A fine-scale state-space model to understand drivers of forest fires in the Himalayan foothills

Abstract

The tropical forests situated in the Himalayan foothills (terai) experience frequent wildfires which can alter the vegetation structure and composition, challenging tiger conservation efforts in this region. Hence, there is a need for better understanding of the drivers of forest fire to aid efficient management, but these efforts are hampered by the deficiency of spatial and temporal data on fire incidences. Advancement in remote sensing technology provides an opportunity to understand the spatial and temporal patterns of wildfires in relation to anthropogenic, ecological, and environmental drivers. We used MODIS fire data from 2001 to 2015 to understand fire incidences in Valmiki Tiger Reserve (VTR), an important tiger habitat area in the Himalayan terai region. We analyzed fire incidences to understand monthly and inter-annual variation of fire incidences at two spatial scales: first, using only climatic variables considering VTR as a single spatial unit and the second, to understand the fire dynamics at 1 km2 spatial resolution using climatic, ecological, and anthropogenic variables. The results show that fire incidences occurred from January to May, 88% of which occurred in March and April. Overall, different variables affected fire incidences in March and April for both the temporal models. Precipitation had a significant negative effect on fire incidences in both March and April, but temperature had a positive effect only in March. Similarly, the fine scale temporal model showed that while ecological (litter load, NPP) and anthropogenic (distance to villages and roads) variables influenced fire incidences in March, altitude and village area surrounding the forest affected fires in April. Litter input, distance to nearest villages, and village area had a non-linear relationship with fire incidences indicating a few inconsistencies with the global patterns of fire with human activity. We show that the Sal dominated forests and terai grasslands at low altitudes (200 m), falling within a zone of 2.5–3 km from villages and with good road connectivity are more prone to fire. The fine-scale fire prediction map of VTR will be helpful to the Tiger Reserve management in developing appropriate strategies for the fire prone areas.