Grouping Land Cover Using Orthophoto in Small Islands: An Application of Low-Cost UAV on Mansinam Island

Abstract

Land cover is crucial for island management, but the lack of accessible and high-resolution remote sensing data has reduced investigations on small islands, including land cover identification. Therefore, this study aimed to investigate land cover using Unmanned Aerial Vehicle (UAV) technology, providing very high-resolution images. Classification and delineation were conducted using automatic segmentation followed by manual reinterpretation and visual verification. The results showed 14 cover classes, consisting of 8 vegetated and six non-vegetated categories. Forest cover on Mansinam island accounted for 75.5% or 302.4 ha, which was evenly distributed. Furthermore, primary forest covered 31.91% or 127.74 ha, and secondary covered 43.63% or 174.68 ha. The classification achieved an overall accuracy of 96% and a kappa coefficient of 0.94. Low-cost UAVs effectively produced high-resolution aerial images of small islands for land cover identification. Therefore, future studies were recommended to consider whether segmentation can reliably distinguish between primary and secondary forests, as well as assess the impact of flight altitude on segmentation accuracy using ground control points. The results were also expected to support spatial planning or sustainable forest and environment management on Mansinam Island.