This Author published in this journals
All Journal Forest and Society
Ayu Shabrina
Research Center for Informatics, Indonesian Institute of Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

Correlation of Climate Variability and Burned Area in Borneo using Clustering Methods Ishardina C. Hidayati; Novinda Nalaratih; Ayu Shabrina; Intan N. Wahyuni; Arnida L. Latifah
Forest and Society Vol. 4 No. 2 (2020): NOVEMBER
Publisher : Forestry Faculty, Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24259/fs.v4i2.9687

Abstract

The island of Borneo has faced seasonal forest fires for decades. This phenomenon is worsening during dry seasons, especially when droughts are concurrent with the El NiƱo-Southern Oscillation (ENSO) phenomenon. Climate is therefore one of the drivers of the fire phenomenon. This paper studies the relationship between climate variables, namely temperature, precipitation, relative humidity, and wind speed, and the occurrence of forest fire using two clustering methods, K-means and Fuzzy C-means (FCM) clustering methods. Borneo is clustered into four areas based on burned area data obtained from Global Fire Emission Data (GFED). It is also clustered according to the combinations of climate variables. Both methods reach the highest correlation between the climate variable and the burned area clusters in September. The K-means method gives a correlation of -0.54 while the FCM gives -0.55. In August until October, relative humidity provides the dominant correlation affecting burned area, even though an additional precipitation or wind variable slightly increases the correlation in the FCM method. In November, temperature largely contributed to the burned area by a positive correlation of 0.31 in K-means and 0.33 in FCM. The evaluation performance of the methods is conducted by an internal validation called the Silhouette index. Both methods have positive index values ranging from 0.39 to 0.69 and the maximum value is influenced by the wind cluster. This indicates that the clustering methods applied in this paper can identify one or a combination of climate variables into dense and well-separated clusters.