Forecasting Post-Wildfire Vegetation Recovery in California using a
  Convolutional Long Short-Term Memory Tensor Regression Network
Jiahe Liu Xiaodi Wang
The study of post-wildfire plant regrowth is essential for developing
successful ecosystem recovery strategies. Prior research mainly examines key
ecological and biogeographical factors influencing post-fire succession. This
research proposes a novel approach for predicting and analyzing post-fire plant
recovery. We develop a Convolutional Long Short-Term Memory Tensor Regression
(ConvLSTMTR) network that predicts future Normalized Difference Vegetation
Index (NDVI) based on short-term plant growth data after fire containment. The
model is trained and tested on 104 major California wildfires occurring between
2013 and 2020, each with burn areas exceeding 3000 acres. The integration of
ConvLSTM with tensor regression enables the calculation of an overall logistic
growth rate k using predicted NDVI. Overall, our k-value predictions
demonstrate impressive performance, with 50% of predictions exhibiting an
absolute error of 0.12 or less, and 75% having an error of 0.24 or less.
Finally, we employ Uniform Manifold Approximation and Projection (UMAP) and KNN
clustering to identify recovery trends, offering insights into regions with
varying rates of recovery. This study pioneers the combined use of tensor
regression and ConvLSTM, and introduces the application of UMAP for clustering
similar wildfires. This advances predictive ecological modeling and could
inform future post-fire vegetation management strategies.