Analisis Genangan Banjir Terhadap Tutupan Lahan Menggunakan Data Citra Sentinel Studi Kasus Wilayah Tangerang

Kenranto, Rizky Aga (2023) Analisis Genangan Banjir Terhadap Tutupan Lahan Menggunakan Data Citra Sentinel Studi Kasus Wilayah Tangerang. Other thesis, Institut Teknologi Sepuluh Nopember.

Text 03311940000087-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only until 3 October 2025. Download (8MB) | Request a copy

Abstract

Banjir merupakan salah satu bencana yang sering terjadi di sebagian wilayah di Indonesia, salah satunya di wilayah Tangerang. Salah satu dampak dari banjir dapat mempengaruhi kondisi tutupan lahan, sehingga perlu adanya upaya mitigasi. Upaya migitasi dapat dilakukan dengan memanfaatkan data citra satelit penginderaan jauh, yaitu menggunakan satelit SAR (Synthetic Aperture Radar). Satelit SAR dapat digunakan untuk pemetaan banjir karena datanya bebas awan dan beroperasi siang dan malam. Banjir sangat berkaitan dengan tutupan lahan, sehingga dalam melakukan pemetaan banjir diperlukan informasi mengenai tutupan lahan di lokasi banjir tersebut. Pada penelitian ini akan melakukan pemetaan spasial genangan banjir di wilayah Tangerang sekaligus analisis hubungannya dengan tutupan lahan pada area tersebut. Pemetaan genangan banjir dilakukan menggunakan pengolahan data citra SAR Sentinel-1 metode change detection dan threshold, sedangkan tutupan lahan menggunakan citra Sentinel-2 dengan metode supervised classification, algoritma Classification and Regression Tress (CART). Hasil dari pengolahan klasifikasi tutupan lahan berupa peta tutupan lahan dengan nilai overall accuracy sebesar 94,84% dan nilai kappa sebesar 93,17%, yang dikategorikan kedalam beberapa klasifikasi tutupan lahan yaitu badan air, lahan terbangun, vegetasi, lahan kosong dan sawah. Kemudian dilakukan pengolahan sebaran genangan banjir dengan nilai threshold 1,20 yang memiliki hasil confusion matrix sebesar 94,34%. Luas genangan banjir yang terjadi pada tanggal 23 Maret 2022 sebesar 14.987 ha. Kelas tutupan lahan yang terkena dampak paling tinggi pada kelas lahan terbangun 5.179,427 ha dan tutupan lahan kelas vegetasi sebesar 5.164,562 ha.
=============================================================================================================================
Flooding is one of the disasters that often occurs in some areas in Indonesia, one of which is in the Tangerang area. One of the impacts of flooding can affect land cover conditions, so mitigation efforts are needed. Mitigation efforts can be carried out by utilizing remote sensing satellite image data, namely using SAR (Synthetic Aperture Radar) satellites. SAR satellites can be used for flood mapping because the data is cloud-free and operates day and night. Flooding is closely related to land cover, so in conducting flood mapping, information on land cover at the flood location is needed. This research will conduct spatial mapping of flood inundation in the Tangerang area as well as analyze its relationship with land cover in the area. Flood inundation mapping is carried out using SAR Sentinel-1 image data processing using change detection and threshold method, while land cover uses Sentinel-2 image with supervised classification method, Classification and Regression Tress (CART) algorithm. The result of land cover classification processing is a land cover map with an overall accuracy value of 94.84% and a kappa value of 93.17%, which is categorized into several land cover classifications, namely water bodies, built-up land, vegetation, vacant land and rice fields. Then the processing of flood inundation distribution is carried out with a threshold value of 1.20 which has a confusion matrix result of 94.34%. The flood inundation area that occurred on March 23, 2022 was 14,987 ha. The highest affected land cover class in the built-up land class is 5,179.427 ha and the vegetation class land cover is 5,164.562 ha.

Item Type:	Thesis (Other)
Additional Information:	RSG 333.731 3 Ken a-1 2023
Uncontrolled Keywords:	Flood, Land Cover, Change Detection, Supervised Classification, Banjir, Tutupan Lahan, Change Detection, Supervised Classification
Subjects:	G Geography. Anthropology. Recreation > G Geography (General) > G70.5.I4 Remote sensing
Divisions:	Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geomatics Engineering > 29202-(S1) Undergraduate Thesis
Depositing User:	Rizky Aga Kenranto
Date Deposited:	01 Aug 2023 06:52
Last Modified:	19 Dec 2023 02:16
URI:	http://repository.its.ac.id/id/eprint/101070

Actions (login required)

View Item