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.

Shrimp farming has expanded over coastal areas in Mexico, particularly in the protected regions of Sonora and Sinaloa. Over the past 30 years, the economic activity associated with these farms has grown so much that the amount of shrimp produced in these ponds now exceeds that harvested from traditional shrimp fisheries. Establishing shrimp ponds has led to significant land changes. The construction of these ponds has fragmented local ecosystems, resulting in permanent alterations to areas such as floodplains, mangrove forests, and dunes, many of which are protected zones. This study aimed to investigate the long-term growth of shrimp farms from 1993 to 2022 and their impact on land-use changes in surrounding ecosystems, focusing on protected areas in the Sinaloa and Sonora coastal regions. We analyzed Landsat images using the Google Earth Engine platform. Our findings indicate that shrimp farm development over the past three decades has been extensive, with protected areas experiencing fragmentation and changes. Remote sensing and platforms like Google Earth Engine enable the effective monitoring of these spatiotemporal changes and their impacts, helping to identify the most affected areas.

Shrimp farming has increasingly taken over coastal areas in Mexico, particularly in the protected regions of Sonora and Sinaloa. Over the past 30 years, the economic activity associated with these farms has grown so much that the amount of shrimp produced in these ponds now exceeds that harvested from traditional shrimp fisheries. Establishing shrimp ponds has led to significant land changes and environmental contamination, introducing organic matter, heavy metals, bacteria, and viruses into the coastal ecosystem of the Gulf of California. The construction of these ponds has fragmented local ecosystems, resulting in permanent alterations to areas such as floodplains, mangrove forests, and dunes, many of which are protected zones. This study aimed to investigate the long-term growth of shrimp farms from 1993 to 2022 and their impact on land-use changes in surrounding ecosystems, focusing on protected areas in the Sinaloa and Sonora coastal regions. We analyzed Landsat images using the Google Earth Engine (GEE) platform. Our findings indicate that shrimp farm development over the past three decades has been extensive, with protected areas experiencing fragmentation and changes. Remote sensing and platforms like GEE enable the effective monitoring of these spatiotemporal changes and their impacts, helping to identify the most affected areas.