Exploring the spatial relationship between ecosystem services (ES) and human disturbance intensity (HDI) is vital for maintaining regional ecological security. This study aims to explore the spatial correlation between ES and HDI in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) and provide meaningful implications for coastal ecological planning. Multi-source remote sensing data, remote sensing software, and geographic information system provided initial data and technical support for this research. We integrated four human pressures (population, land-use, traffic, and energy) to map the HDI in the GBA for 2018. Coastal ES were comprehensively considered and spatially visualized by extracting the ES sources. The geographically weighted Pearson correlation coefficient and bivariate local Moran were used to quantitatively reflect and spatially visualize the detailed relationship between ES and HDI. Our study presents several key findings. First, the central and southern parts of the GBA are under strong HDI, dominated by a dense population and intense land utilization. Second, the kernel density of ES sources can better manifest the spatial distribution of ES objectively in comparison to the traditional model calculation. Provisioning services mainly originate from the periphery of the central cities; cultural services are highly concentrated in the heartland of the GBA; and regulating and maintenance services have high density in the outermost regions. Third, ES and HDI have a significant correlation, and the geographically weighted Pearson correlation coefficient and local indicator of spatial association cluster maps illustrate that unlike the global findings, the local correlation is spatially nonstationary as the local scale is affected by specific human activities, natural conditions, regional development, and other local factors. Four, high-capacity regions of ES provision are mainly under high HDI. Areas with high provisioning service values are mainly affected by population and traffic pressure, whereas regulating and maintenance services and cultural services are mainly dominated by high-density populations. Regulating and maintenance services are also affected by land-use pressure. We determine that human disturbance has negative spillover effects on ES, which should be the focus in regional ecological planning.

Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) can detect an abrupt change that was undetected by Residual Trend analysis (RESTREND), but it is usually combined with the Global Inventory for Mapping and Modeling Studies (GIMMS) Normalized Difference Vegetation Index (NDVI), which cannot detect detailed vegetation changes in small areas. Hence, we used Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD-TR) to analyze the vegetation dynamic of the Pearl River Delta region (PRD) in this study. To choose the most suitable MODIS NDVI from MOD13Q1 (250 m), MOD13A1 (500 m), and MOD13A2 (1 km), whole and local comparison of results of the break year and MOD-TR were used. Meanwhile, a comparison of vegetation change at the city-scale was also implemented. Moreover, to reduce insignificant trend pixels in TSS-RESTREND, a combination method of TSS-RESTREND and RESTREND (CTSS-RESTREND) was proposed. We found that: (1) MOD13Q1 and MOD13A1 two NDVI were suitable for combination with TSS-RESTREND to detect vegetation change in PRD, but MOD13Q1 was a better choice when considering the accuracy of local detailed vegetation change; (2) CTSS-RESTREND could detect more pixels with a significant change (i.e., significant increase and significant decrease) than those of TSS-RESTREND and RESTREND. Also, its effectiveness could be verified by Landsat data; (3) at the city-scale, the CTSS-RESTREND detected that only vegetation decreases in Shenzhen, Foshan, Dongguan, and Zhongshan were higher than vegetation increases, but, significant vegetation changes (i.e., decreases and increases) were mainly concentrated in Huizhou, Jiangmen, Zhaoqing, and Guangzhou.