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Remote Sensing Applied to Marine Species Distribution

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 12957

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Special Issue Editors

Dr. Luis González Vilas

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Guest Editor

Applied Physics Department, Facultad de Ciencias do Mar, University of Vigo, Campus Lagoas-Marcosende, 36213 Vigo, Spain
Interests: ocean color; species distribution; remote sensing; harmful algae bloom (HABs); biological oceanography

Dr. Jesus Torres Palenzuela

E-Mail Website
Guest Editor

Applied Physics Department, Facultad de Ciencias do Mar, University of Vigo, Campus Lagoas-Marcosende, 36213 Vigo, Spain
Interests: ocean color; remote sensing; harmful algae bloom (HABs); oceanography; marine sensors; fishing ecology

Dr. Laura González García

E-Mail Website
Guest Editor

Azorean Biodiversity Group, Centre for Ecology, Evolution and Environmental Changes (CE3C), University of the Azores, Rua Mãe de Deus, 9500-321 Ponta Delgada, Azores, Portugal
Interests: cetacean ecology; species distribution; habitat modelling; oceanography; remote sensing

Special Issue Information

Dear Colleagues,

Different direct and indirect anthropogenic disturbances (e.g., pollution, overfishing, eutrophication, habitat destruction), intensified by the continuous growth of the human population and the effects of climate change and biological invasions, are seriously threatening the sustainability of marine and coastal ecosystems. In recent years, there has been an increasing need to implement a more effective and sustainable planning and decision making for ecosystem and biodiversity conservation.

Ecological models relating species distribution patterns to environmental factors play a key role in assessing ecosystem health and biodiversity in a context of global change. However, estimation of species distribution is often hindered by the availability of reliable field data, especially in marine environments. Remote sensing products can provide continuous data on environmental factors driving the distribution of marine organisms. The increasing availability of Earth observation (EO) data provides an unprecedented opportunity to extend the applicability of ecological models for both predictive and explanatory purposes.

We would like to invite authors to submit their works on innovative methods and applications using remote sensing data to study the temporal and/or spatial distribution of marine organisms, including seagrass, phytoplankton, fish, or marine mammals. Potential topics include but are not necessarily limited to:

Integration of marine remote sensing data and species distribution models;
Identification of key environmental factors for distribution of marine species;
Assessments of marine biodiversity and/or habitat conditions;
Development and application of short- or long-term prediction models of marine species;
Mapping of seagrass meadows or coral reefs;
Analysis of time series of species distributions based on historical remote sensing data;
Evaluation of the status of fish stocks.

The Special Issue will accept both review and research papers.

Dr. Luis González Vilas
Dr. Jesus Torres Palenzuela
Dr. Laura González García
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

remote sensing
species distribution models
habitat suitability models
marine ecosystems
coastal ecosystems
oceanographic variables
seagrass meadow
marine mammals
phytoplankton species
fish species

Published Papers (6 papers)

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Research

16 pages, 2503 KiB

Open AccessArticle

Fishery Resource Evaluation with Hydroacoustic and Remote Sensing in Yangjiang Coastal Waters in Summer

by Xiaoqing Yin, Dingtian Yang, Linhong Zhao, Rong Zhong and Ranran Du

Remote Sens. 2023, 15(3), 543; https://doi.org/10.3390/rs15030543 - 17 Jan 2023

Cited by 2 | Viewed by 2047

Abstract

Yangjiang coastal waters provide vital spawning grounds, feeding grounds, and nursery areas for many commercial fish species. It is important to understand the spatial distribution of fish for the management, development, and protection of fishery resources. In this study, an acoustic survey was conducted from 29 July to 5 June 2021. Meanwhile, remote sensing data were collected, including sea surface temperature (SST), chlorophyll concentration (Chla), sea surface salinity (SSS), and sea surface temperature anomaly (SSTA). The spatial distribution of density and biomass of fish was analyzed based on acoustic survey data using the geostatistical method. Combining with remote sensing data, we explored the relation between fish density and the environment based on the GAMs model. The results showed that fish are mainly small individuals. The horizontal distri-bution of fish density had a characteristic of high nearshore and low offshore. In the vertical direc-tion, fish are mainly distributed in surface-middle layers in shallow waters (<10 m) and in middle-bottom layers in deeper waters (>10 m), respectively. The deviance explained in the optimal GAM model was 59.2%. SST, Chla, SSS, and longitude were significant factors influencing fish density distribu-tion with a contribution of 35.3%, 11.8%, 6.5%, and 5.6%, respectively. This study can pro-vide a scientific foundation and data support for rational developing and protecting fishery re-sources in Yangjiang coastal waters. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Marine Species Distribution)

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33 pages, 9899 KiB

Open AccessArticle

First Estimate Biosiliceous Sedimentation Flux in the Pearl River Estuary from 2000–2020 by Satellite Remote Sensing

by Rong Zhong, Dingtian Yang, Linhong Zhao and Xiaoqing Yin

Remote Sens. 2023, 15(1), 58; https://doi.org/10.3390/rs15010058 - 22 Dec 2022

Cited by 2 | Viewed by 1555

Abstract

Biosiliceous sedimentation, closely related to carbon sedimentation in water, has a significant impact on the marine biogeochemical cycle. However, large-scale monitoring data are scarce due to the constraints of biosiliceous sedimentation flux (BSF) gathering methods. There are few reports on the spatiotemporal variation of BSF in estuaries and offshore waters. Additionally, few studies have used satellite remote sensing methods to retrieve BSF. In the paper, satellite images from 2000 to 2020 were used for the first time to estimate the BSF distribution of the Pearl River Estuary (PRE) over the past 20 years, based on a remote sensing model combined with particulate organic carbon (POC) deposition data and water depth data. The results showed that the BSF ranged from 100 to 2000 mg/(m² × d). The accuracy tests indicated that the correlation coefficient (R²) and significance (P) of Pearson correlation analysis were 0.8787 and 0.0018, respectively. The BSF value varied seasonally and increased every year. The BSF did not follow a simple trend of decreasing along the coast to open water. Shenzhen Bay (SZB) generally had a higher BSF value than the Dragon’s Den Waterway (DDW). The BSF in autumn and winter was investigated using empirical orthogonal function analysis (EOF). In autumn, the BSF of the PRE’s eastern bank showed little change, while the BSF of the western bank showed obvious differences. In winter, the BSF in Hong Kong waters and inlet shoals fluctuated less, whereas the BSF in DDW and Lingding Waterway (LW) fluctuated more. The grey correlation analysis (GRA) identified two factors affecting BSF: chromophoric dissolved organic matter (CDOM) and total suspended solids (TSS). Most BSF were primarily affected by TSS during winter. In spring, the two effects were balanced. TSS affected the east coast in summer, and CDOM was the dominant effect in autumn. Four main parameters influencing the distribution of BSF in the PRE were analyzed: ecosystem, reef, flow field and flocculation. This study showed that using satellite remote sensing to estimate BSF has excellent potential, which is worthy of further discussion in terms of spatiotemporal resolution and model optimization. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Marine Species Distribution)

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17 pages, 4209 KiB

Open AccessArticle

High-Resolution Drone Images Show That the Distribution of Mussels Depends on Microhabitat Features of Intertidal Rocky Shores

by Romina Vanessa Barbosa, Marion Jaud, Cédric Bacher, Yann Kerjean, Fred Jean, Jérôme Ammann and Yoann Thomas

Remote Sens. 2022, 14(21), 5441; https://doi.org/10.3390/rs14215441 - 29 Oct 2022

Cited by 3 | Viewed by 2077

Abstract

In this study, we used orthomosaics and a digital surface model (DSM) generated from drone surveys to (1) characterize the distribution of mussel (Mytilus galloprovincialis) aggregations at high resolution (centimeters), and (2) evaluate the role of topographic features, intertidal height, slope, and orientation angle in determining mussel distribution on two rocky shores oriented differently on both sides of a beach on the French Brittany coast. We first developed and tested a mussel visualization index (MVI) for mapping mussel aggregations from drone images. Then, we analyzed mussel distribution on the two shores. The results showed a contrasted total mussel-occupied area between the two rocky shores, with a higher occupation rate and a clear pattern of distribution depending on topographic features on the rocky shore oriented to the west. Intertidal height, and its associated immersion time, was the main factor determining mussel distribution. An optimum intertidal height was found in the center of the distribution height range, at c.a. 4.5 m above the lowest astronomical tide (LAT), where individuals are under immersion phase on average 43% of the time. Within this optimum, the occupation rate of the mussels was significantly higher in microhabitats facing south and west, particularly at intermediate slope angles. These results demonstrate the role of microhabitat topographic features on the development of intertidal mussels and their final distribution. Furthermore, the results highlight the importance of mesoscale structures of habitats (e.g., 100 m), which seem to be responsible for the differences we observed between the two shores. Our methodological approach highlights the main advantage of using high-resolution drone images to address ecological processes in intertidal ecosystems. Indeed, drone imagery offers the possibility to assess small-scale interactions between individuals and habitat conditions over a wide area, which is technically infeasible from fieldwork approaches or by using satellite remote sensing due to their lower resolution. Scale integration and methodological complementarity are powerful approaches to correctly represent the processes governing the ecology of intertidal ecosystems. We suggest using this methodology to monitor long-term changes of sentinel sessile species. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Marine Species Distribution)

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16 pages, 2224 KiB

Open AccessArticle

Ensemble Three-Dimensional Habitat Modeling of Indian Ocean Immature Albacore Tuna (Thunnus alalunga) Using Remote Sensing Data

by Sandipan Mondal, Yi-Chen Wang, Ming-An Lee, Jinn-Shing Weng and Biraj Kanti Mondal

Remote Sens. 2022, 14(20), 5278; https://doi.org/10.3390/rs14205278 - 21 Oct 2022

Cited by 4 | Viewed by 1360

Abstract

This study evaluated the vertical distribution of immature albacore tuna (Thunnus alalunga) in the Indian Ocean as a function of various environmental parameters. Albacore tuna fishing data were gathered from the logbooks of large-sized Taiwanese longline vessels. Fishery and environmental data for the period from 1998 to 2016 were collected. In addition to the surface variable, the most influential vertical temperature, dissolved oxygen (OXY), chlorophyll, and salinity layers were found at various depths (i.e., 5, 26, and 53 m for SST; 200, 244, and 147 m for OXY; 508, 628, and 411 for SSCI; and 411, 508, and 773 m for SSS) among 20 vertical layers based on Akaike criterion information value of generalized linear model. Relative to the 20 vertical layers base models, these layers had the lowest Akaike information criteria. For the correlation between the standardized and predicted catch per unit effort (CPUE), the correlation values for the generalized linear model (GLM), generalized additive model (GAM), boosted regression tree (BRT), and random forest (RF) model were 0.798, 0.832, 0.841, and 0.856, respectively. The GAM-, BRT-, and RF-derived full models were selected, whereas the GLM-derived full model was excluded because its correlation value was the lowest among the four models. From March to September, a higher immature albacore standardized CPUE was mainly observed from 30°S to 40°S. A northward shift was observed after September, and the standardized CPUE was mainly concentrated at the south coast of Madagascar from November to January. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Marine Species Distribution)

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20 pages, 3490 KiB

Open AccessArticle

Species Distribution Models at Regional Scale: Cymodocea nodosa Seagrasses

by Enrique Casas, Laura Martín-García, Pedro Hernández-Leal and Manuel Arbelo

Remote Sens. 2022, 14(17), 4334; https://doi.org/10.3390/rs14174334 - 01 Sep 2022

Cited by 6 | Viewed by 2243

Abstract

Despite their ecological and socio-economic importance, seagrasses are often overlooked in comparison with terrestrial ecosystems. In the Canarian archipelago (Spain), Cymodocea nodosa is the best-established species, sustaining the most important marine ecosystem and providing ecosystem services (ES) of great relevance. Nevertheless, we lack accurate and standardized information regarding the distribution of this species and its ES supply. As a first step, the use of species distribution models is proposed. Various machine learning algorithms and ensemble model techniques were considered along with freely available remote sensing data to assess Cymodocea nodosa’s potential distribution. In a second step, we used InVEST software to estimate the ES provision by this phanerogam on a regional scale, providing spatially explicit monetary assessments and a habitat degradation characterization due to human impacts. The distribution models presented great predictive capabilities and statistical significance, while the ES estimations were in concordance with previous studies. The proposed methodology is presented as a useful tool for environmental management of important communities sensitive to human activities, such as C. nodosa meadows. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Marine Species Distribution)

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17 pages, 3745 KiB

Open AccessArticle

Reduction of Species Identification Errors in Surveys of Marine Wildlife Abundance Utilising Unoccupied Aerial Vehicles (UAVs)

by Eyal Bigal, Ori Galili, Itai van Rijn, Massimiliano Rosso, Christophe Cleguer, Amanda Hodgson, Aviad Scheinin and Dan Tchernov

Remote Sens. 2022, 14(16), 4118; https://doi.org/10.3390/rs14164118 - 22 Aug 2022

Cited by 2 | Viewed by 2006

Abstract

The advent of unoccupied aerial vehicles (UAVs) has enhanced our capacity to survey wildlife abundance, yet new protocols are still required for collecting, processing, and analysing image-type observations. This paper presents a methodological approach to produce informative priors on species misidentification probabilities based on independent experiments. We performed focal follows of known dolphin species and distributed our imagery amongst 13 trained observers. Then, we investigated the effects of reviewer-related variables and image attributes on the accuracy of species identification and level of certainty in observations. In addition, we assessed the number of reviewers required to produce reliable identification using an agreement-based framework compared with the majority rule approach. Among-reviewer variation was an important predictor of identification accuracy, regardless of previous experience. Image resolution and sea state exhibited the most pronounced effects on the proportion of correct identifications and the reviewers’ mean level of confidence. Agreement-based identification resulted in substantial data losses but retained a broader range of image resolutions and sea states than the majority rule approach and produced considerably higher accuracy. Our findings suggest a strong dependency on reviewer-related variables and image attributes, which, unless considered, may compromise identification accuracy and produce unreliable estimators of abundance. Full article

(This article belongs to the Special Issue Remote Sensing Applied to Marine Species Distribution)

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