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.

This work describes the generation of a database of algal species richness at two spatial scales – regional (Gulf of Mexico and the Caribbean) and global (coastal zones). As a first approach to the definition of the temporal variability, and to produce the corresponding maps, a previously published decision tree is used in order to select the best spatial interpolation technique according to the characteristics of the spatial data. The methods presented are ordinary Kriging (since no relationship exists with any environmental variable that could function as an external variable) and inverse distance squared, for comparative purposes. The methods to generate the spatial layers are evaluated using the leave-one-out cross validation technique. Although the evaluation did not find a large correspondence (in terms of linear regression) between the interpolated and measured values, it was possible to capture the spatial variability of the process and produce the cartography of this variable, with which future ecological analyses can be performed.

Coastal eutrophication due to agricultural runoff is one of the main ecological problems for coastal zones around the world. The increase in nutrients has created multiple consequences on marine ecosystems, such as harmful algal blooms, zones of hypoxia or anoxia, and loss of biodiversity. The coastal areas of México are no exception. In the scientific community, there has been a great effort to estimate the amount of nutrients that enter marine ecosystems. However, a rapid method to make this estimation without field data is still required. In this study, the cultivated area and the entrance of nutrients to the coastal marine environment were determined for one of the main irrigation districts located in the state of Sonora, which drains into the Gulf of California, Mexico. We used an elevation model combined with the digitization of drain canal maps to delimitate sub-basins and allocate the coastal runoff entry points. Landsat 8 satellite data were classified and validated for the period of two agrarian cycles (2015–2016) to obtain the agriculture parcels by sub-basins. The quantity of the nutrient runoff was estimated using the cultivation area and nitrogen runoff percentage, according to values found in published reports. As a result, we identified eight sub-basins, two of which stand out for the amount of nitrogen that drains both into a coastal lagoon and directly to the sea. We confirmed this information by correlation analysis using Moderate Resolution Imaging Spectroradiometer (MODIS) of the diffuse attenuation coefficient at 490 nm K_d(490) detected around the coastal runoff entry points. We concluded that there is a high input in the coastal zones and lagoons of nutrients and other pollutants from the agricultural zones of the Río Mayo irrigation district. We also concluded that the performance of the method applied to estimate the volume of the nitrogen runoff is useful, rapid, and can be improved with in situ data.

Estimation of small-area population counts in an intercensal year and in a future year is a challenging task. This paper presents preliminary results in the development of a geographic-knowledge-guided cellular automata (CA) for modelling growth in small geographic areas. Geographic knowledge contains rules dictating growth patterns that typically cannot be captured by a traditional CA model. Nighttime stable light images and census population counts in censal years are used to determine base-year population counts in each cell in the CA model, and these estimated base-year population counts are used to manually calibrate the model. We use census data in 1990 and 2000 in El Paso County of Texas as the base-year population data, develop a set of rules based on specific urban-growth situations in El Paso and use the model to estimate population counts in block groups in a future year in the study area. Preliminary results in El Paso County suggest that the model has the potential to produce reasonably accurate population counts in sub-county areas in a future year. Future work will include the development of computational procedures that can be used to automate the calibration of the CA model.