<p><em>Information </em><em>on </em><em>the existing land cover is important for land management and planning because it can represent the intensity, location, and pattern of human activities. However, mapping land cover in tropical regions is not easy when using optical remote sensing due to the scarcity of cloud-free images. Therefore, the objective of this study is to map the land cover in Klaten Regency using a time-series Sentinel-1 data. Sentinel-1 data is one of remote sensing image</em><em>s</em><em> with Synthetic Aperture Radar (SAR) system which is well known by its capabilit</em><em>y</em><em> of cloud penetration and all-weather observation. A time-series Sentinel-1 data of both polarisations, VV and VH were automatically classified using an unsupervised classification technique, ISODATA. The results show that the land cover classifications obtained overall accuracies of 79</em><em>.</em><em>26% and 73</em><em>.</em><em>79</em><em>% </em><em>for VV and VH respectively. It is also found that Klaten is still dominated by the vegetated land (agriculture and non-agricultural land).</em><em> T</em><em>hese results suggest the opportunity of mapping land cover using SAR multi temporal data. </em></p><p><strong><em> </em></strong></p><p><strong><em> Keywords</em></strong><em>: </em><em>Land cover; Synthetic Aperture Radar; Time series; Sentinel-1; Klaten</em><em></em></p>
Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery
