DEVELOPING A TECHNOLOGY FOR PRODUCING DRONE-BORNE HYPERSPECTRAL IMAGES TO MONITOR LARGE-AREA MIXED HERITAGE

Abstract

This study compared differences in the shooting and pre-processing techniques between manned and unmanned aerial images, and verified the precision of the images by comparing the ground sample distance between them. GSD of manned aerial image taken at high altitude could not discern tree shapes from 150 cm per pixel, but unmanned aerial image taken at low altitude (200 m) could distinguish trees individually with 30 cm per pixel. It, therefore, found that it is efficient and economically effective to produce unmanned hyperspectral images within the large-area mixed heritage. In addition, the unmanned aerial images have lower atmospheric errors and the ground sample distance that is high enough to distinguish individual trees, so they were found to be applied to the monitoring and the diagnosis for understanding the vegetation management and the health of the large-area mixed heritage.