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Vegetation Density Analysis in Padalarang Bandung Regency Using NDVI Method on Landsat 8 Satellite

Ratna Widyaningtyas  -  Universitas Sebelas Maret, Indonesia
Mini Ambarwati Kusuma Dewi  -  Universitas Sebelas Maret, Indonesia
Maulyda Shofa Azizia  -  Universitas Sebelas Maret, Indonesia
*Hashfi Hawali Abdul Matin orcid scopus publons  -  Universitas Sebelas Maret, Indonesia

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Abstract

Vegetation is an important component in an ecosystem. Padalarang Sub-District is a sub-district in West Bandung Regency with the smallest area but the most densely populated among other sub-districts in West Bandung, with a density of 3,478 people km2. The purpose of this research is to analyze the level of vegetation density using the Normalized Difference Vegetation Index (NDVI) technique as a consideration, especially for the government regarding development program arrangements. The method used was Landsat 8 image interpretation with NDVI, and the results will be classified according to the classification of vegetation density used. As a result, the density of vegetation has decreased from 2013 to 2021, area of non-vegetation and sparse vegetation land indicates, increasing by 5.3% and 4.51%, respectively. In the classification of fairly dense, dense, and very dense vegetation, density decreased by 4.39%, 4.86%, and 0.55%, respectively, which has resulted in reduced green areas becoming built-up areas along with the development of the number and mobility of the population. It is necessary to increase the amount of vegetation and stipulate development regulations that take into account the existence of vegetation as a support for ecological functions.

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Keywords: Vegetation density; NDVI; landsat 8 satellite; Padalarang Bandung

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  1. Aditiya, M.I., Andayani, F., Wulandari, K.C., Ma’sum, M.A., 2021. Analisis Kerapatan Vegetasi Menggunakan Metode Ndvi Di Kecamatan Banguntapan Kabupaten Bantul. Geographia 1, 1–8
  2. Andini, W.S., Prasetyo, Y., Sukmono, A. 2018. Analisis Sebaran Vegetasi dengan Citra Satelit Sentinel menggunakan Metode NDVI dan Segmentasi (Studi Kasus: Kabupaten Demak). J. Geod. Undip 7, 14–24
  3. Danoedoro, P. 2012. Pengolahan Citra digital Teori dan Aplikasinya dalam Bidang Penginderaan Jauh
  4. Ginting, J.A., Jadera, A.M. 2018. Analisa Indeks Vegetasi Menggunakan Citra Satelit Lansat 7 dan Lansat 8 Menggunakan Metode K-Means di Kawasan Gunung Sinabung. Indones. J. Comput. Model. 1, 42–48
  5. Gong, P., Wang, J., Yu, L., Zhao, Yongchao, Zhao, Yuanyuan, Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X., Cheng, Q., Hu, L., Yao, W., Zhang, Han, Zhu, P., Zhao, Z., Zhang, Haiying, Zheng, Y., Ji, L., Zhang, Y., Chen, H., Yan, A., Guo, J., Yu, Liang, Wang, L., Liu, X., Shi, T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C., Clinton, N., Zhu, Z., Chen, Jin, Chen, Jun, 2013. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. Int. J. Remote Sens. 34, 2607–2654
  6. Gumma, M.K., Thenkabail, P.S., Hideto, F., Nelson, A., Dheeravath, V., Busia, D., Rala, A. 2011. Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data. Remote Sens. 3, 816–835
  7. Hansen, M.C., Sohlberg, R., Defries, R.S., Townshend, J.R.G. 2000. Global land cover classification at 1 km spatial resolution using a classification tree approach, International Journal of Remote Sensing
  8. Hardianto, A., Dewi, P.U., Feriansyah, T., Sari, N.F.S., Rifiana, N.S. 2021. Pemanfaatan Citra Landsat 8 Dalam Mengidentifikasi Nilai Indeks Kerapatan Vegetasi (NDVI) Tahun 2013 dan 2019 (Area Studi: Kota Bandar Lampung). J. Geosains dan Remote Sens. 2, 8–15
  9. Hayu, M.K., Ridwana, R. 2019. Analisis Kerapatan Vegetasi Untuk Area Pemukiman Dengan Memanfaatan Citra Satelit Landsat Di Kota Tasikmalaya. J. Geogr. 8, 78
  10. Jia, K., Wei, X., Gu, X., Yao, Y., Xie, X., Li, B. 2014. Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China. Geocarto Int. 29, 941–951
  11. Khairawan, A., Falih, N., Handoko, T.D. 2020. Analisis Perubahan Indeks Kerapatan Vegetasi Memanfaatkan Citra Landsat (Studi Kasus: Provinsi DKI Jakarta). Senamika 1, 62–72
  12. Liu, J.Y., Zhuang, D.F., Luo, D., Xiao, X. 2003. Land-cover classification of China: Integrated analysis of AVHRR imagery and geophysical data. Int. J. Remote Sens. 24, 2485–2500
  13. Lukiawan, R., Purwanto, E.H., Ayundyahrini, M. 2019. Analisis Pentingnya Standar Koreksi Geometrik Citra Satelit Resolusi Menengah Dan Kebutuhan Manfaat Bagi Pengguna. J. Stand. 21, 45
  14. Lulla, K., Nellis, M.D., Rundquist, B. 2013. The Landsat 8 is ready for geospatial science and technology researchers and practitioners. Geocarto Int. 28, 191
  15. Luvi, L.R.D., Yuliantina, A., Dewi, R., Pahlevi, M.Z., Kusumawardhani, N.A. 2021. Komparasi Luas Tutupan Lahan di Kota Bandar Lampung Berdasarkan Algoritma NDVI (Normalized Difference Vegetation Index) dan EVI (Enhanced Vegetation Index). J. Geosains dan Remote Sens. 2, 16–24
  16. Oktaviani, S.I., Hanum, L., Negara, P.Z. 2017. Analisis Vegetasi di Kawasan Terbuka Hijau Industri Gasing. J. Penelit. Sains 19, 124–131
  17. Philiani, I., Saputra, L., Harvianto, L., Muzaki, A.A. 2016. Pemetaan Vegetasi Hutan Mangrove Menggunakan Metode Normalized Difference Vegetation Index ( Ndvi ). Surya Octag. Interdiscip. J. Technol. 1, 211–222
  18. Putra, E.H., 2011. Penginderaan jauh dengan ERMapper. Graha Ilmu, Yogyakarta
  19. Ramadhani, F.R., Arrasyid, R., Fauzi, M.S., Ali, M.F., Setiawan, R.A. 2022. Analisis Pengaruh Persebaran Industri Terhadap Kerapatan Vegetasi Di Kota Cimahi. J. Samudra Geogr. 5, 132–138
  20. Sampurno, R., Thoriq, A. 2016. Klasifikasi Tutupan Lahan Menggunakan Citra Landsat 8 Operational Land Imager (Oli) Di Kabupaten Sumedang. J. Teknotan 10, 61–70
  21. Simarmata, N., Wikantika, K., Tarigan, T.A., Aldyansyah, M., Tohir, R.K., Fauziah, A., Purnama, Y. 2021. Analisis Transformasi Indeks Ndvi, Ndwi Dan Savi Untuk Identifikasi Kerapatan Vegetasi Mangrove Menggunakan Citra Sentinel Di Pesisir Timur Provinsi Lampung. J. Geogr. Geogr. dan Pengajarannya 19, 69–79
  22. Sitanggang, G. 2010. Sistem Penginderaan Jauh Satelit LDCM (Landsat-8). Kaji. Pemanfaat. Satelit Masa Depan 11, 47–58
  23. Statistik, B.P. 2013. Kecamatan Padalarang Dalam Angka 2013. Kabupaten Bandung Barat
  24. Statistik, B.P. 2021. Kecamatan Padalarang Dalam Angka 2021. Kabupaten Bandung Barat
  25. Thenkabail, P.S., Biradar, C.M., Noojipady, P., Dheeravath, V., Li, Y., Velpuri, M., Gumma, M., Gangalakunta, O.R.P., Turral, H., Cai, X., Vithanage, J., Schull, M.A., Dutta, R. 2009. Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. Int. J. Remote Sens. 30, 3679–3733
  26. Timami, S., Sobirin, S., Saraswati, R. 2017. Variasi Spasial Temporal Suhu Permukaan Daratan Kota Metropolitan Bandung Raya Tahun 2014 – 2016. In: Prosiding Industrial Research Workshop and National Seminar. pp. 714–721
  27. Ufiza, S., Salmiati, Ramadhan, H. 2018. Analisis Vegetasi Tumbuhan dengan Metode Kuadrat pada Habitus Herba di Kawasan Pegunungan Deudap Pulo Nasi Aceh Besar. In: Prosiding Seminar Nasional Biotik. pp. 209–215
  28. Wahrudin, U., Atikah, S., Habibah, A. Al, Paramita, Q.P., Tampubolon, H., Sugandi, D., Ridwana, R. 2019. Pemanfaatan Citra Landsat 8 Untuk Identifikasi Sebaran Kerapatan Vegetasi di Pangandaran. Geodika J. Kaji. Ilmu dan Pendidik. Geogr. 3, 90
  29. Winarti, W., Rahmad, R. 2019. Analisis Sebaran Dan Kerapatan Vegetasi Menggunakan Citra Landsat 8 Di Kabupaten Dairi, Sumatera Utara. J. SWARNABHUMI J. Geogr. dan Pembelajaran Geogr. 4

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