Analysis of Mangrove Vegetation Index Using Landsat 8 Images  in Dodinga Bay, West Halmahera

Simon I. Patty; Doni Nurdiansah; Marenda Pandu Rizqi; Nebuchadnezzar Akbar; Rikardo Huwae

doi:10.35800/jip.v13i1.60830

Authors

Simon I. Patty BADAN RISET DAN INOVASI NASIONAL
Doni Nurdiansah Oceanographic Research Center, National Research and Innovation Agency
Marenda Pandu Rizqi Oceanographic Research Center, National Research and Innovation Agency
Nebuchadnezzar Akbar Khairun University
Rikardo Huwae Oceanographic Research Center, National Research and Innovation Agency

DOI:

https://doi.org/10.35800/jip.v13i1.60830

Keywords:

mangrove, Landsat 8 images, vegetation index, Dodinga Bay

Abstract

Mangrove vegetation can be easily recognized from remote-sensing satellite images compared to other terrestrial vegetation. The vegetation index is applied to the satellite images to highlight the aspect of vegetation density. This study aims to determine the correlation between the value of the vegetation index and mangrove canopy cover data to achieve a proper vegetation index to estimate the density of the mangrove canopy. The data needed are satellite imagery from Landsat 8 and mangrove canopy cover in sampling locations along the coast of Dodinga Bay, West Halmahera. Image data analysis includes radiometric correction, image sharpening, masking, classification, and accuracy tests. The vegetation index algorithms used were NDVI, GNDVI, and IM, and regression analysis was carried out for correlation tests. The analysis results obtained four different land cover classes with an overall accuracy of 97.70% and a kappa coefficient of 0.9688. The IM vegetation index showed an excellent correlation with mangrove canopy cover compared to GNDVI and NDVI. The determination coefficient (R²) of the IM is 0.6765; GNDVI (0.4897) and NDVI (0.4825). The IM classification produces four levels of mangrove canopy density, i.e., sparse (7.40 ha), moderate (628.33 ha), dense (921.22 ha), and very dense (16.45 ha).

Keywords: mangrove, Landsat 8 images, vegetation index, Dodinga Bay

Abstrak

Objek vegetasi mangrove paling mudah diidentifikasi dengan menggunakan citra satelit penginderaan jauh dibandingkan objek vegetasi darat lainnya. Indeks vegetasi diterapkan terhadap citra untuk menonjolkan aspek kerapatan vegetasi. Penelitian ini bertujuan mengetahui korelasi antara nilai indeks vegetasi dengan data tutupan kanopi mangrove, sehingga didapatkan indeks vegetasi yang sesuai untuk menduga kerapatan kanopi mangrove. Data yang diperlukan yaitu citra Landsat 8 dan tutupan kanopi mangrove di lapangan. Analisis data citra terdiri dari koreksi radiometrik, penajaman citra, masking, klasifikasi dan uji akurasi. Algoritma indeks vegetasi yang digunakan yaitu NDVI, GNDVI dan IM, serta dilakukan analisis regresi untuk uji korelasi. Hasil analisis mendapatkan empat kelas tutupan lahan yang berbeda dengan overall akurasi sebesar 97,70 % dan kappa coefisien sebesar 0,9688. Indeks vegetasi IM menunjukkan korelasi sangat baik dengan tutupan kanopi mangrove dibandingkan GNDVI dan NDVI. Koefisien determinasi (R²) IM adalah 0,6765; GNDVI (0,4897) dan NDVI (0,4825). Klasifikasi IM menghasilkan empat tingkat kerapatan kanopi mangrove yaitu mangrove jarang (7,40 ha), mangrove sedang (628,33 ha), mangrove lebat (921,22 ha), dan mangrove sangat lebat (16,45 ha).

Kata kunci: mangrove, citra Landsat 8, indeks vegetasi, Teluk Dodinga

References

Akbar, N., I. Marus, I. Haji, S. Abdullah, S. Umalekhoa, F.S. Ibrahim, M. Ahmad, A. Ibrahim, A. Kahar dan I. Tahir, 2017. Struktur komunitas hutan mangrove di Teluk Dodinga, Kabupaten Halmahera Barat, Provinsi Maluku Utara. Jurnal Enggano, 2(1):78–89.

Arhatin, R. E., dan P.I. Wahyuningrum. 2013. Algoritma indeks vegetasi mangrove menggunakan citra landsat ETM+. Buletin PSP 21(2):215-227.

Arindi, Y.N. 2018. Analisis perubahan kerapatan ekosistem mangrove menggunakan algoritma indeks vegetasi NDVI dan SAVI citra satelit multitemporal (studi kasus: pesisir utara Surabaya). Tugas Akhir. Departemen Teknik Geomatika-FTSLK, ITS Surabaya. 101 hal.

Bengen, D.G. 2001. Pedoman teknis pengenalan dan pengelolaan ekosistem mangrove. Pusat Kajian Sumberdaya Pesisir dan Lautan. Institut Pertanian Bogor.

Congalton, R.G., and K. Green. 2009. Thematic and positional accuracy assessment of digital remotely sensed data. Assessing The Accuracy of Remotely Sensed Data: Principles and Practices. New York (US). 179p.

Dharmawan, I.W.E., dan Pramudji. 2014. Panduan monitoring status ekosistem mangrove. COREMAP-CTI LIPI, Jakarta. 35 hlm.

FAO. 2007. The World’s Mangroves 1980-2005. FAO Forestry Paper 153. Rome.

Giri, C., E. Ochieng, L. L. Tieszen, Z. Zhu, A. Singh, T. Loveland, J. Masek and N. Duke. 2011. Status and Distribution of Mangrove Forests of the World Using Earth Observation Satellite Data. Global Ecology and Biogeography 20 (1): 154–59.

Gholizadeh. H., S.M. Robeson, A.F. Rahman, 2015. Comparing the performance of multispectral vegetation indices and machine learning algorithms for remote estimationof chlorophyll content: a case study in the Sundarbans mangrove forest. International journal of remote sensing 36(12):3114-3133.

Gong. P., P.U. Ruiliang, G.S. Biging, M.R. Larraieu, 2003. Estimation of Leaf Area Index Using Vegetation Indices Derived From Hyperion Hyperspectral Data. Journal of IEEE Transactions on Geoscience and Remote Sensing.

Huete, A., K. Didan, W.V. Leeuwen, T. Miura, and E. Glenn. 2011. MODIS vegetation indices. Land remote sensing and global environmental change, 26:579-602. doi: 10.1007/978-1-4419-6749-7_26.

Kementerian Negara Lingkungan Hidup, 2004. Keputusan Menteri Negara Lingkungan Hidup No. 201 Tahun 2004 Tentang Kriteria Baku dan Pedoman Penentuan Kerusakan Mangrove. Jakarta.

Nawangwulan, N.H. 2013. Analisis Pengaruh Perubahan Lahan terhadap Hasil Produksi Tanaman Pangan di Kabupaten Pati Tahun 2001- 2011. Jurnal Geodesi Undip. 2(2):127-140.

Nurdiansah, D. dan , I.W.E.,Dharmawan, 2018. Komunitas mangrove di wilayah pesisir pulau Tidore dan sekitarnya. Jurnal Oseanologi dan Limnologi, 3(1):1–9.

Prahesti, T., N. Bashit dan Y. Wahyuddin, 2021. Analisis perubahan kerapatan tanaman mangrove terhadap perubahan garis pantai di Kabupaten Pati tahun 2017-2020 dengan metode pengindraan jauh dan aplikasi digital shoreline analysis system (DSAS). Jurnal Geodesi, UNDIP. 10(1):143–152.

Prameswari, A.A.S.R., T. Hariyanto dan F. Sidik, 2015. Analisis indeks vegetasi mangrove menggunakan citra satelit Alos Avnir-2 di Estuari Perancak Bali, Jurnal Geold, 11(1):40-45.

Proisy, C., P. Couteron, F. Fromard, 2007. Predicting and Mapping Mangrove Biomass from Canopy Grain Analysis Using Fourier-Based Textural Ordination of IKONOS Images Original Research Article. Remote Sensing of Environment. 109: 379–392.

Rodriguez MG, Estrada JAE, Gonzales MTR, Reynolds MP. 2006. Canopy reflectance indices and its relationship with yield in common bean plants with phosporus supply. International journal of Agriculture and Biology 8(2):203-207

Satyanarayana, B., I.F. Idris, M. Husain, F. Dahdouh-Guebas, 2011. Assessment of Mangrove Vegetation Based on Remote Sensing and Ground-truth Measurements at Tumpat, Kelantan Delta, East Coast of Peninsular Malaysia. International Journal of Remote Sensing. 32(6):1635–1650.

Susilo, S.B. 2000. Penginderaan Jauh Terapan. Fakultas Perikanan dan Ilmu Kelautan. Institut Pertanian Bogor.

Suyarso. 2019. Teknik Eksplorasi Sumber Daya Pesisir (Terumbu Karang dan Mangrove) Berbasis Geospasial. Penerbit ANDI: Yogyakarta.

Winarso, G. dan A.D. Purwanto, 2014. Pendekatan baru indeks kerusakan mangrove menggunakan data penginderaan jauh. Seminar nasional penginderaan jauh. Deteksi parameter geobiofisik dan diseminasi penginderaan jauh, 368–379.