To:  Brenda Brown and Sherry Farwell
From:  Xuexia Chen and Lee Vierling
Re:  Report for travel to the High Spatial Resolution Commercial Workshop held in Reston, VA, March 25-27, 2002.

Xuexia Chen, a Ph.D. student in the lab of Dr. Lee Vierling, participated in the above conference with her travel partially funded by NASA-EPSCoR.  Ms. Chen was one of approximately 12 presenters who were invited by the USGS to present at this meeting, which was convened for scientists and administrators from NASA, USGS, and NIMA to learn about how the commercial satellite imagery purchase program (specifically, for purchasing IKONOS imagery) has been utilized to conduct various scientific research.  Ms. Chen’s presentation was co-authored by participants from SDSM&T, Horizon’s Incorporated of Rapid City, and the EROS Data Center (see abstract and authors listed below).  The presentation was well received and has led to the request and approval for 2 additional IKONOS images to be delivered to the research team for research sites in the Black Hills of South Dakota.  As a result, new and continued opportunities for collaboration among NASA, SDSM&T, Horizon’s, and EROS Data Center have arisen from this conference.

The following paper was submitted and presented at the conference:

 

Relationships Among IKONOS Imagery, Airborne Scanning LIDAR, and Ground-Based Tree Inventory Data in a Ponderosa Pine Forest:  A Multiple Endmember Approach

 X. Chen1, L. Vierling1*, E. Rowell2, D. Dykstra1, W. Capehart1, and T. DeFelice3
1
South Dakota School of Mines and Technology, Institute of Atmospheric Sciences
2
Horizon’s Incorporated, Rapid City, SD
3
USGS EROS Data Center, Sioux Falls, SD
* Corresponding Author:  Lee.Vierling@sdsmt.edu

Estimating forest structure and stand density using remotely sensed data is important for a wide range of scientific and management goals, including assessing biogeochemical budgets (e.g. aboveground carbon storage) and determining the susceptibility of an area to catastrophic fires.  The objective of this study is to determine relationships among ground-collected forest inventory data, high resolution IKONOS imagery, and airborne scanning LIDAR collected at an Ameriflux tower site in the Black Hills of South Dakota.  The site is located in the Black Hills Experimental forest, which is largely dominated by Ponderosa Pine. 

Ground data were collected in the summer of 2001 along eight 10x150 meter belt transects radiating from the tower.  These data included tree species identification, diameter at breast height, tree height, percent and type of ground cover, and effective leaf area index (LAIe) as determined using two LAI-2000 instruments (Li-COR, Lincoln, NE).  IKONOS imagery was obtained over the site on July 28, 2000, and airborne scanning LIDAR was acquired at a 2 meter post spacing (±3m beam footprint) in October 2001.  No thinning or fire activity occurred at the site between data collection dates.  Transect data were subdivided into 10x10 meter plots and co-registered with the IKONOS and LIDAR data for analyses.  A combination of IKONOS multispectral and panchromatic data was used to select image endmembers (i.e. spectrally “pure” components) of bare soil, open grass, and tree/shade.

Effective tree canopy LAI (m2 leaf/m2 ground) ranged from 0 to 2.5 among the 100 m2 plots, and was used as an indicator of forest canopy coverage.  A significant positive correlation existed between LAIe and the IKONOS-derived tree/shade endmember fraction for pixels falling within the transect plots (Pearson’s correlation coefficient [PCC] = 0.76), while LAIe and the open grass endmember fraction was significantly negatively correlated (PCC = -0.67).  The Enhanced Vegetation Index (EVI) was negatively correlated with all measures of tree density, including the canopy LIDAR return fraction (PCC = -0.65) and tree/shade endmember fraction (PCC = -0.94).  Conversely, EVI exhibited a very strong positive correlation with the open grass endmember fraction (PCC = 0.99).  These results indicate that IKONOS data can serve to make the important distinction between tree canopy coverage and exposed understory grasses near peak summertime greenness.


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