Advances in Earth observation and machine learning for quantifying blue carbon

Earth-Science Reviews, Journal Year: 2023, Volume and Issue: 243, P. 104501 - 104501

Published: July 13, 2023

Blue carbon ecosystems (mangroves, seagrasses and saltmarshes) are highly productive coastal habitats, considered some of the most carbon-dense on earth. They an important nature-based solution for both climate change mitigation adaptation. Quantifying blue stocks assessing their dynamics at large scales through remote sensing remains challenging due to difficulties cloud coverage, spectral, spatial temporal limitations multispectral sensors speckle noise synthetic aperture radar (SAR). Recent advances in airborne space-borne SAR imagery Light Detection Ranging (LiDAR) data, sensor platforms such as unmanned aerial vehicles (UAVs), combined with novel machine learning techniques have offered different users a wide-range spatial, multi-temporal information quantifying from space. However, number challenges posed by various traits atmospheric correction, water penetration, column transparency issues environments, multi-dimensionality size LiDAR limitation training samples, backscattering mechanisms acquisition process. As result, existing methodologies face major accurately estimating using these datasets. In this context, emerging innovative artificial intelligence often required robustness reliability estimates, particularly those open-source software signal processing regression tasks. This review provides overview Earth Observation state-of-the-art deep that currently being used quantify above-ground carbon, below-ground soil mangroves, saltmarshes ecosystems. Some key future directions potential use data fusion advanced learning, metaheuristic optimisation also highlighted. summary, quantification approaches holds great contributing global efforts towards mitigating protecting

Language: Английский

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Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(3), P. 446 - 446

Published: Jan. 23, 2024

Since 1971, remote sensing techniques have been used to map and monitor phenomena parameters of the coastal zone. However, updated reviews only considered one phenomenon, parameter, data source, platform, or geographic region. No review has offered an overview that can be accurately mapped monitored with data. This systematic was performed achieve this purpose. A total 15,141 papers published from January 2021 June 2023 were identified. The 1475 most cited screened, 502 eligible included. Web Science Scopus databases searched using all possible combinations between two groups keywords: geographical names in areas platforms. demonstrated that, date, many (103) (39) (e.g., coastline land use cover changes, climate change, urban sprawl). Moreover, authors validated 91% retrieved parameters, 39 1158 times (88% combined together other parameters), 75% over time, 69% several compared results each available products. They obtained 48% different methods, their 17% GIS model techniques. In conclusion, addressed requirements needed more effectively analyze employing integrated approaches: they data, merged

Language: Английский

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Seagrasses on the move: Tracing the multi-decadal species distribution trends in lagoon meadows using Landsat imagery

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102685 - 102685

Published: June 12, 2024

Seagrass meadows play a vital role for lagoon ecosystems and their biota, sustaining multiple ecosystem services. Their distribution functioning are closely tied to the environmental pressures induced by global changes. Long-term monitoring of seagrass species communities is, hence, important depict response past future scenarios. The availability long term open-access satellite data offers new remote sensing perspective dynamics in shallow waters, especially when combined with machine learning algorithms. In this study, seasonal multispectral images (from 1999 2019) were collected from Landsat 5 Thematic Mapper 8 Operational Land Imager satellites map meadows, at community levels, within vast Grado Marano (Northeast Italy) using Random Forest (RF) algorithm. RF models calculated an extensive field training dataset 2010 (n = 426) reached overall accuracy 0.92 0.76 classification respectively. change detection analysis revealed increase 14.16 km2 (+ 39%) whole cover over period, rate 1.59 km2year−1. Despite coarse spatial resolution (30 m) Landsat's images, seagrasses level achieved good (0.76), evidencing Nanozostera noltei as highest (+13.87 time period). observed expansion is likely caused sea water influence that radically modifying Adriatic brackish bodies, emphasizing connection between ongoing changes rapid responses meadows.

Language: Английский

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Providing a framework for seagrass mapping in United States coastal ecosystems using high spatial resolution satellite imagery

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 337, P. 117669 - 117669

Published: March 24, 2023

Seagrasses have been widely recognized for their ecosystem services, but traditional seagrass monitoring approaches emphasizing ground and aerial observations are costly, time-consuming, lack standardization across datasets. This study leveraged satellite imagery from Maxar's WorldView-2 WorldView-3 high spatial resolution, commercial platforms to provide a consistent classification approach at eleven areas the continental United States, representing geographically, ecologically, climatically diverse regions. A single image was selected each of correspond temporally reference data coverage classified into four general classes: land, seagrass, no data. Satellite-derived then compared using either balanced agreement, Mann-Whitney U test, or Kruskal-Wallis depending on format used comparison. Balanced agreement ranged 58% 86%, with better between reference- satellite-indicated absence (specificity 88% 100%) than presence (sensitivity 17% 73%). Results tests demonstrated that percentage cover had moderate large correlations reference-indicated cover, indicative strong Satellite performed best in dense, continuous sparse, discontinuous provided suitable representation distribution within area. demonstrates same methods can be applied scenes spanning varying bioregions, atmospheric conditions, optical water types, which is significant step toward developing consistent, operational mapping national global scales. Accompanying this manuscript instructional videos describing processing workflow, including acquisition, processing, classification. These may serve as management tool complement field- aerial-based efforts ecosystems.

Language: Английский

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Seagrass meadow stability and composition influence carbon storage

Landscape Ecology, Journal Year: 2023, Volume and Issue: 38(12), P. 4419 - 4437

Published: June 16, 2023

Abstract Context Seagrass ecosystems are lauded for storing organic carbon in underlying sediments, but storage is highly variable, even at relatively small spatial scales. While environmental setting and seagrass cover known drivers of capacity, it unclear how other features such as species composition influence storage, whether historical vs. contemporary better predictors storage. Objectives We examined the variables on surface (0–10 cm) sediment meadow-scale (~ 25 km 2 ), addition to drivers. Our study area was located within a subtropical mixed-species meadow along low-energy coastline northeastern Gulf Mexico (Cedar Key, Florida, USA). Methods derived metrics from 14-year monitoring datasets measured densities grain size, biomass composition, well characteristics related hydrology physical disturbance (i.e., relative exposure, elevation, distance navigation channels). assessed bivariate relationships between predictor with linear regression analyses used path analysis assess hypothesized subset densities. Results low global values, Cedar Key meadows varied by an order magnitude. Sediment size strongly densities, had only indirect effects Historical cover, variability diversity were generally than variables. identity–specifically presence Thalassia testudinum –were also significant Conclusions In historically diverse persistent dominated late-successional contained largest stores. results highlight importance site history terms stability (inversely cover) identity enhancing The we comparatively weak however, exposure elevation may not be most relevant hydrological scale. Together, these findings suggest that context scale dependent.

Language: Английский

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A phenomenology-based algorithm for extracting submerged aquatic vegetation in lakes using Sentinel-2 time seriesImagery: a case study of Potamogeton crispus

GIScience & Remote Sensing, Journal Year: 2025, Volume and Issue: 62(1)

Published: March 21, 2025

Language: Английский

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Capturing the Dynamics of Aboveground Carbon Stock in Intertidal Seagrass Meadows using Sentinel-2 Time-Series Imagery

Remote Sensing Applications Society and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 101552 - 101552

Published: April 1, 2025

Language: Английский

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Developing seagrass index for long term monitoring of Zostera japonica seagrass bed: A case study in Yellow River Delta, China

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2022, Volume and Issue: 194, P. 286 - 301

Published: Nov. 9, 2022

Language: Английский

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Multitemporal seagrass carbon assimilation and aboveground carbon stock mapping using Sentinel-2 in Labuan Bajo 2019–2020

Remote Sensing Applications Society and Environment, Journal Year: 2022, Volume and Issue: 27, P. 100803 - 100803

Published: June 21, 2022

Language: Английский

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Mapping the structure of mixed seagrass meadows in the Mexican Caribbean

Frontiers in Marine Science, Journal Year: 2022, Volume and Issue: 9

Published: Dec. 9, 2022

The physical and ecological importance of seagrass meadows in coastal processes is widely recognized, the development tools facilitating characterization their structure distribution important for improving our understanding these processes. Mixed (multi-specific) a Mexican Caribbean reef lagoon were mapped employing multiparameter approach, using PlanetScope remote sensing images, supervised classification based on parameters related to seagrasses meadows, including cover percentages seagrass/algae/sediment, algae thalli shoot densities, canopy heights estimated leaf area index (LAI). cover, obtained ground truth sampling, while LAI was data from long-term monitoring programs. maps do not show differentiation species, but truthing contemplated density Thalassia testudinum, Syringodium filiforme Halodule wrightii respective LAIs. S. dominant species terms density, T. testudinum LAI. In multiparameter-based map four classes defined, structural characteristics, its overall accuracy very high (~90%). Maps sediment alone also had 4 classes, they less accurate than (~70% ~80%, respectively). provided spatially-explicit abundance seagrasses, useful future changes studies that require large-scale meadow structure, such as inventories associated biota, blue carbon storage, or modelling local hydrodynamics.

Language: Английский

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