Impact of topography and climate on post-fire vegetation recovery across different burn severity and land cover types through random forest

Abstract

Wildfires significantly disturb ecosystems by altering forest structure, vegetation ecophysiology, and soil properties. Understanding the complex interactions between topographic and climatic conditions in post-wildfire recovery is crucial. This study investigates the interplay between topography, climate, burn severity, and years after fire on vegetation recovery across dominant land cover types (evergreen forest, shrubs, and grassland) in the Pacific Northwest region. Using Moderate Resolution Imaging Spectroradiometer (MODIS) data, we estimated vegetation recovery by calculating the incremental Enhanced Vegetation Index (EVI) change during post-fire years. A machine learning algorithm, random forest (RF), was employed to map relationships between the input features (elevation, slope, aspect, precipitation, temperature, burn severity, and years after fire) and the target (incremental EVI recovery) for each land cover type. Variable importance analysis and partial dependence plots were generated to understand the influence of individual features. The observed and predicted incremental EVI values showed good matches, with R2 values of 0.99 for training and 0.89–0.945 for testing. The study found that climate variables, specifically precipitation and temperature, were the most important features overall, while elevation played the most significant role among the topographic factors. Partial dependence plots revealed that lower precipitation tended to cause a reduction in vegetation recovery for varying temperature ranges across land cover types. These findings can aid in developing targeted strategies for post-wildfire forest management, considering the varying responses of different land cover types to topographic, climatic, and burn severity factors.

Keywords

• Wildfire
• Remote sensing
• Vegetation recovery
• Random forest