Spatiotemporal Pattern Analysis of Land Use Functions in Contiguous Coastal Cities Based on Long-Term Time Series Remote Sensing Data: A Case Study of Bohai Sea Region, China

Abstract

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.

Keywords

• time series
• land use
• spatial and temporal change patterns
• data mining techniques
• spatial and temporal cubes
• GEE