AN ANALYSIS OF FOREST FIRE AND CLIMATIC PARAMETERS’ TREND USING GEOSPATIAL TECHNOLOGY: A CASE STUDY IN THE STATE OF CHHATTISGARH, INDIA

Featured Image

Photo by Landon Parenteau on Unsplash

Photo by Landon Parenteau on Unsplash

Why is it important?

It is essential to study forest fire occurrences and the climate of any region to address the issue of forest fire vs global warming and global climate change. Studies at regional and global level help to understand the forest fire characterization microscopically. The present study has made an assessment of the long-term forest fire events in the state of Chhattisgarh, India and identified forest fire hotspot areas. We have generated a spatial pattern of climate data and made a statistical analysis. Cramer V coefficient (CVC) was calculated and its relationship with forest fire events was suggested. The study revealed that the Baster and Dantewada districts of Chhattisgarh state of India show the highest forest fire percentage equivalent to 24 and 33 %, respectively. Generally, three forest fire hotspot zones were identified. In January, February and March zone 1 received less rainfall and showed relatively high maximum temperature and potential evapotranspiration when compared with zone 2 and 3. The number of rainy days in January and February in Dantewada and Bastar district (zone 1) was 0.72 to 0.92 and was found lowest among all districts of the state. The climate parameters were more favorable to forest fire events over zone 1 compared to other zones. The evaluation of CVC value of climate data with forest fire events showed that rainfall, maximum temperature, the number of rainy days and potential evapotranspiration were in decreasing order and in the range from 0.74 to 0.32. The highest value (0.74) showed that was closely related with forest fire events. In June, these areas receive adequate rainfall (90–177 mm) which leads to an increase in the moisture content and hinders forest fuel burning capacity. Geospatial technology proved capable of analyzing thematic datasets and various modules/algorithms used in mapping, allowing to draw logical conclusions in solving various research problems.