02549nas a2200133   4500008004100000245010900041210006900150300001300219490000700232520196200239653006902201100002002270856012502290       2002                        eng d00aThe influence of canopy green vegetation fraction on spectral measurements over native tallgrass prairie0 ainfluence of canopy green vegetation fraction on spectral measur  a129 -1350 v813 aSpectral vegetation indices (SVIs) calculated from remotely sensed data are routinely used to monitor spatial and temporal changes in vegetation biophysical characteristics. The most commonly used SVI, the Normalized Difference Vegetation Index (NDVI), has been criticized because of its sensitivity to atmospheric conditions and substrate reflectivity, as well as its insensitivity to increases in vegetation biomass past particular thresholds. Yet, the use of NDVI remains widespread and is attractive because of the ease with which it is calculated. This article examines the utility of NDVI for monitoring the biophysical characteristic of green vegetation fraction (GVF) in comparison to other SVIs suggested as improvements. Statistical relationships between spectral response, presented in the form of SVIs, and GVF of a native tallgrass prairie canopy are explored. Broadband spectra were gathered from close-range during the 1999 growing season at the Konza Prairie Biological Station (KPBS), located in the Flint Hills region of Kansas, USA. Through simple regression analyses, spectra were related to GVF estimates derived from digital color photographs. SVIs evaluated are the NDVI, the Soil Adjusted Vegetation Index (SAVI), and the square of scaled NDVI (N*2). Results show that NDVI and N*2 were statistically related to GVF (R2 for NDVI=.77, N*2=.78) throughout the growing season. The least-squares line defining the relationship between N*2 and GVF approximated a 1:1 line. For June sample dates, all three SVIs were significant statistical predictors of GVF (R2 for NDVI=.89, N*2=.91, SAVI=.89). Regression coefficients for late-season sample dates were weaker, yet still significant in statistical terms (R2 for NDVI=.70, N*2=.70). While encouraging, these results suggest that further analyses are required to determine the usefulness of SVIs calculated from broadband devices for estimation of GVF when leaf litter dominates the scene.10aGreen vegetation fraction; Field spectroscopy; Tallgrass prairie1 aRundquist, B.C.  uhttp://lter.konza.ksu.edu/content/influence-canopy-green-vegetation-fraction-spectral-measurements-over-native-tallgrass01116nas a2200133   4500008004100000245012700041210006900168260004300237300001100280490002100291520059600312100002000908856005400928       2000                        eng d00aFine-scale spatial and temporal variation in the relationship between spectral reflectance and a prairie vegetation canopy0 aFinescale spatial and temporal variation in the relationship bet  aManhattan, KSbKansas State University  a1 -2090 vPhD Dissertation3 a<p>Close-range remote sensing, or field spectroscopy, involves the use of hand-held or boom-mounted radiometers to study relationships between spectral characteristics of Earth surfaces and their biophysical properties as measured in the field environment. Such data often provide the basis for remote sensing work conducted at broader scales, such as those monitored by modern Earth-observing satellites. However, spatial and temporal variation in the relationships between spectral response and surface characteristics usually are not considered in the analysis of remotely sensed data</p>1 aRundquist, B.C.  uhttp://adsabs.harvard.edu/abs/2000PhDT........97R00585nas a2200133   4500008004100000245013700041210006900178300001300247490000700260100002000267700001700287700001600304856013100320       2000                        eng d00aRelating biophysical processes to spatial patterns of spectral reflectance: A multiple-scale analysis of prairie vegetation canopies0 aRelating biophysical processes to spatial patterns of spectral r  a190 -1980 v231 aRundquist, B.C.1 aGoodin, D.G.1 aSmith, A.J.  uhttp://lter.konza.ksu.edu/content/relating-biophysical-processes-spatial-patterns-spectral-reflectance-multiple-scale-analysis