Although the topography of tidal flats is important for understanding their evolution, the spatial and temporal sampling frequency of such data remains limited. The waterline method has the potential to retrieve past tidal flat topography by utilizing large archives of satellite images. This study performs a quantitative analysis of the relationship between the accuracy of tidal flat digital elevation models (DEMs) that are based on the waterline method and the factors that influence the DEMs. The three major conclusions of the study are as follows: (1) the coverage rate of the waterline points and the number of satellite images used to create the DEM are highly linearly correlated with the error of the resultant DEMs, and the former is more significant in indicating the accuracy of the resultant DEMs than the latter; (2) both the area and the slope of the tidal flats are linearly correlated with the error of the resultant DEMs; and (3) the availability analysis of the archived satellite images indicates that the waterline method can retrieve tidal flat terrains from the past forty years. The upper limit of the temporal resolution of the tidal flat DEM can be refined to within one year since 1993, to half a year since 2004 and to three months since 2009.

Urban vitality is the comprehensive form of regional development quality, sustainability, and attractiveness. Urban vitality of various regions within the cities has difference, and the quantitative evaluation of urban vitality within the cities can help guide to future city constructions. Evaluation of urban vitality needs the combination of multi-source data. Existing studies have developed index method and estimation models mainly based on geographic big data to evaluate urban vitality. This study aims to combine remote sensing data with geographic big data to evaluate urban vitality of Shenzhen at street block scale and build the estimation model by random forest method. Indexes and random forest model were built, and some further analyses were conducted. The results were: (1) urban vitality in Shenzhen was high in the coastal areas, business areas, and new towns; (2) compared to indexes, the estimation model had advantages of more accurate results, combination of various data, and the ability to analyze feature contributions; and (3) taxi trajectory, nighttime light, and housing rental data had the strongest influence on urban vitality.

It is commonly believed that the impacts of human activities have decreased the natural vegetation cover, while some promotion of the vegetation growth has also been found. In this study, negative or positive correlations between human impacts and vegetation cover were tested in the Southeast Asia (SEA) region during 2012–2018. The Visible Infrared Imaging Radiometer Suite—Day/Night Band (VIIRS/DNB) nocturnal data were used as a measure of human activities and the moderate resolution imaging spectroradiometer (MODIS)/normalized difference vegetation index (NDVI) diurnal data were used as a measure of vegetation cover. The temporal segmentation method was introduced to calculate features of two sets of time series with spatial resolution of about 500 m, including the overall trend, maximum trend, start date, and change duration. The regions with large variation in human activities (V-change region) were first extracted by the Gaussian fitted method, and 8.64% of the entire SEA (VIIRS overall trend <−0.2 or >0.4) was set as the target analysis area. According to statistics, the average overall VIIRS trend for the V-change region in SEA was about 2.12, with a slight NDVI increment. The time lag effect was also found between vegetation cover and human impacts change, with an average of 10.26 months. Our results indicated a slight green overall trend in the SEA region over the most recent 7 years. The spatial pattern of our trend analysis results can be useful for vegetation management and regional planning.