Hydrology and Earth System Sciences www.hydrol-earth-syst-sci.net 1027-5606 1607-7938 14 2 2010 10.5194/hess-14-383-2010 http://www.hydrol-earth-syst-sci.net/14/383/2010/ http://www.hydrol-earth-syst-sci.net/14/383/2010/hess-14-383-2010.html http://www.hydrol-earth-syst-sci.net/14/383/2010/hess-14-383-2010.pdf 383 392 2010-02-25 Spatial mapping of leaf area index using hyperspectral remote sensing for hydrological applications with a particular focus on canopy interception H. H. Bulcock 204501831@ukzn.ac.za G. P. W. Jewitt School of Bioresources Engineering and Environmental Hydrology, University of KwaZulu-Natal, Pietermaritzburg, South Africa The establishment of commercial forestry plantations in natural grassland vegetation, results in increased transpiration and interception which in turn, results in a streamflow reduction. Methods to quantify this impact typically require LAI as an input into the various equations and process models that are applied. The use of remote sensing technology as a tool to estimate leaf area index (LAI) for use in estimating canopy interception is described in this paper. Remote sensing provides a potential solution to effectively monitor the spatial and temporal variability of LAI. This is illustrated using Hyperion hyperspectral imagery and three vegetation indices, namely the normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI) and Vogelmann index 1 to estimate LAI in a catchment afforested with <i>Eucalyptus</i>, <i>Pinus</i> and <i>Acacia</i> genera in the KwaZulu-Natal midlands of South Africa. Of the three vegetation indices used in this study, it was found that the Vogelmann index 1 was the most robust index with an <i>R</i>² and root mean square error (RMSE) values of 0.7 and 0.3 respectively. However, both NDVI and SAVI could be used to estimate the LAI of 12 year old <i>Pinus patula</i> accurately. If the interception component is to be quantified independently, estimates of maximum storage capacity and canopy interception are required. Thus, the spatial distribution of LAI in the catchment is used to estimate maximum canopy storage capacity in the study area. Baret, F. and Guyot, G.: Potentials and limits of Vegetation Indices for LAI and APAR assessment, Remote Sens. Environ., 35, 161–173, 1991. Beven, K. J.: Rainfall-Runoff modelling: the primer, John Wiley & Sons, Chichester, UK, 2001. Bongonko, M. N.: Hyperspectral Remote Sensing of Soil Moisture Gradients in Millingerwaard, The Netherlands, http://phylares.vub.ac.be/Thesissen/2005%20Michael%20Ndemo%20Bogonko.pdf, last access: 24 February 2009, 2005. Burger, C., Everson, C. S., and Savage, M. J.: Comparative evaporation measurements above commercial forestry and sugarcane canopies in the KwaZulu-Natal Midlands, Ninth South African National Hydrology Symposium, 1–9, 1999. Camp, K. G. T.: The Bioresources Groups of KwaZulu-Natal, Cedara Report N/A/97/6, KwaZulu-Natal Department of Agriculture, Pietermaritzburg, 1997. David, J., Valente, F., and Gash, J. H. C.: Evaporation of intercepted rainfall. In: ed. Anderson, M.G. Encyclopedia of Hydrological Science, John Wiley & Sons Ltd, West Sussex, England, chapter~43, 627–634, 2005. Delegido, J., Fernandez, G., Gandia, S., and Moreno, J.: Retrieval of chlorophyll content and the LAI of crops using hyperspectral techniques: applications of PROBA/CHRIS data, Int. J. Remote Sens., 29(24), 7107–7127, 2008. Dye, P., Megown, R., Jacobs, S., Drew, D., Megown, K., Dicks, M., Mthembu, S., and Pretorius, C.: Determining the water use and growth of forest plantations through the GIS-based integration of remote sensing and field data in the 3-PG model, Water Research Commission, Report No. 1194/1/02. Pretoria, 2002. Everson, C., Moodley, M., Gush, M., Jarmain, C., Govender, M., and Dye, P.: Can effective management of riparian zone vegetation significantly reduce the cost of catchment management and enable greater productivity of land resources, Water Research Commission, Pretoria, Report K5/1284, 2006. Gash, J. H. C., Lloyd, C. R., and Lachaud, G.: Estimating sparse forest rainfall interception with an analytical model, J. Hydrol., 170, 79–86, 1995. Gates, D. M.: Biophysical Ecology, Springer-Verlag, New York, 611~pp., 1980 GCIS: South African Yearbook 2006/7: Water affairs and forestry, http://www.gcis.gov.za/resource_centre/sa_info/yearbook/2007/chapter23.pdf, last access: 14 April 2009, 2007. Ghebremicael, S. T., Smith, C. W., and Ahmed, F. B.: Estimating the leaf area index (LAI) of black wattle from Landsat ETM+ satellite imagery, Southern African For. J., 201, 3–12, 2004. Godsmark, R.: The South African forestry and forest production industry 2007, http://www.forestry.co.za/, last access: 17 March 2009, 2008. Govender, M., Chetty, K. T., and Bulcock, H. H.: A review of hyperspectral remote sensing and its application in vegetation and water resources studies, Water SA, 33, 145–151, 2007. Gush, M. B.: Estimation of Streamflow Reduction Resulting from Commercial Afforestation in South Africa, Unpublished Manuscript, University of Natal. Pietermaritzburg, 2000. Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., and Strachan, I. B.: Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture, Remote Sens. Environ., 90, 337–352, 2004. Hörmann, G.: SIMPEL: A family of simple soil water models, http://www.hydrology.uni-kiel.de/~schorsch/simpel/englisch/simpel_english.pdf, 2003. Jewitt, G. P. W.: 8%–4% debate: Commercial afforestation and water use in South Africa, Southern African J. For., 194, 4–6, 2002. Jewitt, G.P.W.: Water and forests, in: Encyclopedia of Hydrological Science, edited by: Anderson, M. G., John Wiley & Sons Ltd., West Sussex, England, chapter 186, 1–15, 2005. Kongo, V. M. and Jewitt, G. P. W.: Evaporative water use of different landuses in the Thukela river basin assessed from satellite imagery, 13th SANCIAHS Symposium, Cape Town, 2007. Kozak, J. A., Ahuja, L. R., Green, T. R., and Ma, L.: Modelling crop canopy and residue rainfall interception effects on soil hydrological components for semi-arid agriculture, Hydrol. Process., 21, 229–241, 2007. LI-COR.: LAI-2000 Plant canopy analyser: Instruction Manual Ed.I, LI-COR Lincoln, Nebraska, USA, 1992. Landsberg, J. J. and Waring, R. H.: A generalised model of forest productivity using simplified concepts of radiation use efficiency, carbon balance and partitioning, For. Ecol. and Manag., 95, 209–228, 1997. Langrebe, D.: Some fundamentals and methods for hyperspectral image data analysis, P. Soc. Photo-Opt. INS., 3603, 104–113, 1999. Lillesand, T. M. and Kiefer, R. W.: Remote Sensing and Image Interpretation, John Wiley & Sons Inc., New Jersey, 1999. Limousin, J.-M., Rambal, S., Ourcival, J.-M., and Joffre, R.: Modelling rainfall interception in a Mediterranean \textitQuercus ilex ecosystem: Lessons from a throughfall exclusion experiment. J. Hydrol., 357, 57–66. 2008. McGwire, K., Minor, T., and Fenstermaker, L.: Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments, Remote Sens. Environ., 72, 360–374, 2000. Nemani, R., Keeling, C., Hashimoto, H., Jolly, W., Piper, S., Tucker, C., Myneni, R., and Running, S.: Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999, Science, 300, 1560–1563, 2003. Okin, G. S., Roberts, D. A., Murray, B, and Okin, W. J.: Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments, Remote Sens. Environ., 77, 212–225, 2001. Research Systems Inc.: ENVI 4.3: Narrowband Greeness, Boulder, Colorado, 2005. Savenije, H. H. G.: The importance of interception and why we should delete the term evapotranspiration from our vocabulary, Hydrol. Process., 18, 1507–1511, 2004. Schultz, G. A. and Engman, E. T.: Remote Sensing in Hydrology and Water Management, Springer, Heidelberg, Germany, 2000. Schulze, R. E.: Hydrology and Agrohydrology: A Text to Accompany the ACRU 3.00 Agrohydrological Modelling System. Water Research Commission, Pretoria, RSA, WRC Report No. TT69/95, 1995. Sprintsin, M., Karnieli, A., Berliner, P., Rotenberg, E., Yakir, D., and Cohen, S.: The effect of spatial resolution on the accuracy of leaf area index estimation for a forest planted in the desert transition zone, Remote Sens. Environ., 109, 416–428, 2007. Van Dijk, A. I. J. M. and Bruijnzeel, L. A.: Modelling rainfall interception by vegetation of variable density using an updated analytical model. Part 1. Model description, J. Hydrol., 247, 230–238, 2001a. Van Dijk, A. I. J. M. and Bruijnzeel, L. A.: Modelling rainfall interception by vegetation of variable density using an updated analytical model. Part 2. Model validation for a tropical upland mixed cropping system, J. Hydrol., 247, 239–262, 2001b. Vogelmann, J. E., Rock, B. N., and Moss, D. M.: Red Edge Spectral Measurements from Sugar Maple Leaves. Int. J. Remote Sens., 14, 1563–1575, 1993. Von Hoyningen-Huene, J.: Die interzeption des Niederschlages in landwirtschaftlichen Pflanzenbeständen. Arbeitsbericht Deutscher verband fur Wasserwirtschaft und Kulturbau, DVWK, Braunschwig, Germany, 1981. Von Hoyningen-Huene, J.: Die interzeption des Niederschlages in landwirtschaftlichen Pflanzenbeständen, Deutscher Verband fur Wasserwirtschaft und Kulturbau, Verlag Paul Parey-Hamburg, Schirften, 57, 1–66, 1983. Xiao, Q., McPherson, E. G., Ustin, S. L., and Grismer, M. E.: A new approach to model tree rainfall interception, AGU Fall meeting, San Francisco, CA, 1998. Zhang, B., Song, K., Zhang, Y., and Wang, Z.: Study on the relationship between hyperspectral reflectance and soybean LAI, above ground biomass, Int. Geosci. Remote Se., 5, 3583–3586, 2005.