Leaf area index estimation of bamboo forest in Fujian province based on IRS P6 LISS 3 imagery

References

• Atzberger , C. 2004 . Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models . Remote Sensing of Environment , 93 : 53 – 67 .
   Web of Science ®Google Scholar
• Baret , F. and Guyot , G. 1991 . Potentials and limits of vegetation indices for LAI and APAR assessment . Remote Sensing of Environment , 35 : 161 – 173 .
   Web of Science ®Google Scholar
• Berterretche , M. , Hudak , A.T. , Cohen , W.B. , Maiersperger , T.K. , Gower , S.T. and Dungan , J. 2005 . Comparison of regression and geostatistical methods for mapping leaf area index (LAI) with Landsat ETM+ over a boreal forest . Remote Sensing of Environment , 96 : 49 – 61 .
   Web of Science ®Google Scholar
• Brown , L. , Chen , J.M. , Leblanc , S.G. and Cihlar , J. 2000 . A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: an image and model analysis . Remote Sensing of Environment , 71 : 16 – 25 .
   Web of Science ®Google Scholar
• Chen , J.M. 1996 . Evaluation of vegetation indices and a modified simple ratio for boreal applications . Canadian Journal of Remote Sensing , 22 : 229 – 242 .
   Google Scholar
• Chen , J.M. and Black , T.A. 1992 . Defining leaf area index for non-flat leaves . Plant, Cell and Environment , 15 : 421 – 429 .
   Web of Science ®Google Scholar
• Chen , J.M. and Cihlar , J. 1996 . Retrieving leaf area index for boreal conifer forests using Landsat TM images . Remote Sensing of Environment , 55 : 153 – 162 .
   Web of Science ®Google Scholar
• Cohen , W.B. , Maiersperger , T.K. , Gower , S.T. and Turner , D.P. 2003 . An improved strategy for regression of biophysical variables and Landsat ETM+ data . Remote Sensing of Environment , 84 : 561 – 571 .
   Web of Science ®Google Scholar
• Combal , B. , Baret , F. , Weiss , M. , Trubuil , A. , Macé , D. , PragnÈRE , A. , Myneni , R. , Knyazikhin , Y. and Wang , L. 2002 . Retrieval of canopy biophysical variables from bidirectional reflectance: using prior information to solve the ill-posed inverse problem . Remote Sensing of Environment , 84 : 1 – 15 .
   Web of Science ®Google Scholar
• Curran , P.J. 1983 . Multispectral remote sensing for the estimation of green leaf area index . Philosophical Transactions of the Royal Society A , 309 : 257 – 270 .
   Web of Science ®Google Scholar
• Curran , P.J. and Hay , A.M. 1986 . The importance of measurement error for certain procedures in remote sensing at optical wavelengths . Photogrammetric Engineering and Remote Sensing , 52 : 229 – 241 .
   Web of Science ®Google Scholar
• Darvishzadeh , R. , Atzberger , C. , Skidmore , A.K. and Abkar , A.A. 2009 . Leaf area index derivation from hyperspectral vegetation indices and the red edge position . International Journal of Remote Sensing , 30 : 6199 – 6218 .
   Web of Science ®Google Scholar
• Darvishzadeh , R. , Skidmore , A. , Atzberger , C. and Wieren , S.V. 2008 . Estimation of vegetation LAI from hyperspectral reflectance data: effects of soil type and plant architecture . International Journal of Applied Earth Observation and Geoinformation , 10 : 358 – 373 .
   Web of Science ®Google Scholar
• Deutsch , C. and Journel , A. 1998 . GSLIB: Geostatistical Software Library and User's Guide , 2nd , New York : Oxford University Press .
   Google Scholar
• Durbha , S.S. , King , R.L. and Younan , N.H. 2007 . Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer . Remote Sensing of Environment , 107 : 348 – 361 .
   Web of Science ®Google Scholar
• Eklundh , L. , Harrie , L. and Kuusk , A. 2001 . Investigating relationships between Landsat ETM+ sensor data and leaf area index in a boreal conifer forest . Remote Sensing of Environment , 78 : 239 – 251 .
   Web of Science ®Google Scholar
• Fang , H. and Liang , S. 2005 . A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies . Remote Sensing of Environment , 94 : 405 – 424 .
   Web of Science ®Google Scholar
• Friedl , M.A. , Michaelson , J. , Davis , F.W. , Walker , H. and Schimel , D.S. 1994 . Estimating grassland biomass and leaf area index using ground and satellite data . International Journal of Remote Sensing , 15 : 1401 – 1420 .
   Web of Science ®Google Scholar
• Goel , N.S. and Qi , N, W. 1994 . Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: a computer simulation . Remote Sensing Reviews , 10 : 309 – 347 .
   Google Scholar
• Goovaerts , P. 1997 . Geostatistics for Natural Resources Evaluation , New York : Oxford University Press .
   Google Scholar
• Haboudane , D. , Miller , J.R. , Tremblay , N. , Zarco-Tejada , P.J. and Dextraze , L. 2002 . Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture . Remote Sensing of Environment , 81 : 416 – 426 .
   Web of Science ®Google Scholar
• Hall , F.G. , Shimabukuro , Y.E. and Huemmerich , K.F. 1995 . Remote sensing of forest biophysical structure using mixture decomposition and geometric reflectance models . Ecological Applications , 5 : 993 – 1013 .
   Web of Science ®Google Scholar
• Houborg , R. and Boegh , E. 2008 . Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modelling and SPOT reflectance data . Remote Sensing of Environment , 112 : 186 – 202 .
   Web of Science ®Google Scholar
• Hu , B. , Miller , J.R. , Chen , J.M. and Hollinger , A. 2004 . Retrieval of canopy leaf area index in the BOREAS flux tower sites using linear spectral mixture analysis . Remote Sensing of Environment , 89 : 176 – 188 .
   Web of Science ®Google Scholar
• Huete , A.R. 1988 . A soil-adjusted vegetation index (SAVI) . Remote Sensing of Environment , 25 : 295 – 309 .
   Web of Science ®Google Scholar
• Jacquemoud , S. , Bacour , C. , Poilvé , H. and Frangi , J.-P. 2000 . Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode . Remote Sensing of Environment , 74 : 471 – 481 .
   Web of Science ®Google Scholar
• Jacquemoud , S. , Baret , F. , Andrieu , B. , Danson , F.M. and Jaggard , K. 1995 . Extraction of vegetation biophysical parameters by inversion of the PROSPECT+SAIL models on sugar beet canopy reflectance data: application to TM and AVIRIS sensors . Remote Sensing of Environment , 52 : 163 – 172 .
   Web of Science ®Google Scholar
• Jordan , C.F. 1969 . Derivation of leaf area index from quality of light on the forest floor . Ecology , 50 : 663 – 666 .
   Web of Science ®Google Scholar
• Li , X. and Strahler , A.H. 1985 . Geometric-optical modelling of a conifer forest canopy . IEEE Transactions on Geoscience and Remote Sensing , 23 : 705 – 721 .
   Web of Science ®Google Scholar
• Madugundu , R. , Nizalapur , V. and Jha , C.S. 2008 . Estimation of LAI and above-ground biomass in deciduous forests: Western Ghats of Karnataka, India . International Journal of Applied Earth Observation and Geoinformation , 10 : 211 – 219 .
   Web of Science ®Google Scholar
• Mcallister , D.M. and Valeo , C. 2007 . A robust new method for the remote estimation of LAI in montane and boreal forests . International Journal of Remote Sensing , 8 : 1891 – 1905 .
   Google Scholar
• Miller , J.B. 1967 . A formula for average foliage density . Australian Journal of Botany , 15 : 141 – 144 .
   Web of Science ®Google Scholar
• Nemani , R.R. , Pierce , L.L. , Running , S.W. and Band , L. 1993 . Forest ecosystem processes at the watershed scale: sensitivity to remotely sensed leaf area index estimates . International Journal of Remote Sensing , 14 : 2519 – 2534 .
   Web of Science ®Google Scholar
• Peddle , D.R. , Brunke , S.P. and Hall , F.G. 2001 . A comparison of spectral mixture analysis and ten vegetation indices for estimating boreal forest biophysical information from airborne data . Canadian Journal of Remote Sensing , 27 : 627 – 635 .
   Web of Science ®Google Scholar
• Peddle , D.R. , Hall , F.G. and Ledrew , E.F. 1999 . Spectral mixture analysis and geometric–optical reflectance modelling of boreal forest biophysical structure . Remote Sensing of Environment , 67 : 288 – 297 .
   Web of Science ®Google Scholar
• Peterson , D.L. , Spanner , M.A. , Running , S.W. and Teuber , K.B. 1987 . Relationship of thematic mapper simulator data to leaf area index of temperate coniferous forests . Remote Sensing of Environment , 22 : 323 – 341 .
   Web of Science ®Google Scholar
• Pinty , B. and Verstraete , M.M. 1992 . GEMI: a nonlinear index to monitor global vegetation from satellites . Vegetation , 10 : 15 – 20 .
   Google Scholar
• Pu , R. , Gong , P. , Biging , G.S. and 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 .
   Web of Science ®Google Scholar
• Qi , J. , Kerr , Y.H. , Moran , M.S. , Weltz , M. , Huete , A.R. , Sorooshian , S. and Bryant , R. 2000 . Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region . Remote Sensing of Environment , 73 : 18 – 30 .
   Web of Science ®Google Scholar
• Richardson , A.J. and Wiegand , C.L. 1977 . Distinguishing vegetation from soil background information . Photogrammetric Engineering and Remote Sensing , 43 : 1541 – 1552 .
   Web of Science ®Google Scholar
• Roujean , J.L. and Breon , F.M. 1995 . Estimating PAR absorbed by vegetation from bidirectional reflectance measurements . Remote Sensing of Environment , 51 : 375 – 384 .
   Web of Science ®Google Scholar
• Rouse , J.W. , Haas , R.H. , Deering , D.W. , Schell , J.A. and Harlan , J.C. Monitoring vegetation systems in the Great Plains with ERTS . Proceedings, 3rd Earth Resource Technology Satellite (ERTS) Symposium . Washington, DC. pp. 48 – 62 . Washington, DC : NASA, Scientific and Technical Information Office .
   Google Scholar
• Schlerf , M. and Atzberger , C. 2006 . Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data . Remote Sensing of Environment , 100 : 281 – 294 .
   Web of Science ®Google Scholar
• Schlerf , M. , Atzberger , C. and Hill , J. 2005 . Remote sensing of forest biophysical variables using HyMap imaging spectrometer data . Remote Sensing of Environment , 95 : 177 – 194 .
   Web of Science ®Google Scholar
• Soudani , K. , Francois , C. , Maire , G.L. , Dantec , V.L. and Dufrene , E. 2006 . Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands . Remote Sensing of Environment , 102 : 161 – 175 .
   Web of Science ®Google Scholar
• Stenberg , P. , Rautiainen , M. , MANNINEN , T. , Voipio , P. and Smolander , H. 2004 . Reduced simple ratio better than NDVI for estimating LAI in Finnish pine and spruce stands . Silva Fennica , 38 : 3 – 14 .
   Web of Science ®Google Scholar
• Thenkabail , P.S. , Enclona , E.A. , Ashton , M.S. , Legg , C. and Dieu , M.J.D. 2004 . Hyperion, IKONOS, ALI and ETM+ sensors in the study of African rainforests . Remote Sensing of Environment , 90 : 23 – 43 .
   Web of Science ®Google Scholar
• Walthall , C. , Dulaney , W. , Anderson , M. , Norman , J. , Fang , H. and Liang , S. 2004 . A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery . Remote Sensing of Environment , 92 : 465 – 474 .
   Web of Science ®Google Scholar
• Welles , J.M. and Norman , J.M. 1991 . Instrument for indirect measurement of canopy architecture . Agronomy Journal , 83 : 818 – 825 .
   Web of Science ®Google Scholar
• Zarco-Tejada , P.J. , Miller , J.R. , Noland , T.L. , Mohammed , G.H. and Sampson , P.H. 2001 . Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data . IEEE Transactions on Geoscience and Remote Sensing , 39 : 1491 – 1506 .
   Web of Science ®Google Scholar
• Zhang , Z. , He , G. and Wang , X. 2010 . A practical DOS model-based atmospheric correction algorithm . International Journal of Remote Sensing , 31 : 2837 – 2852 .
   Web of Science ®Google Scholar