Spatio-temporal Dynamic Monitoring and Analysis of Mariculture Using Remote Sensing: A Case Study of Zhejiang Province, China (2013–2022)

Monitoring and mapping mariculture is of paramount importance for understanding spatio-temporal evolution patterns of mariculture and facilitating effectively coastal eco-environment management. Currently, there are few studies on long-term monitoring of mariculture types along the coastal zone using efficient and accurate approach. This study investigates the spatio-temporal dynamics of mariculture in the coastal region of Zhejiang Province (CRZJ) from 2013 to 2022, using the CO-TMMMC method (CO represents the proposed cage mariculture recognition index and the object-based visually salient normalized difference vegetation index in the study, and TMMMC denotes an improved Canny edge detection (CED) method by integrating with the total variation model, multi-scale analysis, multi-channel processing, and morphological closing operations). The results indicate that the annual average overall accuracy of the CO-TMMMC method reached 91.07%, surpassing four traditional methods, namely Support Vector Machine, Maximum Likelihood, CED, and Iterative Self-Organizing Data Analysis Technique. The mariculture area experienced a substantial increase from 2013 to 2017, followed by a gradual decrease during 2017−2022. Raft mariculture consistently accounted for more than 82% of the total mariculture area from 2013 to 2022, surpassing cage mariculture by a significant margin. Natural factors (typhoon activity, storm surges) and human factors (coastal management policies) critically influenced the spatio-temporal dynamics of mariculture distribution. Differences in statistical standards and the limitations of single-view remote-sensed images affected the uncertainty in mariculture extraction information. The spatio-temporal dynamics of mariculture in the CRZJ could serve as a scientific reference for the regional planning and implementation of mariculture monitoring and the protection of the marine ecological environment.