Drought Analysis and Forecast Using Landsat-8 Sattelite Imagery, Standardized Precipitation Index and Time Series
(1) Universitas Kristen Satya Wacana
(2) Universitas Kristen Satya Wacana
(3) Universitas Kristen Satya Wacana
(*) Corresponding Author
DOI: https://doi.org/10.23917/khif.v6i1.8863
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R. D’Arrigo and R. Wilson, “El Nino and Indian Ocean influences on Indonesian drought: implications for forecasting rainfall and crop productivity,” Int. J. Climatol. A J. R. Meteorol. Soc., vol. 28, no. 5, pp. 611–616, 2008.
I. G. Hendrawan, K. Asai, A. Triwahyuni, and D. V. Lestari, “The interanual rainfall variability in Indonesia corresponding to El Niño Southern oscillation and Indian Ocean Dipole,” Acta Oceanol. Sin., vol. 38, no. 7, pp. 57–66, 2019.
L. Arumingtyas, “Jawa dan Nusa Tenggara Langganan Bencana Kekeringan, Mengapa?,” Mongabay, 2018.
A. Sulistyo, “Kombinasi Teknologi Aplikasi GPS Mobile dan Pemetaan SIG dalam Sistem Pemantauan Demam Berdarah (DBD),” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 5, no. 1, pp. 6–14, 2019.
A. Syahrial, A. Azmeri, and E. Meilianda, “Analisis Kekeringan Menggunakan Metode Theory of Run di DAS Krueng Aceh,” J. Civ. Eng., vol. 24, no. 2, pp. 167–172, 2017.
L. Wang, G. Huang, and W. Chen, “Towards a theoretical understanding of multiscalar drought indices based on the relationship between precipitation and standardized precipitation index,” Theor. Appl. Climatol., vol. 136, no. 3–4, pp. 1465–1473, 2019.
W. Hatmoko, “Indeks Kekeringan Hidrologi untuk Alokasi Air di Indonesia.” Puslitbang Sumber Daya Air, Bandung, 2012.
M. Hendartyo, “BNPB: 4,87 Juta Jiwa Terdampak Kekeringan,” Tempo, 07-Sep-2018.
Saumlaki, “Maret, Kelaparan Ancam MTB Akibat Krisis Air Parah,” Dhara Pos, 02-Mar-2016.
A. Zubaidah, D. Dirgahayu, and J. M. Pasaribu, “Penginderaan jauh untuk pemantauan kekeringan lahan sawah,” J. Ilm. Widya, vol. 1, no. 1, 2014.
S. Y. J. Prasetyo, K. D. Hartomo, B. H. Simanjuntak, and D. W. Candra, “Mitigation & identification for local aridity, based of vegetation indices combined with spatial statistics & clustering k means,” in Journal of Physics: Conference Series, 2019, vol. 1235, no. 1, p. 12028.
R. P. Gupta, Remote sensing geology. Springer, 2017.
V. A. Bento, I. F. Trigo, C. M. Gouveia, and C. C. DaCamara, “Contribution of land surface temperature (TCI) to vegetation health index: A comparative study using clear sky and all-weather climate data records,” Remote Sens., vol. 10, no. 9, p. 1324, 2018.
S. M. Indirawati, S. Pandia, H. Mawengkang, and W. Hasan, “Inverse Distance Weighted Method and Environmental Health Risks of Plumbum Pollution in Drinking Water in Belawan Coastal Area,” Adv. Sci. Lett., vol. 23, no. 4, pp. 3339–3343, 2017.
S. R. Fitri, E. Saadudin, B. Pranoto, and others, “Comparison of Inverse Distance Weighted (IDW), Natural Neighbour, and Spline Interpolation Methods for Downscaling Data of Solar Energy Potential Map,” Ketenagalistrikan dan Energi Terbarukan, vol. 13, no. 1, pp. 27–38, 2014.
R. Kumar, M. Majid, S. Mir, and M. Shahzad, “Temporal analysis of drought using standard precipitation index (SPI) method,” Indian J. Soil Conserv., vol. 45, no. 3, pp. 348–350, 2017.
I. A. Andika, “Penerapan Metode Standardized Precipitation Index (SPI) untuk Analisa Kekeringan di DAS Ngasinan Kabupaten Trenggalek,” Universitas Brawijaya, 2016.
P. J. Brockwell and R. A. Davis, Introduction to time series and forecasting. springer, 2016.
P. K. Pradhan, S. Dhal, and N. K. Kamila, “Time series least square forecasting analysis and evaluation for natural gas consumption,” Int. J. Recent Innov. Trends Comput. Commun., vol. 5, no. 11, pp. 91–99, 2017.
F. R. Hariri, “Metode Least Square Untuk Prediksi Penjualan Sari Kedelai Rosi,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 7, no. 2, pp. 731–736, 2016.
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