Satellite-Derived Bathymetry with Sediment Classification Using ICESat-2 and Multispectral Imagery: Case Studies in the South China Sea and Australia DOI Creative Commons
Shaoyu Li, Xiao Hua Wang, Yue Ma

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(4), P. 1026 - 1026

Published: Feb. 13, 2023

Achieving coastal and shallow-water bathymetry is essential for understanding the marine environment management. Bathymetric data in shallow sea areas can currently be obtained using SDB (satellite-derived bathymetry) with multispectral satellites based on depth inversion models. In situ bathymetric are crucial validating empirical models but limited remote unapproachable areas. this paper, instead of measured water data, ICESat-2 (Ice, Cloud, Land Elevation Satellite-2) ATL03 points at different acquisition dates imagery from Sentinel-2/GeoEye-1 were used to train evaluate two study regions: Shanhu Island South China Sea, Heron Great Barrier Reef (GBR) Australia. However, sediment types also influenced results. Therefore, three sediments (sand, reef, coral/algae) analyzed Island, four rubble Island. The results show that accuracy generally improved when classification information was considered both For sand showed best performance compared other sediments, mean R2 RMSE values 0.90 1.52 m, respectively, representing a 5.6% improvement latter metric. models, average 0.97 0.65 indicating an 15.5%. Finally, maps generated regions

Language: Английский

Global hotspots of salt marsh change and carbon emissions DOI Creative Commons
Anthony Campbell, Temilola Fatoyinbo,

Liza Goldberg

et al.

Nature, Journal Year: 2022, Volume and Issue: 612(7941), P. 701 - 706

Published: Nov. 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.

Language: Английский

Citations

153
Scientific foundations for an ecosystem goal, milestones and indicators for the post-2020 global biodiversity framework DOI
Emily Nicholson, Kate E. Watermeyer, Jessica A. Rowland

et al.

Nature Ecology & Evolution, Journal Year: 2021, Volume and Issue: 5(10), P. 1338 - 1349

Published: Aug. 16, 2021

Language: Английский

Citations

134
New global area estimates for coral reefs from high-resolution mapping DOI Creative Commons
Mitchell Lyons, Nicholas Murray, Emma Kennedy

et al.

Cell Reports Sustainability, Journal Year: 2024, Volume and Issue: 1(2), P. 100015 - 100015

Published: Feb. 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.

Language: Английский

Citations

23
Space‐Borne Cloud‐Native Satellite‐Derived Bathymetry (SDB) Models Using ICESat‐2 And Sentinel‐2 DOI Creative Commons
Nathan Thomas, Avi Putri Pertiwi, Dimosthenis Traganos

et al.

Geophysical Research Letters, Journal Year: 2021, Volume and Issue: 48(6)

Published: Feb. 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.

Language: Английский

Citations

97
Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine DOI Creative Commons
Jiwei Li,

David Knapp,

Mitchell Lyons

et al.

Remote Sensing, Journal Year: 2021, Volume and Issue: 13(8), P. 1469 - 1469

Published: April 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).

Language: Английский

Citations

89
Reef Cover, a coral reef classification for global habitat mapping from remote sensing DOI Creative Commons
Emma Kennedy, Chris Roelfsema, Mitchell Lyons

et al.

Scientific Data, Journal Year: 2021, Volume and Issue: 8(1)

Published: Aug. 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.

Language: Английский

Citations

87
The distribution of global tidal marshes from Earth observation data DOI Creative Commons
Thomas A. Worthington, Mark Spalding, Emily Landis

et al.

Global Ecology and Biogeography, Journal Year: 2024, Volume and Issue: 33(8)

Published: May 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.

Language: Английский

Citations

17
A remote sensing model for coral recruitment habitat DOI
Ben Radford, Marji Puotinen, Defne Sahin

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 311, P. 114231 - 114231

Published: June 12, 2024

Citations

10
Microplastics in the coral ecosystems: A threat which needs more global attention DOI
Tanmoy Biswas, Subodh Chandra Pal, Asish Saha

et al.

Ocean & Coastal Management, Journal Year: 2024, Volume and Issue: 249, P. 107012 - 107012

Published: Jan. 17, 2024

Language: Английский

Citations

9
Finding genes and pathways that underlie coral adaptation DOI Creative Commons
Oliver Selmoni, Line K. Bay, Moisés Expósito‐Alonso

et al.

Trends in Genetics, Journal Year: 2024, Volume and Issue: 40(3), P. 213 - 227

Published: Feb. 6, 2024

Language: Английский

Citations

9