Evaluasi Ketersediaan Ruang Terbuka Hijau (RTH) Menggunakan Metode Normalized Difference Vegetation Index dan Object Based Image Analysis (Studi Kasus: Surabaya Selatan)

Chusnah, Wachidatin Nisa'ul (2019) Evaluasi Ketersediaan Ruang Terbuka Hijau (RTH) Menggunakan Metode Normalized Difference Vegetation Index dan Object Based Image Analysis (Studi Kasus: Surabaya Selatan). Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Surabaya merupakan salah satu kota metropolitan yang kini tengah fokus dalam pembangunan jaringan infrastruktur. Minimnya ketersediaan lahan di pusat kota menyebabkan pembangunan kian mendesak ke arah sekitarnya, termasuk daerah Surabaya Selatan. Kemudahan akses terhadap pelayanan publik menjadi kunci utama sebagai bahan pertimbangan masyarakat/instansi dalam pembangunan sektor perdagangan, industri, maupun sebagai rumah tinggal. Didukung dengan tingginya angka urbanisasi yang selaras dengan kebutuhkan akan penggunaan lahan terbangun, maka perlu diimbangi dengan tersedianya Ruang Terbuka Hijau (RTH) yang ideal. Ekosistem RTH merupakan salah satu objek yang dapat diidentifikasi melalui teknologi sistem informasi geografis dan penginderaan jauh.Data yang digunakan pada penelitian ini berupa rencana RTH publik Surabaya Selatan yang diatur pada Rencana Detail Tata Ruang (RDTR) Kota Surabaya tahun 2018 dan citra satelit resolusi sangat tinggi Pleiades tahun 2017. RTH diidentifikasi melalui proses klasifikasi metode Normalized Difference Vegetation Index (NDVI) dan Object Based Image Analysis (OBIA).Berdasarkan hasil penelitian, metode klasifikasi OBIA menghasilkan luas kawasan RTH Surabaya Selatan ialah 2.466,153 Ha, sedangkan klasifikasi metode NDVI menghasilkan luas kawasan RTH 2.465,741 Ha. Luas RTH publik pada RDTR ialah 650,968 Ha, namun yang telah terealisasi menjadi kawasan peruntukan hijau sebagaimana mestinya hanya seluas 558,187 Ha atau setara dengan dengan 8,796% luas wilayah Surabaya Selatan. Proporsi tersebut sangat berbeda jauh dengan RTH privat yang mencapai luas 1.907,966 Ha atau setara dengan 30,067% luas wilayah. Hasil uji akurasi menunjukkan bahwa metode OBIA memiliki tingkat keakuratan yang tinggi yakni 93,33% setelah dilakukan uji akurasi dengan 120 titik sampel lapangan (ground truth). Hasil uji korelasi antara metode klasifikasi OBIA dan NDVI menghasilkan nilai korelasi (r) sebesar 0,998 yang menunjukkan adanya hubungan korelasi positif antar keduanya
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Surabaya, one of the metropolitan cities that is currently focusing on building infrastructure networks. The lack of land in the center of city has led the increasingly development in surrounding areas, including the region in South Surabaya. Ease of public access services is the main key as consideration by the community / agencies in the development of the trade sector, industry, and as a home living. The high rate of urbanization in line with the needed of land use, so it needs to be balanced with the availability of ideal Open Green Space. Open green ecosystem is one of the objects that can be identified through geographic information system and remote sensing technology.
The data used in this research are the plans of South Surabaya’s public green open space which is regulated in Detailed Spatial Plan (RDTR) in 2018 and satellite image data of very high resolution Pleiades 1A in 2017. Open green space was identified through the classification process of the Normalized Difference Vegetation Index (NDVI) method and Object Based Image Analysis (OBIA).
Based on the results of the research, the OBIA classification method produces 2,466.153 Ha of green open space area in South Surabaya, while the NDVI classification method produces 2,465.741 Ha of green open space. The area of public green open space in the RDTR is 650.968 Ha, but what has been realized to be a green allotment area only 558.187 Ha or equivalent to 8.796% of the total area of South Surabaya. This proportion is very different from the private green open space, which reached an area of 1,907.966 Ha, equivalent to 30.067% of the total area. Accuracy test shows that the OBIA method has a higher accuracy,it is 93.33% after testing accuracy with 120 ground truth points. Correlation test between the OBIA and NDVI classification methods produce a correlation value (r) of 0.998 which indicates a positive correlation between them

Item Type:	Thesis (Other)
Uncontrolled Keywords:	RTH, NDVI, OBIA, Korelasi
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
Divisions:	Faculty of Civil Engineering and Planning > Geomatics Engineering > 29202-(S1) Undergraduate Thesis
Depositing User:	Wachidatin Nisa'ul Chusnah
Date Deposited:	22 Jul 2026 07:34
Last Modified:	22 Jul 2026 07:34
URI:	http://repository.its.ac.id/id/eprint/65051

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