Improving Clumping and LAI Algorithms Based on Multiangle Airborne Imagery and Ground Measurements

Measurements at more than one angle capture the directional anisotropy of solar radiance reflected from vegetated surfaces. According to our recent research, we propose that the best two view angles for vegetation structural mapping are the following:1) the hotspot, where the Sun and view directions coincide, and 2) the darkspot, where the sensor sees the maximum amount of vegetation structural shadows. The Normalized Difference between Hotspot and Darkspot (NDHD), an angular index generated from Compact Airborne Spectrographic Imager (CASI) data, is found to be highly correlated with the field-measured foliage clumping index. The foliage clumping index characterizes the nonrandomness in the spatial distribution pattern of leaves. It is of comparable importance as the leaf area index (LAI) for quantifying radiation interception and distribution in plant canopies, and it also affects estimated LAI mapping using remote sensing data. As the clumping index can vary considerably within a cover type, it is highly desirable to map its spatial distribution for various ecological applications. We have generated clumping index maps based on the previous algorithms and empirical relationships between field-measured $\Omega$ and CASI-derived NDHD. Through intensive validation using field data, we demonstrate that the combination of the hotspot and darkspot reflectances has the strongest response to changes in vegetation structure. Two crown structural characteristics, namely, crown height and within-crown density, are major factors that impact the NDHD and clumping index difference between the mature and young (regrowth) coniferous forests. The study area is located near Sudbury in the northern Ontario, Canada.