FIGURE 1
Satellite remote sensing and the Marine Biological Observation Network. Red squares denote the original demonstration nodes in waters of the Arctic, Central California, Southern California Bight, and Florida Keys. Green squares indicate nodes added in 2019 in the Northern California Current/ Pacific Northwest and the Gulf of Maine. The blue square locates an ongoing partnership site with the Smithsonian Institution. Each node represents a unique coastal environment with variability in biophysical dynamics where in situ observations complement remote sensing. The figure highlights biological and physical variables that are all available with current remote sensing, including sea ice, sea surface temperature (SST), sea surface height (SSH), salinity (SAL), and chlorophyll a (Chl-a), along with chromophoric dissolved organic matter (CDOM) from ocean color (see section on Remote Sensing and Biodiversity: Challenges and Current Capacity).
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Coastal ecosystems are rapidly changing due to human-caused global warming, rising sea level, changing circulation patterns, sea ice loss, and acidification that in turn alter the productivity and composition of marine biological communities. In addition, regional pressures associated with growing human populations and economies result in changes i...
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Context 1
... 2014, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), the National Science Foundation (NSF), and the Bureau of Ocean and Energy Management (BOEM) pooled resources under the National Ocean Partnership Program (NOPP) to initiate a pilot Marine Biodiversity Observation Network (MBON). The goal was to establish a scalable and transferable observational model for detecting biodiversity and marine habitat variability with direct application to resource management and decision-making (Figure 1) . ...
Context 2
... Global Ocean Observing System created the system of Essential Ocean Variables (EOVs), several of which can be observed synoptically from space. Relevant to MBON, biodiversity, and ocean health, satellite EOVs include sea surface temperature (microwave and near-infrared radiometry measurements), sea surface height and currents (altimetry), roughness (scatterometry), salinity (scatterometry and microwave radiometry), sea ice (microwave radiometry), and a broad category of ocean color (visible radiometry) (Figure 1). The diversity and biomass of phytoplankton, and foundation groups such as seagrass, mangroves, macroalgae, and corals, are also each listed as an EOV and are attainable using satellite or suborbital remote sensing. ...
Citations
... Amongst a widening array of monitoring technologies, remote sensing is increasingly being used to monitor water quality and the ecological status of aquatic environments. Satellite Earth Observation can provide repeated synoptic coverage of ecosystems at multiple scales (Kavanaugh et al., 2021;Maberly et al., 2020;Tormos et al., 2021), with spatially resolved data better capturing horizontal heterogeneities in the various parameters monitored. In addition, the ever-improving spatial, spectral, and temporal resolutions of satellite Earth Observation underscore the need to improve the quality and robustness of the various estimation algorithms for chlorophyll a (Chl-a) concentration, colored dissolved organic matter (CDOM) concentration, surface water temperature, and cloud cover ( Ansper and Alikas, 2018;Topp et al., 2020). ...
Lake water quality assessment requires quantification of phytoplankton abundance. Optical satellite imagery allows us to map this information within the entire lake area. The ESA Climate Change Initiative (ESA-CCI) estimates Chl-a concentrations, based on medium resolution satellite data, on a global scale. Chl-a concentrations provided by the ESA-CCI consortium were analyzed to assess their representativeness for water quality monitoring and subsequent phenology studies in Lake Geneva. Based on vertically resolved in-situ data, those datasets were evaluated through match-up comparisons. Because the underlying algorithms do not take into account the vertical distribution of phytoplankton, a specific analysis was performed to evaluate any potential biases in remote sensing estimation, and consequences for observed phenological trends. Different approaches to data averaging were performed to reconstruct Chl-a estimates provided by the remote sensing algorithms. Strong correlation (R-value > 0.89) and acceptable discrepancies (rmse ∼ 1.4 mg.m−3) were observed for the ESA-CCI data. This approach permitted recalibration of the ESA CCI data for Lake Geneva. Finally, merging satellite and in-situ data provided a consistent time series for long term analysis of phytoplankton phenology and its interannual variability since 2002. This combination of in-situ and satellite data improved the temporal resolution of the time series, enabling a more accurate identification of the timing of specific spring events characterising phytoplankton phenology.
... Inaccuracies in LBI are thus important for large organizations and their products and services but have small and likely insignificant effects on the impacts for most individual labor outputs. Other than the accurate altimetry of water surface and depth, satellite remote sensing for monitoring freshwater and marine biodiversity is still challenging and research is ongoing to improve such methods (28). ...
The Impact Measurement and Conservation System (IMACS) was developed to calculate environmental and human condition impacts and to apply conservation required to neutralize such impacts for products and services purchased by end-user consumers (1- 6). With its implementation, the IMACS system would allow the fastest return to the best approximation of pre-industrial sustainable conditions (Global warming reversal and wildlife area restoration). All environmental impacts take place on a location. Location Based Impacts (LBIs) are environmental impacts assigned to parcels (land) or designated areas (marine). Under IMACS, LBIs are distributed in a dynamic fashion over the products made and services rendered using these areas. This article focuses on the use of remote sensing instruments systems used to accurately measure the underlying variables needed for parcel and designated area delineation and the environmental impacts taking place on them (LBIs). These impacts include landscape change and the subsequent use as cultivated area, changes in biodiversity, greenhouse gas emissions, fresh water consumption, soil and surface water acidification, soil & sediment loss, coastal area at risk of flooding, atmospheric ozone layer damage and includes all applicable types of conserving impacts, including wildlife area conservation, carbon storage and protection of coastal areas from flooding due to sea level rise. Using currently available remote sensing technology and after training using ground data, area mapping, parcel delineation and the measurement of most environmental impact variables can be done using satellites, by using aerial sensors or by using combinations. Implementation of IMACS requires the development of data products that combine remote sensing based environmental data with civic databases (users of parcel and designated areas), allowing easy, automated and low-cost extraction of LBI data.
... Despite the high accuracy of field data measurement, these approaches are costly in operation and timely in field collection [3,6], leading to a gap in bathymetry map data in several regions [1,7]. The remote estimation of biological and physical parameters using satellite images has become essential to a variety of research domains in recent decades [8]. This approach is cost-effective compared to other survey techniques, is a well-developed sensing technology, is easy to integrate with artificial intelligence (AI) models, and is accurate in thematic mapping [9]. ...
Bathymetry data is indispensable for a variety of aquatic field studies and benthic resource inventories. Determining water depth can be accomplished through an echo-sounding system or remote estimation utilizing space-borne and air-borne data across diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces challenges due to the complex inherent optical properties of water bodies (e.g., turbid water), satellite spatial resolution limitations, and constraints in the performance of
retrieval models. This study focuses on advancing the remote sensing-based method by harnessing the non-linear learning capabilities of the machine learning (ML) model, employing advanced feature selection through a meta-heuristic algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we
validate the predictive capabilities of six ML models: Random Forest (RF), Support Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light Gradient Boosting Machine (LGBM),
and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swarm Optimization, and Grey Wolf Optimization), to accurately ascertain
water depth. This is achieved using a diverse input dataset derived from multi-spectral Landsat 9 imagery captured on a cloud-free day (19 September 2023) in a shallow, turbid lagoon. Our findings indicate the superior performance of LGBM coupled with Particle Swamp Optimization (R2 = 0.908, RMSE = 0.31 m), affirming the consistency and reliability of the feature extraction and selection-based framework, while offering novel insights into the expansion of bathymetric mapping in complex aquatic environments.
... Visual surveys and manual collection, which take a lot of time and labor, are currently used methods for finding plastic garbage on beaches. Several distinct strategies, such as aerial or satellite imagery, have been investigated in recent years as potential instrumental methods for marine remote sensing applications such as oil spill detection, habitat mapping, and marine litter detection (Kavanaugh et al. 2021;Silveira et al. 2021;Papakonstantinou et al. 2021;Topouzelis et al. 2020). With the advent of Artificial Intelligence algorithms, RGB imagery has proven sufficient to identify specific application types of plastic waste, further enhancing the efficiency of detection (Sami et al. 2020;Gnann et al. 2022;Tamin 2022). ...
Plastic pollution is a rising environmental issue, with millions of tons of plastic debris collecting in the world's seas and on its shores. Hyperspectral imaging (HSI) has become increasingly widely used as a more precise approach that can identify targets in remote sensing aquatic missions. The interference from other beach materials, and the need for proper identification of litter types can make identifying dumped plastics on sand-surrounded beaches challenging. This study lays the groundwork for a physical laboratory setting for images captured by a hyperspectral (HS) imager. The suggested testing setup included the development of a fluorescence signature for the target theater of operations (low-density polyethylene (LD-PE) and wood surrounded by sand) for detecting polymers in a simulated beach environment using the laser-induced fluorescence (LIF) approach. Initially using broadband-spectrum light, strong sample diffuse reflectance contrast is observed in the imaging at wavelengths between 400 and 460 nm. Next, a dedicated LIF system for plastic litter discovery was developed using an ultraviolet (UV) laser source. Initial findings show that there is a distinct fluorescence signal for plastics at 450 nm and at 750 nm for wood. Our pilot studies support current efforts to determine the optimum spectral signature that these polymers will appear with clarity on shorelines using an inexpensive imagery combined with our UV LIF approach, which may have an impact on applications for the detection of beach pollution. The knowledge gained from this study can be used to construct reliable aerial conventional cameras for plastic waste environmental monitoring and management.
... For example, remote sensing platforms such as uncrewed aerial vehicles and satellites have been used to produce high resolution maps of sensitive marine habitats [8] and to study important physical characteristics such as salinity [9] and temperature [10]. However, these technologies are limited in the achievable depth of observations by water turbidity and solar illumination [11]. Deep sea tools including gliders [12], seafloor mounted buoys [13], and autonomous underwater vehicles [14] can explore to significant depths but with mission duration often limited by battery capacity. ...
The vast majority of the ocean's volume remains unexplored, in part because of limitations on the vertical range and measurement duration of existing robotic platforms. In light of the accelerating rate of climate change impacts on the physics and biogeochemistry of the ocean, the need for new tools that can measure more of the ocean on faster timescales is becoming pressing. Robotic platforms inspired or enabled by aquatic organisms have the potential to augment conventional technologies for ocean exploration. Recent work demonstrated the feasibility of directly stimulating the muscle tissue of live jellyfish via implanted microelectronics. We present a biohybrid robotic jellyfish that leverages this external electrical swimming control, while also using a 3D printed passive mechanical attachment to streamline the jellyfish shape, increase swimming performance, and significantly enhance payload capacity.
A six-meter-tall, 13,600-liter saltwater facility was constructed to enable testing of the vertical swimming capabilities of the biohybrid robotic jellyfish over distances exceeding 35 body diameters. We found that the combination of external swimming control and the addition of the mechanical forebody resulted in an increase in swimming speeds to 4.5 times natural jellyfish locomotion. Moreover, the biohybrid jellyfish were capable of carrying a payload volume up to 105\% of the jellyfish body volume. The added payload decreased the intracycle acceleration of the biohybrid robots relative to natural jellyfish, which could also facilitate more precise measurements by onboard sensors that depend on consistent platform motion. While many robotic exploration tools are limited by cost, energy expenditure, and varying oceanic environmental conditions, this platform is inexpensive, highly efficient, and benefits from the widespread natural habitats of jellyfish.
The demonstrated performance of these biohybrid robots suggests an opportunity to expand the set of robotic tools for comprehensive monitoring of the changing ocean.
... Currently, in the use of EO data there is a lack of a dedicated process of understanding and quantifying CC effects on urban areas. Indeed, over the past decades technical advancements of satellite technologies have enabled and improved the monitoring of the natural environment, with several applications in the field of agriculture [1] [2], oceans monitoring [3] [4], land uses [5] and biodiversity preservation [6] [7]. However, human settlements and urban environments still represent a relevant challenge for the full exploitation of EO data end the development of dedicated services. ...
... Many researchers had studied the application of remote sensing in the ocean [3][4][5] for sensing the marine environment, marine and coastal environment detection. SAR was widely used in marine monitoring: oil spill monitoring [6] and marine biodiversity observation [7]. Ships are necessary equipment for marine development, energy transportation, national defence construction and other activities. ...
The synthetic aperture radar (SAR) image ship detection system needs to adapt to an increasingly complicated actual environment, and the requirements for the stability of the detection system continue to increase. Adversarial attacks deliberately add subtle interference to input samples and cause models to have high confidence in output errors. There are potential risks in a system, and input data that contain confrontation samples can be easily used by malicious people to attack the system. For a safe and stable model, attack algorithms need to be studied. The goal of traditional attack algorithms is to destroy models. When defending against attack samples, a system does not consider the generalization ability of the model. Therefore, this paper introduces an attack algorithm which can improve the generalization of models by based on the attributes of Gaussian noise, which is widespread in actual SAR systems. The attack data generated by this method have a strong effect on SAR ship detection models and can greatly reduce the accuracy of ship recognition models. While defending against attacks, filtering attack data can effectively improve the model defence capabilities. Defence training greatly improves the anti-attack capacity, and the generalization capacity of the model is improved accordingly.
... The geological, physical, chemical, and biological makeup of coastal and nearshore habitats is complex [2]. Due to this complexity, ecological specificity, and high spatiotemporal variability, coastal zones harbor some of the most endangered ecosystems on Earth [3] and offer valuable and often unique services. ...
... Wang [8] (p. 2) gives an explicit definition of RS: 'Remote sensing refers to art, science, and technology for Earth system data acquisition through nonphysical contact sensors or sensor systems mounted on space-borne, airborne, and other types of platforms; data processing and interpretation from automated and visual analysis; information generation under computerized and conventional mapping facilities; and applications of generated data and information for societal benefits and needs.' In the broadest sense, the societal benefits and needs catered to by RS are echoed by the Sustainable Development Goals (SDGs) of the United Nations (UN). ...
... RS is also a good methodology for monitoring biological patterns and processes, e.g., by providing information on biomass or dominant taxa of lower trophic levels from observing surface watercolor [2]. Additionally, biological and physical parameters that structure biodiversity patterns can be derived from RS data [9]. ...
The background of this feature article is a necessity to systematize a vast array of issues pertinent to the latest applications of remote sensing in coastal and marine conservation. Hence the purpose of this study: stocktaking of cutting-edge research articles in this field and eliciting the essential trends and issues shaping the knowledge and future research and technical development perspectives in coastal and marine nature conservation, which is pivotal for meeting the United Nations Sustainable Development Goals till 2030. A hierarchical cluster analysis was undertaken with the KH Coder 3.0 tool to elicit topical co-occurrence networks for thematic words in academic papers from 2015 to 2021 on the topic quarried from Scholar Google. The article’s main findings are the elicited four main trending themes in applications of remote sensing in coastal and marine conservation: (1) Remote Sensing-Based Classification and Modelling; (2) Conservation of Tropical Coastal and Marine Habitats; (3) Mapping of Habitats and Species Distribution; (4) Ecosystem and Biodiversity Conservation and Resource Management. The main conclusion of the article is that habitat vulnerability is a key factor to take into consideration for the future hybrid applications of remote sensing and “citizen science” inputs.
... The main reason is that the development of ocean remote sensing is driven by the environment in which marine economy prospered and ocean exploitation strategies arised in the early 21st century. To achieve the healthy and sustainable development of marine economy, we must rely on the development of high-tech, such as marine remote sensing, which are complementary [68][69][70][71]. ...
The ocean is of great significance in the climate system, global resources and strategic decision making. With the continuous improvement in remote sensing technology, ocean remote sensing research has increasingly become an important topic for resource development and environmental protection. This paper uses bibliometric analysis method and VOSviewer visual software to conduct analysis. The analysis focuses on the period from 1990 to 2020. The analysis results show that articles have been steadily increasing over the past two decades. Scholars and researchers form the United States, China and Europe (mainly Western European countries), as well as NASA, Chinese Academy of Sciences and NOAA have bigger influence in this field to some extent. Among them, the United States and NASA holds the core leading position. Moreover, global cooperation in this field presents certain characteristics of geographical distribution. This study also reveals journals that include the most publications and subject categories that are highly relevant to related fields. Cluster analysis shows that remote sensing, ocean color, MODIS (or Moderate Resolution Imaging Spectroradiometer), chlorophy, sea ice and climate change are main research hotspots. In addition, in the context of climate warming, researchers have improved monitoring technology for remote sensing to warn and protect ocean ecosystems in hotspots (the Arctic and Antarctica). The valuable results obtained from this study will help academic professionals keep informed of the latest developments and identify future research directions in the field related to ocean remote sensing.
... Bravo et al. used artificial intelligence to evaluate macroalgae and sessile organisms on rocky shores across the American continent, from Patagonia (Argentina) to Canada, including the Galapagos Islands (Ecuador). Livore et al. expanded a study of rocky shore biodiversity to include satellite-derived assessments of biogeography or "Seascapes" of Kavanaugh et al. (2021). ...
