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• Proposed the STLCF to modify the unrealistic land cover change. • The STLCF is based on the spatio-temporal transition probability. • The STLCF significantly improved the spatio-temporal consistency of annual land cover maps. Annual analyses of land cover dynamics in urban areas provide a thorough understanding of the urbanization effects on environment and valuable information for the improvement of urban growth modeling. However, most current studies focus on major land cover changes, such as urbanization and vegetation loss. The most feasible way to evaluate the complex interactions among different land cover types is the post-classification change detection, but the temporal inconsistency in the time series of land cover maps impedes the high-frequency and long-term analyses. This study proposed a spatio-temporal land cover filter (STLCF) to remove the illogical land cover change events in the time series of land cover maps, and analyzed the annual land cover dynamics in urban areas. The knowledge of illogical land cover change events was 'learned' from the land cover maps through the spatio-temporal transition probability matrix, instead of experts' knowledge. The illogical change was modified with the land cover of the maximum probability calculated from the naïve Bayesian equation. The STLCF was tested in Wuhan, a typical densely urbanized Chinese city. The annual land cover maps from 2000 to 2013 were derived from multi-date Landsat images using the Decision Tree (DT) classifier. Results showed that the STLCF improved the mean overall accuracy of annual change detection by about 6%. Additionally, the amount of land cover trajectories with unrealistically frequent changes was significantly decreased. During the study period, 7.86% of the pixels experienced one land cover change, and about 0.57% of the pixels experienced land cover changes more than once. The annual analyses demonstrated the non-linear increasing trend in urbanization as well as the corresponding trend in vegetation loss in the study area. We also found the conversion from built-up areas to vegetation near rivers and lakes and in the reserves and rural areas, mainly caused by the restoration of built-up areas to the park or green belt/wedges along rivers and new roads in the metropolitan areas, and to the cropland and woods in the rural areas. Results of this study showed the importance of the spatio-temporal consistency check with knowledge derived from land cover maps of the study area, which facilitates the annual analyses of major and subtle land cover dynamics in urban areas. [ABSTRACT FROM AUTHOR]

Nighttime light imagery is a powerful tool to study urbanization because it can provide a uniform metric, lit area, to delimit urban extents. However, lit area is much larger than actual urban area, so thresholds of digital number (DN) values are usually needed to reduce the lit area. The threshold varies greatly among different regions, but at present it is still not very clear what factors impact the changes of the threshold. In this study, urban extent by province for China is mapped using official statistical data and four intercalibrated and geometrically corrected nighttime light images between 2004 and 2010. Lit area in the imagery for most provinces is at least 94% greater than the official amount of urban area. Regression analyses show a significant correlation between optimal thresholds and GDP per capita, and larger thresholds more commonly indicate higher economic level. Size and environmental condition may explain a province’s threshold that is disproportionate to GDP. Findings indicate one threshold DN is not appropriate for multiple (adjacent) province urban extent mapping, and optimal thresholds for one year may be notably different than the next. Province-level derived thresholds are not appropriate for other geographic levels. Brightness of nighttime lights is an advantage over imagery that relies on daylight reflection, and decreases in brightness indicate faster growth in the horizontal direction than the vertical. A province’s optimal threshold does not always maintain an increase with population and economic growth. In the economically developed eastern provinces, urban population densities decreased (and this is seen in the brightness data), while urban population increased. [ABSTRACT FROM AUTHOR]

Brightness of nighttime lights (NTL) collected by the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) is compatible across different times of images thanks to the on-board calibration system. However, the NTL radiance observed by the S-NPP VIIRS shows clear seasonality corresponding to the seasonal changes in the albedo of land surface. Additionally, the existence of many uncertain factors (e.g. complex atmospheric conditions) renders it inappropriate to directly use the NTL radiances to derive changes on the ground. In this study, we adopt a statistical procedure of time series analysis, namely seasonal and trend decomposition using Loess (STL), to model the time series observations of NTL brightness by decomposing the observations into three separable time series components (i.e. trend, seasonality, and remainder). Based on the time series model, forecast can be made for short-term future with confidence measure, and by comparing the model forecast with observed NTL brightness, significant changes can then be detected at pixel levels. We applied this method to the Puerto Rico area to detect and assess the damages caused by Hurricanes Irma and Maria, and to monitor the recovery after the disaster. Our results show that the proposed method successfully captures the changes of NTL brightness due to the damage of the hurricanes and general economic decline. Moreover, we also find that after removing the seasonal and remainder components, the time series of NTL image can more accurately reflect the temporal trends of economic status in Puerto Rico. • Time-series radiance of NTL from VIIRS-DNB image composites is decomposed by STL. • Seasonal component and noise are removed to detect actual changes in urban areas. • Significant changes in brightness of NTL are detected at pixel levels with confidence levels. • Damages caused by Hurricanes Irma and Maria in Puerto Rico are assessed by our method. [ABSTRACT FROM AUTHOR]