## 1 Introduction
In the study area, a pixel-based classification approach was applied for mapping and monitoring mangrove coverage [8; 31], but most of these studies are limited in the long-term monitoring of mangrove. For example, [PERSON] et al. (2019) [8] applied two SPOT datasets (in 2000 and 2011) with a spatial resolution of 10 m to analyze the changes in mangrove cover, but the spatiotemporal trends and variations were not presented in detail. For instance, the research described changes in mangrove areas for the whole study site, but it did not investigate the pattern and rate of change per pixel. In addition, the users must pay a substantial cost for acquiring SPOT data to conduct the research, while the spatial resolution is similar to Sentinel-2 imagery. Moreover, spectralindices have been applied widely for the identification and monitoring of mangrove patterns from non-magnrove vegetation classes in areas rich in mangrove diversity, and using time series to examine spatiotemporal variations is also more justifiable compared to the straight-forward statistics methods (e.g., overlaying two images) [20; 32]. The Normalized Difference Vegetation Index (NDVI) is an indicator which has been used in various applications related to ecosystem monitoring [33]. The NDVI was developed by [PERSON] et al. (1974) [34], using reflectance values of Red and Near-Infrared (NIR) bands of the multispectral electromagnetic spectrum. It quantifies the variations in chlorophyll content via absorption, and in spongy mesophyll via reflected near-infrared radiation within the vegetation canopy. Previous studies applied the NDVI widely to investigate mangrove forests at both global [35] and regional scales [30; 36]. Recently, the Google Earth Engine (GEE), an online data provider portal that allow users to access a wide range of time series satellite imagery, has enabled better support to research communities in undertaking Earth observation studies [37; 38]. This tool is currently available on request to a number of groups for testing and developing applications [39]. The GEE Application Program Interface (API) enables users to access and develop algorithms on the full Earth Engine data archive using Google's cloud computing and processing platform [37; 39]. As such, using GEE facilitates a better performance on spectral vegetation indices for the examination of the pattern of mangrove forest variations. Our study aims at answering the research question related to determining the effectiveness of using the multi-temporal NDVI derived from Sentinel-2 through the Google Earth Engine for mapping and monitoring the mangrove patterns. The specific objectives are to: (i) investigate the reflectance of the NDVI to mangrove coverage derived from Sentinel-2; (ii) characterize the spatial distribution of mangrove forests through three ecological zones of the Biosphere Reserve area; and (iii) assess the relationship between land-use change and variation in the mangrove forest area. The Can Gio district was selected as a case study to demonstrate the effectiveness of integrating time-series data and spatial analysis techniques for mangrove forest change monitoring and assessment. The results will provide an important basis for proposing appropriate strategies in mangrove ecosystem management and provide a reference for wider global application.