Mapping swamp timothy (Crypsis schoenoides) seed productivity using spectral values and vegetation indices in managed wetlands.
This work examines the potential to predict the annual seed productivity of swamp timothy (Crypsis schoenoides) in two Central California managed wetlands by correlating spectral reflectance values and associated spectral vegetation indices (SVIs) calculated from two sets of high-resolution aerial images (May and June 2006) to collected vegetation data. An object-based segmentation approach incorporating image textural properties was also investigated. The June image provided better predictive capacity relative to May, a result that underscores the importance of imagery timing to coincide with optimal vegetation status. The simple ratio (SR) derived from the June image proved to be the best predictor of swamp timothy dry seed productivity (R2=0.566, standard error (SE)=29.3 g m-2). Addition of object-based texture information did not significantly increase the accuracy of seed mass estimations. Using the SR-seed biomass model, a seed productivity map was created demonstrating the potential utility of this approach as a tool for resource managers.