A Google Earth Engine-Based Framework to Identify Patterns and Drivers of Mariculture Dynamics in an Intensive Aquaculture Bay in China

Abstract

Although mariculture contributes significantly to regional/local economic development, it also promotes environmental degradation. Therefore, it is essential to understand mariculture dynamics before taking adaptive measures to deal with it. In the present study, a framework that integrates the Google Earth Engine (GEE) based methods and GeoDetector software was developed to identify patterns and drivers of mariculture dynamics. This framework was then applied to Zhao’an Bay, which is an intensive aquaculture bay in Coastal China, based on Landsat 8 OLI (2013–2022) and Sentinel-2 (December 2015–May 2022) data. The results show that the GEE-based method produces acceptable classification accuracy. The overall accuracy values for the interpretation are >85%, where the kappa coefficients are >0.9 for all years, excluding 2015 (0.83). Mariculture increased in the study area from 2013 to 2022, and this is characterised by distinct spatiotemporal variations. Cage mariculture is primarily concentrated around islands, whereas raft mariculture is dominant in bay areas, and pond and mudflat mariculture types are mostly in nearshore areas. The growth of mariculture in Zhao’an Bay is attributed to a combination of geographic and human factors. The initial area associated with mariculture in a grid significantly impacted the expansion of the raft, cage, and mudflat mariculture. The distance to an island, spatial proximity to similar types of mariculture and types of mariculture are the main drivers of change in mariculture. Human activities greatly contribute to the dynamics of mudflat mariculture; regulation regarding the clearing of waterways directly impacts the dynamics of mariculture. The present study demonstrates that the proposed framework facilitates the effective monitoring of the mariculture dynamics and identification of driving factors. These findings can be exploited for the local planning and management of mariculture in similar coastal bays.

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...…”

Automatic Extraction Method of Aquaculture Sea Based on Improved SegNet Model

“…Xing et al [26] extracted the algae culture area by using the differential vegetation index and revealed that it has no inevitable relationship with the formation of green tide in the Yellow Sea by retrieving the spatiotemporal development process through remote sensing, which has great significance to guiding the spatial planning of mariculture and the prevention and control of green tides in the Yellow Sea. Wang et al [27] extracted the aquaculture sea area based on the GEE framework and random forest model and determined the dynamic pattern and driving factors of mariculture. However, the traditional index method and machine learning supervised classification method cannot escape the phenomena of "same spectral characteristics, different matter" and "same substance, different spectrum" and cannot automatically extract the original feature information.…”