Object-Based on Land Cover Classification on LAPAN-A3 Satellite Imagery Using Tree Algorithm (Case Study: Rote Island)

Abstract

LAPAN became serious about making a remote sensing satellite on its third-generation satellite. Launched a year after LAPAN-A2, the third-generation satellite, LAPAN-A3, brought LISAT as the main payload. LISAT is a multispectral camera with 4 bands (Red, Green, Blue, NIR) that can be used for land classification, agriculture monitoring, drought monitoring, and land use changing<em>. LAPAN-A3 is the third generation of micro-satellite developed by Satellite Technology Center – LAPAN. This satellite carries a multispectral push-broom sensor that can record the earth's surface at the visible and near-infrared spectrum. </em>This paper aims to determine object-based land cover classification in Rote Island using the LAPAN-A3 satellite image using the tree method algorithm. This classification technique is expected to increase the accuracy of land cover classification. This classification used the LAPAN-A3 satellite imagery of Rote Island. The first process was determined the segmentation with scale parameter 60, shape 0.5, and compactness 0.5. The result shows that OBIA classification on Rote Island, the area of the open land class is 233.67 km², the area of the settlement is 11.57 km², the body of water is 2006.21 km2, the area of low vegetation is 525.93 km², the area of high vegetation is 437.5 km², and there is no data (cloud and cloud shadows) on the LAPAN-A3 image of 45.78 km². The accuracy values obtained were producer 86.67%, KIA 83.02%, Helden 92.86%, Short 86.7%, KIA per class 82.72%, and 85.96%. This object-based classification can meet international and national land cover classification standards, namely at 80%.