Quantifying Forest Fire and Post-Fire Vegetation Recovery in the Daxin’anling Area of Northeastern China Using Landsat Time-Series Data and Machine Learning

Abstract

Many post-fire on-site factors, including fire severity, management strategies, topography, and local climate, are concerns for forest managers and recovery ecologists to formulate forest vegetation recovery plans in response to climate change. We used the Vegetation Change Tracker (VCT) algorithm to map forest disturbance in the Daxing’anling area, Northeastern China, from 1987 to 2016. A support vector machine (SVM) classifier and historical fire records were used to separate burned patches from disturbance patches obtained from VCT. Afterward, stepwise multiple linear regression (SMLR), SVM, and random forest (RF) were applied to assess the statistical relationships between vegetation recovery characteristics and various influential factors. The results indicated that the forest disturbance events obtained from VCT had high spatial accuracy, ranging from 70% to 86% for most years. The overall accuracy of the annual fire patches extracted from the proposed VCT-SVM algorithm was over 92%. The modeling accuracy of post-fire vegetation recovery was excellent, and the validation results confirmed that the RF algorithm provided better prediction accuracy than SVM and SMLR. In conclusion, topographic variables (e.g., elevation) and meteorological variables (e.g., the post-fire annual precipitation in the second year, the post-fire average relative humidity in the fifth year, and the post-fire extreme maximum temperature in the third year) jointly affect vegetation recovery in this cold temperate continental monsoon climate region.