Forest growth models can provide valuable support tools for forest recovery assessment and forestry management, whether in the form of diagnostic or prognostic. Furthremore, they can be applied to characterize each phytophysiognomy in terms of vegetation growth parameters that and can be applied to gauge the spatiotemporal progress of recovery processes. Up to date, such parameters remain mostly unknown. In this paper, we explore a modelling framework aimed at providing computer-aided prognostics of forest recovery based on the diffusive-logistic growth (DLG) model and present case studies for a number of four preservation areas located in the Brazilian Atlantic rainforest biome. The modelling framework involves the application of vegetation indices derived from satellite images and a computational implementation of the DLG model. The objective of the study is to illustrate how forest restoration and recovery projects could gain from the proposed methodology, due to the fact that the likely outcomes of management practices could be assessed in advance. Additionally, it aims to determine the characteristic parameters of forest growth for a portion of the Atlantic rainforest biome. The diffusion and growth rate parameters from the DLG model were calibrated and they show the evolution of forest density over the years. The results show that the forest recovery process can take several decades to stabilize in the absence of negative interventions in the forest growth. The model and the implementation presented in this work are freely available and they can be an important tool for decision and policy making in what concerns forest recovery.

森林生长模型可以以诊断或预测的形式为森林恢复评估和林业管理提供有价值的支持工具。此外，它们可用于根据植被生长参数表征每种植物地貌，并可用于评估恢复过程的时空进程。迄今为止，此类参数大多仍然未知。在本文中，我们探索了一个基于扩散逻辑增长（DLG）模型的旨在提供计算机辅助森林恢复预测的建模框架，并针对位于巴西大西洋雨林生物群落中的四个保护区进行了案例研究。该建模框架涉及从卫星图像中获取植被指数的应用以及DLG模型的计算实现。该研究的目的是说明由于可以预先评估管理做法的可能结果这一事实，森林恢复和恢复项目可以从提议的方法中受益。此外，它的目的是确定大西洋雨林生物群落一部分森林生长的特征参数。校准了DLG模型的扩散和生长速度参数，这些参数显示了多年来森林密度的演变。结果表明，在没有负面影响森林生长的情况下，森林恢复过程可能需要数十年才能稳定下来。此工作中介绍的模型和实施是免费的，它们可以成为有关森林恢复的决策和决策的重要工具。