By Russell G. Congalton
Congalton does an outstanding task featuring distant sensing accuracy review techniques. as well as the idea, he presents useful examples to aid in utilizing the speculation to genuine international situations.
The booklet turns out means over-priced for its dimension.
Read Online or Download Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition (Mapping Science) PDF
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Extra resources for Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition (Mapping Science)
While total acreage is useful, it is far more important to know where a specific land cover or vegetation type exists. Therefore, this second epoch was relatively short-lived and quickly led to the age of site-specific assessments. , percentage correct) presented. This method far exceeded the non-site-specific assessment, but lacked information about individual land cover/vegetation categories. Only overall map accuracy was assessed. Site-specific assessment techniques were the dominant method until the late 1980s.
Also relying on Greenwalt and Schultz (1962, 1968), the NSSDA specifies that positional accuracy be characterized using RMSE, requires that accuracy be reported in ground distance units at the “95% confidence level,”† and provides guidance on how samples are to be selected. NSSDA continues to be the accepted standard on positional accuracy assessment. It is often used in conjunction with the ASPRS largescale map standards, with NSSDA providing standardized processes for assessing positional accuracy and the ASPRS (1990) standards setting the maximum errors allowable for different map scales.
Inaccurate measurements can be very precise, and accurate measurements can be imprecise. 2 illustrates the concepts of accuracy and precision with an example of multiple measurements made of one location. In positional accuracy assessment, we are interested in characterizing the accuracy of a geospatial data set. We take samples to determine if a bias (systematic inaccuracy) exists in the data set, and we estimate the magnitude and precision of the bias. We also strive to ensure that our measurements of each sample’s reference and geospatial data set’s locations are themselves accurate, and we must take enough samples so that our estimate of the bias (if it exists) is precise.