Effects of silvicultural thinning in planted stands on surface and crown wildfire behavior (Case study: Malekroud districts of Siahkal Forest)

Document Type : Research Article

Authors

1 Assistant Professor of Forest Sciences, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

2 Associate Professor of Surveying, Department of Surveying and Geomatics Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

Abstract

Under rapid climate change, desertification, and land degradation, a critical management issue in forests of northern Iran is how to increase forest resilience to wildfires. For decades, forest managers have been employing silvicultural treatments to restore the Hyrcanian forest stand's structure, composition, and diversity, but there is no scientific evidence to support that these treatments reduce the wildfire risk in the area. In this study, based on the wildfire behavior modeling approach using FlamMap MTT, we evaluated spatial relationships between silvicultural fuel reduction treatment (e.g., thinning) and surface and crown burn probabilities in the 6 and 7 Malekroud districts of Siahkal forest. We first analyzed fuel characteristics after the development of silvicultural treatments in the period 2007-2017 in the study area to evaluate low thinning and heavy thinning as restoration tools for similar fuel conditions. Furthermore, we conducted analyses of variance to determine the influence of the silvicultural thinning treatment on surface and canopy burn probabilities. Modeling results at the landscape scale confirm the important role of thinning in the mitigation of crown burn probability (almost 11%) in the study area. However, the effect of thinning on the surface burn probability was not statistically significant (P> 0.05). Comparing the two types of thinning in terms of intensity, heavy thinning had a greater effect on changing the surface and crown (i.e., 10% increase and 39% decrease, respectively, in conifer plantation) burn probability than the low thinning. The modeling approach in the study offers new insights to improve the spatial mapping of wildfire likelihood, include variables sensitive to thinning operation in the models, and provide useful information to prevent extreme wildfire behavior through effective silvicultural treatments.

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Main Subjects


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  • Receive Date: 22 April 2022
  • Revise Date: 20 May 2023
  • Accept Date: 03 June 2023
  • First Publish Date: 03 June 2023
  • Publish Date: 23 September 2023