Chengfa Benjamin Lee

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

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Dimosthenis Traganos

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

Alina Blume

European Space Research Institute

Avi Putri Pertiwi

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

Peter Reinartz

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

Dimitris Poursanidis

Foundation for Research and Technology Hellas

Annabelle Cupidon

Island Conservation Society

Matthew Morgan

Island Conservation Society

Isabel Caballero

Instituto de Ciencias Marinas de Andalucía

A. Faure

Island Conservation Society

Jeanne A. Mortimer

University of Florida

Cooperative Institutions

University of Würzburg

Island Conservation Society

University of Queensland

Freie Universität Berlin

Consejo Superior de Investigaciones Científicas

Institut de Ciències del Mar

Universitat de Barcelona

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

Centre National de la Recherche Scientifique

Instituto de Ciencias Marinas de Andalucía

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Predicting resilience of migratory birds to environmental change

Proceedings of the National Academy of Sciences (2024)

Simeon Lisovski Bethany J. Hoye Jesse R. Conklin Phil F. Battley Richard A. Fuller

The pace and scale of environmental change represent major challenges to many organisms. Animals that move long distances, such as migratory birds, are especially vulnerable to change since they need chains of intact habitat along their migratory routes. Estimating the resilience of such species to environmental changes assists in targeting conservation efforts. We developed a migration modeling framework to predict past (1960s), present (2010s), and future (2060s) optimal migration strategies across five shorebird species (Scolopacidae) within the East Asian-Australasian Flyway, which has seen major habitat deterioration and loss over the last century, and compared these predictions to empirical tracks from the present. Our model captured the migration strategies of the five species and identified the changes in migrations needed to respond to habitat deterioration and climate change. Notably, the larger species, with single or few major stopover sites, need to establish new migration routes and strategies, while smaller species can buffer habitat loss by redistributing their stopover areas to novel or less-used sites. Comparing model predictions with empirical tracks also indicates that larger species with the stronger need for adaptations continue to migrate closer to the optimal routes of the past, before habitat deterioration accelerated. Our study not only quantifies the vulnerability of species in the face of global change but also explicitly reveals the extent of adaptations required to sustain their migrations. This modeling framework provides a tool for conservation planning that can accommodate the future needs of migratory species.

Flyway

Environmental change

Vulnerability

Resilience

10.1073/pnas.2311146121

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Citations (10)

A Simple Cloud-Native Spectral Transformation Method to Disentangle Optically Shallow and Deep Waters in Sentinel-2 Images

Remote Sensing (2022)

Chengfa Benjamin Lee Dimosthenis Traganos Peter Reinartz

This study presents a novel method to identify optically deep water using purely spectral approaches. Optically deep waters, where the seabed is too deep for a bottom reflectance signal to be returned, is uninformative for seabed mapping. Furthermore, owing to the attenuation of light in the water column, submerged vegetation at deeper depths is easily confused with optically deep waters, thereby inducing misclassifications that reduce the accuracy of these seabed maps. While bathymetry data could mask out deeper areas, they are not always available or of sufficient spatial resolution for use. Without bathymetry data and based on the coastal aerosol blue green (1-2-3) bands of the Sentinel-2 imagery, this study investigates the use of band ratios and a false colour HSV transformation of both L1C and L2A images to separate optically deep and shallow waters across varying water quality over four tropical and temperate submerged sites: Tanzania, the Bahamas, the Caspian Sea (Kazakhstan) and the Wadden Sea (Denmark and Germany). Two supervised thresholds based on annotated reference data and an unsupervised Otsu threshold were applied. The band ratio group usually featured the best overall accuracies (OA), F1 scores and Matthews correlation coefficients, although the individual band combination might not perform consistently across different sites. Meanwhile, the saturation and hue band yielded close to best performance for the L1C and L2A images, featuring OA of up to 0.93 and 0.98, respectively, and a more consistent behaviour than the individual band ratios. Nonetheless, all these spectral methods are still susceptible to sunglint, the Sentinel-2 parallax effect, turbidity and water colour. Both supervised approaches performed similarly and were superior to the unsupervised Otsu’s method—the supervised methods featuring OA were usually over 0.70, while the unsupervised OA were usually under 0.80. In the absence of bathymetry data, this method could effectively remove optically deep water pixels in Sentinel-2 imagery and reduce the issue of dark pixel misclassification, thereby improving the benthic mapping of optically shallow waters and their seascapes.

Seabed

Spectral bands

10.3390/rs14030590

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Citations (7)

Subtidal seagrass and blue carbon mapping at the regional scale: a cloud-native multi-temporal Earth Observation approach

GIScience & Remote Sensing (2024)

Mar Roca Chengfa Benjamin Lee Avi Putri Pertiwi Alina Blume Isabel Caballero

The seagrass ecosystems are among the most important organic carbon sinks on Earth, having a key role as climate change buffers. Among all seagrasses, Posidonia oceanica, an endemic seagrass species in the Mediterranean Sea, has been observed to feature the highest carbon stock and sequestration rate among all seagrasses. We developed a satellite-based workflow to complement in situ seagrass monitoring efforts in the Balearic Islands (Western Mediterranean), reducing field expenses while covering regional spatial scales. Our synoptic tool uses Sentinel-2 A/B satellite imagery at 10 m spatial resolution to generate a multi-temporal composite (2016–2022) of the Balearic Islands' coastal waters within the Google Earth Engine cloud computing platform, optimizing image processing and highlighting the importance of a high-resolution bathymetric dataset to increase seagrass mapping accuracies. Machine learning algorithms have been applied to perform seagrass detection, obtaining a seagrass cartography up to 30 m of depth, estimating 505.6 km2 of seagrass habitat extent. Using existing in situ soil carbon stock (Cstock) data, we estimated a mean Cstock value of 12.27 ± 2.1 million megagram (Mg) Corg, while mapping a total annual C fixation (Cfix) and C sequestration (Cseq) rates of P. oceanica of 1,116.3 Mg Corg and 227 Mg Corg, according to depth. Our methodology highlights the key role of using a large image archive to generate the multi-temporal optical composite and an optimized bathymetry dataset to better map and account blue carbon in seagrass ecosystems across depth, showing the importance to integrate this Earth Observation approach to ensure a seagrass ecosystem monitoring at regional scales. This information aims to support the development of blue carbon strategies with synoptic time- and cost-efficient seagrass monitoring in the Mediterranean Sea.

Earth observation

Carbon fibers

10.1080/15481603.2024.2438838

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Citations (1)

Mapping the National Seagrass Extent in Seychelles Using PlanetScope NICFI Data

Remote Sensing (2023)

Chengfa Benjamin Lee L. D. Martin Dimosthenis Traganos Sylvanna Antat Stacy K. Baez

Seagrasses provide ecosystem services worth USD 2.28 trillion annually. However, their direct threats and our incomplete knowledge hamper our capabilities to protect and manage them. This study aims to evaluate if the NICFI Satellite Data Program basemaps could map Seychelles’ extensive seagrass meadows, directly supporting the country’s ambitions to protect this ecosystem. The Seychelles archipelago was divided into three geographical regions. Half-yearly basemaps from 2015 to 2020 were combined using an interval mean of the 10th percentile and median before land and deep water masking. Additional features were produced using the Depth Invariant Index, Normalised Differences, and segmentation. With 80% of the reference data, an initial Random Forest followed by a variable importance analysis was performed. Only the top ten contributing features were retained for a second classification, which was validated with the remaining 20%. The best overall accuracies across the three regions ranged between 69.7% and 75.7%. The biggest challenges for the NICFI basemaps are its four-band spectral resolution and uncertainties owing to sampling bias. As part of a nationwide seagrass extent and blue carbon mapping project, the estimates herein will be combined with ancillary satellite data and contribute to a full national estimate in a near-future report. However, the numbers reported showcase the broader potential for using NICFI basemaps for seagrass mapping at scale.

Archipelago

10.3390/rs15184500

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Citations (7)

Spatially Explicit Seagrass Extent Mapping Across the Entire Mediterranean

Frontiers in Marine Science (2022)

Dimosthenis Traganos Chengfa Benjamin Lee Alina Blume Dimitris Poursanidis Hrvoje Čižmek

The seagrass Posidonia oceanica is the main habitat-forming species of the coastal Mediterranean, providing millennial-scale ecosystem services including habitat provisioning, biodiversity maintenance, food security, coastal protection, and carbon sequestration. Meadows of this endemic seagrass species represent the largest carbon storage among seagrasses around the world, largely contributing to global blue carbon stocks. Yet, the slow growth of this temperate species and the extreme projected temperature and sea-level rise due to climate change increase the risk of reduction and loss of these services. Currently, there are knowledge gaps in its basin-wide spatially explicit extent and relevant accounting, therefore accurate and efficient mapping of its distribution and trajectories of change is needed. Here, we leveraged contemporary advances in Earth Observation—cloud computing, open satellite data, and machine learning—with field observations through a cloud-native geoprocessing framework to account the spatially explicit ecosystem extent of P. oceanica seagrass across its full bioregional scale. Employing 279,186 Sentinel-2 satellite images between 2015 and 2019, and a human-labeled training dataset of 62,928 pixels, we mapped 19,020 km 2 of P. oceanica meadows up to 25 m of depth in 22 Mediterranean countries, across a total seabed area of 56,783 km 2 . Using 2,480 independent, field-based points, we observe an overall accuracy of 72%. We include and discuss global and region-specific seagrass blue carbon stocks using our bioregional seagrass extent estimate. As reference data collections, remote sensing technology and biophysical modelling improve and coalesce, such spatial ecosystem extent accounts could further support physical and monetary accounting of seagrass condition and ecosystem services, like blue carbon and coastal biodiversity. We envisage that effective policy uptake of these holistic seagrass accounts in national climate strategies and financing could accelerate transparent natural climate solutions and coastal resilience, far beyond the physical location of seagrass beds.

Posidonia oceanica

Blue carbon

Marine protected area

10.3389/fmars.2022.871799

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Citations (25)

Bahamian seagrass extent and blue carbon accounting using Earth Observation

Frontiers in Marine Science (2023)

Alina Blume Avi Putri Pertiwi Chengfa Benjamin Lee Dimosthenis Traganos

Seagrasses are among the world’s most productive ecosystems due to their vast ‘blue’ carbon sequestration rates and stocks, yet have a largely untapped potential for climate change mitigation and national climate agendas like the Nationally Determined Contributions of the Paris Agreement. To account for the value of seagrasses for these agendas, spatially explicit high-confidence seagrass ecosystem assessments guided by nationally aggregated data are necessary. Modern Earth Observation advances could provide a scalable technological solution to assess the national extent and blue carbon service of seagrass ecosystems. Here, we developed and applied a scalable Earth Observation framework within the Google Earth Engine cloud computing platform to account the national extent, blue carbon stock and sequestration rate of seagrass ecosystems across the shallow waters of The Bahamas—113,037 km 2 . Our geospatial ecosystem extent accounting was based on big multi-temporal data analytics of over 18,000 10-m Sentinel-2 images acquired between 2017-2021, and deep feature engineering of multi-temporal spectral, color, object-based and textural metrics with Random Forests machine learning classification. The extent accounting was trained and validated using a nationwide reference data synthesis based on human-guided image annotation, recent space-borne benthic habitat maps, and field data collections. Bahamian seagrass carbon stocks and sequestration rates were quantified using region-specific in-situ seagrass blue carbon data. The mapped Bahamian seagrass extent covers an area up to 46,792 km 2 , translating into a carbon storage of 723 Mg C, and a sequestration rate of 123 Mt CO 2 annually. This equals up to 68 times the amount of CO 2 emitted by The Bahamas in 2018, potentially rendering the country carbon-neutral. The developed accounts fill a vast mapping blank in the global seagrass map—29% of the global seagrass extent—highlighting the necessity of including their blue carbon fluxes into national climate agendas and showcasing the need for more cost-effective conservation and restoration efforts for their meadows. We envisage that the synergy between our scalable Earth Observation technology and near-future nation-specific in-situ observations can and will support spatially-explicit seagrass and ocean ecosystem accounting, accelerating effective policy-making, blue carbon crediting, and relevant financial investments in and beyond The Bahamas.

Blue carbon

Carbon accounting

10.3389/fmars.2023.1058460

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Citations (17)

Shedding light with harmonic radar: Unveiling the hidden impacts of streetlights on moth flight behavior

Proceedings of the National Academy of Sciences (2024)

Jacqueline Degen Mona Storms Chengfa Benjamin Lee Andreas Jechow Anna Stöckl

One of the most dramatic changes occurring on our planet is the ever-increasing extensive use of artificial light at night, which drastically altered the environment to which nocturnal animals are adapted. Such light pollution has been identified as a driver in the dramatic insect decline of the past years. One nocturnal species group experiencing marked declines are moths, which play a key role in food webs and ecosystem services such as plant pollination. Moths can be easily monitored within the illuminated area of a streetlight, where they typically exhibit disoriented behavior. Yet, little is known about their behavior beyond the illuminated area. Harmonic radar tracking enabled us to close this knowledge gap. We found a significant change in flight behavior beyond the illuminated area of a streetlight. A detailed analysis of the recorded trajectories revealed a barrier effect of streetlights on lappet moths whenever the moon was not available as a natural celestial cue. Furthermore, streetlights increased the tortuosity of flights for both hawk moths and lappet moths. Surprisingly, we had to reject our fundamental hypothesis that most individuals would fly toward a streetlight. Instead, this was true for only 4% of the tested individuals, indicating that the impact of light pollution might be more severe than assumed to date. Our results provide experimental evidence for the fragmentation of landscapes by streetlights and demonstrate that light pollution affects movement patterns of moths beyond what was previously assumed, potentially affecting their reproductive success and hampering a vital ecosystem service.

Light Pollution

10.1073/pnas.2401215121

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Citations (4)

Climate Change Mitigation in The Bahamas - The Power of Earth Observation for Blue Carbon Accounting

Alina Blume Dimosthenis Traganos Avi Putri Pertiwi Chengfa Benjamin Lee Marie‐Hélène Rio

The seagrass ecosystem can sequester and store vast amounts of carbon in their soils and biomass, which renders them a strong natural climate solution for climate change mitigation. The carbon uptake capabilities of this coastal marine ecosystem have important implications for Multilateral Environmental Agreements like the National Determined Contributions of the Paris Agreement and the Sustainable Development Goals. However, the  value of seagrasses for these agendas is often overlooked due to a lack of spatially-explicit extent and carbon data. Modern Earth Observation advances can provide time- and cost-efficient solutions to minimise these data gaps.We utilised multi-temporal Sentinel-2 data within the cloud computing platform Google Earth Engine to quantify the current Bahamian seagrass extent, associated carbon stocks, and sequestration rates. Our approach combines big satellite data, pixel and object-based feature analysis, and scalable machine learning algorithms. We are envisaging to assess ecosystem extent changes using historic image archives (e.g. Landsat), and the integration of biophysical variables into our models (e.g. bathymetry, meadow patchiness).We estimate the current seagrass ecosystem extent to cover an area of up to 46,792 km2, storing 723 Mg carbon and sequestering about 68 times the amount of carbon dioxide that was emitted by The Bahamas in 2018.Our generated data highlights the importance of the seagrass ecosystem for climate change mitigation in The Bahamas and beyond, and showcases the necessity of including seagrass blue carbon in national climate agendas. This data and our developed earth observation approach can support policy makers and scientists from a national to a global climate action context.

Blue carbon

Earth observation

10.5194/egusphere-egu23-5542

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Earth observation for ecosystem accounting: spatially explicit national seagrass extent and carbon stock in Kenya, Tanzania, Mozambique and Madagascar

Remote Sensing in Ecology and Conservation (2022)

Dimosthenis Traganos Avi Putri Pertiwi Chengfa Benjamin Lee Alina Blume Dimitris Poursanidis

Abstract Seagrass ecosystems are globally significant hot spots of blue carbon storage, coastal biodiversity and coastal protection, rendering them a so‐called natural climate solution. Their potential as a natural climate solution has been largely overlooked in national and international climate strategies and financing. This stems mainly from the lack of standardized, spatially explicit mapping and region‐specific carbon inventories. Here, we introduce a novel seagrass ecosystem accounting framework that harnesses machine learning, big satellite data analytics and open region‐specific reference data within the Google Earth Engine cloud computing platform. Leveraging a biennial percentile composite, assembled from 16 453 Sentinel‐2 surface reflectance image tiles at 10‐m spatial resolution, and 20 820 reference data points, we applied the cloud‐native framework to produce the first national inventories of seagrass extent and total seagrass carbon stocks in Kenya, Tanzania, Mozambique and Madagascar. We estimated 4316 km 2 of regional seagrass extent (mean F1‐score of 59.3% and overall accuracy of 84.3%) up to 23 m of depth. Pairing country‐specific in situ carbon data and our spatially explicit seagrass extents, we calculated total regional seagrass blue carbon stocks between 11.2–40.2 million MgC, with the largest national carbon pool in Kenya (8–29.2 million MgC). We envisage that improvements in the remote sensing components of the framework guided by a necessary influx of region‐specific data on seagrass stocks and fluxes could reduce uncertainties in our current spatially explicit ecosystem extent and carbon accounts, enhancing the incorporation of seagrasses into Multilateral Environmental Agreements for future resilient ecosystems, societies and economies.

Blue carbon

10.1002/rse2.287

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Citations (15)