Range Management Research Site Logo
ARS Home About Us Helptop nav spacerContact Us En Espanoltop nav spacer
Printable VersionPrintable Version     E-mail this pageE-mail this page
Agricultural Research Service United States Department of Agriculture
Search
  Advanced Search
 
Programs and Projects
Subjects of Investigation
Ecological Site Descriptions
Monitoring & Assessment
Long Term Ecological Research
Long Term Agricultural Research
Landscape Toolbox
Data Catalogs
EcoTrends
 

Title: Linking ground observations, simulation model output, and remote sensing data to characterize phenology across diverse arid landscapes

Authors
item Anderson, John - NEW MEXICO STATE UNIV
item Peters, Debra
item Rango, Albert
item Steele, Caiti - NEW MEXICO STATE UNIV

Submitted to: American Society for Photogrammetry and Remote Sensing Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: September 15, 2007
Publication Date: N/A

Technical Abstract: We combined long-term data on plant phenology with simulation modeling output and remote sensing data to characterize diverse landscapes at the Jornada Experimental Range in the northern Chihuahuan Desert of southern New Mexico. Phenology of 15 key species in Chihuahuan Desert plant communities have been monitored monthly for 15 sites since 1992. Phenological state (non-reproductive, in bud, in flower, dormant) is noted for all plants of selected grass and shrub species at three replicate sites of five major plant communities (upland grasslands, playa grasslands, creosotebush shrublands, mesquite shrublands, tarbush shrublands). We combined these long-term data with simulation model results of key species to extrapolate back in time and to forecast future dynamics under a changing climate. We used a daily timestep model of soil water dynamics (SOILWAT) to simulate recruitment of the dominant grass for the entire Jornada Basin. We also compared the long-term data with remotely sensed images through time for one year from the ASTER satellite. The ability of the ASTER images to sense phonological changes varied by community type. Linking different technologies has great potential for improving understanding and prediction for arid landscapes that vary both temporally and spatially.

   
 
 
Last Modified: 05/25/2013
ARS Home | USDA.gov | Site Map | Policies and Links 
FOIA | Accessibility Statement | Privacy Policy | Nondiscrimination Statement | Information Quality | USA.gov | White House