Remote Sensing and GIS Accuracy Assessment
The development of robust accuracy assessment methods for the validation of spatial data represents a difficult challenge for the geospatial science community. The importance and timeliness of this issue are 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 increasingly 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 typically suffer from the problem of spatial autocorrelation, which violates the important statistical assumption of “independent” data.
Positional errors tend to introduce 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.
0 Comments