Forest Fire Trend and Influence of Climate Variability in India: A Geospatial Analysis at National and Local Scale

Abstract

Climate change and its severity play an important role in forest fire regime. Analysing the forest fires events becomes a prerequisite for safeguarding the forest from further damage. We have made an assessment of the long-term forest fire events at the district level in India and identified the forest fire hotspot districts. The spatial seasonal (January to June) district wise pattern and forest fire trend were analysed. In the second part of the study area (central part of India), we have evaluated the forest fire events in grid format with respect to the climatic/weather datasets, and the statistical analysis Cramer V coefficient (CVC) was performed to understand its association/relationship with forest fire events.The study revealed that Karbi Anglong and North Cachar Hills districts of Assam of India have the highest forest fire percent among all districts equivalent to 3.4 and 3.2% respectively. Dantewada district of Chhattisgarh and Garhchiroli district of Maharashtra of India occupied 3rd and 4th rank with value 3.1 and 3.0% respectively. The grid-based evaluation (local scale) revealed that most of the fire equivalent of 80% was found in the month of March and April. Forest fire frequency of the month of April is spread over 88 % of the grids over the study area. The 11 years average seasonal month-wise (February to June) maximum temperature, wind velocity, relative humidity, and solar radiation were found in the range of (25.9 to 40.6), (1.69 to 2.7), (0.301 to 0.736) and (14.21 to 22.98) respectively. The percentage increase (in the month of March) of maximum temperature, wind velocity, and solar radiation were 36, 39 and 62% respectively, when compared with the preceding month; whereas, a 60% decrease to relative humidity that was observed in the same month is usually the major cause of forest fire events in the month of March onwards.The evaluation of Cramer V coefficient (CVC) values of rainfall, relative humidity, potential evapotranspiration, maximum temperature, wind velocity, and solar radiation were in decreasing order and in the range of 0.778 to 0.293. The highest value of rainfall (0.778) showed its strongest association with the forest fire events. In the month of June, these areas receive adequate rainfall, which leads to an increase in the soil moisture and a reduction in forest fuel burning capacity by absorbing the moisture and it is a strong reason for less forest fire events during this month. Geospatial technology provides an opportunity to evaluate large datasets over various spatial and temporal scales and help in decision making/formulating various policies.

Forest covers, land fire hotspots, and atmospheric pollutions (CO, SO2) in the lower Batanghari River Basin landscape during dry season (June-August 2021)

“…In forests in India, most of the fire hotspots were found in the midyear period between the months of March and April. The percentage increase observed in the month of March of maximum temperature, wind velocity, and solar radiation were 36, 39, and 62%, and a 60% decrease in relative humidity that was observed in the same month is the major cause of fire hotspots in the month of March onwards (Ahmad and Goparaju 2019). The similar effects of the climate and drought season on fire hotspots were also confirmed in a recent study in Sumatra.…”

An Inventory on Black Carbon and Organic Carbon Emission by the Different Vegetative Ecosystem Over India

“…A study on Greenhouse gas inventory in Thailand also indicated similar results of more than 80% of emissions by the agricultural sector 51 . The decrease in forest res contribution maybe because of land clearing for collecting Mahua (Madhuca Indica) owers and the shifting cultivation by the tribal community, which accounts for 23% of annual deforestation in India 52 .…”

“…A Subside in emission is observed in June, which may be attributed to the onset of monsoon season as soil moisture increases due to rainfall. The results in reducing woodburning capacity by absorbing moisture in the woods 52 . However, for cropland res, observed temporal variation of carbonaceous aerosol emission indicates that the emission is high during March (56%) to May (28%) for the entire study period, which is a stubble burning season in India, especially in northern parts of the country 73 .…”

Spatiotemporal Patterns of Burned Areas Based on the Geographic Information System for Fire Risk Monitoring

“…As fires often occur in spatially vast and dangerous areas, the use of geospatial technology is considered the most appropriate choice to handle such fires [8]. Geospatial technology allows analyzing forest and land fires at various spatial and temporal scales [9]. Geospatial analysis can also prevent future fires and help conserve forest and land resources [10].…”