**1. Introduction**

Mangroves are a species of woody plants which comprise unique, halophytic communities in the tropical and sub-tropical inter-tidal coastlines of the world [1]. When meeting accepted definitions based on attributes including height, diameter and canopy closure, mangroves can qualify as forest [2]. Areas not qualifying as forest are peripheral parts of wider mangrove ecosystems, including expanses dominated by submerged, dwarf or scrub, and fringe plants [1,3–5]. Mangrove ecosystems —both forest and non-forest—are found in 102 countries and 21 territories [5]. The value of mangrove ecosystems is multifaceted, including the provisioning of critical goods (e.g., fuel wood, fish, shellfish, medicine, fiber, and timber) and services (e.g., shoreline stabilization, storm protection, and cultural, recreational and tourism opportunities) to millions of people residing in coastal communities [6–9]. In addition, mangrove ecosystems are incredibly biodiverse, providing habitat for numerous species, many of which are rare, at-risk, or endangered [10–12]. Mangrove forests are also incredibly carbon-dense and meet or exceed many of their terrestrial peers in sequestration and storage [13–15]. Increasingly, the conservation, restoration and managed-use of mangrove ecosystems is being pursued through payments for ecosystem services (PES) programs, including forest carbon initiatives (e.g., REDD+, Plan Vivo) [16,17].

Despite their multifaceted value, global mangrove loss is widespread. In the last two decades of the 20th century the world lost an estimated 35% of mangrove forest cover [18]. While globally the rate of loss has thus far slowed in the 21st century—an estimated 4% from 1996 to 2016—many parts of the world, notably SE Asia, remain loss hotspots [19–21]. The primary driver of mangrove loss is anthropogenic activities including aquaculture, agriculture, urban development, and unmanaged harvest [22]. Accurate, reliable, contemporary, and easily updated information representing the extent of mangrove ecosystems is required by decision makers and managers and to help countries pursue and meet environmental targets (e.g., Millennium Development Goals and Ramsar Convention on Wetlands of International Importance especially as Waterfowl Habitat) [23–25]. Remotely sensed data have a well-established utility for mapping and monitoring the multi-date distribution of mangrove ecosystems and quantifying change over time; however, the remote sensing of mangrove environments has its own unique set of challenges which must be overcome to produce accurate results, including the variable presence or absence of water associated with daily tidal fluctuations [23,26]. Fluctuating tides can drastically influence the spectral properties of mangrove ecosystems making information on tidal condition at time of image acquisition vital [27]. Many mangrove studies have ignored variable tidal conditions, combining images ranging from low to high tide [23]. Recently, studies have used image composites that include imagery acquired during selective tides (i.e., high and/or low); however, these have covered limited areas (e.g., a single bay within a single Landsat scene) where reliable local tidal stations or modeled tidal products are available, and have not evaluated dynamics [27–29]. Other studies demonstrated the potential to use remote sensing or models to calibrate tides across larger areas; however, these approaches depend on substantial expertise to run specialized or customized software and the models depend on high quality training data—which is not always available—making them too complex and inaccessible for most potential users [30–33].

Beyond tidal considerations, conventional mapping techniques—while successful and informative—remain limited by imagery availability, required computing resources, and necessary technical expertise [34]. A single uncompressed Landsat 8 scene is larger than 1.6 gigabytes, and applications using multiple scenes require computing resources that present a barrier to many practitioners [35]. Emerging tools and technologies are ushering in a new era for land-cover mapping and monitoring [26,36]. Cloud-based platforms, most notably Google Earth Engine (GEE), provide unprecedented volumes of ready-to-use geospatial data, including the entire Landsat archive (i.e., radiometrically and geometrically corrected), and tool and computing resources for rapid and seamless processing [34]. GEE stores data and completes processing on numerous remote servers (i.e., parallel processing), removing the need to download and process data on local stand-alone computers. This eliminates many barriers related to the hardware and technical expertise required for remote sensing. All that is required to use GEE is a computer capable of running a modern web browser and an internet connection—for development, research, or educational purposes, access is freely granted through Google, LLC (Limited Liability Corporation), by signing up through the GEE Homepage. These advancements allow for developing and carrying out mapping methodologies over unprecedented spatial extents with drastically increased speed (e.g., University of Maryland Global Forest Dynamics), making advanced remote sensing applications accessible to considerably broader audiences [34,37]. In addition, tools built for GEE and distributed over the Internet can facilitate methodological repeatability while providing opportunities for adaptability and customization [38].

To date, several studies have explored and demonstrated the utility of GEE for mapping mangroves yielding encouraging results and improvements over conventional methods [39–43]. While there is clear utility for mapping and monitoring mangrove ecosystems using GEE, published methodologies remain inaccessible to many would-be users. To replicate published methods requires an advanced level of specialized expertise with remote sensing, geospatial processing techniques, and/or coding. To date, no intuitive and accessible version of a mangrove mapping methodology within GEE has been proposed which caters to a wider audience of non-specialist conservation managers and decision makers. In addition, existing tools fail to fully capitalize on the wealth of local knowledge and understanding often held by coastal managers. Lastly, no single methodology comprehensively incorporates all of the best available options for mapping and monitoring mangrove ecosystems from across existing published studies and includes a widely applicable approach toward tidal calibration.

Herein we present a comprehensive, intuitive, accessible, and replicable methodology encapsulated in a new tool—the Google Earth Engine Mangrove Mapping Methodology (i.e., the GEEMMM). The GEEMMM was designed to provide a ready-to-go methodology for non-expert practitioners to map and monitor mangrove ecosystems, enabling them to combine their local knowledge with GEE's cloud computing capabilities. We developed the GEEMMM following a thorough review of mangrove remote sensing literature and incorporating the best available practices. In addition, our approach to tidal calibration operates completely within GEE based entirely on shoreline reflectance (i.e., image-based). To demonstrate the tool, we present an example of multi-date, desk-based (i.e., involving no field work) mapping and change assessment for Myanmar (Burma)—a global loss hotspot [19]. The GEEMMM—freely accessible to non-profit users—runs on detailed and well commented code within the GEE environment and is adaptable to any mangrove area of interest. GEEMMM outputs include multi-date classified maps, accuracies, and dynamic assessments. To set the stage for trailing the GEEMMM for Myanmar and contextualizing the outputs, and similar to methods detailed in Gandhi and Jones [19], all existing single- and multi-date mangrove maps for Myanmar were inventoried, described, and compared, with an emphasis on existing information on distribution and dynamics. We introduce the pilot area of interest (i.e., AOI), describe existing datasets, overview the GEEMMM tool, and compare the results to existing datasets.

#### **2. Materials and Methods**

#### *2.1. Google Earth Engine Mangrove Mapping Methodology (GEEMMM) Pilot AOI*
