IDENTIFIKASI GENANGAN BANJIR DI PERSAWAHAN MENGGUNAKAN CITRA SATELIT SENTINEL-1 (STUDI KASUS : KABUPATEN DEMAK PROVINSI JAWA TENGAH)

Abstract

Banjir merupakan meluapnya aliran sungai akibat hujan terus-menerus di hulu yang tidak tertampung oleh sungai, sehingga menggenangi wilayah sekitar (Ningrum & Ginting, 2020). Pada 19 Maret 2024, banjir melanda Kabupaten Demak, Jawa Tengah, merendam pemukiman, jalan raya, dan persawahan. Penelitian ini memanfaatkan data Citra Sentinel-1 sebelum dan saat banjir untuk memetakan sebaran banjir di area persawahan menggunakan metode Change Index (Amitrano dkk., 2017) dan pengolahan data di ArcGIS melalui tahapan raster calculator, clip data, raster to polygon, pemilihan atribut, dan perhitungan luas. Hasilnya, banjir teridentifikasi melanda ±14.498,091 ha area persawahan, dengan dampak terbesar di Kecamatan Karang Anyar (4.460,99 ha atau 30,77%) dan terkecil di Kecamatan Kebon Agung (7,83 ha atau 0,05%).

Kata kunci: : Banjir, Sentinel-1, Change Index

ABSTRACT

Flooding is defined as the overflow of river discharge resulting from continuous upstream rainfall that exceeds the river's capacity, leading to the inundation of surrounding areas (Ningrum & Ginting, 2020). On March 19, 2024, a significant flood event occurred in Demak Regency, Central Java, inundating residential areas, major roads, and agricultural lands, particularly rice fields. This study employs Sentinel-1 satellite imagery—both pre-flood and during-flood datasets—to accurately delineate the extent of flooding across agricultural zones. The analysis utilizes the Change Index method as proposed by Amitrano et al. (2017), with data processing conducted in ArcGIS through several stages: raster calculation, data clipping, raster-to- polygon conversion, attribute indexing, and flood area computation. The findings reveal that approximately 14,498.091 hectares of rice fields were affected, with Karang Anyar District experiencing the most extensive flooding (4,460.99 ha or 30.77%), while Kebon Agung District recorded the smallest affected area (7.83 ha or 0.05%). These results underscore the critical value of remote sensing and geospatial analysis in supporting disaster response and agricultural impact assessment.

Key words: Flood, Sentinel-1, Change Index