Use of vegetation index "fingerprints" from hyperion data to characterize vegetation states within land cover/land use types in an Australian tropical savanna

Suites of spectral indices may be derived from hyperspectral sensors such as Hyperion on EO-1. Spectral indices linked to vegetation and landscape function that are scalable to multi-spectral global sensors, could provide "fingerprints" for vegetation states in tropical savannas. In this study, Hyperion images were acquired on three occasions throughout the dry season over each of two consecutive years in the tropical savanna near Darwin, Northern Territory, Australia (12 degrees 25'N, 130 degrees 50'E) during 2005 and 2006. This paper examines the changes in fractional cover of photosynthetic and non-photosynthetic vegetation and bare soil and key diagnostic narrow band vegetation indices for major land cover/land use (LCLU) types over two contrasting post-monsoon seasons. The fractional cover proportions and vegetation indices responded strongly to the additional month of full monsoon rains in 2006 versus 2005. There were differences in vegetation indices sensitive to pigments, canopy water and cellulose between LU and LC classes, but within class variation was very high for large sized sample areas. When fine scale variation in vegetation indices and fractional cover were examined as "fingerprints" for small, more uniform areas of specific LC, distinct differences were evident. Vegetation indices and derived vegetation properties can be used to characterize vegetation states at the scale of natural and management-induced variation. The vegetation indices and fractional cover methods used here can be translated and scaled-up to current and new global sensors to improve description of vegetation structure and function in savannas

Monitoring Changes On The Sheyenne National Grassland Using Multitemporal Landsat Data

Tallgrass prairies are one of the rarest ecosystems on the planet as up to 99% of their historical extent has been converted to agriculture. Once a prairie is converted there is often a loss of ecosystem services such as soil retention, carbon storage, water quality and a loss of biodiversity. It can take centuries to restore a native prairie after conversion has taken place. The Sheyenne National Grassland is managed by the U.S. Forest Service and contains the largest publicly owned tract of tallgrass prairie remaining in North America making it a highly valuable for conservation. Ordinary least squares regression was implemented to evaluate statistically significant trends at a per pixel basis in selected Vegetation Indices (VI) between the years of 1984 and 2011 on the Sheyenne National Grassland. VIs included NDVI, NDII RGR and SWIR32. Additionally, a Composite Index which sought to combine information from the original four indexes was created to evaluate the usefulness of combining indexes. A random forest regression model was also used to evaluate which independent variables were the most useful in predicting VI values through time. Between 1984 and 2011 the NDVI and NDII have increased while the RGR and SWIR32 have decreased. This indicates that greenness and wetness have increased through time while stress and non-photosynthetic vegetation have decreased. It is likely that the increase in NDVI is driven by a complex relationship between the influence of climate change and cattle grazing on the relative abundance of C3 and C4 plants. It is hypothesized that continuously stocked cattle grazing has reduced the vigor and competitive ability of native C4 grasses which competitively releases C3 grasses that are more tolerant of grazing and are primarily invasive. In addition to the competitive release of cattle grazing, C3 establishment is promoted through increased spring precipitation which has increased over the last century