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GEOSPATIAL DATA ACCURACY ASSESSMENT
Citation:
Lunetta, R S. AND J G. Lyon. GEOSPATIAL DATA ACCURACY ASSESSMENT. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-03/064, 2004.
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:
The development of robust accuracy assessment methods for the validation of spatial data represent's a difficult scientific challenge for the geospatial science community. The importance and timeliness of this issue is related directly to the dramatic escalation in the development and application of spatial data throughout the latter 20th century. This trend, which is expected to continue, will become evermore pervasive, and continue to revolutionize future decision making processes. However, our current ability to validate large-area spatial data sets, represents a major impediment to many future applications. Problems associated with assessing
spatial data accuracy are primarily related to their valued characteristic of being continuous data, and to the associated geometric or positional errors implicit with all spatial data. Continuous data routinely suffer from the problem of spatial autocorrelation which violate the important statistical assumption of "independent" data, while positional errors tend to introduce excessive (anomalous) errors with the combining of multiple data sets or layers. The majority of large-area spatial data coverages are derived from remote sensor data and subsequently analyzed in a GIS, to provide baseline information for data driven assessments to facilitate the decision making process.