Estimasi Suhu Udara Di Kabupaten Manokwari Melalui Pemanfaatan Citra Satelit Landsat 8

Authors

DOI:

https://doi.org/10.31172/jmg.v23i1.753

Keywords:

Normalized Difference Vegetation Index (NDVI), suhu udara, citra satelit

Abstract

Air temperature is the main parameter in determining agricultural land. However, most areas in Manokwari do not have air temperature data due to limited meteorological stations. The utilization of Landsat 8 satellite imagery is one of the alternative solutions for providing air temperature data. This study aims to examine the performance of Landsat 8 satellite imagery in estimating air temperature in Manokwari. The air temperature is estimated using the Normalized Difference Vegetation Index (NDVI) approach. Seven (7) statistical parameters i.e mean error (ME), mean absolute error (MAE), root mean square error (RMSE), relative bias (RBIAS), mean bias factor (MBIAS), percent bias (PBIAS), and the Pearson correlation coefficient (r) are used in the test. Besides, a paired T-test was also used to determine the significance of the difference between the estimated and observed data. A total of 33 Landsat 8 satellite imagery recordings from 2015 to 2020 and air temperature data obtained from the climatological station were used. The results showed that the estimated temperature had good accuracy with ME = 0.50 oC, MAE = 2.73 oC, RMSE = 3.45 oC, RBIAS = 0.09, MBIAS = 1.00, and PBIAS = 9,16% compared with climatological data. Besides, the estimated temperature does not have a significant difference to observed data although it has a weak correlation with r = 0.31. Therefore, Landsat 8 satellite imagery can be used as an alternative solution in providing air temperature in Manokwari for supporting agricultural land development.

References

Bappenas, “Rancangan Teknokratik Rencana Pembangunan Jangka Menengah Nasional 2020 - 2024 : Indonesia Berpenghasilan Menengah - Tinggi Yang Sejahtera, Adil, dan Berkesinambungan,” 2019. doi: 10.1017/CBO9781107415324.004.

Menteri Pertanian, “Keputusan Menteri Pertanian RI Nomor 830/Kpts/RC.040/12/2016 Tentang Lokasi Pengembangan Kawasan Pertanian Nasional,” Jakarta, 2016.

S. Ritung, K. Nugroho, A. Mulyani, and E. Suryani, Petunjuk Teknis Evaluasi Lahan untuk Komoditas Pertanian, 2nd ed. Bogor: Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian, 2011.

BMKG, “Metadata Stasiun,” 2018. https://dataonline.bmkg.go.id/mcstation_metadata (accessed Nov. 07, 2018).

World Meteorological Organization, Commission for Instruments and Methods of Observation (WMO-No. 1064), no. 1064. 2010.

BPS Kabupaten Manokwari, “Kabupaten Manokwari Dalam Angka 2021,” Manokwari, 2021.

Department of the Interior U.S. Geological Survey, Landsat 8 (L8) Data User Handbook, 2nd ed. U.S. Geological Survey, 2016.

M. Abrams and S. Hook, ASTER User Handbook. California: Jet Population Library, 2003.

European Space Agency, “GOES-R (Geostationary Operational Environmental Satellite-R) 3rd Generation Series,” 2018. https://earth.esa.int/web/eoportal/satellite-missions/g/goes-r.

NASA, NOAA KLM User’s Guide, no. August 2014. 2014.

A. Faisol, Indarto, E. Novita, and Budiyono, “Pemanfaatan Citra Satelit untuk Membangkitkan Informasi Suhu Udara Guna Mendukung Pengelolaan Sumberdaya Air,” in Peran Keanekaragaman Hayati untuk Mendukung Indonesia sebagai Lumbung Pangan Dunia, 2018, vol. 2, no. 1, p. B.10-B.18.

R. H. Al-Anbari, O. Z. Jasim, and Z. T. Mohammed, “Estimation High Resolution Air Temperature Based on Landsat 8 images and Climate Datasets,” in 2nd International Conference on Sustainable Engineering Techniques (ICSET 2019), 2019, vol. 518, no. 2, doi: 10.1088/1757-899X/518/2/022033.

I. Hough, A. Just, B. Zhou, M. Dorman, J. Lepeule, and I. Kloog, “A multi-resolution air temperature model for France from MODIS and Landsat thermal data,” Environ. Res., vol. 183, no. April, p. 109244, 2020, doi: https://doi.org/10.1016/j.envres.2020.109244.

S. B. Lee, D. Lee, J. Kim, and J. Kim, “Development and Validation of a Daily Maximum Temperature Estimation Algorithm Using Landsat-8,” in International Geoscience and Remote Sensing Symposium (IGARSS) 2018, 2018, pp. 2523–2526, doi: https://doi.org/10.1109/IGARSS.2018.8518682.

S. Hong, “Mapping Regional Distributions of Energy Balance Components Using Optical Remotely,” New Mexico Institute of Mining and Technology, 2008.

J. G. Liu and P. J. Mason, Essential Image Processing and GIS for Remote Sensing, 1st ed. Hoboken: Wiley & Sons, Ltd, 2009.

US Geological Survey, “Landsat Normalized Difference Vegetation Index,” 2019. https://www.usgs.gov/core-science-systems/nli/landsat/landsat-normalized-difference-vegetation-index?qt-science_support_page_related_con=0#qt-science_support_page_related_con.

E. Omranian, H. O. Sharif, and A. A. Tvakoly, “How Well Can Global Precipitation Measurement ( GPM ) Capture Hurricanes ? Case Study : Hurricane Harvey,” Remote Sens., p. 14, 2018, doi: 10.3390/rs10071150.

R. Saeidizand, S. Sabetghadam, E. Tarnavsky, and A. Pierleoni, “Evaluation of CHIRPS Rainfall Estimates over Iran,” Adv. Remote Sens. pf Rainfall Snowfall, vol. 144, no. May, pp. 282–291, 2018, doi: 10.1002/qj.3342.

L. C. Lelis, R. W. D. Bosquilia, and S. N. Duarte, “Assessment of Precipitation Data Generated by GPM and TRMM Satellites,” Rev. Bras. Meteorol., vol. 33, no. 1, pp. 153–163, 2018.

R. Wang, J. Chen, and X. Wang, “Comparison of IMERG Level-3 and TMPA 3B42V7 in Estimating Typhoon-Related Heavy Rain,” Water, vol. 9, no. 276, pp. 1–15, 2017, doi: 10.3390/w9040276.

F. J. P. Trejo, H. A. Barbosa, M. A. Peñaloza-Murillo, M. A. Moreno, and A. Farías, “Intercomparison of Improved Satellite Rainfall Estimation with CHIRPS Gridded Product and Rain Gauge Data over Venezuela,” Atmósfera, vol. 29, no. 4, pp. 323–342, 2016, doi: 10.20937/ATM.2016.29.04.04.

M. A. Azka, P. A. Sugianto, A. K. Silitonga, and I. R. Nugraheni, “Uji Akurasi Produk Estimasi Curah Hujan Satelit GPM IMERG di Surabaya, Indonesia,” J. Sains Teknol. Modif. Cuaca, vol. 19, no. 2, pp. 83–88, 2018.

D. Machiwal and M. K. Jha, Hydrologic Time Series Analysis: Theory and Practice, 1st ed. New Delhi: Springer International Publishing, 2012.

Nuryadi, T. D. Astuti, E. S. Utami, and M. Budiantara, Dasar-Dasar Statistik Penelitian, 1st ed. Yogyakarta: Sibuku Media, 2017.

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Published

2022-03-01

How to Cite

Mashudi, M., & Faisol, A. (2022). Estimasi Suhu Udara Di Kabupaten Manokwari Melalui Pemanfaatan Citra Satelit Landsat 8. Jurnal Meteorologi Dan Geofisika, 23(1), 55–61. https://doi.org/10.31172/jmg.v23i1.753

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Vol. 23 No. 1 (2022)

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Article

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