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Despite providing many valuable ecosystem services, seagrasses are a threatened habitat and their global distribution is not fully known. For example, Venezuela lacks a national seagrass map. An established regional mapping approach for seagrass exists for the Google Earth Engine (GEE) platform, but requires a long time window to obtain sufficient data to overcome cloud and other challenges. Recently, GEE has released a Cloud Score+ quality band product for the purpose of cloud masking. Cloud masking could potentially reduce the time window needed for a representative multitemporal composite, which would allow for temporal analyses. We compare the performance of Cloud Score+ derived products against previously established multitemporal image composites acquired in different time ranges, and the ACOLITE‐processed single image composite. The Sentinel‐2 (S2) Level‐1C (L1C) imagery for the whole Venezuelan coastline was processed following three different approaches: (a) using a multitemporal composition of the full S2 L1C archive available and processed in GEE using the Dark Object Subtraction; (b) integrating Cloud Score+ data set into the previous approach; and (c) using a single‐image offline approach applying ACOLITE atmospheric correction. Additional raster features were generated and a two‐step classification approach was performed with five classes, namely sand, seagrass, turbid water, deep water, and coral, and bootstrapped 20 times. Quantitatively, the performance within the Cloud Score+ derived products were largely similar. While the full archive approach had the best quantitative results, the ACOLITE approach produced the best maps qualitatively. With this, we produced the first national seagrass map for Venezuela. 

Coastal wetlands are vulnerable to accelerated sea-level rise, yet knowledge about their extent and distribution is often limited. We developed a land cover classification of wetlands in the coastal plains of the southern United States along the Gulf of Mexico (Texas, Louisiana, Mississippi, Alabama, and Florida) using 6161 very-high (2 m per pixel) resolution WorldView-2 and WorldView-3 satellite images from 2012 to 2015. Area extent estimations were obtained for the following vegetated classes: marsh, scrub, grass, forested upland, and forested wetland, located in elevation brackets between 0 and 10 m above sea level at 0.1 m intervals. Sea-level trends were estimated for each coastal state using tide gauge data collected over the period 1983–2021 and projected for 2100 using the trend estimated over that period. These trends were considered conservative, as sea level rise in the region accelerated between 2010 and 2021. Estimated losses in vegetation area due to sea level rise by 2100 are projected to be at least 12,587 km2, of which 3224 km2 would be coastal wetlands. Louisiana is expected to suffer the largest losses in vegetation (80%) and coastal wetlands (75%) by 2100. Such high-resolution coastal mapping products help to guide adaptation plans in the region, including planning for wetland conservation and coastal development. 

In September of 2017, Hurricane Irma made landfall within the Rookery Bay National Estuarine Research Reserve of southwest Florida (USA) as a category 3 storm with winds in excess of 200 km h−1. We mapped the extent of the hurricane’s impact on coastal land cover with a seasonal time series of satellite imagery. Very high-resolution (i.e., <5 m pixel) satellite imagery has proven effective to map wetland ecosystems, but challenges in data acquisition and storage, algorithm training, and image processing have prevented large-scale and time-series mapping of these data. We describe our approach to address these issues to evaluate Rookery Bay ecosystem damage and recovery using 91 WorldView-2 satellite images collected between 2010 and 2018 mapped using automated techniques and validated with a field campaign. Land cover was classified seasonally at 2 m resolution (i.e., healthy mangrove, degraded mangrove, upland, soil, and water) with an overall accuracy of 82%. Digital change detection methods show that hurricane-related degradation was 17% of mangrove forest (~5 km2). Approximately 35% (1.7 km2) of this loss recovered one year after Hurricane Irma. The approach completed the mapping approximately 200 times faster than existing methods, illustrating the ease with which regional high-resolution mapping may be accomplished efficiently.