Synergistic Integration of Time Series Optical and SAR Satellite Data for Mariculture Extraction

Abstract

Mariculture is an important part of aquaculture, and it is important to address global food security and nutrition issues. However, seawater environmental conditions are complex and variable, which causes large uncertainties in the remote sensing spectral features. At the same time, mariculture types are distinct because of the different types of aquaculture (cage aquaculture and raft aquaculture). These factors bring great challenges for mariculture extraction and mapping using remote sensing. In order to solve these problems, an optical remote sensing aquaculture index named the marine aquaculture index (MAI) is proposed. Based on this spectral index, using time series Sentinel-1 and Sentinel-2 satellite data, a random forest classification scheme is proposed for mapping mariculture by combining spectral, textural, geometric, and synthetic aperture radar (SAR) backscattering. The results revealed that (1) MAI can emphasize the difference between mariculture and seawater; (2) the overall accuracy of mariculture in the Bohai Rim is 94.10%, and the kappa coefficient is 0.91; and (3) the area of cage aquaculture and raft aquaculture in the Bohai Rim is 16.89 km2 and 1206.71 km2, respectively. This study details an effective method for carrying out mariculture monitoring and ensuring the sustainable development of aquaculture.

Marine Infrastructure Detection with Satellite Data—A Review

“…Just over half of all studies used Machine Learning algorithms, with over 40% using Deep Learning approaches alone. With seven applications, Random Forest accounts for the majority of Traditional Machine Learning approaches [51,53,54,65,66,79,81], followed by Support Vector Machine [9,65] and Decision Tree [64,65] (two each) along with Fuzzy C-means [59] and K-means [81] (one each). This indicates that Random Forest plays a prominent role in recognition due to its flexibility and adaptability.…”

“…Raft aquaculture was the most studied (46%) [31,32,37,39,46,50,[55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73] and 12% of aquaculture studies looked specifically at cages [40,52,[74][75][76][77]. The remaining 42% investigated different types of aquaculture including raft, cage or longline in combination [33][34][35][47][48][49]53,54,[78][79][80][81][82][83][84][85][86][87][88][8...…”

“…The zoomed-in map (Figure 8) of East and Southeast Asia clearly shows that aquaculture is being studied along the entire Chinese coast. The hotspots here are Sansha Bay in Fujian Province (16 studies) [33][34][35]37,47,49,56,65,69,73,76,80,83,85,86,88], the offshore area around Dalian in Liaoning Province (13 studies) [37,39,49,50,53,[57][58][59][60]62,67,68,73], and Haizhou Bay in Jiangsu Province (9 studies) [31,32,55,61,63,66,[71][72][73]. Further site and local studies of aquaculture were carried out on the coast of Japan [59,70,93,106], on the south coast of South Korea [81,82], the south coast of Vietnam [43,…”

“…Only nine authors used multispectral data in combination with radar data as the basis for their studies. Of these nine, seven dealt with aquaculture [46,53,65,66,73,74,79] and two with platforms [30,94]. A total of 47 of the 89 studies (53%) dealt with the detection of offshore infrastructure based on multispectral satellite data.…”

Dynamic Evolution of Aquaculture along the Bohai Sea Coastline and Implications for Eco-Coastal Vegetation Restoration Based on Remote Sensing

“…Degradation of coastal vegetation has led to a decrease in the biodiversity of coastal zones and a gradual increase in seawater pollution in this coastal area [ 13 ]. In addition, the aquaculture model in this area is very homogenous and rugged, and does not consider the unity of aquatic plants and aquatic animals [ 14 ]. In 2022, the production of aquatic plants in aquaculture around the Bohai Sea was 1.16 million tonnes, accounting for about 42% of China’s total production.…”