@conference {50284,
	title = {The contribution of vegetation cover and bare soil to pixel reflectance in an arid ecosystem},
	booktitle = {AGU 2008 Fall Meeting},
	series = {AGU 2008 Fall Meetings},
	year = {2008},
	month = {December 15-19, },
	address = {San Francisco, CA},
	abstract = {The heterogeneity of vegetation and soils in arid and semi-arid environments complicates the analysis of medium spatial resolution remotely sensed imagery. A single pixel may contain several different types of vegetation, as well as a sizeable proportion of bare soil. We have used linear mixture modeling to explore the contribution of vegetation cover and bare soil to pixel reflectance. In October, 2006, aerial imagery (0.25 m spatial resolution) was acquired for our study sites in the Jornada Experimental Range, southern New Mexico. Imagery was also acquired from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) for June and November, 2006. These data corresponded with pre- and post monsoon conditions. Object-based feature extraction was used to classify the aerial imagery to shrub, grass and bare ground cover classes. Percent cover was then calculated for each cover class. Visible-near-infrared and shortwave infrared ASTER reflectance data from both dates were combined into a single 18-band dataset (30 m spatial resolution). A vector overlay from the classification results of the aerial imagery was used to define pure endmember pixels in the ASTER imagery. Estimates of the proportions of shrub, grass and bare ground cover from the linear mixture modeling approach were compared with cover calculated using feature extraction from the aerial imagery. The results indicate that reflectance in ASTER pixels is likely to be a linear combination of the cover proportions of the three main cover types (shrubs, grass, bare ground). However, noticeable outliers in the relationship between cover calculated from each method, indicate there may be other variables that affect the accuracy with which can estimate cover using linear mixture modeling.},
	keywords = {aerial imagery, ASTER, pixel},
	author = {Caitriana Steele and Alistair Smith and A. Campanella and A. Rango}
}
