A scalable model of vegetation transitions using deep neural networks.

ABSTRACT: In times of rapid global change, anticipating vegetation changes and assessing their impacts is of key relevance to managers and policy makers. Yet, predicting vegetation dynamics often suffers from an inherent scale mismatch, with abundant data and process understanding being available at a fine spatial grain, but the relevance for decision-making is increasing with spatial extent.We present a novel approach for scaling vegetation dynamics (SVD), using deep learning to predict vegetation transitions. Vegetation is discretized into a large number (10³-10⁶) of potential states based on its structure, composition and functioning. Transition probabilities between states are estimated via a deep neural network (DNN) trained on observed or simulated vegetation transitions

SUBMITTER: Rammer W 

PROVIDER: S-EPMC6582592 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature