Measures of spatio-temporal accuracy for time series land cover data |
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| Institution: | 1. Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;2. Joint Center for Global Change Studies, Beijing 100875, China;3. Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA;4. UNESCO-IHE, Westvest 7, Delft 2611, AX, the Netherlands;1. Department of Environmental Resources Engineering, State University of New York, College of Environmental Science and Forestry, 1 Forestry Drive, Syracuse, NY 13210, United States;2. Department of Forest and Natural Resources Management, State University of New York, College of Environmental Science and Forestry, 1 Forestry Drive, Syracuse, NY 13210, United States |
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| Abstract: | Remote sensing is a useful tool for monitoring changes in land cover over time. The accuracy of such time-series analyses has hitherto only been assessed using confusion matrices. The matrix allows global measures of user, producer and overall accuracies to be generated, but lacks consideration of any spatial aspects of accuracy. It is well known that land cover errors are typically spatially auto-correlated and can have a distinct spatial distribution. As yet little work has considered the temporal dimension and investigated the persistence or errors in both geographic and temporal dimensions. Spatio-temporal errors can have a profound impact on both change detection and on environmental monitoring and modelling activities using land cover data. This study investigated methods for describing the spatio-temporal characteristics of classification accuracy. Annual thematic maps were created using a random forest classification of MODIS data over the Jakarta metropolitan areas for the period of 2001–2013. A logistic geographically weighted model was used to estimate annual spatial measures of user, producer and overall accuracies. A principal component analysis was then used to extract summaries of the multi-temporal accuracy. The results showed how the spatial distribution of user and producer accuracy varied over space and time, and overall spatial variance was confirmed by the principal component analysis. The results indicated that areas of homogeneous land cover were mapped with relatively high accuracy and low variability, and areas of mixed land cover with the opposite characteristics. A multi-temporal spatial approach to accuracy is shown to provide more informative measures of accuracy, allowing map producers and users to evaluate time series thematic maps more comprehensively than a standard confusion matrix approach. The need to identify suitable properties for a temporal kernel are discussed. |
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| Keywords: | Remotely sensed data Spatio-temporal analysis Accuracy assessment Geographically weighted model Principal component analysis |
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