@article {Geerken:2005:0143-1161:4499,
title = "Differentiation of rangeland vegetation and assessment of its status: field investigations and MODIS and SPOT VEGETATION data analyses",
journal = "International Journal of Remote Sensing",
parent_itemid = "infobike://tandf/tres",
publishercode ="tandf",
year = "2005",
volume = "26",
number = "20",
publication date ="2005-10-20T00:00:00",
pages = "4499-4526",
itemtype = "ARTICLE",
issn = "0143-1161",
eissn = "1366-5901",
url = "https://www.ingentaconnect.com/content/tandf/tres/2005/00000026/00000020/art00008",
doi = "doi:10.1080/01431160500213425",
author = "Geerken, R. and Batikha, N. and Celis, D. and DePauw, E.",
abstract = "In the Syrian Steppe we carried out a field experiment using repeated hyperspectral measurements throughout the 2001 growing season to investigate the separability of vegetation types based on their temporalspectral signatures. We studied two different perennial shrubs and annual grasses showing differences in the length of their growing period. These differences cause seasonal variations in spectral plant reflectance, also giving hints to functional vegetation characteristics such as palatability, soil stabilization or certain hydrometeorological characteristics. By submitting biophysical and spectral data to statistical analyses we identified the dry season period as the most suitable to discriminate between the studied plants or their functional characteristics respectively. NDVI time series using MODIS or SPOT VEGETATION NDVI bands perform nearly as well as an optimum narrow banded index. For a balanced assessment of the biomass from different functional vegetation groups, our study recommends a narrow banded (9nm width) vegetation index that uses the 630nm band together with the 755nm band. Its particular advantage, compared with indices calculated from MODIS and SPOT VEGETATION bands, is the better correlation with biomass from annual grasses. Conclusions from the field experiment were tested for their transferability to remote sensing conditions. Using the noisefiltered shapes of NDVI cycles as a primary classifier, we identify the distribution and the fractional cover of species with an extended growing period. Pixels with a dominant cover of species with an extended growing period were classified by correlating a reference NDVI cycle with all image pixels, where the calculated correlation coefficient is the measure for shape similarity, and the corresponding slope value the measure for vegetation cover relative to the reference.",
}