Estimating the shrimp farm’s production and their future growth prediction by remote sensing: Case study Gulf of California

Abstract

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Shrimp farms are within the main aquaculture activities and In the last 30 years, shrimp farms have expanded along the Gulf of California. Because the shrimp farm’s production is related to the pond area and the extended location of shrimp farms, we employed a multi-disciplinary approach to study the process of annual shrimp farm expansion, in addition to predicting shrimp production in the short term. Our study hypothesizes that semi-intensive shrimp farm production is dependent on the pond area which can be accurately quantified by using remote sensing image classification combined with the historical production data of shrimp aquaculture. We could calculate the long-term expansion of the total pond area and forecast the total annual shrimp production in the short-term. Our results highlight the development over the last 28 years. The total surface area of the shrimp ponds grew by more than 1100%. The linear regression model between the shrimp farms area, calculated using remote sensing, and statistical data on shrimp production, was strongly positive and significant (r2 = 0.874, p =2.209e-11). Likewise, the linear regression model for the total pond area as a function of time resulted strong positive and statistically significant (r2 = 0.936, p =2.917e-15). We demonstrated that the shrimp production could be assessed based on the total pond area by year, obtained by remote sensing. We estimated the past long-term shrimp farm expansion in the study zone using remote sensing data. This methodology is valuable for monitoring food security, aquaculture management decisions, and coastal ecology.

Keywords

• aquaculture production forecast
• google earth engine
• satellite image classification
• aquaculture expansion forecast
• prediction model