The influence of vegetation index and spatial resolution on a two-date remote sensing-derived relation to C4 species coverage

Changes in composition of plant species are expected to accompany a warming climate. In the northern mixed grass prairie, such changes are predicted to take the form of shifts in the relative ground cover of C3 and C4 photosynthetic types. In this study, we explore the feasibility of using two-date remote sensing data as a potential tool for monitoring these shifts. Our approach is based on the well-described asynchronous seasonality of C3 and C4 species. We hypothesize that the ratios of early-season to late-season aboveground live biomass (B-early/B-late) will decrease as sites become more C4-dominated, and that if B-early and B-late can be reliably estimated using spectral data, it may be feasible to predict C4 species coverage (%C4) from commercially available satellite information. Using spectral and botanical measurements from three upland communities in the Canadian mixed grass prairie, we (a) examined the relationship between various spectral vegetation indices and aboveground live biomass, (b) investigated the nature of the relationship between remotely sensed estimated of B-early/B-late and %C4 at multiple sample resolutions (0.5 m, 2.5 m, 10 m and 50 m), and (c) assessed whether these relationships were dependent on the vegetation index used to estimate biomass. We found a log-linear relationship between each spectral index and aboveground live biomass. Negative linear relationships were found between %C4 and remotely sensed B-early/B-late at all sampling resolutions. These relationships were strongest at sampling resolutions of 10 m and 50 m. The strengths and forms of relationships were found to be partially vegetation index-dependent. Stronger relationships between variants at coarser resolutions likely result from the smoothing of fine-scale variation in aboveground live biomass and C4 species coverage. Our results suggest that commercially available satellite data at resolutions of 10 m to 50 m (e.g., Landsat Thematic Mapper) may offer the potential for estimating coverage of C4 species and that the choice of vegetation index used to estimate biomass is relatively unimportant. However, we caution that for this technique to be operationally useful, statistical model performance must be strengthened and developed to provide both temporal and spatial generality. Further investigation is needed to examine the applicability of this approach to other growing seasons, community types, and grassland regions. (C) Elsevier Science Inc., 2001.