The long-term accumulated remote sensing data and the emerging cloud-based geospatial processing platform Google Earth Engine (GEE) enable the mining of the spatiotemporal pattern of land-use (LU) functional changes in the contiguous area of large coastal cities. This study proposes a spatiotemporal pattern mining technique for land use function in a large area, which consists of two parts: (1) long-term time series land cover mapping based on the random forest (RF) classification algorithm in the GEE platform and a pixel-by-pixel temporal consistency correction, and (2) spatiotemporal pattern mining based on the constructed spatial temporal cubes (STCs). Specifically, for each LU functional series, we constructed the STC and applied change point detection, time series clustering, and emerging hot spot analysis to mine the spatiotemporal change patterns of LU functions. The study shows that (1) the construction land in the Bohai Sea region from 1990 to 2020 expanded significantly, with the development intensity increasing from 2.08% to 9.77%, having formed a contiguous area of large cities; at the same time, the arable land area decreased significantly, from 57.94% to 47.83%; (2) the emerged construction land experienced three periods: fluctuation, rise, and decline, with 2004 and 2014 being the change points during the period; and (3), the spatial and temporal pattern of the expansion of construction land shows a spatial gradient change in the scale and rate of expansion along the central cities and major axes. This study demonstrates the potential of using long-term time series remote sensing data towards cognizing the generation mechanisms of contiguous coastal big cities.

Human activities along with climate change have unsustainably changed the land use in coastal zones. This has increased demands and challenges in mapping and change detection of coastal zone land use over long-term periods. Taking the Bohai rim coastal area of China as an example, in this study we proposed a method for the long time-series mapping and change detection of coastal zone land use based on Google Earth Engine (GEE) and multi-source data fusion. To fully consider the characteristics of the coastal zone, we established a land-use function classification system, consisting of cropland, coastal aquaculture ponds (saltern), urban land, rural settlement, other construction lands, forest, grassland, seawater, inland fresh-waters, tidal flats, and unused land. We then applied the random forest algorithm, the optimal classification method using spatial morphology and temporal change logic to map the long-term annual time series and detect changes in the Bohai rim coastal area from 1987 to 2020. Validation shows an overall acceptable average accuracy of 82.30% (76.70–85.60%). Results show that cropland in this region decreased sharply from 1987 (53.97%) to 2020 (37.41%). The lost cropland was mainly transformed into rural settlements, cities, and construction land (port infrastructure). We observed a continuous increase in the reclamation with a stable increase at the beginning followed by a rapid increase from 2003 and a stable intermediate level increase from 2013. We also observed a significant increase in coastal aquaculture ponds (saltern) starting from 1995. Through this case study, we demonstrated the strength of the proposed methods for long time-series mapping and change detection for coastal zones, and these methods support the sustainable monitoring and management of the coastal zone.