# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
## 1 Introduction  
Islands consist of isolated land surrounded by water, making them particularly sensitive to environmental and anthropogenic changes. Climate change, overpopulation, and other human activities have considerable impacts on island ecosystems and the environment. Rapid island development and utilization can quickly cause irreversible damage to the environment [1]. Some rapidly developing islands are undergoing significant changes due to urbanization. Land cover change is a significantindicator to help identify land-system dynamics and environmental development [2; 3; 4]. Therefore, mapping land cover change in a developing island is critical for supporting sustainable development and land-use policy-making. Mapping of land cover change using satellite remote sensing data has been reported by many researchers [3; 5]. For instance, [PERSON] et al. (2019) [6] monitored farmland dynamics on Pingtan Island using ENVI software. [PERSON] et al. (2016) [2] assessed the spatial patterns of land cover change using land cover change indicators. [PERSON] et al. (2019) [4] used the advanced feature selection and classification techniques to assess multi-temporal land use and land change (LULC) changes. [PERSON] et al. (2019) [7] proposed a novel method to classify long-term satellite images. These studies successfully tracked land cover over time and the main challenge for wider adoption is the requirement of computers with substantial processing capability and specialized software (e.g., ENVI, ArcGIS, ERDAS, etc.) to classify remote sensing images. These specialized software packages need significant computer resources and time to process multiple years of imagery over large spatial extents, and it is challenging to process remote sensing images because of the data size of the imagery. In contrast to the traditional remote sensing processes, Google Earth Engine (GEE) has shown great potential for land cover classification [8]. It is a cloud computing platform for environmental monitoring and analysis ([[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/))) [9; 10], which can handle big data processing rapidly [8]. In addition to various automatic mapping approaches, it also hosts a vast public data catalog, including a series of remote sensing data (e.g., Landsat images, MODIS), topography, land cover, weather, and population [10], which are updated as new data products become available. GEE provides multiple examples to support imagery classification, including filter image collection, simple cloud score, Landsat simple composite, and cloud masking. These examples help users to classify LULC more efficiently and easily. This platform has been used by researchers in numerous studies for forest change detection [11; 12], habitat tracking [13], urban land mapping [14; 15], and human activity [16], indicating that GEE is very useful in observation and environmental analysis. For instance, forest cover and change analysis has been performed in GEE, which helps to monitor and improve the sustainability of the regional environment. [PERSON] et al. (2020) [17] concluded that the most-used datasets in GEE are: vegetation indices (27%), Hansen global forest (17%), land cover (17%), and canopy height (5%), which are all related to forests. Besides estimating forest biophysical parameters, it is used to explore the forest change at global, national, and regional scales. Measuring trends in forest change have been reported in previous research, such as using the Normalized Difference Vegetation Index (NDVI) change to detect the land cover change [18; 19; 20] and Enhanced Vegetation Index (EVI) change to monitor land degradation and restoration [21]. However, few studies discuss the process and trends of LULC changes on a rapidly developing and urbanizing island. In China, many cities have experienced unprecedented urbanization over the past three decades [4].