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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. 

Abstract People depend on biodiversity—the heart of healthy ecosystems—in many ways and every day of our lives. Yet usable knowledge of marine life is a missing link in the way we have designed marine observing and information systems. We lack critical biodiversity information to inform sustainable development from local levels to global scales—information on Essential Ocean Variables such as how many types and how much plankton, seagrasses, macro-algae, mangroves, corals and other invertebrates, fish, turtles, birds, and mammals are in any location at any one time, the value we may derive from that combination of organisms, and how this is changing with time and why. Marine Life 2030 is a program endorsed by the Ocean Decade to develop a coordinated system to deliver such actionable, transdisciplinary knowledge of ocean life to those who need it, promoting human well-being, sustainable development, and ocean conservation. Marine Life 2030 is an open network that invites partners to join us with ideas and energy to connect communities, programs, and sectors into a global, interoperable network, transforming the observation and forecasting of marine life for the future and for the benefit of all people. 

We examine the main drivers that may elevate biomass and biodiversity of non-chemosynthetic benthic megafauna of the lower bathyal (800-3500m depth) of the Mid-Atlantic Ridge in the North Atlantic Ocean (MAR). Specifically: 1. Primary production in surface waters (10°-48°N) from remote sensing data 2002-2020 over the MAR was not significantly different from abyssal regions to the east and west. We reject the hypothesis that presence of a mid ocean ridge may enhance surface primary production. 2. The quantity of particulate organic matter reaching the sea floor was estimated as a proportion of surface export production scaled by bathymetry. Flux was 1.3 to 3.0 times greater on the MAR as a function of shorter vertical transport distance from the surface than on adjacent abyssal regions. 3. Depth variation effect on species richness. Demersal fishes living between 41° and 60°N showed a maximum of species richness at 2000 m depth and linear increase in regional (Gamma) diversity of 32 species per 1,000 m elevation of the MAR above the abyss. Elevated topography provides niches for species that cannot otherwise survive. 4. Substrate heterogeneity. The MAR >95% covered with soft sediment with frequent hard rocky patches spaced at a mean nearest neighbour distance of <500 m. Over 90% were <1 km apart. Animals are readily able to disperse between such patches increasing biodiversity through the additive effect of soft and hard substrate fauna on the MAR. 5. Presence of a biogeographic overlap zone. The MAR harbours bathyal species known from Western Atlantic and Eastern Atlantic continental slopes with meridional asymmetry resulting in bias toward predominance of Eastern species. The mix of species contributes to increased diversity to the east of the MAR. Multiple factors support increase in biomass and biodiversity on the MAR. Biological data are almost entirely absent from 12° to 33°N, the part of the MAR which may be mined for polymetallic sulphide ore deposits. This study enables some predictions of biomass and biodiversity but there is urgent need for intensive biological sampling across the MAR throughout the proposed mining areas south of the Azores. 

Abstract Marine Life 2030 is a programme endorsed by the United Nations Decade of Ocean Science for Sustainable Development (the Ocean Decade) to establish a globally coordinated system that delivers knowledge of ocean life to those who need it, promoting human well-being, sustainable development, and ocean conservation. It is an open network to unite existing and new programmes into a co-designed, global framework to share information on methods, standards, observations, and applications. Goals include realizing interoperable information and transforming the observation and forecasting of marine life for the benefit of all people. Co-design, sharing local capacity, and coordination between users of ocean resources across regions is fundamental to enable sustainable use and conservation. A novel, bottom-up networking structure is now engaging members of the ocean community to address local issues, with Marine Life 2030 facilitating the linkage between groups across different regions to meet the challenges of the Ocean Decade. A variety of metrics, including those proposed by the Group on Earth Observations, will be used to track the success of the co-design process. 

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.