Assessing Spatiotemporal differences in Shrimp ponds using Remote sensing data and Machine learning algorithms

Abstract

Shrimp farming and exporting is the main income source for the southern coastal districts of the Mekong delta. Monitoring these shrimp ponds is helpful in identifying losses incurred due to natural calamities like floods, sources of water pollution by chemicals used in shrimp farming, and changes in the area of cultivation with an increase in demand for shrimp production. Satellite imagery, which is consistent with good spatial resolution and helpful in providing frequent information with temporal imagery, is a better solution for monitoring these shrimp ponds remotely for larger spatial extent. The shrimp ponds of Cai Doi Vam Township, Ca Mau Province, Viet Nam were mapped using DMC-3 (TripleSat) and Jilin-1 high-resolution satellite imagery for years of 2019 and 2022. The 3m spatial resolution shrimp pond extent product showed an overall accuracy of 87.5% with a producer’s accuracy of 90.91% (errors of omission = 11.09%), and a user’s accuracy of 90.91% (errors of commission = 11.09%) for the shrimp pond class. It was noted that 66 ha of shrimp ponds in 2019 were observed to be dry in 2022, and 39 ha of other ponds had been converted into shrimp ponds in 2022. The continuous monitoring of shrimp ponds helps achieve sustainable aquaculture and acts as crucial input for the decision-makers for any interventions.