Sentinel-2 & Sentinel-1 SAR (comparison) for mapping aquaculture pond distribution in Indonesia

Nurul Afdal Haris

This study aims to explore the accuracy of the gray level of co-occurrence model (GCLM) textures of multitemporal Sentinel-1 data for aquaculture pond mapping in Brebes Regency, Central Java Province, Indonesia. In addition, single-date Sentinel-2 optical imagery was used to compare the results from Sentinel-1 data. The Sentinel-2 data has been identified using supervised classifications, e.g., maximum likelihood (ML), minimum distance (MD), random forest (RF), and K-nearest neighbor (KNN) algorithms, and the most accurate algorithm was selected to classify the Sentinel-1 data using GLCM textures.




Open Atlas, how it works

Find an algorithm

Contact the author

Create a partnership

Endless opportunities