Integration of Texture and PCA Information from Sentinel-1 SAR Data for Land Cover-Analysis using Random Forest Classifier Method in Sidoarjo Regency, Indonesia

Filsa Bioresita (Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Geomatics Engineering Building, ITS Campus, Sukolilo, Surabaya 60111)
Trisya Sayyidah Fithri Larastika (Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Geomatics Engineering Building, ITS Campus, Sukolilo, Surabaya 60111)
Muhammad Taufik (Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Geomatics Engineering Building, ITS Campus, Sukolilo, Surabaya 60111)
Noorlaila Hayati (Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Geomatics Engineering Building, ITS Campus, Sukolilo, Surabaya 60111)
Chelsea Alfarelia Putri Taslyanto (Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Geomatics Engineering Building, ITS Campus, Sukolilo, Surabaya 60111)

Forum Geografi
Vol. 39 No. 1 (2025): April 2025

Land cover has an important role in modelling to spatially analyse natural phenomena that occur on the earth's surface. The identification of land cover can also be used to determine the availability of green space and the percentage of built-up land in an area. Through this information, it can help the government to formulate policies related to development planning in an area. Currently, land cover identification can be done with remote sensing technology, generally using optical imagery. However, there are obstacles when using optical imagery, namely, if the cloud cover in an area is thick enough, it will affect the accuracy of the land cover results. To anticipate this, land cover identification can be done using active or radar imagery, one of which is the Sentinel-1 GRD image. The active image is not influenced by clouds and can record information without being constrained by weather both during the day and night. Sentinel-1 GRD data contains backscattering information that can be extracted using texture analysis and Principal Component Analysis (PCA). The Random Forest classifier was employed early in this study to analyze Sentinel-1 data, enabling classification using various inputs. Land cover classification from several inputs, namely, sigma, gamma, and beta from backscattering data, resulted in overall accuracy of 86.154%, 87.692%, and 86.154%.