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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/nhess-2019-338
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-338
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 13 Jan 2020

Submitted as: research article | 13 Jan 2020

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A revised version of this preprint is currently under review for the journal NHESS.

Evaluating forest fire probability under the influence of human activity based on remote sensing and GIS

Wei Yang and Xiaoli Jiang Wei Yang and Xiaoli Jiang
  • Taiyuan Normal University, Jinzhong, Shanxi, China

Abstract. Fires are an important factor involved in the disturbance of forest ecosystems, causing resource damage and the loss of human life. Evaluating forest fire probability can provide an effective method to minimize these losses. In this study, a comprehensive method that integrates remote-sensing data and geographic information systems is proposed to evaluate forest fire probability. In our analysis, we selected four probability indicators: drought index, vegetation condition, topographical factors and anthropogenic factors. To evaluate the influence of anthropogenic factors on fire probability, a distance analysis from fire locations to settlements or roads was conducted to see which distance was associated with a higher probability. The forest fire probability index (FFPI) was calculated to assess the probability level in Heilongjiang Province, China. According to the FFPI, five classes were identified: very low, low, moderate, high, and very high. A receiver operating characteristics (ROC) curve was used as the validation method, and the results of the ROC analysis showed that the proposed model performed well in terms of forest fire probability prediction. The results of this study provide a technical framework for the Department of Forest Resource Management to predict occurrence of fires.

Wei Yang and Xiaoli Jiang

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Wei Yang and Xiaoli Jiang

Wei Yang and Xiaoli Jiang

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