Resilience to Forest Fires: An Indicator-Based Study in a Tropical Forest of Uttarakhand, India

The present study uses remote sensing (RS)-based indicators to quantify the engineering resilience of a seasonal tropical forest ecosystem of Uttarakhand, India. The forest region in Uttarakhand, including the Jim Corbett National Park, witnessed a severe fire event in the year 2016. Resilience is examined using five indicators, namely, recovery time, maximum impact, cumulative impact, maximum impact per time, and cumulative impact per time, developed using the normalized burn ratio (NBR) time series derived from MODIS imagery. We developed a framework to estimate the engineering resilience of forests (to wildfires) that are subjected to seasonal changes. The findings suggest that deciduous broadleaf forests exhibit lower resilience with longer recovery time, followed by evergreen broadleaf forests and evergreen needle leaf forests, while grasslands and broadleaf cropland have higher resilience with shorter recovery time and low impacts. A positive precipitation anomaly during the recovery period influences the consistent recovery of vegetation across all land cover classes, impacting seasonal forest resilience. Leaf area index (LAI) dynamics, together with NBR, can provide a comprehensive assessment of resilience in seasonal forests in India. The accessibility of extended temporal datasets from the satellite platforms facilitates accuracy in studying postfire dynamics, thereby enabling stakeholders to make informed decisions regarding postfire ecosystem monitoring and restoration.