## 3 Study Area and Methodologies
### Data Acquisition and Processing  
Downloading and pre-processing images with the help of traditional remote sensing analysis means facing the problems of huge data volume and low processing efficiency, while the cloud platform of Google Earth Engine (GEE) has already carried out the pre-processing work of atmospheric correction, geometric alignment, radiometric calibration, and other pre-processing work for commonly used images, and it can quickly realize the image acquisition, batch processing, computation, and analysis by means of on-line programming, which greatly improves the operation efficiency [76]. Therefore, land use classification based on the GEE cloud platform has obvious characteristic advantages compared with using local software (e.g., ENVI 5.3, ArcGIS 10.8). Accordingly, the Google Earth Engine (GEE) was initially employed to perform a supervised classification of Landsat satellite images from 2000 to 2020, which allowed us to obtain the 30 m resolution of land-use data for Putian City, China. The findings indicated that the Kappa test's coefficient values exceeded 0.8 for all time periods, and the overall accuracy for the remote-sensing imagery at each of the three stages surpassed 90%. Land-use categories were delineated into several types based on the unique conditions and research goals of the study area: cultivated land (CL), forest land (FL), orchard land (OL), grassland (GL), water bodies (WB), built-up areas (BL), and unused land (UL). Subsequently, a land-use spatial database for Putian City was constructed using ArcGIS 10.8 by allocating codes ranging from 1 to 7 to each specific land-use type. For instance, the code 12 represents a shift from cultivated land to forest land, while the code 26 indicates a change from forest land to built-up land. The remaining codes are assigned in accordance with the same systematic approach. After experimentation, the 1 km \(\times\) 1 km grid scale was ultimately selected for the study area, considering the scale effect on measurement results. The Create Fishnet function of ArcGIS 10.8 was utilized to divide the study area into grids (a total of 4296 grid cells). Subsequently, the Kriging tool of ArcGIS 10.8 software was employed for interpolation analysis, and target information was collected to establish a foundation for further research. The following is a comprehensive list of the software used, including version numbers, manufacturers, and access information:  
Google Earth Engine (GEE): Version 1.0.1, developed by Google LLC. GEE is a cloud-based platform for geospatial data analysis that is freely accessible to users ([[https://earthegine.google.com/](https://earthegine.google.com/)]([https://earthegine.google.com/](https://earthegine.google.com/)), accessed on 17 October 2023). ArcGIS 10.8: Version 10.8, developed by Esri. ArcGIS is a comprehensive Geographic Information System software suite used for spatial analysis and map creation. It requires a license, which can be obtained through Esri or its authorized distributors. GeoDa 1.20: Version 1.20, developed by the GeoDa Center for Geospatial Analysis and Computation at Arizona State University. GeoDa is free, open-source software for spatial data analysis ([[https://geodacenter.github.io/](https://geodacenter.github.io/)]([https://geodacenter.github.io/](https://geodacenter.github.io/)), accessed on 11 December 2023). Microsoft Excel Version 2021, developed by Microsoft Corporation. Excel was employed for basic data management and calculations. It is part of the Microsoft Office suite and requires a subscription. Origin 2022: Version 2022, developed by OriginLab Corporation. Origin is a data analysis and graphing software used for scientific research. It is proprietary software and requires a license ([[https://www.originlab.com/](https://www.originlab.com/)]([https://www.originlab.com/](https://www.originlab.com/)), accessed on 23 October 2023).