Microatolls are massive corals on flat reefs with the colony dying in the center or there is a hole in the middle due to erosion by algae or other micro-borers. This study aims to measure the ability of UAV RGB imagery to identify micro atolls located on reef flats and objects associated with them on small islands. High resolution satellite images were also used to compare their accuracy in estimating the size of micro atolls. Some of the important steps carried out are field surveys, aerial and google earth photo acquisitions as well as mosaics, classification with segmentation based on, shape and color, texture, and shape of object proximity. RGB UAV is used to collect data and it is processed by object-based image analysis (OBIA) classification algorithm and supervised classification. This paper represents the result of UAV data analysis is more accurate and precise than high resolution satellite image data analysis. The diameter of the micro atoll can be calculated and the associated community around it. This technology allows it to be used for monitoring shallow water ecosystems bordering community activities on the mainland of small islands and producing a higher scale.
Remote sensing has provided some water quality data that can be used to assess the suitability of water quality for cultivation. It is the main approach used to map seaweed farming. The aims of this study were to map the seaweed farming potential of the study area and calculate the amount of plastic waste likely to be produced from the use of plastic buoys in seaweed aquaculture. SPOT 6 and Sentinel 2 provide high and moderate resolution satellite imagery that can be used as primary data to map seaweed farming at fine and broad scales. ArcMap application was applied to analyse the vector data of water quality. Weighting and scoring were carried out to give value to the parameters for culture suitability. The plastic waste used as a buoy during cultivation is calculated based on primary data from field surveys. The results suggest that the simple analysis of high and moderate spatial resolution imagery with digitation on screen method can delineate seaweed farming areas efficiently. Furthermore, this method could be applied in the future to monitor the extent of seaweed farms and to predict the amount of plastic waste produced by seaweed farming activities.
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