Ground and remote estimation of leaf area index in Rocky Mountain forest stands, Kananaskis, Alberta

Abstract

Leaf area index (LAI) is an important measure of canopy structure that is related to biomass, carbon and energy exchange, and is an important input to ecological and climate change models. LAI can be estimated using algorithms applied to airborne and satellite imagery, with ground-based measurements of LAI being required for calibration and validation. A variety of methods exist for ground-based and remote estimation of LAI, and this can lead to confusion and uncertainty regarding selection of methods, experimental design, and instrumentation. As a contribution towards clarifying these protocols, this paper investigated and compared three optical methods and an allometric technique for ground-based estimation of LAI, and these were related to remote LAI estimates derived from the compact airborne spectrographic imager (casi) using three vegetation indices (normalized difference, weighted difference, and soil-adjusted vegetation indices, or NDVI, WDVI, and SAVI, respectively) and subpixel-scale spectral mixture analysis (SMA). The study was conducted in the Kananaskis region of Alberta in the Canadian Rocky Mountains and considered four species compositions within a montane ecological subregion: lodgepole pine, white spruce, composite deciduous (aspen and balsam poplar), and mixedwood (mixture of deciduous and lodgepole pine or white spruce). LAI data were obtained in the field using a LI-COR, Inc. LAI-2000 instrument, a tracing radiation and architecture of canopies (TRAC) system, an integrated (LAI-2000 and TRAC) method, and an allometric technique that used the ratio of sapwood basal area to leaf area. A subsample of plots was assessed with hemispherical photographs and LAI-2000 data from which similar effective leaf area index (eLAI) values were derived for two of the four species analyzed. The results highlight the importance of ensuring that samples represent the range of stand structures and canopy architecture inherent in the species group being assessed. Foliage clumping was observed to be similar in both coniferous and deciduous species and an important element to measure. LAI estimates were influenced by the field methods used to estimate LAI, species and their canopy architecture, and the form of the vegetation index or subpixel-scale mixing derived from the casi image. Of the three vegetation indices, the SAVI was the statistically strongest predictor of LAI for mixedwood species, but all were poor LAI estimators for lodgepole pine and deciduous species. The subpixel-scale scene fractions from SMA provided the best prediction of LAI for white spruce compared with the three vegetation indices. The result for white spruce provides an encouraging basis for further investigation of SMA as a sampling tool to scale from field to high-resolution airborne and satellite imagery for local to landscape-level biophysical estimation.