Abstract:
Wildfire is a common disaster that hits Indonesia
every dry season, especially on the islands of Kalimantan and
Sumatra. In order to reduce the impact of fire hazards,
preventive measures are needed before the occurrence of fires.
One of them is by setting up an information system such as
EWS. The aim of this study is to create an effective image- and
machine learning-based predictive model of the severity
of
forest and land fires based on vegetation conditions prior to
burning. Three parameters of prefire vegetation conditions,
namely vegetation greenness indices, vegetation moisture, and
vegetation senescence, were selected as independent variables to
predict the postfire dependent variable, i.e., fire severity. There
are 25 vegetation greenness index options tested, using either
ANN regression or multiple linear regression. The vegetation
moisture information is represented by the Normalized
Difference Moisture Index (NDMI). The vegetation senescence
information is extracted using the Plant Senescence Reflectance
Index (PSRI). Meanwhile, the wildfire severity is measured
using the Burned Area Index for Sentinel-2 (BAIS2). All
vegetation conditions and wildfire severity information were
extracted from Sentinel-2 imageries. The topology of ANN
regression models is configured from one to six hidden layers.
More than 100,000 pixels are used as samples, which are then
separated into training samples and validation samples. The
results of model development and testing show that ANN
regression with Inverted Red-Edge Chlorophyll Index (IRECI)
as a vegetation greenness parameter is the model that has the
highest accuracy in predicting wildfire severity.
