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Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS

Author:
Nami, M.H., Jaafari, A., Fallah, M., Nabiuni, S.
Source:
International journal of environmental science and technology 2018 v.15 no.2 pp. 373-384
ISSN:
1735-1472
Subject:
ecoregions, geographic information systems, humans, infrastructure, landscapes, models, moderate resolution imaging spectroradiometer, prediction, probability, risk assessment, surveys, wildfires, wildland fire management, Iran
Abstract:
Accurate estimates of wildfire probability and production of distribution maps are the first important steps in wildfire management and risk assessment. In this study, geographical information system (GIS)-automated techniques were integrated with the quantitative data-driven evidential belief function (EBF) model to predict spatial pattern of wildfire probability in a part of the Hyrcanian ecoregion, northern Iran. The historical fire events were identified using earlier reports and MODIS hot spot product as well as by carrying out multiple field surveys. Using the GIS-based EBF model, the relationships among existing fire events and various predictor variables predisposing fire ignition were analyzed. Model results were used to produce a distribution map of wildfire probability. The derived probability map revealed that zones of moderate, high, and very high probability covered nearly 60% of the landscape. Further, the probability map clearly demonstrated that the probability of a fire was strongly dependent upon human infrastructure and associated activities. By comparing the probability map and the historical fire events, a satisfactory spatial agreement between the five probability levels and fire density was observed. The probability map was further validated by receiver operating characteristic using both success rate and prediction rate curves. The validation results confirmed the effectiveness of the GIS-based EBF model that achieved AUC values of 84.14 and 81.03% for success and prediction rates, respectively.
Agid:
5906185