Factors controlling vegetation fires in protected and non-protected areas of myanmar

Abstract

Fire is an important disturbance agent in Myanmar impacting several ecosystems. In this study, we quantify the factors impacting vegetation fires in protected and non-protected areas of Myanmar. Satellite datasets in conjunction with biophysical and anthropogenic factors were used in a spatial framework to map the causative factors of fires. Specifically, we used the frequency ratio method to assess the contribution of each causative factor to overall fire susceptibility at a 1km scale. Results suggested the mean fire density in non-protected areas was two times higher than the protected areas. Fire-land cover partition analysis suggested dominant fire occurrences in the savannas (protected areas) and woody savannas (non-protected areas). The five major fire causative factors in protected areas in descending order include population density, land cover, tree cover percent, travel time from nearest city and temperature. In contrast, the causative factors in non-protected areas were population density, tree cover percent, travel time from nearest city, temperature and elevation. The fire susceptibility analysis showed distinct spatial patterns with central Myanmar as a hot spot of vegetation fires. Results from propensity score matching suggested that forests within protected areas have 11% less fires than non-protected areas. Overall, our results identify important causative factors of fire useful to address broad scale fire risk concerns at a landscape scale in Myanmar.

Fig 1. A map showing the protected areas in Myanmar with boundaries in dark green color.

MODIS land cover map is shown in the background.

Fig 2. Histograms of Frequency Ratios for each causative factors in protected and non-protected forests.

Fig 3. Spatial distribution of the frequency ratios (FR’s) for causative factors of fire in Myanmar.

Fig 4. Fire Susceptibility Map of Myanmar.

For a given pixel, the Fire Susceptibility was calculated by summing the individual frequency ratios for each causative factor.

Fig 5. Correlogram of covariates used in propensity matching analysis.

Blue indicates positive correlation and red negative correlation. Darker shades indicate stronger correlation.