Extraction of Aquaculture Pond Region in Coastal Waters of Southeast China Based on Spectral Features and Spatial Convolution

Multiple datasets related to pond and marine aquaculture have been published using diverse remote sensing technologies, yet a comprehensive dataset detailing spatial distribution on both land and sea sides is lacking. Firstly, a meticulous comparison of datasets which we selected related to aquaculture ponds and marine, ensuring consistency in trends. Subsequently, the datasets published by our team were edited and integrated to illustrate aquaculture activities on both sides of China’s coastal zone. Finally, a spatial differentiation of coastal aquaculture in major provinces was analyzed. This analysis also utilizes the types of coastline and statistical data, guiding coordinated resource management efforts. The results unveil a distinctive spatial distribution pattern, concentrating aquaculture in the northern regions—Bohai Sea, Jiangsu, Fujian, and Pearl River coasts in Guangdong. The provinces rich in aquaculture resources, such as Shandong, Guangdong, and Liaoning, exhibit extensive coastlines. However, remote sensing monitoring suggests an underestimation of Liaoning’s marine aquaculture compared to statistical yearbook data. Furthermore, southern provinces like Guangdong and Fujian exhibit significantly higher aquaculture output than Liaoning. Zhejiang leads in fishing output. The paper outlines the future development direction of coastal aquaculture, emphasizing a strategic, integrated land–sea approach for sustainable development.

Section: Introductionmentioning

confidence: 99%

Spatial Distribution and Differentiation Analysis of Coastal Aquaculture in China Based on Remote Sensing Monitoring

Meng,

Yang,

Wang

et al. 2024

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

Wang

Huang

et al. 2023

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.

Extracting Water Surfaces of the Dike-Pond System from High Spatial Resolution Images Using Deep Learning Methods

Zhou,

Fu,

Liang

et al. 2024

A type of aquaculture pond called a dike-pond system is distributed in the low-lying river delta of China’s eastern coast. Along with the swift growth of the coastal economy, the water surfaces of the dike-pond system (WDPS) play a major role attributed to pond aquaculture yielding more profits than dike agriculture. This study aims to explore the performance of deep learning methods for extracting WDPS from high spatial resolution remote sensing images. We developed three fully convolutional network (FCN) models: SegNet, UNet, and UNet++, which are compared with two traditional methods in the same testing regions from the Guangdong–Hong Kong–Macao Greater Bay Area. The extraction results of the five methods are evaluated in three parts. The first part is a general comparison that shows the biggest advantage of the FCN models over the traditional methods is the P-score, with an average lead of 13%, but the R-score is not ideal. Our analysis reveals that the low R-score problem is due to the omission of the outer ring of WDPS rather than the omission of the quantity of WDPS. We also analyzed the reasons behind it and provided potential solutions. The second part is extraction error, which demonstrates the extraction results of the FCN models have few connected, jagged, or perforated WDPS, which is beneficial for assessing fishery production, pattern changes, ecological value, and other applications of WDPS. The extracted WDPS by the FCN models are visually close to the ground truth, which is one of the most significant improvements over the traditional methods. The third part is special scenarios, including various shape types, intricate spatial configurations, and multiple pond conditions. WDPS with irregular shapes or juxtaposed with other land types increases the difficulty of extraction, but the FCN models still achieve P-scores above 0.95 in the first two scenarios, while WDPS in multiple pond conditions causes a sharp drop in the indicators of all the methods, which requires further improvement to solve it. We integrated the performances of the methods to provide recommendations for their use. This study offers valuable insights for enhancing deep learning methods and leveraging extraction results in practical applications.