Monitoring Bencana Banjir Pada Kawasan Lumbung Padi Nasional Berbasis Citra Sentinel-1 (Studi Kasus: Kecamatan Laren, Kabupaten Lamongan)

Azizah, Luthfia (2023) Monitoring Bencana Banjir Pada Kawasan Lumbung Padi Nasional Berbasis Citra Sentinel-1 (Studi Kasus: Kecamatan Laren, Kabupaten Lamongan). Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Banjir merupakan salah satu bencana alam yang dapat dipengaruhi oleh kondisi alam seperti geografi, iklim, dan bentuk aliran sungai dapat menimbulkan kerugian berupa gagal panen pada sektor persawahan. Luas panen padi terbesar tahun 2021 membuat Jawa Timur sebagai kawasan lumbung padi nasional atau daerah penghasil padi yang besar. Namun berdasarkan data statistik BPS pada tahun 2019, 2020, dan 2021 hasil panen padi tidak memberikan hasil yang sama, berupa jumlah produksi padi yang menurun di salah satu kecamatan yaitu Kecamatan Laren dengan wilayahnya yang berbatasan langsung dengan Sungai Bengawan Solo. Sehingga diperlukan monitoring area sawah tergenang banjir yang menjadi kawasan lumbung padi nasional dalam upaya mitigasi dan evaluasi penanganan ketahanan pangan. Teknologi penginderaan jauh dengan SAR berupa Sentinel-1 dapat digunakan untuk mengidentifikasi wilayah genangan banjir karena sensornya yang dapat bekerja di segala cuaca dan dapat menembus awan saat terjadi banjir yang tertutup awan akibat hujan yang berlangsung terus menerus. Penelitian ini menggunakan Sentinel-1 GRD dengan metode change detection dan thresholding 1,1 melalui platform cloud computing GEE di tiga waktu yaitu 13 Maret 2019; 20 Desember 2020; dan 2 Maret 2021. Dilakukan perhitungan Prediksi banjir menggunakan metode Cellular Automata dan ANN (CA-ANN) melalui plugin MOLUSCE. Hasil identifikasi banjir menunjukkan posisi banjir yang selalu berada di bagian selatan Kecamatan Laren. Tiga kelurahan dengan peringkat banjir terluas yaitu Kelurahan Gelap, Kelurahan Jabung, dan Kelurahan Pelangwot berdasarkan tiga waktu banjir yang diidentifikasi. Hasil prediksi banjir dengan CA-ANN menunjukkan bahwa metodenya yang dapat digunakan untuk mengevaluasi area sebaran banjir berdasarkan data spasiotemporal banjir yang memperlihatkan area banjir yang masih berada di bagian selatan Kecamatan Laren. Hasil prediksi banjir menunjukkan posisi banjir yang hampir sama dengan banjir di tiga waktu studi namun berkurang dalam rentang 8,92 % - 33,62 % dari banjir 2021.
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Natural conditions such as geography, climate, and the form of river flow can cause losses in the form of crop failure in the rice field sector due to natural disasters such as floods. The largest rice harvest area in 2021 makes East Java a national rice granary or a large rice-producing area. However, based on BPS statistical data in 2019, 2020, and 2021 the rice harvest did not provide the same results, in the form of a decreasing amount of rice production in one of the sub-districts, namely Laren District with its area directly adjacent to the Bengawan Solo River. So it is necessary to monitor rice fields that become national rice barn areas to mitigate and evaluate food security handling. Remote sensing technology with SAR in Sentinel-1 helps identify flood inundation areas because the sensor can work in all weather and penetrate clouds during cloud-covered floods due to continuous rain. This research uses Sentinel-1 GRD with the change detection method and thresholding 1.1 through the GEE cloud computing platform thrice on March 13, 2019; December 20, 2020; and March 2, 2021. s research uses flood prediction with the Cellular Automata and ANN (CA-ANN) method through the MOLUSCE plugin. The results of flood identification show that the flood position is always in the southern part of the Laren Sub-district. Based on the three flood times identified, the three villages with the widest flood ranking are Kelurahan Gelap, Kelurahan Jabung, and Kelurahan Pelangwot. The results of flood prediction with CA-ANN showed that the method successfully evaluated the flood distribution area based on the spatiotemporal flood data, which showed that the flood area was still in the southern part of Kecamatan Laren. The flood prediction results showed a flood position almost the same as the flood at the three study times but reduced in the range of 8.92% - 33.62% of the 2021 flood.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Banjir, Change Detection, Lahan Sawah, Prediksi, Flood, Change Detection, Rice Fields, Prediction
Subjects:	G Geography. Anthropology. Recreation > G Geography (General) > G70.212 ArcGIS. Geographic information systems. G Geography. Anthropology. Recreation > G Geography (General) > G70.217 Geospatial data G Geography. Anthropology. Recreation > G Geography (General) > G70.5.I4 Remote sensing G Geography. Anthropology. Recreation > GB Physical geography > GB1399.9 Floods
Divisions:	Faculty of Civil, Environmental, and Geo Engineering > Geomatics Engineering > 29202-(S1) Undergraduate Theses
Depositing User:	Luthfia Azizah
Date Deposited:	20 Nov 2023 01:09
Last Modified:	20 Nov 2023 01:09
URI:	http://repository.its.ac.id/id/eprint/101578

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