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18 pages, 2509 KiB

Open AccessArticle

Links between Phenology of Large Phytoplankton and Fisheries in the Northern and Central Red Sea

by John A. Gittings, Dionysios E. Raitsos, Robert J. W. Brewin and Ibrahim Hoteit

Remote Sens. 2021, 13(2), 231; https://doi.org/10.3390/rs13020231 - 11 Jan 2021

Cited by 19 | Viewed by 4741

Abstract

Phytoplankton phenology and size structure are key ecological indicators that influence the survival and recruitment of higher trophic levels, marine food web structure, and biogeochemical cycling. For example, the presence of larger phytoplankton cells supports food chains that ultimately contribute to fisheries resources. [...] Read more.

Phytoplankton phenology and size structure are key ecological indicators that influence the survival and recruitment of higher trophic levels, marine food web structure, and biogeochemical cycling. For example, the presence of larger phytoplankton cells supports food chains that ultimately contribute to fisheries resources. Monitoring these indicators can thus provide important information to help understand the response of marine ecosystems to environmental change. In this study, we apply the phytoplankton size model of Gittings et al. (2019b) to 20-years of satellite-derived ocean colour observations in the northern and central Red Sea, and investigate interannual variability in phenology metrics for large phytoplankton (>2 µm in cell diameter). Large phytoplankton consistently bloom in the winter. However, the timing of bloom initiation and termination (in autumn and spring, respectively) varies between years. In the autumn/winter of 2002/2003, we detected a phytoplankton bloom, which initiated ~8 weeks earlier and lasted ~11 weeks longer than average. The event was linked with an eddy dipole in the central Red Sea, which increased nutrient availability and enhanced the growth of large phytoplankton. The earlier timing of food availability directly impacted the recruitment success of higher trophic levels, as represented by the maximum catch of two commercially important fisheries (Sardinella spp. and Teuthida) in the following year. The results of our analysis are essential for understanding trophic linkages between phytoplankton and fisheries and for marine management strategies in the Red Sea. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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14 pages, 1436 KiB

Open AccessArticle

Developing an Atlas of Harmful Algal Blooms in the Red Sea: Linkages to Local Aquaculture

by Elamurugu Alias Gokul, Dionysios E. Raitsos, John A. Gittings and Ibrahim Hoteit

Remote Sens. 2020, 12(22), 3695; https://doi.org/10.3390/rs12223695 - 11 Nov 2020

Cited by 12 | Viewed by 4797

Abstract

Harmful algal blooms (HABs) are one of the leading causes of biodiversity loss and alterations to ecosystem services. The Red Sea is one of the least studied large marine ecosystems (LMEs), and knowledge on the large-scale spatiotemporal distribution of HABs remains limited. We [...] Read more.

Harmful algal blooms (HABs) are one of the leading causes of biodiversity loss and alterations to ecosystem services. The Red Sea is one of the least studied large marine ecosystems (LMEs), and knowledge on the large-scale spatiotemporal distribution of HABs remains limited. We implemented the recently developed remote sensing algorithm of Gokul et al. (2019) to produce a high-resolution atlas of HAB events in the Red Sea and investigated their spatiotemporal variability between 2003 and 2017. The atlas revealed that (i) the southern part of the Red Sea is subject to a higher occurrence of HABs, as well as long-lasting and large-scale events, in comparison to the northern part of the basin, and (ii) the Red Sea HABs exhibited a notable seasonality, with most events occurring during summer. We further investigated the potential interactions between identified HAB events and the National Aquaculture Group (NAQUA), Al-Lith (Saudi Arabia)—the largest aquaculture facility on the Red Sea coast. The results suggest that the spatial coverage of HABs and the elevated chlorophyll-a concentration (Chl-a) (> 1 mg m⁻³; a proxy for high nutrient concentration), in the coastal waters of Al-Lith during summer, increased concurrently with the local aquaculture annual production over a nine-year period (2002–2010). This could be attributed to excessive nutrient loading from the NAQUA facility’s outfall, which enables the proliferation of HABs in an otherwise oligotrophic region during summer. Aquaculture is an expanding, high-value industry in the Kingdom of Saudi Arabia. Thus, a wastewater management plan should ideally be implemented at a national level, in order to prevent excessive nutrient loading. Our results may assist policy-makers’ efforts to ensure the sustainable development of the Red Sea’s coastal economic zone. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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17 pages, 3884 KiB

Open AccessFeature PaperArticle

A Major Ecosystem Shift in Coastal East African Waters During the 1997/98 Super El Niño as Detected Using Remote Sensing Data

by Zoe L. Jacobs, Fatma Jebri, Meric Srokosz, Dionysios E. Raitsos, Stuart C. Painter, Francesco Nencioli, Kennedy Osuka, Melita Samoilys, Warwick Sauer, Michael Roberts, Sarah F. W. Taylor, Lucy Scott, Hellen Kizenga and Ekaterina Popova

Remote Sens. 2020, 12(19), 3127; https://doi.org/10.3390/rs12193127 - 23 Sep 2020

Cited by 15 | Viewed by 4528

Abstract

Under the impact of natural and anthropogenic climate variability, upwelling systems are known to change their properties leading to associated regime shifts in marine ecosystems. These often impact commercial fisheries and societies dependent on them. In a region where in situ hydrographic and [...] Read more.

Under the impact of natural and anthropogenic climate variability, upwelling systems are known to change their properties leading to associated regime shifts in marine ecosystems. These often impact commercial fisheries and societies dependent on them. In a region where in situ hydrographic and biological marine data are scarce, this study uses a combination of remote sensing and ocean modelling to show how a stable seasonal upwelling off the Kenyan coast shifted into the territorial waters of neighboring Tanzania under the influence of the unique 1997/98 El Niño and positive Indian Ocean Dipole event. The formation of an anticyclonic gyre adjacent to the Kenyan/Tanzanian coast led to a reorganization of the surface currents and caused the southward migration of the Somali–Zanzibar confluence zone and is attributed to anomalous wind stress curl over the central Indian Ocean. This caused the lowest observed chlorophyll-a over the North Kenya banks (Kenya), while it reached its historical maximum off Dar Es Salaam (Tanzanian waters). We demonstrate that this situation is specific to the 1997/98 El Niño when compared with other the super El-Niño events of 1972,73, 1982–83 and 2015–16. Despite the lack of available fishery data in the region, the local ecosystem changes that the shift of this upwelling may have caused are discussed based on the literature. The likely negative impacts on local fish stocks in Kenya, affecting fishers’ livelihoods and food security, and the temporary increase in pelagic fishery species’ productivity in Tanzania are highlighted. Finally, we discuss how satellite observations may assist fisheries management bodies to anticipate low productivity periods, and mitigate their potentially negative economic impacts. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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18 pages, 14008 KiB

Open AccessArticle

Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine

by Zhe Sun, Juhua Luo, Jingzhicheng Yang, Qiuyan Yu, Li Zhang, Kun Xue and Lirong Lu

Remote Sens. 2020, 12(18), 3086; https://doi.org/10.3390/rs12183086 - 21 Sep 2020

Cited by 57 | Viewed by 7857

Abstract

Global rapid expansion of the coastal aquaculture industry has made great contributions to enhance food security, but has also caused a series of ecological and environmental issues. Sustainable management of coastal areas requires the explicit and efficient mapping of the spatial distribution of [...] Read more.

Global rapid expansion of the coastal aquaculture industry has made great contributions to enhance food security, but has also caused a series of ecological and environmental issues. Sustainable management of coastal areas requires the explicit and efficient mapping of the spatial distribution of aquaculture ponds. In this study, a Google Earth Engine (GEE) application was developed for mapping coastal aquaculture ponds at a national scale with a novel classification scheme using Sentinel-1 time series data. Relevant indices used in the classification mainly include the water index, texture, and geometric metrics derived from radar backscatter, which were then used to segment and classify aquaculture ponds. Using this approach, we classified aquaculture ponds for the full extent of the coastal area in Vietnam with an overall accuracy of 90.16% (based on independent sample evaluation). The approach, enabling wall-to-wall mapping and area estimation, is essential to the efficient monitoring and management of aquaculture ponds. The classification results showed that aquaculture ponds are widely distributed in Vietnam’s coastal area and are concentrated in the Mekong River Delta and Red River delta (85.14% of the total area), which are facing the increasing collective risk of climate change (e.g., sea level rise and salinity intrusion). Further investigation of the classification results also provides significant insights into the stability and deliverability of the approach. The water index derived from annual median radar backscatter intensity was determined to be efficient at mapping water bodies, likely due to its strong response to water bodies regardless of weather. The geometric metrics considering the spatial variation of radar backscatter patterns were effective at distinguishing aquaculture ponds from other water bodies. The primary use of GEE in this approach makes it replicable and transferable by other users. Our approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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15 pages, 5391 KiB

Open AccessArticle

Evaluating a Suitable Aquaculture Site Selection Model for Cobia (Rachycentron canadum) during Extreme Events in the Inner Bay of the Penghu Islands, Taiwan

by Yan-Lun Wu, Ming-An Lee, Lu-Chi Chen, Jui-Wen Chan and Kuo-Wei Lan

Remote Sens. 2020, 12(17), 2689; https://doi.org/10.3390/rs12172689 - 20 Aug 2020

Cited by 14 | Viewed by 3452

Abstract

Despite numerous studies on the effect of a cold weather disaster on fisheries in 2008, no operational systems have been developed to monitor the threat of such an event to mariculturists in the Penghu Islands (PHI) region of Taiwan. The present study employed [...] Read more.

Despite numerous studies on the effect of a cold weather disaster on fisheries in 2008, no operational systems have been developed to monitor the threat of such an event to mariculturists in the Penghu Islands (PHI) region of Taiwan. The present study employed a suitable aquaculture site selection map of the inner bay of the PHI to reduce aquaculture losses and mortality rates of cobia (Rachycentron canadum) during extreme events. Daily marine environmental data, including sea surface temperature (SST), chlorophyll-a concentration (chl-a), and wind speed in the winter, were collected. An extreme event was defined as a period of over 11 days in a month of strong winds (>6 m/s). Four parameters in the PHI inner bay, including SST, cold-water intrusion days, chl-a, and offshore distance to the PHI coastline, were used to evaluate suitable aquaculture sites for cobia culture. The results indicated that La Niña events could not be used as a factor to detect cold-water intrusion events and select suitable aquaculture sites in the PHI. The evaluated suitable aquaculture site selection map, obtained using an arithmetic mean model and a geometric mean model, revealed that the avoidance sites during extreme events were concentrated in the northern and northwestern PHI. Suitable areas were concentrated in the southeastern areas. We further suggested that commercial cobia aquaculture operations in the PHI inner bay could be moved to the suitable sites in southeastern PHI during extreme events. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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15 pages, 2623 KiB

Open AccessArticle

Ensemble Modelling of Skipjack Tuna (Katsuwonus pelamis) Habitats in the Western North Pacific Using Satellite Remotely Sensed Data; a Comparative Analysis Using Machine-Learning Models

by Robinson Mugo and Sei-Ichi Saitoh

Remote Sens. 2020, 12(16), 2591; https://doi.org/10.3390/rs12162591 - 12 Aug 2020

Cited by 34 | Viewed by 5801

Abstract

To examine skipjack tuna’s habitat utilization in the western North Pacific (WNP) we used an ensemble modelling approach, which applied a fisher- derived presence-only dataset and three satellite remote-sensing predictor variables. The skipjack tuna data were compiled from daily point fishing data into [...] Read more.

To examine skipjack tuna’s habitat utilization in the western North Pacific (WNP) we used an ensemble modelling approach, which applied a fisher- derived presence-only dataset and three satellite remote-sensing predictor variables. The skipjack tuna data were compiled from daily point fishing data into monthly composites and re-gridded into a quarter degree resolution to match the environmental predictor variables, the sea surface temperature (SST), sea surface chlorophyll-a (SSC) and sea surface height anomalies (SSHA), which were also processed at quarter degree spatial resolution. Using the sdm package operated in RStudio software, we constructed habitat models over a 9-month period, from March to November 2004, using 17 algorithms, with a 70:30 split of training and test data, with bootstrapping and 10 runs as parameter settings for our models. Model performance evaluation was conducted using the area under the curve (AUC) of the receiver operating characteristic (ROC), the point biserial correlation coefficient (COR), the true skill statistic (TSS) and Cohen’s kappa (k) metrics. We analyzed the response curves for each predictor variable per algorithm, the variable importance information and the ROC plots. Ensemble predictions of habitats were weighted with the TSS metric. Model performance varied across various algorithms, with the Support Vector Machines (SVM), Boosted Regression Trees (BRT), Random Forests (RF), Multivariate Adaptive Regression Splines (MARS), Generalized Additive Models (GAM), Classification and Regression Trees (CART), Multi-Layer Perceptron (MLP), Recursive Partitioning and Regression Trees (RPART), and Maximum Entropy (MAXENT), showing consistently high performance than other algorithms, while the Flexible Discriminant Analysis (FDA), Mixture Discriminant Analysis (MDA), Bioclim (BIOC), Domain (DOM), Maxlike (MAXL), Mahalanobis Distance (MAHA) and Radial Basis Function (RBF) had lower performance. We found inter-algorithm variations in predictor variable responses. We conclude that the multi-algorithm modelling approach enabled us to assess the variability in algorithm performance, hence a data driven basis for building the ensemble model. Given the inter-algorithm variations observed, the ensemble prediction maps indicated a better habitat utilization map of skipjack tuna than would have been achieved by a single algorithm. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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19 pages, 5368 KiB

Open AccessArticle

Multisatellite-Based Feeding Habitat Suitability Modeling of Albacore Tuna in the Southern Atlantic Ocean

by Ali Haghi Vayghan, Ming-An Lee, Jinn-Shing Weng, Sandipan Mondal, Ching-Te Lin and Yi-Chen Wang

Remote Sens. 2020, 12(16), 2515; https://doi.org/10.3390/rs12162515 - 5 Aug 2020

Cited by 19 | Viewed by 4545

Abstract

Decision strategies in fisheries management are often directed by the geographic distribution and habitat preferences of target species. This study used remote sensing data to identify the optimal feeding habitat of albacore tuna in the Southern Atlantic Ocean (SAO) using an empirical habitat [...] Read more.

Decision strategies in fisheries management are often directed by the geographic distribution and habitat preferences of target species. This study used remote sensing data to identify the optimal feeding habitat of albacore tuna in the Southern Atlantic Ocean (SAO) using an empirical habitat suitability model applying longline fisheries data during 2009–2015. An arithmetic mean model with sea surface temperature (SST) and sea surface chlorophyll-a concentration (SSC) was determined to be suitable for defining the albacore habitat in the SAO. The optimal ranges of SST and SSC for the habitat were approximately 16.5 °C–19.5 °C and 0.11–0.33 mg/m³, respectively. The study revealed a considerable positive trend between the suitable habitat area and standardized catch per unit effort (r = 0.97; p < 0.05); due to the west-to-east and northward development of the suitable habitat, albacore schools moved to the northeast of the SAO, thus increasing catch probability in April to August in that region. Overall, the frontal structure of SST and SSC plays an essential role in the formation of potential albacore habitats in the SAO. Our findings could contribute to the establishment of regional ecosystem-based fisheries management in the SAO. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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18 pages, 7012 KiB

Open AccessArticle

Association of Environmental Factors in the Taiwan Strait with Distributions and Habitat Characteristics of Three Swimming Crabs

by Muhamad Naimullah, Kuo-Wei Lan, Cheng-Hsin Liao, Po-Yuan Hsiao, Yen-Rong Liang and Ting-Chen Chiu

Remote Sens. 2020, 12(14), 2231; https://doi.org/10.3390/rs12142231 - 11 Jul 2020

Cited by 20 | Viewed by 4765

Abstract

Information regarding the oceanic environment is crucial for determining species distributions and their habitat preferences. However, in studies on crustaceans, especially swimming crabs, such information remains poorly utilized, and its effects on crab communities in the Taiwan Strait (TS) has not been well [...] Read more.

Information regarding the oceanic environment is crucial for determining species distributions and their habitat preferences. However, in studies on crustaceans, especially swimming crabs, such information remains poorly utilized, and its effects on crab communities in the Taiwan Strait (TS) has not been well documented. The purpose of this study was to understand the relationship between the catch rates of three swimming crab species and environmental factors in the TS. We fitted generalized additive models (GAMs) to logbooks and voyage data recorder data from Taiwanese crab vessels (2011–2015), developed a species distribution model, and predicted catch rates for these three swimming crab species based on the GAM output. The chlorophyll-a (Chl-a) concentration was related to the high catch rates of Chrybdis feriatus and Portunus sanguinolentus, whereas bottom temperature (BT) was related to high catch rates of Portunus pelagicus. The variance percentages for each crab species indicated that high catch rates of C. feriatus and P. sanguinolentus occurred in a Chl-a concentration > 0.5 mg/m³, whereas P. pelagicus catch rates exhibited negative correlations with BTs > 25 °C. The model predicted high catch rates of C. feriatus in the north of the TS during autumn and winter, whereas P. pelagicus was observed to the south during summer and autumn. P. sanguinolentus was predicted to be widely distributed around the TS and distributed further to the northern area during autumn and winter. These findings revealed that each species responds to spatiotemporal environmental variations. Understanding the distributions and habitats of these three crabs is vital in fisheries resource management and conservation planning. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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26 pages, 10813 KiB

Open AccessArticle

Pen Culture Detection Using Filter Tensor Analysis with Multi-Temporal Landsat Imagery

by Yanxin Xi, Luyan Ji and Xiurui Geng

Remote Sens. 2020, 12(6), 1018; https://doi.org/10.3390/rs12061018 - 22 Mar 2020

Cited by 4 | Viewed by 2962

Abstract

Aquaculture plays an important role in China’s total fisheries production nowadays, and it leads to a few problems, for example water quality degradation, which has damaging effect on the sustainable development of environment. Among the many forms of aquaculture that deteriorate the water [...] Read more.

Aquaculture plays an important role in China’s total fisheries production nowadays, and it leads to a few problems, for example water quality degradation, which has damaging effect on the sustainable development of environment. Among the many forms of aquaculture that deteriorate the water quality, disorderly pen culture is especially severe. Pen culture began very early in Yangchenghu Lake and Taihu Lake in China and part of the pen culture still exists. Thus, it is of great significance to evaluate the distribution and area of the pen culture in the two lakes. However, the traditional method for pen culture detection is based on the factual measurement, which is labor and time consuming. At present, with the development of remote sensing technologies, some target detection algorithms for multi/hyper-spectral data have been used in the pen culture detection, but most of them are intended for the single-temporal remote sensing data. Recently, a target detection algorithm called filter tensor analysis (FTA), which is specially designed for multi-temporal remote sensing data, has been reported and has achieved better detection results compared to the traditional single-temporal methods in many cases. This paper mainly aims to investigate the pen culture in Yangchenghu Lake and Taihu Lake with FTA implemented on the multi-temporal Landsat imagery, by determining the optimal time phases combination of the Landsat data in advance. Furthermore, the suitability and superiority of FTA over Constrained Energy Minimization (CEM) in the process of pen culture detection were tested. It was observed in the experiments on the data of those two lakes that FTA can detect the pen culture much more accurately than CEM with Landsat data of selected bands and of limited number of time phases. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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16 pages, 5887 KiB

Open AccessArticle

Modeling the Spatial Distribution of Swordfish (Xiphias gladius) Using Fishery and Remote Sensing Data: Approach and Resolution

by Nan-Jay Su, Chia-Hao Chang, Ya-Ting Hu, Wei-Chuan Chiang and Chen-Te Tseng

Remote Sens. 2020, 12(6), 947; https://doi.org/10.3390/rs12060947 - 15 Mar 2020

Cited by 11 | Viewed by 4290

Abstract

Swordfish, Xiphias gladius (Linnaeus, 1758), is a commercially important species that is widely distributed throughout three oceans. This species inhabits oceanic waters with preferred environmental ranges and migrates vertically to the surface layer for feeding. However, the spatial distribution pattern and habitat preferences [...] Read more.

Swordfish, Xiphias gladius (Linnaeus, 1758), is a commercially important species that is widely distributed throughout three oceans. This species inhabits oceanic waters with preferred environmental ranges and migrates vertically to the surface layer for feeding. However, the spatial distribution pattern and habitat preferences of swordfish have been rarely studied in the Pacific Ocean due to the wide geographic range of this species. This study examined the spatial distribution and preferred ranges of environmental variables for swordfish using two approaches, generalized additive models and habitat suitability index methods, with different spatio-temporal data resolution scales. Results indicated that sea surface temperature is the most important factor determining swordfish spatial distribution. Habitat spatial pattern and preferred environmental ranges, estimated using various modeling approaches, were robust relative to the spatio-temporal data resolution scales. The models were validated by examining the consistency between predictions and untrained actual observations, which all predicted a high relative density of swordfish in the tropical waters of the central Pacific Ocean, with no obvious seasonal movement. Results from this study, based on fishery and remote sensing data with wide spatial coverage, could benefit the conservation and management of fisheries for highly migratory species such as swordfish and tuna. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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17 pages, 3790 KiB

Open AccessArticle

Spatial Habitat Shifts of Oceanic Cephalopod (Ommastrephes bartramii) in Oscillating Climate

by Irene D. Alabia, Sei-Ichi Saitoh, Hiromichi Igarashi, Yoichi Ishikawa and Yutaka Imamura

Remote Sens. 2020, 12(3), 521; https://doi.org/10.3390/rs12030521 - 5 Feb 2020

Cited by 15 | Viewed by 4436

Abstract

Short- and long-term climate oscillations impact seascapes, and hence, marine ecosystem structure and dynamics. Here, we explored the spatio-temporal patterns of potential squid habitat in the western and central North Pacific across inter-decadal climate transitions, coincident with periods of persistent warming and cooling. [...] Read more.

Short- and long-term climate oscillations impact seascapes, and hence, marine ecosystem structure and dynamics. Here, we explored the spatio-temporal patterns of potential squid habitat in the western and central North Pacific across inter-decadal climate transitions, coincident with periods of persistent warming and cooling. Potential habitat distributions of Ommastrephes bartramii were derived from the outputs of multi-ensemble species distribution models, developed using the most influential environmental factors to squid distribution and occurrence data. Our analyses captured the underlying temporal trends in potential squid habitat in response to environmental changes transpiring at each climatic transition, regulated by phase shifts in Pacific decadal oscillation (PDO) from 1999–2013. The spatial differences in environmental conditions were apparent across transitions and presumably modulate the local changes in suitable squid habitat over time. Specifically, during a cold to warm PDO shift, decreases in the summer potential habitat (mean rate ± standard deviation: −0.04 ± 0.02 habitat suitability index (HSI)/yr) were observed along the southern edge of the subarctic frontal zone (162°E–172°W). Coincidentally, this area also exhibits a warming trend (mean temporal trend: 0.06 ± 0.21 °C/yr), accompanied with the prevalence of cold-core mesoscale eddies, west of the dateline (mean temporal trend in sea surface height: −0.19 ± 1.05 cm/yr). These conditions potentially generate less favorable foraging habitat for squid. However, a warm-to-cold PDO transition underpins a northward shift of suitable habitat and an eastward shift of regions exhibiting the highest rate of potential squid habitat loss (170–160°W; mean temporal trend: −0.05 ± 0.03 HSI/yr). Nonetheless, the emergence of the areas with increasingly suitable habitat regardless of climate transitions suggests the ecological importance of these regions as potential squid habitat hotspots and climatic refugia. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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18 pages, 15405 KiB

Open AccessArticle

Spatio-Temporal Patterns of Coastal Aquaculture Derived from Sentinel-1 Time Series Data and the Full Landsat Archive

by Dorothee Stiller, Marco Ottinger and Patrick Leinenkugel

Remote Sens. 2019, 11(14), 1707; https://doi.org/10.3390/rs11141707 - 18 Jul 2019

Cited by 45 | Viewed by 6418

Abstract

Asia is the major contributor to global aquaculture production in quantity, accounting for almost 90%. These practices lead to extensive land-use and land-cover changes in coastal areas, and thus harm valuable and sensitive coastal ecosystems. Remote sensing and GIS technologies contribute to the [...] Read more.

Asia is the major contributor to global aquaculture production in quantity, accounting for almost 90%. These practices lead to extensive land-use and land-cover changes in coastal areas, and thus harm valuable and sensitive coastal ecosystems. Remote sensing and GIS technologies contribute to the mapping and monitoring of changes in aquaculture, providing essential information for coastal management applications. This study aims to investigate aquaculture expansion and spatio-temporal dynamics in two Chinese river deltas over three decades: the Yellow River Delta (YRD) and the Pearl River Delta (PRD). Long-term patterns of aquaculture change are extracted based on combining a reference layer on existing aquaculture ponds for 2015 derived from Sentinel-1 data with annual information on water bodies extracted from the long-term Landsat archive. Furthermore, the suitability of the proposed approach to be applied on a global scale is tested based on exploiting the Global Surface Water (GSW) dataset. We found enormous increases in aquaculture area for the investigated target deltas: an 18.6-fold increase for the YRD (1984–2016), and a 4.1-fold increase for the PRD (1990–2016). Furthermore, we detect hotspots of aquaculture expansion based on linear regression analyses for the deltas, indicating that hotspots are located in coastal regions for the YRD and along the Pearl River in the PRD. A comparison with high-resolution Google Earth data demonstrates that the proposed approach can detect spatio-temporal changes of aquaculture at an overall accuracy of 89%. The presented approach has the potential to be applied to larger spatial scales covering a time period of more than three decades. This is crucial to define appropriate management strategies to reduce the environmental impacts of aquaculture expansion, which are expected to increase in the future. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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17 pages, 19213 KiB

Open AccessArticle

Assessment of Coastal Aquaculture for India from Sentinel-1 SAR Time Series

by Kumar Arun Prasad, Marco Ottinger, Chunzhu Wei and Patrick Leinenkugel

Remote Sens. 2019, 11(3), 357; https://doi.org/10.3390/rs11030357 - 11 Feb 2019

Cited by 70 | Viewed by 12251

Abstract

Aquaculture is one of the fastest growing primary food production sectors in India and ranks second behind China. Due to its growing economic value and global demand, India’s aquaculture industry experienced exponential growth for more than one decade. In this study, we extract [...] Read more.

Aquaculture is one of the fastest growing primary food production sectors in India and ranks second behind China. Due to its growing economic value and global demand, India’s aquaculture industry experienced exponential growth for more than one decade. In this study, we extract land-based aquaculture at the pond level for the entire coastal zone of India using large-volume time series Sentinel-1 synthetic-aperture radar (SAR) data at 10-m spatial resolution. Elevation and slope from Shuttle Radar Topographic Mission digital elevation model (SRTM DEM) data were used for masking inappropriate areas, whereas a coastline dataset was used to create a land/ocean mask. The pixel-wise temporal median was calculated from all available Sentinel-1 data to significantly reduce the amount of noise in the SAR data and to reduce confusions with temporary inundated rice fields. More than 3000 aquaculture pond vector samples were collected from high-resolution Google Earth imagery and used in an object-based image classification approach to exploit the characteristic shape information of aquaculture ponds. An open-source connected component segmentation algorithm was used for the extraction of the ponds based on the difference in backscatter intensity of inundated surfaces and shape metrics calculated from aquaculture samples as input parameters. This study, for the first time, provides spatial explicit information on aquaculture distribution at the pond level for the entire coastal zone of India. Quantitative spatial analyses were performed to identify the provincial dominance in aquaculture production, such as that revealed in Andhra Pradesh and Gujarat provinces. For accuracy assessment, 2000 random samples were generated based on a stratified random sampling method. The study demonstrates, with an overall accuracy of 0.89, the spatio-temporal transferability of the methodological framework and the high potential for a global-scale application. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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Jump to: Research

22 pages, 411 KiB

Open AccessReview

Remote Sensing of Ocean Fronts in Marine Ecology and Fisheries

by Igor M. Belkin

Remote Sens. 2021, 13(5), 883; https://doi.org/10.3390/rs13050883 - 27 Feb 2021

Cited by 69 | Viewed by 10513

Abstract

This paper provides a concise review of the remote sensing of ocean fronts in marine ecology and fisheries, with a particular focus on the most popular front detection algorithms and techniques, including those proposed by Canny, Cayula and Cornillon, Miller, Shimada et al., [...] Read more.

This paper provides a concise review of the remote sensing of ocean fronts in marine ecology and fisheries, with a particular focus on the most popular front detection algorithms and techniques, including those proposed by Canny, Cayula and Cornillon, Miller, Shimada et al., Belkin and O’Reilly, and Nieto et al.. A case is made for a feature-based approach that emphasizes fronts as major structural and circulation features of the ocean realm that play key roles in various aspects of marine ecology. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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