Leaf Area Index Estimation using Lidar and Forest Reflectance Modelling of Airborne Hyperspectral Data

Insect-induced damages in forests are a major concern for timber production, landscape conservation and ecosystem research. Early detection methods based on remote sensing data can document the severity and spatial extent of ongoing attacks and might aid in designing mitigation measures or even prevention where necessary. In southeastern Norway, a large-scale insect defoliation of pine trees is ongoing. The larvae of the Pine sawfly Neodiprion sertifer create it with its mass attacks during their feeding on needles in June and July. In the winter before the attack, egg galleries are evident in the needles. This provides a test case for early detection methods and remote sensing techniques for monitoring forest health. In the context of an ongoing project on REMote sensing of FORest health (REM-FOR) in Norway, we approach this problem by mapping leaf area index (LAI) before and after the attack in a test area (size around 20 km²). LAI is used as a proxy for the crown density, and decreasing trends not related to phenology (non-periodic) indicate defoliation. Estimates for effective LAI for two different years, 2005 and 2007, were derived using airborne laser scanning (LIDAR) calibrated with ground-based point measurements with the LAI-2000 Plant Canopy Analyzer (LI-CORreg, USA). These estimates are based on an application of the Beer-Lambert law and a threshold-based separation of laser reflections from the ground and from the canopy. We also obtained airborne high-resolution hyperspectral images (HySpex^TM, Norsk Elektrooptikk, Norway) for the same reference years to investigate the spectral response of the affected forest. The set of two cameras deliver 330 different spectral channels in the wavelength range 400 to 1800 nm. The analysis might be done using advanced multivariate methods or spectral unmixing using spectral libraries. Here, it was performed using a physically-based model emphasizing geometrical and optical properties of canopies, - - the Forest Reflectance Model FRT. FRT was designed for the application to (managed) Northern European Forests and is based on conventional forest inventory data, species-dependent parametrized crown shapes, canopy LAI, needle clumping index, and needle optical properties. Here, however, we run the model in an inverse mode, by iteratively minimizing the discrepancy between measured and simulated reflectances, and predicting the LAI, keeping well-constrained parameters of the model (e.g. tree density, tree height, optical properties of needles) fixed while calibrating others (e.g. needle weight per tree, average shoot length, shoot self-shading). A set of 14 sample trees felled in 2005 further constrain the range of the calibrated parameters, excluding local model inversion minima with unrealistic parameter estimates. The LAI values predicted by the model are then compared to those obtained with airborne laser-scanning with a spatial resolution of 20 m times 20 m for the pine- dominated part of the scenes. In doing so, the spectra from each pixel (size 25 cm times 25 cm) were aggregated by calculating channel-wise median values. In effect, the modelling setup results in determining geometrical and optical properties of forest plots, trees and needles from hyperspectral images. The project data are complemented by images from a range of satellite-based sensors, including MODIS, SPOT, and Hyperion to cover larger regions and as a basis for operationalizing the approach for future insect attacks.