Advantages of unmanned aerial vehicle (UAV) photogrammetry for landscape analysis compared with satellite data: A case study of postmining sites in Indonesia

• Published in: Cogent geoscience Vol. 4; no. 1; p. 1498180
• Main Authors: Iizuka, Kotaro, Itoh, Masayuki, Shiodera, Satomi, Matsubara, Takashi, Dohar, Mark, Watanabe, Kazuo
• Format: Journal Article
• Language: English
• Published: London Cogent 01.01.2018; Taylor & Francis Ltd
• Subjects: Environmental changes; GIS; Land use; Land use planning; PALSAR-2; Photogrammetry; Remote sensing; Satellites; structure from motion; UAV; Unmanned aerial vehicles; Indonesia
• ISSN: 2331-2041; 2331-2041
• DOI: 10.1080/23312041.2018.1498180

This study presents the advantages of detailed landscape analysis by UAV (drone hereafter) photogrammetry compared with satellite remote sensing data. First, satellite data are used for generating a coarse-scale land use/land-cover (LULC) map of the study region using conventional GIS techniques. The Advanced Land Observation Satellite-2 (ALOS-2) Phased Array L-band Synthetic Aperture Radar-2 (PALSAR-2) L-Band backscattering data are processed with a multilayer perceptron (MLP) supervised classification for generating a categorical map. The satellite-derived classification map resulted in eight general land-cover types with a ground resolution of 7.5 m, providing a moderate-resolution representation of the island landscapes. Second, the drone's image data are used to collect ground survey information and microscale information of the local site by implementing a structure from motion (SfM) technique to develop mosaicked orthorectified images of the sites. The orthophoto and digital surface model (DSM) derived from the drone-based data had resolutions of 0.05 m and 0.1 m, respectively. The SAR-based LULC map showed an overall accuracy of 78.1%, and the drone-based LULC map had an overall average accuracy of 92.3%. The subset area of the SAR map was compared with the drone-based map and showed average Kappa statistics of 0.375, demonstrating that satellite data cause challenges in correctly delineating the local land environment. The terrain information generated by the SfM method provided a good representation of the topography showing the drastic changes in the environment. The results indicate the usefulness of drone-based landscape analysis for future land-use planning at a local village scale.