Salt pond detection on Sentinel 2 Satellite Imagery using Object-Based Image Analysis (OBIA) Approach

Abstract

Abstract Object-based image analysis (OBIA) is an image classification that is oriented to object patterns that use image objects as the basis for processing, calculates characteristics per object, and extracts land cover information from remotely sensed images. This study aims to detect salt ponds using Sentinel 2 satellite data with an object-based classification model. The center of salt production, which is also an experimental area for the development of industrial salt from the ministry of maritime affairs and fisheries on the north coast of the island of Java was selected as the study area. The unit of analysis for this classification is the segmented object of sentinel image. The classification scheme built to detect salt ponds using OBIA consists of level 1, level 2, and level 3. Level 1 is to separate land and water using a Near Infrared canal. Level 2 is to separate land use from object segmentation results in land class at level 1 using NDVI transformation, and level 3 is to separate salt and non-salt ponds from the segmentation results of land use at level 2 using sentinel image transformation algorithm for the distribution of chlorophyll-a. The result shows chlorophyll-a estimation image transformation from sentinel useful to separate salt and non-salt ponds. Many researchers have been reported that chlorophyll-a does not live in the salinity range of salt ponds greater than 50 ppt, meanwhile, in non-salt ponds, chlorophyll-a is used as natural feed for cultivated animals. Furthermore, the research shows a classification scheme of salt ponds and non-salt ponds can be derived from sentinel 2 imagery with OBIA approach