Repository logo
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log In
    or
    New user? Click here to register.Have you forgotten your password?
Repository logo
  • Communities & Collections
  • Research Outputs
  • Fundings & Projects
  • People
  • Statistics
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log In
    or
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Indian Institute of Technology Madras
  3. Publication2
  4. Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India
 
  • Details
Options

Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India

Date Issued
01-09-2021
Author(s)
Achu, A. L.
Thomas, Jobin
Aju, C. D.
Gopinath, Girish
Kumar, Satheesh
Reghunath, Rajesh
DOI
10.1016/j.ecoinf.2021.101348
Abstract
The recurrent forest fires have been a serious management concern in southern Western Ghats, India. This study investigates the applicability of various geospatial data, machine learning techniques (MLTs) and spatial statistical tools to demarcate the forest fire susceptible regions of the forested landscape of the Wayanad district in the southern Western Ghats (Kerala, India). The inventory map of 279 forest fire locations (period = 2001–2018) was developed via Sentinel 2A satellite images, NASA fire archives, and field visits. The forest fire susceptibility modelling involves twelve influencing factors, such as ambient air temperature, wind speed, rainfall, relative humidity, atmospheric water vapor pressure (WVP), elevation, slope angle, topographical wetness index (TWI), slope aspect, land use/land cover (LU/LC), distance from the road and distance from the villages. Considering the varying level of performances (i.e., receiver operating characteristics-area under curve (ROC-AUC) values ranging from 0.869 to 0.924 in the testing phase) of the MLTs, viz., artificial neural network (ANN), generalized linear model (GLM), multivariate adaptive regression splines (MARS), Naïve Bayesian classifier (NBC), K-nearest neighbour (KNN), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), adaptive boosting (AdaBoost) and maximum entropy (MaxEnt), we propose a weighted approach to characterize the forest fire susceptibility of the region using the outputs of the different MLTs. The proposed method demonstrates improvement in accuracy (AUC = 0.890) for mapping the forest fire susceptibility of the region compared to the individual MLTs (AUC = 0.715 to 0.869) while validating with the recent forest fire data (i.e., 2019–2021). This study suggests that roughly one-third of the study area is highly susceptible to the occurrence of forest fires, implying the severity of the disturbance regime. The analysis also indicates the role of anthropogenic factors in the occurrence of forest fires in the region. It is expected that the demarcation and prioritization of the forest fire susceptibility zones in the region, which is a part of one of the global biodiversity hotspots, have significant implications on biodiversity conservation at a regional scale.
Volume
64
Subjects
  • Forest fire

  • GIS

  • Hot spot analysis

  • India

  • Machine-learning

  • Western Ghats

Indian Institute of Technology Madras Knowledge Repository developed and maintained by the Library

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback