Mapping and Classification of Crystallization Ponds in Pangasinan Salterns Using LandSat Imagery for Salt Production Estimation Rodel T. Utrera(1), Nadine Sharinette R. Bravo(2), Julius Jonar L. Butay(3), Nathaniel R. Alibuyog(4) and Lord Ian R. Galano(5)
(1)Research Directorate, Mariano Marcos State University, rtutrera[at]mmsu.edu.ph
(2)Research Directorate, Mariano Marcos State University, nrbravo[at]mmsu.edu.ph
(3)Planning Directorate, Mariano Marcos State University, jbutay[at]mmsu.edu.ph
(4)College of Engineering, Mariano Marcos State University, nralibuyog[at]mmsu.edu.ph
(5)Research Directorate, Mariano Marcos State University, lrgalano[at]mmsu.edu.ph
Abstract
Crystallization ponds are the final and most essential component of solar salt production systems, where salt precipitates and is harvested after successive evaporation stages. Mapping the spatial extent of these ponds is crucial not only for monitoring salt farm infrastructure but also for estimating potential salt production output. By accurately identifying and delineating crystallization ponds, it becomes possible to project salt yields across wider areas, providing valuable data to support the revitalization and planning of the salt industry throughout the Philippines.
This study focused on classifying and mapping crystallization ponds within existing salterns in Pangasinan using remote sensing and GIS-based techniques. LandSat eight (8) satellite imagery was processed using the Supervised Classification tool in ArcGIS to extract crystallization pond features based on their unique spectral characteristics. A refined training dataset enabled distinction from similar land uses such as evaporation ponds, fishponds, and agricultural fields.
To evaluate classification accuracy, a total of 151 validation points were collected through extensive ground truthing, including field visits and drone-assisted aerial surveys. Among these, 124 points were correctly classified, resulting in an overall accuracy of 82.12%. This reliable classification demonstrates the potential of integrating remote sensing, GIS, and field validation to generate high-quality spatial datasets. The delineation revealed a total crystallization pond area of 430.52 hectares, with individual pond sizes ranging from 0.058 ha to 36.806 ha
The resulting maps serve as a foundation for estimating salt production potential by correlating pond area with yield estimates. This approach can be scaled nationally, offering a cost-effective method for identifying underutilized salt farming areas and informing data-driven policies for sustainable salt industry development in the Philippines.
Keywords: Crystallization Ponds, Remote Sensing, Supervised Classification, Geospatial Analysis, Salt Production Mapping
