Classification of the sensitivity of the forests in Lordegan County based on structural and biophysical characteristics

Document Type : Scientific article

Authors

1 Ph.D. Student of Forest management, Department of Forest Sciences, Faculty of Natural Resources and Earth Sciences, ShahreKord University, ShahreKord, I.R. Iran.

2 Assistant Professor, Department of forest sciences, Faculty of natural resources and earth sciences, Shahrekord University, Shahrekord, Iran

3 Department of forest sciences, Faculty of Natural Resources and Earth Science, Shahrekord University, Shahrekord, I. R. Iran.

Abstract

Background and Objective: Forests around the world have become increasingly vulnerable due to their exposure to a range of environmental and human-induced threats. As the detrimental effects of forest degradation and declining ecosystem quality are widely recognized as major risks, it is essential to develop effective tools and strategies to prevent or mitigate these impacts. Understanding the degree of forest sensitivity is a critical step in this process. This study aims to classify the sensitivity of forest ecosystems in Lordegan County based on both structural characteristics of forest stands and biophysical factors, including topographic and hydrological features.
Material and Methods: This research assessed the sensitivity levels of forest habitats in Lordegan County using key indicators of forest health—specifically the Leaf Area Index (LAI) and forest density—alongside physiographic variables (slope, aspect, and elevation), rainfall patterns, and land use/land cover data. Structural and land use maps were generated and validated using satellite imagery, while physiographic data were derived from the province’s topographic maps. Average annual precipitation was calculated using long-term monthly rainfall data from nearby synoptic and rain gauge stations. A regression model was developed to estimate annual rainfall variability at each station, which was then spatially mapped. All input layers were converted into raster format in ArcGIS 10.7 and classified into four sensitivity categories: low, moderate, high, and very high. The standardized indicator maps were weighted using the Delphi method, and a composite sensitivity index map was created by averaging the weighted layers. The relationship between the sensitivity index and the contributing indicators was examined using Pearson correlation analysis.
Results: The integrated sensitivity analysis revealed that 18,386.34 hectares (14.44%) of the forest area were categorized as low sensitivity, 48,333.58 hectares (37.96%) as moderate, 38,179.18 hectares (30%) as high, and 22,405.90 hectares (17.60%) as very high sensitivity. Statistical analysis showed that spatial variations in forest sensitivity were strongly influenced by a positive correlation with both LAI and forest density, while precipitation had a significant negative correlation with sensitivity. Additionally, the combination of physiographic variables demonstrated that sensitivity and vulnerability increased as slope, aspect, and elevation values approached higher threshold classes (i.e., classes 3 and 4).
Conclusion: This study highlights that a high-resolution spatial evaluation of sensitivity indicators—using a 30x30 meter grid—can effectively reveal the relationships between key factors influencing forest vulnerability in Lordegan. The projected increase in climatic variability and its growing impact on forest sensitivity underscore the urgent need for proactive monitoring and management. Without timely intervention, these forests are likely to face escalating threats from climate change, natural hazards, and human activities, leading to further degradation. Future research should incorporate the other two core dimensions of vulnerability—exposure and adaptive capacity—alongside sensitivity. Emphasizing the role of local communities within a social-ecological systems framework could offer a more comprehensive understanding of forest vulnerability and inform more resilient conservation strategies.

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Forest Research and Development
Volume 11, Issue 1
June 2025
Pages 1-23

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