Application of Estuarine and Coastal Classifications in Marine Spatial Management

Pittman, Simon J; Swanborn, Denise J.B.; Connor, David W.; Wright, Dawn J.

doi:10.1016/b978-0-323-90798-9.00040-8

Cited by 2 publications

(2 citation statements)

References 245 publications

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“…Spatial classification methods provide a more integrated approach to spatial management, enhancing coordination and facilitating objective comparisons across local to international scales. This method supports FAO's promotion of aquaculture by strategically allocating space through the designation of aquaculture zones (AZA), which helps to minimize spatial conflicts and environmental and socio-economic impacts [39]. Coastal classification and mapping are instrumental in marine spatial management, aiding in biodiversity conservation, fisheries management, and the development of a sustainable blue economy.…”

Section: Discussionmentioning

confidence: 86%

Chinese Coastal Fishing Ports Classification Based on Remote Sensing Images

Liu,

Luan,

Tian

et al. 2024

Land

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Fishing ports are important fishery production platforms, and the transformation of these has had a profound impact on the fishing industry. A reasonable classification solution is crucial for scientific understanding, development, and management of fishing ports. Current research on the use of spatial distributional characterization and the construction of a classification system for fishing ports to improve their management is limited. Therefore, in this study, a fishing port classification system was constructed using remote sensing images, with fishing port boundaries accurately extracted for classification. Using graphical summarization, fishing ports were classified into five types, including inland, estuarine, shoreline, gulf, and islands. A port type identification system was also constructed based on distance from the shoreline, water area, and circulation. Finally, fishing port characteristics and differences were investigated based on spatial superposition. The results demonstrate that shoreline and gulf types are the most prevalent fishing port types along the Chinese coast, accounting for 43% and 26% of the total number of fishing ports, respectively. This provides a strong foundation for China’s fishery production, processing, and trade. Through the establishment of a comprehensive scientific classification system, fishing port management can be modernized, supporting the sustainable development and utilization of coastal zones.

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Section: Discussionmentioning

confidence: 86%

Chinese Coastal Fishing Ports Classification Based on Remote Sensing Images

Liu,

Luan,

Tian

et al. 2024

Land

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“…Proper land planning in the coastal areas of Mozambique requires the costly process of evaluating landscape dynamics, performing a climate change assessment, and carrying out an integrated socio-environmental analysis [86][87][88][89]. The lowest possible cost when monitoring the coastal areas of Mozambique can be achieved by evaluating the difference between land cover types and performing a patch analysis using the image classification and quantification of landscape fragments [90]. This is possible using cartographic mapping and a time series analysis of the satellite images [91][92][93].…”

Section: Study Areamentioning

confidence: 99%

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

Lemenkova

2024

Coasts

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Mapping coastal regions is important for environmental assessment and for monitoring spatio-temporal changes. Although traditional cartographic methods using a geographic information system (GIS) are applicable in image classification, machine learning (ML) methods present more advantageous solutions for pattern-finding tasks such as the automated detection of landscape patches in heterogeneous landscapes. This study aimed to discriminate landscape patterns along the eastern coasts of Mozambique using the ML modules of a Geographic Resources Analysis Support System (GRASS) GIS. The random forest (RF) algorithm of the module ‘r.learn.train’ was used to map the coastal landscapes of the eastern shoreline of the Bight of Sofala, using remote sensing (RS) data at multiple temporal scales. The dataset included Landsat 8-9 OLI/TIRS imagery collected in the dry period during 2015, 2018, and 2023, which enabled the evaluation of temporal dynamics. The supervised classification of RS rasters was supported by the Scikit-Learn ML package of Python embedded in the GRASS GIS. The Bight of Sofala is characterized by diverse marine ecosystems dominated by swamp wetlands and mangrove forests located in the mixed saline–fresh waters along the eastern coast of Mozambique. This paper demonstrates the advantages of using ML for RS data classification in the environmental monitoring of coastal areas. The integration of Earth Observation data, processed using a decision tree classifier by ML methods and land cover characteristics enabled the detection of recent changes in the coastal ecosystem of Mozambique, East Africa.

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Application of Estuarine and Coastal Classifications in Marine Spatial Management

Cited by 2 publications

References 245 publications

Chinese Coastal Fishing Ports Classification Based on Remote Sensing Images

Chinese Coastal Fishing Ports Classification Based on Remote Sensing Images

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

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