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Temporal and Spatial Analysis of the Relationship Between Climate Parameter Changes and Fire in the Forests and Rangelands in the Province of Gilan | ||
| تحقیقات حمایت و حفاظت جنگلها و مراتع ایران | ||
| Article 11, Volume 21, Issue 1 - Serial Number 41, September 2023, Pages 164-186 PDF (923.66 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.22092/ijfrpr.2023.361993.1577 | ||
| Authors | ||
| Saeedeh Eskandari* 1; Fatemeh Ahmadloo2; Hamid Reza Pourghasemi3; Yazdanfar Ahangaran4; Zolfaghar Rezapour5 | ||
| 1Assistant Prof., Forest Research Division, Research Institute of Forests and Rangelands, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran | ||
| 2Assistant Prof., Poplar Research Division, Research Institute of Forests and Rangelands, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran | ||
| 3Prof., Department of Soil Science, College of Agriculture, Shiraz University, Shiraz, Iran. | ||
| 4Senior expert, Head of Plant Pathology Group, Protection and Conservation Office, Natural Resources and Watershed Organization of Iran, Tehran, Iran | ||
| 5Senior expert, Head of Applied Meteorology Development Group, Kohgiluyeh and Boyer Ahmad Meteorological Administration, Yasouj, Iran. | ||
| Abstract | ||
| Fire is one of the destructive phenomena that have devastated a significant portion of forests and grasslands in Gilan Province in recent years. This study aimed to investigate the temporal and spatial relationship between climatic variables and wildfires in Gilan Province. The wildfire variables included the number and extent of wildfires, and the climatic variables consisted of seven parameters over the past 26 years (2001-2026). Pearson correlation and regression analysis were utilized to examine the temporal relationship. The relative importance of climatic variables in wildfire occurrence was determined using Mean Decrease Gini (MDG) and Mean Decrease Accuracy (MDA) statistics. For modeling and generating probability maps of wildfire occurrence, 70% of wildfire locations and various machine learning models (Logistic Regression, Random Forest, Support Vector Machine, and SVM-RF Hybrid) were employed using the R programming language. Model validation was conducted using 30% of wildfire locations and the Area Under the Curve (AUC) metric. The temporal results showed that during the 26-year period (2001-2026), a significant negative correlation was observed between the number of wildfires and the average seasonal precipitation, while positive correlations were found between the number of wildfires and the average seasonal wind speed and maximum wind speed at a 95% confidence level. Furthermore, a significant negative correlation was observed between the extent of wildfires and the average seasonal precipitation at a 95% confidence level. The spatial relationship analysis indicated that the average maximum temperature, average seasonal precipitation, and average relative humidity had the highest importance in wildfire occurrence within the geographical extent of Gilan Province. Model validation results revealed that the Random Forest model (AUC: 0.82) and the SVM-RF Hybrid model (AUC: 0.79) outperformed others in predicting the occurrence of wildfires. Therefore, predicting wildfires resulting from climatic factors in the forests and grasslands of Gilan Province using the aforementioned maps is feasible and can significantly aid natural resource managers in implementing protective measures in high-risk wildfire areas. Hence, it is imperative that proactive measures be taken by the Natural Resources and Watershed Management Organization of the province to prevent future wildfires with greater sensitivity. | ||
| Keywords | ||
| Climatic variables; number and area of fire; fire spatial and temporal analysis; linear regression; machine learning models | ||
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