Discriminating between cool season and warm season grassland cover types in northeastern Kansas

Spatially weighted supervised classification for remote sensing

“…In addition, change in such properties may be monitored through multi-temporal analysis and remotely sensed images can be used to calibrate and assess the accuracy of process models that allow spatial prediction to be extended through time (e.g., Liu et al, 2001;Peterson et al, 2002).…”

Using multitemporal satellite imagery to characterize forest wildlife habitat: The case of ruffed grouse

“…However, delineation of understory vegetation requires an additional winter image, as well as elevation data. Multitemporal images can also be used to differentiate grassland communities (Peterson et al, 2002), monitor wetlands (Lunetta and Balogh, 1999), and serve as baseline data for detecting changes in landuse over longer temporal scales (Pinder et al, 1999), making their use in wildlife habitat studies cost-justifiable. With the incorporation of simple adjustments for local forest plant species phenology into the model, it may be used to better classify wildlife habitat of similar species in areas with comparable forest communities and topography.…”

Estimating above-ground biomass on mountain meadows and pastures through remote sensing

“…Therefore, it seems that wetness indices perform better in high canopy covers than vegetation indices, although more research with field validation should be done. Another interesting fact regarding predictors is that the NDVI index, broadly used in literature (Maselli et al, 2009;Paruelo et al, 1997;Peterson et al, 2002;Piñeiro et al, 2006;Schino et al, 2003;Xie et al, 2009), has a lower significance in AGB models compared to EVI or TCG,. This could be explained by the NDVI index saturation effect in grasslands, which begins when biomass exceed 100-150 g m −2 (Vescovo and Gianelle, 2008) as in the case of our study area (see Table 1).…”