# Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery
## 4 Discussion  
Our estimate of 578.54 km\({}^{2}\) of mangrove ecosystem area is substantially different from the two most recent estimates: 478.31 km\({}^{2}\) as of 2016 [22] and 721.69 km\({}^{2}\) as of 2017 [20]. However, these results should not be interpreted as indicating that Belizean mangrove cover has either increased by approximately 100 km\({}^{2}\) or decreased by approximately 140 km\({}^{2}\) between 2016 and 2020. Instead, the discrepancies between our estimate and those of [20] and [22] are attributable primarily to differences in methodology and data resolution. The 10 m Sentinel-2 data allowed us to map mangrove caves that are likely too small to be identifiable in the coarser-resolution data used by [20] and [22] (see Figure 4 for a comparison). Additionally, we identified several areas of mangrove on the Belizean mainland that were not included in [22]'s estimate. The inclusion of these small caves and large mainland stands is likely why our area estimate is substantially larger than that of [22]. The differences between our mangrove inventory and these two previous inventories underscore the importance of creating higher-resolution national mangrove maps, particularly with cloud-based geospatial processing programs such as Google Earth Engine making data- and computing-intensive efforts more feasible than ever before. Not only do high-resolution data offer the potential for more accurate estimates of national mangrove area, but they also offer the potential for maps that more accurately and explicitly depict mangrove distribution at local scales. Mangrove decline, conservation, and restoration take place in specific locations and within specific contexts [38; 39]. Therefore, accurate and spatially explicit maps of local mangrove extent and distribution will be crucial components for ongoing mangrove conservation efforts. By refining previous estimates of Belizean mangrove area, our map will be of immediate use in helping Belize fulfill its NDC  
Figure 4: Comparison of mangrove cover in caves identified by (**A**) this study and (**B**) [PERSON] et al. [22], demonstrating the advantage of higher-resolution data for mapping small mangrove caves. commitments for mangrove ecosystems. The incorporation of our new area calculation will improve estimates of the amount of carbon stored within Belize's mangroves. Mangroves will be increasingly threatened by both anthropogenic and natural processes in the near future [38; 40; 41; 42]. Many of these changes take place at subtle scales [13], which 30 m resolution data may miss. Furthermore, small mangrove patches play a substantial role relative to their size in habitat connection and ecosystem service provision [43], and these small patches are the most immediately threatened. For both of these reasons, high-resolution monitoring will be essential for early detection and intervention in threatened mangrove ecosystems. Because our methodology places the burden of data storage and computation on Google Earth Engine's online servers, our classification methodology can be used by anyone with an internet connection. Expensive computers with advanced processing capabilities are not required. Therefore, our methodology is an affordable and accessible option that local stakeholders throughout the region can use for a quick and efficient update of mangrove maps for their area of interest, whether that area is local-, national-, or MAR-scale. We are developing workshops to train stakeholders in the use of our methodology, so that they can use it to \"plug and play\" high-resolution imagery to monitor future changes against our baseline. The accuracy of our map also contributes further evidence to the effectiveness of random forest classification approaches for mangrove mapping. Prior studies that have used random forest classification [22; 25; 28; 29; 30; 31] to map mangroves have usually attained overall classification accuracies between 85% and 95%, and Kappa coefficients between 80% and 90%.