Analisis Kerapatan Vegetasi Kota Ambon Menggunakan Data Citra Satelit Sentinel-2 dengan Metode MSARVI Berbasis Machine Learning pada Google Earth Engine

Philia Christi Latue; Heinrich  Rakuasa; Daniel Anthoni Sihasale

doi:10.56211/sudo.v2i2.270

Authors

Philia Christi Latue Universitas Pattimura, Ambon
Heinrich Rakuasa Universitas Pattimura, Ambon
Daniel Anthoni Sihasale Universitas Pattimura, Ambon

DOI:

https://doi.org/10.56211/sudo.v2i2.270

Keywords:

Ambon; Kerapatan Vegetasi; Google Earth Engine; MSARVI; Machine Learning

Abstract

Pertumbuhan kota Ambon yang pesat serta dapat mempengaruhi penurunan Indeks kerapatan vegetasi di Kota Ambon. Penelitian ini bertujuan untuk mengnalisis kerapatan vegetasi Kota Ambon menggunakan data citra satelit sentinel-2 dengan metode MSARVI berbasis machine learning pada google earth engine. Penelitian ini menggunakan data Citra Satelit Sentinel-2 yang dianalisis menggunakan Google Earth Engine dengan metode Modified Soil-Adjusted Vegetation Index. Hasil analisis kerapatan vegetasi menggunakan metode Metode MSARVI (Modified Soil-Adjusted Vegetation Index) menunjukan bahwa daerah yang memiliki kerapatan vegetasi tinggi memiliki luas sebesar 32.856,03 ha atau 85%, daerah yang memiliki kerapatan vegetasi sedang memiliki luas sebesar 3.508,67 ha atau 9,11 % dan daerah yang memiliki kerapatan vegetasi rendah memiliki luas sebesar 2.169,64 ha atau 5,63 %. Nilai kerapatan vegetasi di Kota Ambon pada tahun 2023 yaitu nilai terendah -0,481341 dan nilai tertinggi 0,978457. Hasil penelitian ini dapat digunakan untuk monitoring perubahan lingkungan, mengidentifikasi area dengan kualitas lingkungan yang buruk, mengukur dampak perubahan iklim dan menyediakan informasi bagi pengambil keputusan.

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References

J. H. Pietersz, J. Matinahoru, and R. Loppies, “Pendekatan Indeks Vegetasi Untuk Mengevaluasi Kenyamanan Termal Menggunakan Data Satelit Landsat-Tm Di Kota Ambon,” Agrologia, vol. 4, no. 2, Feb. 2018, doi: 10.30598/a.v4i2.208.

G. S. Heinrich Rakuasa, “Analisis Spasial Kesesuaian dan Evaluasi Lahan Permukiman di Kota Ambon,” J. Sains Inf. Geogr. (J SIG), vol. 5, no. 1, pp. 1–9, 2022, doi: DOI: http://dx.doi.org/10.31314/j%20sig.v5i1.1432.

H. Latue, P. C., Septory, J. S. I., & Rakuasa, “Perubahan Tutupan Lahan Kota Ambon Tahun 2015, 2019 dan 2023,” JPG (Jurnal Pendidik. Geogr., vol. 10, no. 1, pp. 177–186, 2023, doi: http://dx.doi.org/10.20527/jpg.v10i1.15472.

H. Rakuasa, “ANALISIS SPASIAL TEMPORAL SUHU PERMUKAAN DARATAN/ LAND SURFACE TEMPERATURE (LST) KOTA AMBON BERBASIS CLOUD COMPUTING: GOOGLE EARTH ENGINE,” J. Ilm. Inform. Komput., vol. 27, no. 3, pp. 194–205, Dec. 2022, doi: 10.35760/ik.2022.v27i3.7101.

M. M. F. Wong, J. C. H. Fung, and P. P. S. Yeung, “High-resolution calculation of the urban vegetation fraction in the Pearl River Delta from the Sentinel-2 NDVI for urban climate model parameterization,” Geosci. Lett., vol. 6, no. 1, p. 2, Dec. 2019, doi: 10.1186/s40562-019-0132-4.

S. Abdullah and D. Barua, “Combining Geographical Information System (GIS) and machine learning to monitor and predict vegetation vulnerability: An Empirical Study on Nijhum Dwip, Bangladesh,” Ecol. Eng., vol. 178, p. 106577, 2022, doi: https://doi.org/10.1016/j.ecoleng.2022.106577.

Y. Zeng et al., “Optical vegetation indices for monitoring terrestrial ecosystems globally,” Nat. Rev. Earth Environ., vol. 3, no. 7, pp. 477–493, May 2022, doi: 10.1038/s43017-022-00298-5.

X. Geng et al., “Vegetation coverage of desert ecosystems in the Qinghai-Tibet Plateau is underestimated,” Ecol. Indic., vol. 137, p. 108780, Apr. 2022, doi: 10.1016/j.ecolind.2022.108780.

K. V. Ticman, S. G. Salmo III, K. E. Cabello, M. Q. Germentil, D. M. Burgos, and A. C. Blanco, “MONITORING POST-DISASTER MANGROVE FOREST RECOVERIES IN LAWAAN-BALANGIGA, EASTERN SAMAR USING TIME SERIES ANALYSIS OF MOISTURE AND VEGETATION INDICES,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XLVI-4/W6-, pp. 295–301, Nov. 2021, doi: 10.5194/isprs-archives-XLVI-4-W6-2021-295-2021.

M. M. Moura et al., “Temporal analysis of desertification vulnerability in Northeast Brazil using Google Earth Engine,” Trans. GIS, vol. 26, no. 4, pp. 2041–2055, Jun. 2022, doi: 10.1111/tgis.12926.

I. M. Cipta, F. A. Sobarman, H. Sanjaya, and M. R. Darminto, “Analysis of Mangrove Forest Change from Multi-Temporal Landsat Imagery Using Google Earth Engine Application : (Case Study: Belitung Archipelago 1990 - 2020),” in 2021 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS), Sep. 2021, pp. 90–95. doi: 10.1109/AGERS53903.2021.9617354.

R. Latuconsina, G. Mardiatmoko, and J. D. Putuhena, “VARIATION OF NDVI VEGETATION INDEX IN LANDSCAPE CHANGE OF AMBON CITY, MALUKU PROVINCE,” J. HUTAN PULAU-PULAU KECIL, vol. 4, no. 1, pp. 1–13, Apr. 2020, doi: 10.30598/jhppk.2020.4.1.1.

M. Amiri and H. R. Pourghasemi, “Mapping the NDVI and monitoring of its changes using Google Earth Engine and Sentinel-2 images,” in Computers in Earth and Environmental Sciences, Elsevier, 2022, pp. 127–136. doi: 10.1016/B978-0-323-89861-4.00044-0.

S. L. Ermida, P. Soares, V. Mantas, F.-M. Göttsche, and I. F. Trigo, “Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series,” Remote Sens., vol. 12, no. 9, p. 1471, May 2020, doi: 10.3390/rs12091471.

J. Aryal, C. Sitaula, and S. Aryal, “NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia,” Land, vol. 11, no. 3, p. 351, Feb. 2022, doi: 10.3390/land11030351.

A. Latue, P. C., Rakuasa, H., Somae, G., & Muin, “Analisis Perubahan Suhu Permukaan Daratan di Kabupaten Seram Bagian Barat Menggunakan Platform Berbasis Cloud Google Earth Engine,” Sudo J. Tek. Inform., vol. 2, no. 2, pp. 45–51., 2023, doi: https://doi.org/10.56211/sudo.v2i2.261.

L. K. Onisimo Muntaga, “Google Earth Engine Applications,” remotesensing, pp. 11–14, 2019, doi: 10.3390/rs11050591.

V. F. Kovyazin, A. Y. Romanchikov, D. T. L. Anh, D. V. Hung, and V. Van Hung, “Predicting Forest Land Cover Changesin Ba Be National Park of Vietnam,” {IOP} Conf. Ser. Earth Environ. Sci., vol. 574, p. 12038, Oct. 2020, doi: 10.1088/1755-1315/574/1/012038.

Y. Rakuasa, H., & Pakniany, “Spatial Dynamics of Land Cover Change in Ternate Tengah District, Ternate City, Indonesia,” Forum Geogr., vol. 36, no. 2, pp. 126–135, 2022, doi: DOI: 10.23917/forgeo.v36i2.19978.

P. C. (2023). Letedara, R., Rakuasa, H., & Latue, “Cellular Automata Markov Chain Application For Prediction Of Land Cover Changes In The Wae Batu Gantung Watershed, Ambon City, Indonesia,” ournal Multidiscip. Sci., vol. 2, no. 2, pp. 113-122., 2023, doi: https://doi.org/10.58330/prevenire.v2i2.191.

H. Sugandhi, N., Supriatna, S., Kusratmoko, E., & Rakuasa, “Prediksi Perubahan Tutupan Lahan di Kecamatan Sirimau, Kota Ambon Menggunakan Celular Automata-Markov Chain,” JPG (Jurnal Pendidik. Geogr., vol. 9, no. 2, pp. 104–118, 2022, doi: http://dx.doi.org/10.20527/jpg.v9i2.13880.

P. C. Latue and H. Rakuasa, “ANALISIS SPASIAL PERUBAHAN TUTUPAN LAHAN DI DAS WAE BATUGANTONG, KOTA AMBON,” J. Tanah dan Sumberd. Lahan, vol. 10, no. 1, pp. 149–155, Jan. 2023, doi: 10.21776/ub.jtsl.2023.010.1.17.

M. C. Rakuasa, H., Salakory, M., & Mehdil, “Prediksi perubahan tutupan lahan di DAS Wae Batu Merah, Kota Ambon menggunakan Cellular Automata Markov Chain,” J. Pengelolaan Lingkung. Berkelanjutan (Journal Environ. Sustain. Manag., vol. 6, no. 2, pp. 59–75, 2022, doi: https://doi.org/10.36813/jplb.6.2.59-75.