Open AccessNumerical Analysis using Empirical Orthogonal Function Based on Multivariate Singular Value Decomposition on Indonesian Forest Fire Signal
Author(s) -
Pandu Septiawan,
Sri Nurdiati,
Ardhasena Sopaheluwakan
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/303/1/012053
Subject(s) - empirical orthogonal functions , indonesian , singular spectrum analysis , environmental science , precipitation , climatology , carbon stock , geography , forestry , meteorology , climate change , physical geography , mathematics , singular value decomposition , ecology , algorithm , geology , philosophy , linguistics , biology
Over the past years, fires have burn Indonesian forest annually. The strength of the fire and many of fire points is vary each year. In 1997 there was 9.75 million ha forest burned in one year with 206,6 million ton of carbon emissions. In 2015 there was 2.089 million ha forest and 805 million tons carbon emissions. Both years involved the biggest incidences of burned forest in Indonesia. The purpose of this research is to analyze the pattern of burned forest in Indonesia and to analyze linkages of variables that affect it. Datasets that used in this research is monthly data from 1997 until 2016 Global Fires Emissions Database (GFED) with variable burned area, GFEDs with variable carbon emissions, and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Using Singular Value Decomposition and Empirical Orthogonal Function analysis, for the overlapping period detected that there is annual signal that dominate the incidences of fires in Indonesia, especially in Riau, Palembang, South Kalimantan and Central Kalimantan. There was 4 annual signal that can explain 72% of fires incidences in Indonesia in 1997-2016. In Riau there is another signal that have 6 months period caused by 6 months rainfall period.