Environmental Modeling & Assessment

, Volume 19, Issue 6, pp 533–546 | Cite as

Assessment of Wildfire Hazards with a Semiparametric Spatial Approach

A Case Study of Wildfires in South America
  • Ricardo Acevedo-CabraEmail author
  • Yolanda Wiersma
  • Donna Ankerst
  • Thomas Knoke


Rural households in agricultural economies are vulnerable to several environmental risks such as fires, floods, and droughts that may affect their productivity in whole or in part. These hazards are especially relevant in developing countries where farmers have few or no access to traditional risk-transfer techniques, such as insurance and finance, and where low governmental investments in rural infrastructure, risk assessment techniques, or early warning systems makes the aftermath of such hazards more expensive and results in slower recovery for those who are affected. In this paper, we use historical satellite data (Terra) of burned areas in South America to fit a semiparametric spatial model, based on kernel smoothing and on a nonlinear relationship between average time between events and damage, to assess the environmental hazard affecting the land’s productivity. The results were twofold: first, we were able to develop a spatial assessment of fire hazard, and second, we were able to evaluate how much a farmer may lose in terms of productivity per hectare due to the environmental hazard. The methodology may be easily adapted to other world regions; to different environmental hazards such as floods, windbreak, windthrow, or related land-use changes; or to integrate various environmental hazards simultaneously, as long as they can be monitored via remote sensing (e.g., satellite imagery, aerial photographs, etc).


Environmental risk assessment Kernel smoothing Semiparametric Average time between events Fire risk Satellite imagery 



We are most thankful for the financial support of the “Deutsche Forschungsgemeinschaft” (DFG), project KN 586/5-2 and to the members of the research group “FOR 816” whose research initiative and support made this study possible. We are grateful to Professor Claudia Klueppelberg for her valuable comments and to Christian Schemm for the preparation of the dataset with ArcGIS.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ricardo Acevedo-Cabra
    • 1
    Email author
  • Yolanda Wiersma
    • 2
  • Donna Ankerst
    • 3
  • Thomas Knoke
    • 1
  1. 1.Institute of Forest ManagementTechnische Universität MünchenFreisingGermany
  2. 2.Department of BiologyMemorial UniversitySt. John’sCanada
  3. 3.Chair of Mathematical StatisticsTechnische Universität MünchenMunichGermany

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