal Chemists, 143-167
Data source: Dialnet