Global hotspots of salt marsh change and carbon emissions

Nature, Год журнала: 2022, Номер 612(7941), С. 701 - 706

Опубликована: Ноя. 30, 2022

Abstract Salt marshes provide ecosystem services such as carbon sequestration 1 , coastal protection 2 sea-level-rise (SLR) adaptation 3 and recreation 4 . SLR 5 storm events 6 drainage 7 mangrove encroachment 8 are known drivers of salt marsh loss. However, the global magnitude location changes in extent remains uncertain. Here we conduct a systematic change analysis Landsat satellite imagery from years 2000–2019 to quantify loss, gain recovery ecosystems then estimate impact these on blue stocks. We show net loss globally, equivalent an area double size Singapore (719 km ), with rate 0.28% year −1 2000 2019. Net losses resulted 16.3 (0.4–33.2, 90% confidence interval) Tg CO e emissions 2019 0.045 (−0.14–0.115) reduction burial. Russia USA accounted for 64% losses, driven by hurricanes erosion. Our findings highlight vulnerability systems climatic intensification storms cyclones.

Язык: Английский

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Scientific foundations for an ecosystem goal, milestones and indicators for the post-2020 global biodiversity framework

Nature Ecology & Evolution, Год журнала: 2021, Номер 5(10), С. 1338 - 1349

Опубликована: Авг. 16, 2021

Язык: Английский

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New global area estimates for coral reefs from high-resolution mapping

Cell Reports Sustainability, Год журнала: 2024, Номер 1(2), С. 100015 - 100015

Опубликована: Фев. 1, 2024

Coral reefs underpin the environmental, social, and economic fabrics of much world's tropical coast. Yet, fine-scale distribution composition coral have never been reported consistently across planet. Here, we present new area estimates enabled by global geomorphic zone benthic substrate maps at 5 m pixel resolution. We revise reef to 348,361 km2 shallow 80,213 (46,237–106,319 km2, 95% confidence interval) habitat. The mapping used more than 1.5 million training samples supported 480+ data contributions deploy a classification over 100 trillion pixels from Sentinel-2 satellites Planet Dove CubeSat constellation. publicly available are accessible via Allen Atlas Google Earth Engine already being thousands people improve conservation, management, research ecosystems.

Язык: Английский

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The distribution of global tidal marshes from Earth observation data

Global Ecology and Biogeography, Год журнала: 2024, Номер 33(8)

Опубликована: Май 9, 2024

Abstract Aim Tidal marsh ecosystems are heavily impacted by human activities, highlighting a pressing need to address gaps in our knowledge of their distribution. To better understand the global distribution and changes tidal extent, identify opportunities for conservation restoration, it is critical develop spatial base occurrence. Here, we globally consistent map year 2020 at 10‐m resolution. Location Global. Time period 2020. Major taxa studied marshes. Methods location world's marshes resolution, applied random forest classification model Earth observation data from We trained with reference dataset developed support mapping coastal ecosystems, predicted between 60° N S. validated using standard accuracy assessment methods, final having an overall score 0.85. Results estimate extent be 52,880 km 2 (95% CI: 32,030 59,780 ) distributed across 120 countries territories. centred temperate Arctic regions, nearly half occurring Northern Atlantic (45%) region. At national scale, over third (18,510 ; 11,200–20,900) occurs within USA. Main conclusions Our analysis provides most detailed on date shows that occur more greater proportion coastline than previous studies. fills major gap regarding baseline needed measuring estimating value terms ecosystem services.

Язык: Английский

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Space‐Borne Cloud‐Native Satellite‐Derived Bathymetry (SDB) Models Using ICESat‐2 And Sentinel‐2

Geophysical Research Letters, Год журнала: 2021, Номер 48(6)

Опубликована: Фев. 19, 2021

Abstract Shallow nearshore coastal waters provide a wealth of societal, economic, and ecosystem services, yet their topographic structure is poorly mapped due to reliance upon expensive time intensive methods. Space‐borne bathymetric mapping has helped address these issues, but remained largely dependent in situ measurements. Here we fuse ICESat‐2 lidar data with Sentinel‐2 optical imagery, within the Google Earth Engine cloud platform, create openly available spatially continuous high‐resolution maps at regional‐to‐national scales Florida, Crete Bermuda. classified photons are used train three Satellite Derived Bathymetry (SDB) methods, including Lyzenga, Stumpf, Support Vector Regression algorithms. For each study site Lyzenga algorithm yielded lowest RMSE (approx. 10%–15%) when compared validation data. We demonstrate means using for both model calibration validation, thus cementing pathway fully space‐borne estimates bathymetry shallow, clear water environments.

Язык: Английский

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Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine

Remote Sensing, Год журнала: 2021, Номер 13(8), С. 1469 - 1469

Опубликована: Апрель 10, 2021

Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed automated mapping approach based on the Sentinel-2 surface reflectance dataset in Google Earth Engine. created a new method for generating clean-water mosaic tailored automatic estimation algorithm. then evaluated performance of models at six globally diverse sites (Heron Island, Australia; West Coast Hawaiʻi Hawaiʻi; Saona Dominican Republic; Punta Cana, St. Croix, United States Virgin Islands; The Grenadines) using 113,520 field sampling points. Our derived accurate waters, Root Mean Square Error (RMSE) values ranging from 1.2 1.9 m. This automatic, efficient, robust was applied map global scale, especially areas which have biodiversity (i.e., coral reefs).

Язык: Английский

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Reef Cover, a coral reef classification for global habitat mapping from remote sensing

Scientific Data, Год журнала: 2021, Номер 8(1)

Опубликована: Авг. 2, 2021

Abstract Coral reef management and conservation stand to benefit from improved high-resolution global mapping. Yet classifications underpinning large-scale mapping date are typically poorly defined, not shared or region-specific, limiting end-users’ ability interpret outputs. Here we present Reef Cover , a coral geomorphic zone classification, developed support both producers end-users of global-scale habitat maps, in transparent version-based framework. Scalable classes were created by focusing on attributes that can be observed remotely, but whose membership rules also reflect deep knowledge form functioning. Bridging the divide between earth observation data geo-ecological reefs, maximises trade-off applicability at scales, relevance accuracy local scales. Two case studies demonstrate application classification scheme its scientific benefits: 1) detailed Cairns Management Region Great Barrier 2) Caroline Mariana Island chains Pacific for purposes.

Язык: Английский

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A remote sensing model for coral recruitment habitat

Remote Sensing of Environment, Год журнала: 2024, Номер 311, С. 114231 - 114231

Опубликована: Июнь 12, 2024

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Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine

Drones, Год журнала: 2020, Номер 4(3), С. 50 - 50

Опубликована: Авг. 28, 2020

While coral reef ecosystems hold immense biological, ecological, and economic value, frequent anthropogenic environmental disturbances have caused these to decline globally. Current monitoring methods include in situ surveys analyzing remotely sensed data from satellites. However, are often expensive inconsistent terms of time space. High-resolution satellite imagery can also be acquire subject conditions that conceal target features. gathered piloted aircraft systems (RPAS or drones) is an inexpensive alternative; however, processing drone for analysis time-consuming complex. This study presents the first semi-automatic workflow image with Google Earth Engine (GEE) free open source software (FOSS). With this workflow, we processed 230 images Heron Reef, Australia classified coral, sand, rock/dead substrates Random Forest classifier. Our classification achieved overall accuracy 86% mapped live cover 92% accuracy. The presented enable efficient any environment useful when calibrating validating imagery.

Язык: Английский

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Tiger sharks support the characterization of the world’s largest seagrass ecosystem

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Ноя. 1, 2022

Abstract Seagrass conservation is critical for mitigating climate change due to the large stocks of carbon they sequester in seafloor. However, effective and its potential provide nature-based solutions hindered by major uncertainties regarding seagrass extent distribution. Here, we describe characterization world’s largest ecosystem, located The Bahamas. We integrate existing spatial estimates with an updated empirical remote sensing product perform extensive ground-truthing seafloor 2,542 diver surveys across tiles. also leverage assessments movement data obtained from instrument-equipped tiger sharks, which have strong fidelity ecosystems, augment further validate predictions. report a consensus area at least 66,000 km 2 up 92,000 habitat Bahamas Banks. Sediment core analysis stored organic confirmed global relevance blue stock this ecosystem. Data sharks proved important supporting mapping estimates. This work provides evidence knowledge gaps ocean benefits partnering marine animals address these gaps, underscores support rapid protection oceanic sinks.

Язык: Английский

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