Reflectance measures of grassland biophysical structure

This is an article published by Taylor and FrancisLeaf area index (LAI), as an easily obtained variable, has been demonstrated to be a good indicator for estimating vegetation biophysical properties, and is simultaneously well correlated with remote sensing vegetation indices (VIs). The goal of this study is to develop an efficient method to retrieve vegetation biophysical properties based on ground LAI measurements and satellite data, and thus avoid the labour-intensive and time-consuming process for collecting biomass and canopy height in the future. The field data was conducted in Grasslands National Park (GNP), Saskatchewan, Canada and the biophysical properties (i.e. green biomass, dead biomass and canopy height) of the grassland community were collected. Two VIs, the adjusted transformed soil-adjusted ; vegetation index (ATSAVI) and the renormalized difference vegetation index (RDVI), were derived from SPOT 4 HRV images to estimate LAI and to prepare LAI and biophysical maps for the GNP. The results of the linear regressions demonstrated strong relationships between LAI and both of these selected VIs. However, a detailed assessment of the accuracy of the regression models indicated that the ATSAVI was likely to be better than the RDVI in estimating and mapping LAI for a northern mixed grassland ecosystem. The accuracy of the LAI map, derived from the ATSAVI, was calculated to be 66.7% and this map represented the spatial distribution of the vegetation acceptably. The significant relationship between measured LAI and the biophysical data solves the difficulty for mapping biophysical information due to insufficient sampling coverage for GNP. Our results demonstrate that, in native prairie ecosystems, reflectance can be used to measure the vegetation biophysical properties and thus to aid management decision making.This work was sponsored by University of Saskatchewan, and Western and Northern Service Center