Abstract
Accuracy is increasingly recognized as an important dimension in geospatial information and analyses. A strategy well suited for map users who usually have limited information about map lineages is proposed for location-specific characterization of accuracy in land cover change maps. Logistic regression is used to predict the probabilities of correct change categorization based on local patterns of map classes in the focal three by three pixel neighborhood centered at individual pixels being analyzed, while kriging is performed to make corrections to regression predictions based on regression residuals at sample locations. To promote uncertainty-informed accuracy characterization and to facilitate adaptive sampling of validation data, standard errors in both regression predictions and kriging interpolation are quantified to derive error margins in the aforementioned accuracy predictions. It was found that the integration of logistic regression and kriging leads to more accurate predictions of local accuracies through proper handling of spatially-correlated binary data representing pixel-specific (in)correct classifications than kriging or logistic regression alone. Secondly, it was confirmed that pixel-specific class labels, focal dominances and focal class occurrences are significant covariates for regression predictions at individual pixels. Lastly, error measures computed of accuracy predictions can be used for adaptively and progressively locating samples to enhance sampling efficiency and to improve predictions. The proposed methods may be applied for characterizing the local accuracy of categorical maps concerned in spatial applications, either input or output.
Highlights
The provision of spatio-temporal land cover information is becoming increasingly important for landscape ecology and environmental monitoring
The proposed methods for mapping local accuracies in land cover change maps can be usefully explored in combination with work on landscape dynamics monitoring and modelling, such as that described by Millington et al [1] and Rutherford et al [2], and related research efforts [28,30,31,32]
This paper has focused on situations where users of land cover change maps would like to evaluate such maps’ local accuracies based on some validation samples and by exploiting the association between the occurrences ofcorrect classifications and local patterns of class occurrences in the map being assessed
Summary
The provision of spatio-temporal land cover information is becoming increasingly important for landscape ecology and environmental monitoring. Spatio-temporal land cover information may be acquired and derived from the combined use of field survey data and remotely-sensed images through statistical and other pattern analysis techniques in a semi-automatic fashion. Fuller et al [4] examined issues concerning the detection, measurement and characterization of landscape changes by remote sensing and other means, while Chen et al [5] described an effective and feasible strategy for operational global land cover mapping at 30-m resolution. Land cover information can be represented as categorical maps (or area-class maps) [6], either by polygons in vector format or contiguous blocks of grid cells in raster format
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