Accurate vegetation models can produce further insights into the complex
interaction between vegetation activity and ecosystem processes. Previous
research has established that long-term trends and short-term variability of
temperature and precipitation affect vegetation activity. Motivated by the
recent success of Transformer-based Deep Learning models for medium-range
weather forecasting, we adapt the publicly available pre-trained FourCastNet to
model vegetation activity while accounting for the short-term dynamics of
climate variability. We investigate how the learned global representation of
the atmosphere's state can be transferred to model the normalized difference
vegetation index (NDVI). Our model globally estimates vegetation activity at a
resolution of \SI{0.25}{\degree} while relying only on meteorological data. We
demonstrate that leveraging pre-trained weather models improves the NDVI
estimates compared to learning an NDVI model from scratch. Additionally, we
compare our results to other recent data-driven NDVI modeling approaches from
machine learning and ecology literature. We further provide experimental
evidence on how much data and training time is necessary to turn FourCastNet
into an effective vegetation model. Code and models will be made available upon
publication.

利用 Transformer-based 深度学习模型，我们在考虑气候变化的短期动态的同时，通过转移与大气状态有关的全球学习表示，仅依靠气象数据全球性地估计了 0.25° 分辨率的植被活动，并且证明了与从头学习的 NDVI 模型相比，利用预训练的天气模型可以提高 NDVI 估计结果。

使用预训练的气象变换器进行全球植被建模

Global Vegetation Modeling with Pre-Trained Weather Transformers

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.

通过开发 ConvLSTMTR 网络，结合张量回归方法来预测和分析火灾后植物恢复情况，并利用 UMAP 和 KNN 聚类技术识别恢复趋势，该研究开拓了张量回归和 ConvLSTM 的结合应用，并推进了预测生态建模，可为未来火灾后植被管理策略提供指导。