Assessment of coastal sustainable development along the maritime silk road using an integrated natural-economic-social (NES) ecosystem

Zuo, Jian; Li, Zhang; Chen, Bo-Wei; Liao, Jingjuan; Hashim, Mazlan; Sutrisno, Dewayany; Hasan, Mohammad Emran; Mahmood, Riffat; Sani, Dalhatu Aliyu

doi:10.1016/j.heliyon.2023.e17440

Cited by 5 publications

(1 citation statement)

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“…However, the rapid expansion of coastal aquaculture [12][13][14][15][16][17] has resulted in significant competition for land [18]. This competition can cause direct and severe damage to coastal ecosystems, leading to environmental problems such as the shrinkage or disappearance of native coastal wetlands, water pollution, the destruction of natural habitats, the fragmentation of landscapes, the loss of biodiversity, and the increased vulnerability of coastal zones [19][20][21][22][23]. Competition for land is highly relevant to SDG14, which aims to protect oceans and marine resources.…”

Section: Introductionmentioning

confidence: 99%

An Object-Based Approach to Extract Aquaculture Ponds with 10-Meter Resolution Sentinel-2 Images: A Case Study of Wenchang City in Hainan Province

Hu,

Zhang,

Chen

et al. 2024

Remote Sensing

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Coastal aquaculture has made an important contribution to global food security and the economic development of coastal zones in recent decades. However, it has also damaged these coastal zones’ ecosystems. Moreover, coastal aquaculture is poised to play a key role in the achievement of Sustainable Development Goals (SDGs). Consequently, extracting aquaculture has become crucial and valuable. However, due to the limitations of remote sensing image spatial resolution and traditional extraction methods, most research studies focus on aquaculture areas containing dikes rather than individually separable aquaculture ponds (ISAPs). This is not an accurate estimation of these aquaculture areas’ true size. In our study, we propose a rapid and effective object-based method of extracting ISAPs. We chose multi-scale segmentation to generate semantically meaningful image objects for various types of land cover, and then built a decision tree classifier according to the unique features of ISAPs. The results show that our method can remove small rivers and other easily confused features, which has thus far been difficult to accomplish with conventional methods. We obtained an overall precision value of 85.61% with a recall of 84.04%; compared to the support vector machine’s (SVM) overall precision value of 78.85% and recall rate of 61.21%, our method demonstrates greater accuracy and efficiency. We used this method to test the transferability of the algorithm to nearby areas, and the obtained accuracy exceeded 80%. The method proposed in this study could provide a readily available solution for the simple and efficient extracting of ISAPs and shows high spatiotemporal transferability.

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