Predicting post-fire forest recovery using the 3-PG model with bi-temporal Landsat imagery in high-severity burned areas of Great Xing’an Mountain

Abstract

High-severity fire altered the landscape pattern and destroyed forest resilience in the Great Xing’an Mountain. Predicting the spatiotemporal patterns of forest recovery in high-severity burned areas is critical for formulating effective post-fire management measures. Previous studies attempted to address this topic by fitting the recovery trajectory based on time-series remote sensing vegetation indices. However, these methods typically require long-term time series of satellite images for prediction and have sometimes been demonstrated as an unrealistic representation of the post-fire forest patterns due to the saturation issues of vegetation indices. In this study, we proposed a novel approach to predict post-fire forest recovery patterns by coupling a process-based Physiological Principles in Predicting Growth (3-PG) model with bi-temporal satellite imagery. First, based on tree core data, we calibrated the physiological parameters of the 3-PG model using a Model-Independent Parameter Estimation (PEST) model. Second, we used vegetation indices derived from bi-temporal Landsat data to estimate site-specific parameters of the 3-PG model and extrapolated the meteorological factors by a Mountain Microclimate Simulation Model (MTCLIM). Third, we used these variables as input parameters to drive the 3-PG model, resulting in the simulation of the post-fire 50-year regeneration dynamic of the leaf area index (LAI). The calibrated 3-PG model was validated by the independent sites, providing good estimates of DBH and biomass components with R2 of greater than 0.994 and RRMSE of less than 15.7 %. The 3-PG model predicted LAI maps were evaluated by referenced LAI retrieved from Landsat data, showing a high correlation with reference but the accuracy decreased with the prediction time increased. The model predicted forest recovery to pre-fire LAI within 32 years where the longest recovery interval was observed in the low seedling density and soil fertility. This study shows great potential for reasonable prediction and assessment of forest recovery after the fire occurrence. By combining more simulated scenarios, our approach also facilitates the exploration of the impact of artificial measures and climate on post-forest recovery.