@conference {50080,
	title = {Prospects of phenological monitoring in an arid southwestern U.S. rangeland using field observations with hyperspatial and moderate resolution imagery},
	booktitle = {American Geophysical Union Fall Meeting},
	year = {2009},
	month = {12/2009},
	address = {San Francisco, California},
	abstract = {<p>Relating field observations of plant phenological events to remotely sensed depictions of land surface phenology remains a challenge to the vertical integration of data from disparate sources. This research conducted at the Jornada Basin Long-Term Ecological Research site in southern New Mexico capitalizes on legacy datasets pertaining to reproductive phenology and biomass and hyperspatial imagery. Large amounts of exposed bare soil and modest cover from shrubs and grasses in these arid and semi-arid ecosystems challenge the integration of field observations of phenology and remotely sensed data to monitor changes in land surface phenology. Drawing on established field protocols for reproductive phenology, hyperspatial imagery (4 cm), and object-based image analysis, we explore the utility of two approaches to scale detailed observations (i.e., field and 4 cm imagery) to the extent of long-term field plots (50 x 50m) and moderate resolution Landsat Thematic Mapper (TM) imagery (30 x 30m). Very high resolution color-infrared imagery was collected June 2007 across 15 LTER study sites that transect five distinct vegetation communities ranging from grass to shrub dominance. We examined two methods for scaling spectral vegetation indices (SVI) at 4 cm resolution: pixel averaging and object-based integration. Pixel averaging yields the mean SVI value for all pixels within the plot or TM pixel. Alternatively, the object-based method is based on a weighted average of SVI values that correspond to discrete image objects (e.g., individual shrubs or grass patches). Object-based image analysis of 4 cm imagery provides a detailed depiction of ground cover and allows us to extract species-specific contributions to upscaled SVI values. The ability to discern species- or functional-group contributions to remotely sensed signals of vegetation greenness can greatly enhance the design of field sampling protocols for phenological research. Furthermore, imagery from unmanned aerial vehicles (UAV) is a cost-effective and increasingly available resource and generation of UAV mosaics has been accomplished so that larger study areas can be addressed. This technology can provide a robust basis for scaling relationships for phenology-based research applications.</p>
},
	keywords = {computational methods, data processing, phenological, remote sensing},
	author = {Dawn M. Browning and Andrea S. Laliberte and A. Rango and Jeffrey E. Herrick}
}
