Analisis Tingkat Perubahan Vegetasi Hutan Mangrove Terhadap Tingkat Salinitas Menggunakan Citra Landsat

Authors

  • Rafly Rizqyta Ramadhan Universitas Udayana
  • Winardi Tjahyo Baskoro Program Studi Fisika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana
  • Kadek Sumaja Stasiun Meteorologi Kelas I Ngurah Rai, Badan Meteorologi, Klimatologi, dan Geofisika
  • Komang Ngurah Suarbawa Program Studi Fisika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana
  • Putu Tedy Indrayana Program Studi Fisika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana
  • Ni Nyoman Ratini Program Studi Fisika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

DOI:

https://doi.org/10.29408/kpj.v8i2.23729

Keywords:

NDVI, SI, NDSI, VSSI, Landsat 8, Mangrove

Abstract

Research on the analysis of the level of changes of mangrove forest vegetation on the level of seawater salinity has been conducted with remote sensing methods by using Landsat 8 imagery in Tahura Ngurah Rai. The data used in this research is secondary data from the official website of the USGS (United States Geological Survey). This study aims to determine the levels of changes in mangrove forest vegetation cover in Tahura Ngurah Rai and to determine how much influence the level of mangrove forest vegetation cover has on the level of salinity in Tahura Ngurah Rai. The vegetation values were obtained by processing the NDVI index data and the salinity values were obtained by processing the SI, NDSI and VSSI index. from the results of the analysis that has been conducted, vegetation cover greatly influences the NDSI index with an increase in the total NDVI value of 25,48% and a decrease in the total NDSI value of -25,48%, with a correlation coefficient and determination of NDVI-NDSI 0.998 and 0.996 respectively.

Author Biography

Rafly Rizqyta Ramadhan, Universitas Udayana

Department of Mathematics and Natural Sciences

References

Asfaw, E., Suryabhagavan, K. V., & Argaw, M. (2018). Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia. Journal of the Saudi Society of Agricultural Sciences, 17(3), 250–258. https://doi.org/10.1016/j.jssas.2016.05.003

Campbell, J. B., & Wynne, R. H. (2011). History and Scope of Remote Sensing. In Introduction to Remote Sensing (5th ed., p. 6). New York City: The Guilford Press. Retrieved from https://www.guilford.com/excerpts/campbell2_old_ch1.pdf?t=1

Darmo, K., Suarbawa, K. N., & Gusti Agung Widagda, I. (2018). Analysis of Changes in Area of Mangrove Density at Ngurah Rai Bali Grand Forest Park Using Image of Landsat 8 Satellite. BULETIN FISIKA, 19(2), 58–63. https://doi.org/10.24843/BF.2018.v19.i02.p04

Dehni, A., & Lounis, M. (2012). Remote Sensing Techniques for Salt Affected Soil Mapping: Application to the Oran Region of Algeria. Procedia Engineering, 33, 188–198. https://doi.org/https://doi.org/10.1016/j.proeng.2012.01.1193

Dittmann, S., Mosley, L., Stangoulis, J., Nguyen, V. L., Beaumont, K., Dang, T., … McGrath, A. (2022). Effects of Extreme Salinity Stress on a Temperate Mangrove Ecosystem. Frontiers in Forests and Global Change, 5. https://doi.org/10.3389/ffgc.2022.859283

Landgrebe, D. A. (2003). Introduction and Background. In Signal Theory Methods in Multispectral Remote Sensing (pp. 1–27). John Wiley & Sons, Ltd. https://doi.org/https://doi.org/10.1002/0471723800.ch1

Purwadhi, S. H. (2001). Interpretasi Citra Digital. Jakarta: Grasindo.

Putra, A., Tanto, T. Al, Farhan, A. R., Husrin, S., & Pranowo, W. S. (2018). Pendekatan Metode Normalized Difference Vegetation Index (NDVI) Dan Lyzenga Untuk Pemetaan Sebaran Ekosistem Perairan Di Kawasan Pesisir Teluk Benoa, Bali. Jurnal Ilmiah Geomatika, 23(2), 87. https://doi.org/10.24895/jig.2017.23-2.729

Putranto, T. T., & Kusuma, K. I. (2012). Permasalahan Airtanah Pada Daerah Urban. Teknik, 30(1), 48–56. Retrieved from https://ejournal.undip.ac.id/index.php/teknik/article/view/1824

Salisbury, J. W., & D’Aria, D. M. (1992). Emissivity of terrestrial materials in the 8–14 μm atmospheric window. Remote Sensing of Environment, 42(2), 83–106. https://doi.org/https://doi.org/10.1016/0034-4257(92)90092-X

Sukojo, B. M., & Arindi, Y. N. (2019). Analisa Perubahan Kerapatan Mangrove Berdasarkan Nilai Normalized Difference Vegetation Index Menggunakan Citra Landsat 8 (Studi Kasus: Pesisir Utara Surabaya). Geoid, 14(2), 1–5.

Suryana, Y., Nur, H. S., & Hilmi, E. (1998). Hubungan Antara Keberadaan Lebar Jalur Mangrove dengan Kondisi Biofisik Ekosistem Mangrove. Bandung: Fakultas Kehutanan Universitas Winayamukti.

Wijaya, N. I., & Huda, M. (2018). Monitoring Sebaran Vegetasi Mangrove Yang Direhabilitasi Di Kawasan Ekowisata Mangrove Wonorejo Surabaya. Jurnal Ilmu Dan Teknologi Kelautan Tropis, 10(3), 747–755. https://doi.org/10.29244/jitkt.v10i3.21271

Yastika, P. E., Vipriyanti, N. U., & Doan, T.-U. T. (2023). Analisis Spatio-Temporal Perubahan Tutupan Vegetasi Mangrove di Taman Hutan Raya Ngurah Rai Berbasis Citra Landsat 7 ETM+ dan Landsat 8 OLI. Jasintek, 4(2), 82–92.

Zainab, S., & Handajani, N. (2022). Analisis Sebaran Salinitas Air Tanah Akibat Intrusi Air Laut Dan Curah Hujan Di Pesisir Bangkalan Madura Berbasis Data Citra Satelit. Surabaya. Retrieved from http://repository.upnjatim.ac.id/6060/1/Laporan_Penelitian%202022_Perpustakaan.pdf

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Published

2024-08-01

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