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Leaf Area Index Modeling Using Hyperspectral and Radar Data

WONG Kwan Kit, Frankie

Leaf area index (LAI) is defined as the total one-sided area of all leaves in the canopy within a defined region. The significance of LAI found on its relationship with the energy, mass and gas exchange process and the net primary production of terrestrial ecosystem. The success of LAI estimation can model the ecological process and predict the response of ecosystem. As LAI is functionally linked to spectral reflectance, remote sensing is effective in retrieving LAI over large areas particularly for mangrove areas which are ecologically-sensitive and generally inaccessible. Optical satellite images have long been an important data source for estimation of stand characteristics, biomass and LAI but with various degrees of success. Radio Detection and Ranging (RADAR) is gaining importance with its all-weather capability that enables timely data acquisition. The longer wavelength enables radar signal to penetrate the canopy and acquire under-canopy vegetation parameters and surface characteristics. Moreover, radar is sensitive to dielectric constant related to water availability which is potentially suitable to mangroves studies as they flourish in tide inundation zone where water availability and quantity change seasonally.

The research acquired Hyperion and Envisat-ASAR C-band data in November 2008 supplemented with in situ field survey to understand the current biophysical condition of mangrove in Mai Po Ramsar Site of Hong Kong. Ground LAI was acquired by optical measurement of diffuse radiation beneath the canopy through close-range hemispherical photo capture using the WinSCANOPY imaging system from Régent Instruments Inc. Gap fraction analysis with clumping correction was used to compute LAI and other canopy parameters. Several vegetation indices were computed from hyperspectral narrowbands while textural variables were extracted from ASAR based on GLCM. LAI was then modeled by regressing field-measured LAI against vegetation indices (VIs), backscatter and textural measures. The relationship was further explored in terms of different LAI classes (low to high LAI).

Fig. 1 The WinSCANOPY imaging system


Fig. 2 Hemispherical Photo (index photo)