Impact of Topography and Climate on Post-fire Vegetation Recovery Across Different Burn Severity and Land Cover Types through Machine Learning
arxiv(2024)
摘要
Wildfire 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
data, we estimated vegetation recovery by calculating the incremental enhanced
vegetation index (EVI) change during post-fire years. A machine learning
technique, 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 between 0.89 and 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.
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