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Sequential design in quality control and validation of land cover databases
Author(s) -
Carfagna Elisabetta,
Marzialetti Johnny
Publication year - 2009
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.742
Subject(s) - quality (philosophy) , land cover , cover (algebra) , database , sample (material) , computer science , interpretation (philosophy) , sample size determination , interpreter , statistics , data mining , data quality , land use , mathematics , service (business) , mechanical engineering , philosophy , chemistry , civil engineering , economy , epistemology , chromatography , engineering , economics , programming language
We have faced the problem of evaluating the quality of land cover databases produced through photo‐interpretation of remote‐sensing data according to a legend of land cover types. First, we have considered the quality control, that is, the comparison of a land cover database with the result of the photo‐interpretation made by a more expert photo‐interpreter, on a sample of the polygons. Then we have analysed the problem of validation, that is, the check of the photo‐interpretation through a ground survey. We have used the percentage of area correctly photo‐interpreted as a quality measure. Since the kind of land cover type and the size of the polygons affect the probability of making mistakes in the photo‐interpretation, we stratify the polygons according to two variables: the land cover type of the photo‐interpretation and the size of the polygons. We have proposed an adaptive sequential procedure with permanent random numbers in which the sample size per stratum is dependent on the previously selected units but the sample selection is not, and the stopping rule is not based on the estimates of the quality parameter. We have proved that this quality control and validation procedure allows unbiased and efficient estimates of the quality parameters and allows reaching high precision of estimates with the smallest sample size. Copyright © 2009 John Wiley & Sons, Ltd.