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Estimation of Forest Leaf Area Index Using Meteorological Data: Assessment of Heuristic Models

S. Karimi1*, A. H. Nazemi1, A. A. Sadraddini1, T. R. Xu2*, S. M. Bateni3, and A. F. Fard1

  1. Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz 5166616471, Iran
  2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  3. Department of Civil and Environmental Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, Hawaii 96822, USA

*Corresponding author. Tel.: +984133340081; fax: +984133341316. E-mail address: (S. Karimi).
*Corresponding author. Tel.: +86-10-58807455; fax: +86-10-58805274. E-mail address: (T. R. Xu).


Leaf Area Index (LAI) is an important structural feature of our ecosystem as it affects energy, carbon, and water exchanges between the land surface and overlying atmosphere. Global scale LAI datasets have been obtained by regression, heuristic data driven, and radiative transfer models using remotely sensed land surface reflectance data. However, the estimation of LAI from remotely sensed data is limited only to clear sky conditions. Also, it is problematic to estimate LAI in forests by using conventional remote sensing image analysis of multi-spectral data. Due to the above-mentioned shortcomings of estimating LAI from remotely sensed data, this study obtained LAI from meteorological data using the Gene Expression Programming (GEP) technique. The new approach was tested in different forest sites with broad-leaf and needle-leaf trees in USA. The results showed that the GEP technique can accurately estimate LAI from meteorological data in different forest sites.

Keywords: meteorological data, leaf area index, gene expression programming

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