Research Gap Analysis of Remote Sensing Application in Fisheries: Prospects for Achieving the Sustainable Development Goals
Abstract
:1. Introduction
2. Research Gaps in the Current Understanding of RS in Fisheries
3. Materials and Methods
3.1. Research Materials
3.2. Cooperative Network Connections
3.3. Comparison of the Cooperative Organizational Structures of The Main Research-Sponsoring Countries
3.4. Distribution of Hotspots and Gaps in Academic Research
4. Results
4.1. Progress in the International Application of RS in Fisheries
4.2. Network Pattern of the Application of RS in Fisheries
- United States (84);
- China (33);
- India (22);
- Australia (20);
- Canada (19);
- France (19);
- UK (18);
- Japan (12);
- Taiwan (11);
- Germany (10);
- Brazil (9);
- Italy (9).
4.3. Hotspots and the Gaps in Applying RS to Fisheries
5. Discussion
6. Conclusions
- the northeastern marine area of the United States;
- the high seas area of the North Atlantic Ocean;
- the surrounding sea areas of France, Spain and Portugal;
- the peripheral areas of the Indian Ocean;
- the East China Sea, Yellow Sea and Bohai Sea areas to the north of Taiwan.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Country | Total Number of Authorships during the Study Period (2009–2018) | 1st Five-Year Period (2009–2013) | 2nd Five-Year Period (2014–2018) | Growth Rate |
---|---|---|---|---|
USA | 366 | 172 | 194 | 13% |
China | 130 | 30 | 100 | 233% |
Others * | 115 | 43 | 72 | 67% |
India | 79 | 25 | 54 | 116% |
UK | 61 | 42 | 19 | −55% |
France | 51 | 27 | 24 | −11% |
Italy | 51 | 23 | 28 | 22% |
Japan | 45 | 21 | 24 | 14% |
Australia | 38 | 8 | 30 | 275% |
Taiwan | 37 | 13 | 24 | 85% |
Canada | 35 | 16 | 19 | 19% |
Brazil | 32 | 15 | 17 | 13% |
Germany | 31 | 12 | 19 | 58% |
Spain | 28 | 3 | 25 | 733% |
Belgium | 22 | 18 | 4 | −78% |
Argentina | 21 | 4 | 17 | 325% |
Mexico | 17 | 3 | 14 | 367% |
Norway | 16 | 9 | 7 | −22% |
South Africa | 15 | 3 | 12 | 300% |
Philippines | 14 | 0 | 14 | - |
South Korea | 14 | 9 | 5 | −44% |
Indonesia | 13 | 0 | 13 | - |
Turkey | 13 | 7 | 6 | −14% |
Portugal | 10 | 2 | 8 | 300% |
Rank | Country | International | Domestic | Total |
---|---|---|---|---|
1 | USA | 39 | 45 | 84 |
2 | China | 13 | 20 | 33 |
3 | India | 2 | 20 | 22 |
4 | Australia | 16 | 4 | 20 |
4 | France | 17 | 3 | 20 |
5 | Canada | 12 | 7 | 19 |
6 | UK | 16 | 2 | 18 |
7 | Japan | 8 | 4 | 12 |
8 | Taiwan | 7 | 4 | 11 |
9 | Germany | 8 | 2 | 10 |
10 | Brazil | 4 | 5 | 9 |
10 | Italy | 6 | 3 | 9 |
11 | Spain | 5 | 3 | 8 |
12 | South Africa | 6 | 1 | 7 |
13 | Belgium and Norway | 6 | 0 | 6 |
14 | Argentina | 1 | 4 | 5 |
14 | Mexico | 2 | 3 | 5 |
15 | Indonesia | 4 | 0 | 4 |
15 | Kenya, Malaysia and the Netherlands | 3 | 1 | 4 |
1 5 | New Caledonia, New Zealand, Poland, Portugal and Russia | 4 | 0 | 4 |
16 | South Korea | 2 | 1 | 3 |
16 | Sri Lanka | 3 | 0 | 3 |
17 | Cambodia, Fiji, Iran, Madagascar and Sweden | 2 | 0 | 2 |
17 | Finland, Singapore and Thailand | 1 | 1 | 2 |
18 | Austria, Cameroon, Chile, Costa Rica, Cote d’Ivoire, Egypt, Falkland Islands, French Polynesia, Greece, Iceland, Iraq, Kiribati, Malawi, Malta, Mauritania, Morocco, Myanmar, Namibia, Panama, Philippines, Switzerland, Tanzania, Uruguay, Vietnam and Zambia | 1 | 0 | 1 |
18 | Turkey and Ecuador | 0 | 1 | 1 |
Total | 250 | 139 | 389 |
Rank | Authors | Title | Total Citation | Publication Year | Level of Collaboration |
---|---|---|---|---|---|
1 | Anderson, D. M. | Approaches to monitoring, control and management of harmful algal blooms (HABs) | 207 | 2009 | Single author |
2 | Lavik, G.; Stührmann, T.; Brüchert, V.; Van der Plas, A.; Mohrholz, V.; Lam, P.; Mussmann, M.; Fuchs, B. M.; Amann, R.; Lass, U.; and Kuypers, M. M. | Detoxification of sulphidic African shelf waters by blooming chemolithotrophs | 181 | 2009 | International |
3 | Richards, D. R.; and Friess, D. A. | Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012 | 171 | 2016 | Domestic |
4 | Zhu, C.; Zhou, H.; Wang, R.; and Guo, J. | A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image Based on Shape and Texture Features | 165 | 2010 | Domestic |
5 | Hamilton, S. E.; and Casey, D. | Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21) | 140 | 2016 | Domestic |
6 | Heumann, B. W. | Satellite remote sensing of mangrove forests: Recent advances and future opportunities | 136 | 2011 | Domestic |
7 | Murphy, H. M.; and Jenkins, G. P. | Observational methods used in marine spatial monitoring of fishes and associated habitats: a review | 123 | 2010 | Domestic |
8 | Pittman, S. J.; and Brown, K. A. | Multi-Scale Approach for Predicting Fish Species Distributions across Coral Reef Seascapes | 117 | 2011 | International |
9 | Li, J.; Wang, X.; Wang, X.; Ma, W.; and Zhang, H. | Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area, China | 114 | 2009 | Domestic |
10 | Corbane, C.; Najman, L.; Pecoul, E.; Demagistri, L.; and Petit, M | A complete processing chain for ship detection using optical satellite imagery | 85 | 2010 | International |
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Yen, K.-W.; Chen, C.-H. Research Gap Analysis of Remote Sensing Application in Fisheries: Prospects for Achieving the Sustainable Development Goals. Remote Sens. 2021, 13, 1013. https://doi.org/10.3390/rs13051013
Yen K-W, Chen C-H. Research Gap Analysis of Remote Sensing Application in Fisheries: Prospects for Achieving the Sustainable Development Goals. Remote Sensing. 2021; 13(5):1013. https://doi.org/10.3390/rs13051013
Chicago/Turabian StyleYen, Kuo-Wei, and Chia-Hsiang Chen. 2021. "Research Gap Analysis of Remote Sensing Application in Fisheries: Prospects for Achieving the Sustainable Development Goals" Remote Sensing 13, no. 5: 1013. https://doi.org/10.3390/rs13051013
APA StyleYen, K. -W., & Chen, C. -H. (2021). Research Gap Analysis of Remote Sensing Application in Fisheries: Prospects for Achieving the Sustainable Development Goals. Remote Sensing, 13(5), 1013. https://doi.org/10.3390/rs13051013