ArticlePDF Available
Remote sensing and the UN Ocean Decade: high
expectations, big opportunities
Vincent Lecours
, Mathias Disney
, Kate He
, Nathalie Pettorelli
, J. Marcus Rowcliffe
Temuulen Sankey
& Kylie Scales
School of Forest, Fisheries, & Geomatics Sciences, University of Florida, 7922 NW 71st St., Gainesville Florida, 32653, USA
Department of Geography, University College London, Gower Street, London WC1E 6BT, UK
Department of Biological Sciences, Murray State University, Murray Kentucky, 42071, USA
Zoological Society of London, Institute of Zoology, Regent’s Park, London NW1 4RY, UK
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff Arizona, 86011, USA
Global-Change Ecology Research Group, University of the Sunshine Coast, Maroochydore Queensland 4556, Australia
doi: 10.1002/rse2.241
This year officially marks the beginning of the United
Nations Decade of Ocean Science for Sustainable Devel-
opment (20212030)the Ocean Decade. A primary
objective of this coordination framework is to support
scientific research and technological developments that
can contribute to the conservation and sustainable man-
agement of the world’s oceans. One of the seven Decade
Outcomes is to secure healthy and resilient oceans where
marine biodiversity is mapped and protected; however,
fulfilling this goal will require data, knowledge, and tech-
nology. The use of remote sensing is now established in
marine research and management and is crucial in devel-
oping our understanding of ocean patterns and processes
at multiple spatial and temporal scales (e.g., Jawak et al.,
2015). As such, remote sensing technology is expected to
play a critical role in achieving the vision set by the
Ocean Decade.
In the last 20 years, technological developments in
remote sensing have boosted our ability to monitor the
distribution and status of previously understudied ecosys-
tems, from tidal flats and mangroves (Goldberg et al.,
2020; Murray et al., 2019) to continental shelves (Pygas
et al., 2020) and the deep sea (Lim et al., 2021). These
developments have also enabled the mapping of marine
physical and biogenic habitats and ecosystems at spatial
resolutions never achieved before. For example, Lyons
et al. (2020) recently demonstrated how coral reef habitats
ranging from individual reefs (~200 km
) to entire barrier
reef systems (200 000 km
) could be mapped across vast
ocean extents (>6 000 000 km
) using global multiscale
earth observations, generating high-resolution maps that
can be used to support ecosystem risk assessments and to
inform management. Deeper seafloor habitats can now be
mapped and imaged at a centimeter scale using autono-
mous underwater vehicles and sensors like synthetic aper-
ture sonars (e.g., Thorsnes et al., 2019). Maps produced
by such efforts are invaluable communication tools; they
have become key for data integration and synthesis to
inform decision-making in a variety of contexts (Guisan
et al., 2013; Harris & Baker, 2020). These mapping exer-
cises can also be used to predict the distribution of spe-
cies, communities, or ecosystems based on their
associations with the physical and chemical characteristics
of the environment and can support seascape ecology
studies that relate spatial patterns with ecological pro-
cesses (Pittman, 2018).
Passive sensors mounted on unoccupied aerial vehicles
(UAVs) and satellites are commonly used to map and
monitor characteristics and components of the marine
environment, such as sea surface temperature, salinity,
marine mammal distribution, primary productivity, and
harmful algal blooms (Pettorelli, 2019). Satellite radar
altimeters have also long been used to study the oceans
and derive coarse-scale digital bathymetric models (e.g.,
Dixon et al., 1983). The information compiled by differ-
ent sensors can then be integrated to delineate broad
marine biogeographic units such as ecoregions (e.g., Sayre
et al., 2017; Spalding et al., 2007). At finer scales, UAV-
mounted lidar sensors have enabled increased above-
ground biomass monitoring in coastal systems such as
mangroves (e.g., Qiu et al., 2019), while bathymetric lidar
systems have boosted data collection efforts in submerged
coastal areas, where it is often too dangerous and resource
intensive to collect acoustic data and challenging for radar
altimeters to differentiate land from water (Sandwell
et al., 2002).
While active underwater cameras mounted on remotely
operated vehicles or towed or dropped platforms have
been extensively used to collect species and seafloor data
and create photomosaics of the seafloor (e.g., Jones, 2009;
Sward et al., 2021), optical remote sensing is usually lim-
ited to shallow and optically clear waters. This means
ª2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and
distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
that, in most situations, acoustic remote sensing repre-
sents the most effective source of data for ecologists inter-
ested in marine biodiversity. Acoustic remote sensing can
be passive (i.e., using hydrophones to capture sounds in
the environment) or active (i.e., using sonars that pro-
duce directional sound and listen for returns); both have
their place in support of marine ecology and conserva-
tion. For example, multibeam echosounders enable the
production of high-resolution digital bathymetric models,
from which different terrain attributes (e.g., slope, rugos-
ity) known to be direct or indirect surrogates of species
distributions can be derived (Lecours et al., 2015,2016;
McArthur et al., 2010). Multibeam backscatter data and
sidescan sonar imagery can also provide information
about the distribution of sediment and seafloor habitat
characteristics important to many species. Most often
used in fisheries, singlebeam echosounders can provide
critical information about what lives in the water column,
while passive acoustic remote sensing can contribute spe-
cies occurrence and distribution data and inform abun-
dance and behavioral research (Stowell & Sueur, 2020).
There is no doubt that the UN Ocean Decade will pro-
vide exciting opportunities for the field of remote sensing
and its applications to marine and coastal environments.
Active acoustic remote sensing technologies have histori-
cally been associated with military uses and the field of
hydrography rather than with the remote sensing commu-
nity of practice; this has slowed the integration of data
processing and analysis methods that have proven effec-
tive in the study of terrestrial environments. This gap
offers new research opportunities that remain unexplored
in marine environments. For example, because raw multi-
beam echosounder data are displayed as point clouds that
share many characteristics with lidar point clouds,
Table 1. A meta-analysis of original research articles published in Remote Sensing in Ecology and Conservation highlights an increase in coastal
and marine studies and a strong reliance on optical remote sensing and, to a lesser extent, passive acoustics.
References Topics Remote sensing approaches
Weishampel et al. (2016) Mapping of sea turtle nesting patterns in
Satellite-based visible and infrared sensors
Asner et al. (2017) Coral reef mapping Satellite multispectral imagery
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maps and species distribution models
Multibeam echosounder bathymetric and backscatter
Di Iorio et al. (2018)Posidonia oceanica meadows monitoring Hydrophones (passive acoustic monitoring)
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Lyons et al. (2020) Coral reef mapping Satellite multispectral imagery, airborne hyperspectral sensor,
satellite-derived bathymetry, bathymetric data compilations
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Theoretical passive acoustic detectors and cameras
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Articles are listed chronologically.
268 ª2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
Editorial V. Lecours
acoustic data processing workflows might benefit from
algorithms developed for processing lidar data. The oppo-
site is also true; the commonly used CUBE (Combined
Uncertainty and Bathymetry Estimator) algorithm for the
generation of digital bathymetric models and the com-
bined storage of bathymetry and uncertainty layers within
a single BAG (Bathymetric Attributed Grid) file format
may benefit other types of remotely sensed data like lidar-
derived digital surface and terrain models. Data fusion
techniques offer opportunities for the production of
seamless digital surface models spanning the terrestrial
and marine environments that combine both optical and
acoustic remotely sensed data (e.g., Linklater et al., 2018).
New developments in image processing tools, analytical
methods like object-based image analysis, and artificial
intelligence have the potential to enhance marine ecology
and seascape ecology research (Pittman et al., 2021). New
ways to study the marine environment, such as multi-
beam water column data (e.g., Schimel et al., 2020), mul-
tispectral acoustic systems (e.g., Brown et al., 2019), and
satellite-derived bathymetry (Ashphaq et al., 2021), high-
light the need for more research into how remote sensing
can contribute to the understanding and conservation of
the world’s oceans.
The issues targeted by the Ocean Decade, such as cli-
mate change and unsustainable exploitation of marine
resources, are global and, as such, will require collabora-
tive efforts and data from around the world. However,
both ocean science and remote sensing capacities are
unevenly distributed. Mapping marine ecosystems and
biodiversity in places or through organizations that can-
not count on well-funded initiatives must rely on existing,
publicly available datasets such as the GEBCO (General
Bathymetric Chart of the Oceans) global bathymetric
dataset, archived satellite imagery, or marine biodiversity
datasets like those compiled on OBIS (Ocean Biodiversity
Information System). This highlights the need for open-
source multidisciplinary data in both remote sensing and
the marine sciences that can be spatially integrated accu-
rately; it also highlights the need for a common platform
where information gathered by these communities can be
shared and scientific agendas synchronized. Since its
inception, the editorial board of Remote Sensing in Ecology
and Conservation has welcomed contributions to coastal
and marine ecology and conservation that rely on remote
sensing (Pettorelli et al., 2015). In 2017, the editorial
board made it a goal to increase their engagement with
communities working in marine systems and acoustic
remote sensing (Pettorelli et al., 2017). The number of
published “original research” articles on coastal or marine
environments has steadily increased every year since 2016,
reaching 21% of all contributions in 2020 (Table 1).
However, the use of active acoustic remote sensing is still
underrepresented, with only one article published since
the launch of our journal. With efforts like the Seabed
2030 Project, which aims to map the world’s seafloor by
2030 and relies heavily on acoustic remote sensing tech-
nologies (Mayer et al., 2018), we expect the availability of
seafloor data to increase and, with them, the opportuni-
ties to better understand the ecology of our seas and
oceans. We thus want to reiterate our commitment to
marine remote sensing developments and applications
and hope that the increased opportunities will be reflected
in the submissions to come.
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V. Lecours Editorial
... These sensors are uniquely suited to detect standing water, monitor soil moisture, estimate above-ground biomass, and produce topographic maps. Other active sensors include acoustic techniques such as side-scan sonar, and single-and multi-beam echo sounders, which are commonly used approaches for mapping and monitoring submerged aquatic vegetation (Gumusay et al., 2019;Lecours et al., 2022). ...
... The progressive development of sensors and platforms has increased the amount and complexity of RS data available for terrestrial and marine observations, leading to rapid and continuous advances in techniques and algorithms to process and retrieve more information from the data (Pettorelli et al., 2014). As RS products become more affordable and accessible, RS is becoming an important tool for biodiversity, conservation, and climate change research (Byrd et al., 2018;Lecours et al., 2022;Turner et al., 2003). Currently, blue carbon science is still a relatively new research field, and progressive developments in RS technologies offer opportunities for delivering the full potential of BCEs as natural climate solutions. ...
Full-text available
Blue carbon ecosystems (BCEs), such as mangroves, saltmarshes, and seagrasses, are increasingly recognized as natural climate solutions. Evaluating the current extent, losses, and gains of BCEs is crucial to estimating greenhouse gas emissions and supporting policymaking. Remote sensing approaches are uniquely suited to assess the factors driving BCEs dynamics and their impacts at various spatial and temporal scales. Here, we explored trends in the application of remote sensing in blue carbon science. We used bibliometric analysis to assess 2193 published papers for changes in research focus over time (1990 – June 2022). Over the past three decades, publications have steadily increased, with an annual growth rate of 16.9%. Most publications focused on mangrove ecosystems and used the optical spaceborne Landsat mission, presumably due to its long‐term, open‐access archives. Recent technologies such as LiDAR, UAVs, and acoustic sensors have enabled fine‐scale mapping and monitoring of BCEs. Dominant research topics were related to mapping and monitoring natural and human impacts on BCEs, estimating vegetation and biophysical parameters, machine and deep learning algorithms, management (including conservation and restoration), and climate research. Based on corresponding author affiliations, 80 countries contributed to the field, with United States (27.2%), China (15.0%), Australia (7.5%), and India (6.0%) holding leading positions. Overall, our results reveal the need to increase research efforts for seagrasses, saltmarshes, and macroalgae, integrate technologies, increase the use of remote sensing to support carbon accounting methodologies and crediting schemes, and strengthen collaboration and resource sharing among countries. Rapid advances in remote sensing technology and decreased image acquisition and processing costs will likely enhance research and management efforts focused on BCEs.
... Management and conservation strategies need to be strengthened, and in the SDG in general, it is necessary to include education (Shah and Atisa, 2021), and communication for holistic, sustainable development of the mangrove wetlands, applying Education for Sustainable Development (EDS) advocated by the United Nations (UN, 2019) that connects the SDG4 with other SDGs to think about the sustainable development, for example for the mangrove ecosystem. Recently, the United Nations Ocean Decade initiated the Ocean Literacy program (UNESCO-IOC, 2021) in order to build science around marine-coastal ecosystems (UN, 2021b), such as mangroves (Lecours et al., 2021). In addition to education, it is also expected that scientific interest can give more visibility to the role of mangroves in the decade of action, listing the SDGs in order to fill in the knowledge gaps. ...
... Some of the biodiversity paƩerns highlighted here (e.g., hotspots in Belize, the south of Cuba, northeast of Puerto Rico) mirror recent Caribbean-wide esƟmaƟons of corals morpho-funcƟonal diversity (Melo-Merino et al., 2022), and could indicate priority target for regional conservaƟon iniƟaƟves. Lecours et al., 2022). A hub centralizing different data sources on the state of the reef ecosystem will be of paramount importance in the future, as it will facilitate the establishment of links between all the different information layers. ...
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Motivation: Host to intricate networks of marine species, coral reefs are among the most biologically diverse ecosystems on Earth. Over the past few decades, major degradations of coral reefs have been observed worldwide, which is largely attributed to the effects of climate change and local stressors related to human activities. Now more than ever, characterizing how the environment shapes the dynamics of the reef ecosystem (e.g., shifts in species abundance, community changes, emergence of locally adapted populations) is key to uncovering the environmental drivers of reef degradation, and developing efficient conservation strategies in response. To achieve these objectives, it is pivotal that environmental data describing the processes driving such ecosystem dynamics, which occur across specific spatial and temporal scales, are easily accessible to coral reef researchers and conservation stakeholders alike. Main types of variable contained : Multiple environmental variables characterizing various facets of the reef environment, including water chemistry and physics (e.g., temperature, pH, chlorophyll concentration), local anthropogenic pressures (e.g., boat traffic, distance from agricultural or urban areas) and sea currents patterns. Spatial location and grain: Worldwide reef cells of 5 by 5 km. Time period and grain: Last 3–4 decades, monthly and yearly resolution. Major taxa and level of measurement: Environmental data important for coral reefs and associated biodiversity. Software format: Interactive web application available at
... The use of ports as field labs for evolutionary studies will be strongly dependent on (i) knowledge of the environmental conditions within and between ports, (ii) the ability to compare these conditions with those pertaining to the native ranges of the species under study, and (iii) the need to properly quantify propagule pressure. Large-scale and time-detailed ecological conditions are accessible through remote sensing (temperature, salinity, chlorophyll, etc.; Lecours et al., 2021), but their availability at the scale of ports appears to be limited. Additionally, propagule pressure is a difficult parameter to estimate and relies on surveys, proxies, and model estimations (e.g., Drake et al., 2015). ...
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Humans have built ports on all the coasts of the world, allowing people to travel, exploit the sea, and develop trade. The proliferation of these artificial habitats and the associated maritime traffic are not predicted to fade in the coming decades. Ports share common characteristics: species find themselves in novel singular environments, with particular abiotic properties ‐e.g., pollutants, shading, protection from wave action‐ within novel communities in a melting‐pot of invasive and native taxa. Here we discuss how this drives evolution, including setting‐up of new connectivity hubs and gateways, adaptive responses to exposure to new chemicals or new biotic communities, and hybridization between lineages that would have never come into contact naturally. There are still important knowledge gaps however, such as the lack of experimental tests to distinguish adaptation from acclimation processes, the lack of studies to understand the putative threats of port lineages to natural populations, or to better understand the outcomes and fitness effects of anthropogenic hybridization. We thus call for further research examining “biological portuarization”, defined as the repeated evolution of marine species in port‐ecosystems under human‐altered selective pressures. Furthermore, we argue that ports act as giant mesocosms often isolated from the open sea by seawalls and locks, and so provide replicated life‐size evolutionary experiments essential to support predictive evolutionary sciences.
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The redistribution of marine ecosystem engineers in response to changing climate is restructuring endemic benthic communities globally. Therefore, developing and implementing efficient monitoring programs across the complete depth range of these marine ecosystem engineers is often an urgent management priority. Traditionally, many monitoring programs have been based on a systematically selected set of survey locations that, while able to track trends at those sites through time, lack inference for the overall region being monitored. This study trialled a probabilistic sampling design to address this need, taking advantage of an important prerequisite for such designs, extensive multibeam echosounder (MBES) mapping, to inform a spatially balanced sample selection. Here, we allocated 170 remotely operated vehicles (ROVs) transects based on a spatially balanced probabilistic sampling design across three locations with extensive mapping. Generalized additive models were used to estimate the density and associated barren cover of the range‐expanding ecosystem engineer, the long spined urchin (Centrostephanus rodgersii). Estimates were generated at a reef‐wide scale across three locations on the east coast of Tasmania, Australia, representing the leading edge of the species recent range extension. Model‐based estimates of urchin density and barren cover incorporated seabed structure attributes, such as depth and ruggedness, with differences in these modelled relationships being identified between locations. Estimates ranged from 0.000065 individuals m−2 and 0.018% barren cover in the Tasman Peninsula to 0.167 individuals m−2 and 2.10% barren cover at Governor Island Marine Reserve, reflecting a north to south distributional gradient. This study highlights the value of combining probabilistic sampling designs, ROV transects, stereo video, and MBES mapping to generate reliable and robust estimates of important ecosystem species needed to protect reef‐based fishery and conservation values via adaptive and informed management. Many marine monitoring programs are based on systematically selected set of survey locations that, while able to track trends at those sites through time, lack inference for the overall region being monitored. This study trialled a probabilistic sampling design to address this need, taking advantage of an important prerequisite for such designs, extensive multibeam echosounder mapping, to inform a spatially balanced sample. Precise model estimates of urchin density and barren habitat were possible, highlighting the value of combining probabilistic sampling designs, remotely operated vehicle transects, stereo video, and fine‐scale mapping to generate reliable and robust estimates of important ecosystem species, which are needed to protect reef‐based fishery and conservation values via adaptive and informed management.
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The number of civilian, commercial and military applications are dependant on accurate knowledge of bathymetry of coastal regions. Conventionally, hydrographic surveying methods are used for bathymetric surveys carried by ship-based acoustic systems, but needs high-cost resources. Space technology has provided a cost-effective alternate means for charting near shore and inaccessible waters. The optical satellite data have capabilities to offer alternate solution in near-shore region, which has been researched for past 50 years, using evolving algorithms to estimate Satellite Derived Bathymetry (SDB). However, there is no agreement on use of terms like approach, model, method and techniques, which have been used varyingly and interchangeably as per context of SDB research. This paper suggests a classification scheme for SDB algorithms which is also applicable to other Marine Remote Sensing studies. In this paper, based on literature available on SDB for the past five decades, an insight on SDB classification has been offered grounded in research philosophy. The SDB Approaches, models, methods and techniques have been elaborated with chronological development, along with SDB studies based on them, their accuracy and errors in SDB retrieval. We have suggested a matrix of prerequisite satellite data, in-situ data resolution, methods and algorithms of SDB based on level of accuracy needs to be achieved, which will guide future researchers to select one as per their context of research.
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Seascape ecology, the marine-centric counterpart to landscape ecology, is rapidly emerging as an interdisciplinary and spatially explicit ecological science with relevance to marine management, biodiversity conservation and restoration. While important progress in this field has been made in the past decade, there has been no coherent prioritisation of key research questions to help set the future research agenda for seascape ecology. We used a two-stage modified Delphi method to solicit applied research questions from academic experts in seascape ecology and then asked respondents to identify priority questions across nine interrelated research themes using two rounds of selection. We also invited senior management/conservation practitioners to prioritise the same research questions. Analyses highlighted congruence and discrepancies in perceived priorities for applied research. Themes related to both ecological concepts and management practice, and those identified as priorities include seascape change, seascape connectivity, spatial and temporal scale, ecosystem-based management, and emerging technologies and metrics. Highest priority questions (upper tercile) received 50% agreement between respondent groups and lowest priorities (lower tercile) received 58% agreement. Across all three priority tiers, 36 of the 55 questions were within a ±10% band of agreement. We present the most important applied research questions as determined by the proportion of votes received. For each theme, we provide a synthesis of the research challenges and the potential role of seascape ecology. These priority questions and themes serve as a roadmap for advancing applied seascape ecology during, and beyond, the UN Decade of Ocean Science for Sustainable Development (2021–2030).
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Cold-water coral (CWC) habitats are considered important centers of biodiversity in the deep sea, acting as spawning grounds and feeding area for many fish and invertebrates. Given their occurrence in remote parts of the planet, research on CWC habitats has largely been derived from remotely-sensed marine spatial data. However, with ever-developing marine data acquisition and processing methods and non-ubiquitous nature of infrastructure, many studies are completed in isolation resulting in large inconsistencies. Here, we present a concise review of marine remotely-sensed spatial raster data acquisition and processing methods in CWC habitats to highlight trends and knowledge gaps. Sixty-three studies that acquire and process marine spatial raster data since the year 2000 were reviewed, noting regional geographic location, data types (‘acquired data’) and how the data were analyzed (‘processing methods’). Results show that global efforts are not uniform with most studies concentrating in the NE Atlantic. Although side scan sonar was a popular mapping method between 2002 and 2012, since then, research has focused on the use of multibeam echosounder and photogrammetric methods. Despite advances in terrestrial mapping with machine learning, it is clear that manual processing methods are largely favored in marine mapping. On a broader scale, with large-scale mapping programs (INFOMAR, Mareano, Seabed2030), results from this review can help identify where more urgent research efforts can be concentrated for CWC habitats and other vulnerable marine ecosystems.
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The lack of detailed spatial information on coastal resources, notably shallow water coral reefs and associated benthic habitats, impedes our ability to protect and manage them in the face of global climate change and anthropogenic impacts. Here, we develop a semi‐automated workflow in the cloud that uses freely available Sentinel‐2 data from the European Space Agency (ESA) Copernicus programme to derive information on near‐shore coral reef habitats in the Quirimbas National Park (QNP), a recently declared biosphere reserve in northern Mozambique. We use an end‐to‐end cloud‐based framework within the Google Earth Engine cloud geospatial platform to process imagery from raw pixels to cloud‐free composites which are corrected for glint and surface artefacts, water column and derived estimated depth and then classified into four benthic habitats. Using independent training and validation data, we apply three supervised classification algorithms: random forests (RF), support vector machine (SVM) and classification and regression trees (CART). Our results show that random forests are the most accurate supervised algorithm with over 82% overall accuracy. We mapped over 105 000 ha of shallow water habitat inside the protected area, of which 18% are dominated by coral and hardbottom; 27.5% are seagrass and submerged aquatic vegetation and another 23.4% are soft and sandy substrates, and the remaining area is optically deep water. We employ satellite‐derived bathymetry to assess slope, bathymetric position, rugosity and underwater topography of these habitats. Finally, a spectral unmixing model provides further sub‐pixel–level information of habitats with the potential to monitor changes over time. This effort provides the first, consistent and repeatable and also scalable coastal information system for an east African tropical marine protected area, which hosts shallow‐water ecosystems which are of great significance to local communities and building resilience towards climate change.
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Abstract Characterizing and monitoring changes in biogenic 3‐dimensional (3D) structures at multiple scales over time is challenging within the practical constraints of conventional ecological tools. Therefore, we developed a structure‐from‐motion (SfM)‐based photogrammetry method, coupled with inspection and mesh processing software, to estimate important ecological parameters of underwater worm colonies (hummocks) constructed by the sabellariid polychaete Sabellaria alveolata, using non‐destructive, 3D modeling and mesh analysis. High resolution digital images of bioconstructions (hummocks) were taken in situ under natural conditions to generate digital 3D models over different sampling periods to analyse the morphological evolution of four targeted hummocks. 3D models were analysed in GOM Inspect software, a powerful and freely available mesh processing software to follow growth as well as morphology changes over time of each hummock. Linear regressions showed 3D models only slightly overestimated the real dimensions of the reference objects with an average error
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The distribution of emperor penguins is circumpolar, with 54 colony locations currently reported of which 50 are currently extant as of 2019. Here we report on eight newly discovered colonies and confirm the rediscovery of three breeding sites, only previously reported in the era before Very High Resolution satellite imagery was available, making a total of 61 breeding locations. This represents an increase of ~20% in the number of breeding sites, but, as most of the colonies appear to be small, they may only increase the total population by around 5–10%. The discoveries have been facilitated by the use of Sentinel2 satellite imagery, which has a higher resolution and more efficient search mechanism than the Landsat data previously used to search for colonies. The small size of these new colonies indicates that considerations of reproductive output in relation to metabolic rate during huddling is likely to be of interest. Some of the colonies exist in offshore habitats, something not previously reported for emperor penguins. Comparison with recent modelling results show that the geographic locations of all the newly found colonies are in areas likely to be highly vulnerable under business‐as‐usual greenhouse gas emissions scenarios, suggesting that population decreases for the species will be greater than previously thought. Sentinel2 enables us to track and discover emperor penguin colonies. We use the new technology to discover 11 new colony sites, increasing the number of known colonies by 20%. However, it is not all good news for emperors, as all the newly found colonies are in areas vulnerable to future sea ice loss, and the new discoveries actually make the species more vulnerable to climate change than previously thought.
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Global mangrove loss has been attributed primarily to human activity. Anthropogenic loss hotspots across Southeast Asia and around the world have characterized the ecosystem as highly threatened, though natural processes such as erosion can also play a significant role in forest vulnerability. However, the extent of human and natural threats has not been fully quantified at the global scale. Here, using a Random Forest‐based analysis of over one million Landsat images, we present the first 30‐meter resolution global maps of the drivers of mangrove loss from 2000‐2016, capturing both human‐driven and natural stressors. We estimate that 62% of global losses between 2000‐2016 resulted from land‐use change, primarily through conversion to aquaculture and agriculture. Up to 80% of these human‐driven losses occurred within six Southeast Asian nations, reflecting the regional emphasis on enhancing aquaculture for export to support economic development. Both anthropogenic and natural losses declined between 2000‐2016, though slower declines in natural loss caused an increase in their relative contribution to total global loss area. We attribute the decline in anthropogenic losses to the regionally‐dependent combination of increased emphasis on conservation efforts and a lack of remaining mangroves viable for conversion. While efforts to restore and protect mangroves appear to be effective over decadal time scales, the emergence of natural drivers of loss presents an immediate challenge for coastal adaptation. We anticipate that our results will inform decision making within conservation and restoration initiatives by providing a locally‐relevant understanding of the causes of mangrove loss.
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Unmanned aerial vehicles (UAVs) are cost‐effective remote sensing tools useful for generating very high‐resolution (VHR) aerial imagery. Habitat maps generated from UAV imagery are a fundamental component of marine spatial planning, essential for the designation and governance of marine protected areas (MPAs). We investigated whether UAV survey altitude affects habitat classification performance and the classification accuracy of thematic maps from a tropical shallow water environment. We conducted repeated UAV flights at 75, 85, and 110 m, using a fixed‐wing UAV on the Turneffe Atoll, Belize. Flights were ground truthed with snorkel surveys. Images were mosaiced to form orthomosaics and transformed into thematic maps through semi‐automatic object‐based image analysis (OBIA). Three subset areas (4000 m2, 17 000 m2 and 17 000 m2) from two cayes on the atoll were selected to investigate the effect of survey altitude. A linear regression demonstrated that for every 1 m increase in survey altitude, there was a ~1% decrease in the overall classification accuracy. A low survey altitude of 75 m produced a higher classification accuracy for thematic maps and increased the representation of mangrove, seagrass and sand. The variability in classified cover was driven by altitude, although the direction and extent of this relationship was specific to each class. For coral and sea, classified cover decreased with increased altitude. Mangrove classified cover was non‐sensitive to altitude changes, demonstrating a lesser need for a consistent survey altitude. Sand and seagrass had a greater sensitivity to altitude, due to classified cover variability between altitudes. Our findings suggest that survey altitude should be minimized when classifying tropical marine environments (coral, seagrass) and, given that most fixed‐wing UAVs are restricted to a minimum altitude of 70 m, we recommend an altitude of 75 m. Survey altitude should be a major consideration when targeting habitats with greater sensitivity to altitude variability. A fixed‐wing unmanned aerial vehicle (UAV) was operated to acquire very high‐resolution aerial imagery of two cayes on the Turneffe Atoll, Belize. Three subset areas (4000 km2, 17 000 km2 and 17 000 km2) were selected from the UAV surveys to investigate the effect of altitude on habitat classification. A linear regression demonstrated that for every 1 m increase in altitude, there was a ~1% decrease in the overall classification accuracy. A low survey altitude of 75 m produced higher classification accuracy for thematic maps, and increased the representation of mangrove, seagrass, and sand. The variability in classified cover was driven by altitude, although the direction and extent of this relationship was specific to each class. Our findings suggest that survey altitude should be minimised to 75 m when classifying tropical marine environments (coral, seagrass). A consistent altitude should be considered for surveys targeting habitats with greater sensitivity to altitude variability.