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APPLICATION OF A "VITURAL FIELD REFERENCE DATABASE" TO ASSESS LAND-COVER MAP ACCURACIES
Citation:
Iiames, J., D Pilant, AND R S. Lunetta. APPLICATION OF A "VITURAL FIELD REFERENCE DATABASE" TO ASSESS LAND-COVER MAP ACCURACIES. Presented at American Society for Photogrametry and Remote Sensing (ASPRS) 2001 Annual Conference, St. Louis, MO, April 23-27, 2000.
Impact/Purpose:
Our research objectives are to: (a) develop new methods using satellite remote sensor data for the rapid characterization of LC condition and change at regional to national scales; (b) evaluate the utility of the new NASA-EOS MODIS (Moderate Resolution Imaging Spectrometer) leaf area index (LAI) measurements for regional scale application with landscape process models (e.g., biogenic emissions and atmospheric deposition); (c) provide remote sensor derived measurement data to advance the development of the next generation of distributed landscape process-based models to provide a predictive modeling capability for important ecosystem processes (e.g., nutrients, sedimentation, pathogens, etc.); and (d) integrate in situ monitoring measurement networks with UAV and satellite based remote sensor data to provide a continuous environmental monitoring capability.
Description:
An accuracy assessment was performed for the Neuse River Basin, NC land-cover/use
(LCLU) mapping results using a "Virtual Field Reference Database (VFRDB)". The VFRDB was developed using field measurement and digital imagery (camera) data collected at 1,409 sites over a period of two years (1998-99) and was designed to support detailed assessments of remote sensor derived LCLU products by providing a robust database characterizing representative cover types throughout the study area. To account for plot Heterogeneity two independent interpreters assigned class labels to the VFRDB reference data set corresponding to a hierarchical classification system. Interpretations were based on the detailed field measurement and imagery (camera) data contained in the VFRDB. Correspondence between interpreters was analyzed at multiple classification levels. A high level of correspondence between interpreters was attributed to the high quality source of measurement and imagery data to guide class assignments. Disagreements, between interpreters were a result of landscape heterogeneity. Results demonstrate the importance of a reference that can be repetitively interpreted to provide reference data with known variability, to support the quantitative assessments of remote sensor derived LCLU products