Random Forests Algorithm for Two Levels of Coral Reef Ecosystem Mapping Using Planetscope Image in Malalayang Beach, Manado

Abstract

The coral reef ecosystem has a significant physical and biological function and is also one of the coastal ecosystem components apart from the seagrass and mangrove ecosystem. Besides their ecological function, the coral reef also has an economic function. The condition of the coral reef ecosystem in Malalayang Beach has been changing for years. The utilization of remote sensing images can monitor current conditions. This research aims to map the coral reef ecosystem mapping in Malalayang Beach, Manado and conduct a test for the accuracy of coral reef ecosystem mapping using field survey data as a classification and validation sample. PlanetScope multispectral image has four channels to detect underwater objects: red, green, blue and near infrared. PlanetScope level 3B image for the research has a surface reflectance value for its pixel. The image processing stages of this research consist of sunglint correction, water column correction, and then continue to classify the coral reef ecosystem using random forests algorithm. Classification and accuracy training sample data were obtained using the photo transect technique. The sunglint correction regression equation is between 0.27 – 0.38. The coefficient of attenuation ratio in B1 is 0.927797938, B2 is 0.168841585, and B3 is 0.29033029. This value then becomes the input for the Lyzenga formula. The classification accuracy for level one using random forests is 72,54%, and the accuracy for level two mapping is 37,61%.Keywords: Coral Reef Ecosystem, Planetscope, Random Forests