Spatial Modelling of Seablite Distribution

Abstract

Foods demands are increasing today opposed to the change in environmental degradation. In this work, we studied new seablite distribution modelling. We aimed to identify substantial basic factors related to spatial distribution of seablites to make a meaningful explanation or accurate prediction in a coastal region of Samut Sakhon, Thailand. Virtual field survey and field survey data of physical geography were used to form a structure of spatial model and build a predictive model. We found that important underlying factors of spatial distribution of seablites were soil salinity, soil pH, soil moisture, air temperature, height above sea level, distance from seashore, and wind direction. Our predictive model improves understanding upon a distribution of seablites and environments. This preliminary work supported to simulate the environments to establish and thrive seablites for smart agriculture system.