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Abstract We present an open-source computational tool for the 2D simulation of the Diffusive-Logistic Growth (DLG) model. The r.recovery module offers a complete environment for the simulation of forestry regeneration in conservation areas and includes a built-in tool for calibration and validation of the model parameters through the use of standard and freely available satellite imagery. It was implemented as an add-on to the GRASS software, a largely applied open-source Geographic Information System (GIS). To illustrate its application, we present a complete case study of forest regeneration carried out in the Espigão Alto State Park (EASP), Brazil, from which we assess typical values of forest diffusion and growth rate parameters, along with the prognostics of forest density status for the coming decades. We observe that the r.recovery tool can be advantageously applied by forestry managers and policy-makers as a form of acquiring technical and scientifically-based information for strategy development and decision-making. Highlights • This research makes a state-of-the-art forestry modeling framework available for a large public. • The module can be coupled to hydrological models in long-term simulations. • The module is user-friendly and can enhance the efficiency and response time of forestry management processes. • A complete walkthrough is presented to illustrate the application of the module. • The module is open-source and freely available for download at http://modelagemambientaluffs.blogspot.com.br/. [ABSTRACT FROM AUTHOR]

Riparian buffer strips (RBS) play an important role in the conservation of water bodies. Yet to date the increasing degradation of riparian vegetation is a major concern due to the inherent risk it poses to quality and quantity of drinkable water. In turn, forest regeneration is known to be a process that evolves in a time-scale of years, such that the regeneration of a forested area after a perturbation can take several decades. In this context, the exploration of models of vegetation growth can contribute to the understanding and acknowledgement of diverse aspects of such phenoma. Further, from the standpoint of conservation projects, the application of models allows the assessment of different future scenarios resulting from the application of different management practices, thus allowing anticipating the likely outcomes of the management process. In this paper, we study the application of the diffusive-logistic model (DLG model) to the problem of forest regeneration. We concentrate in vegetation belonging to permanent preservation areas, such as RBS. The main contribution of this study is to propose and illustrate a thorough methodology applicable as auxiliary assessment and design tool for conservation projects. The study is developed in two steps. We build upon recent results that study on the performance of riparian buffer strips in the removal of nitrogen in agricultural catchments in which an Artificial Neural Network Ensemble (ANNE) was applied to evaluate the riparian buffer strip width needed to accomplish filtering of 90% of the residual nitrogen. On this basis, we calibrate and validate the parameters of the diffusive-logistic model of forest growth to estimate the evolution of regeneration in riparian buffer strips, considering the buffer widths obtained by the ANNE. We implement the solution in GPGPU (General Purpose Graphics Processing Unit) and then simulate for forty years ahead to verify the likely effect of conservation upon the riparian buffer strips in the watershed. The results of calibration and validation show that the diffusive-logistic model provides a rather accurate temporal and spatial account of forest regeneration. The application of the model to a case study of the Ligeiro River watershed, Brazil, shows that the model can provide valuable insights for decision-making in the context of environmental conservation and management. [ABSTRACT FROM AUTHOR]

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. • A diffusive-logistic model is applied to study forest growth in the Atlantic Rainforest. • Growth parameters can help gauge the spatiotemporal progress of recovery processes. • Atlantic rainforest conservation units can reach solid density recovery in 60 years. [ABSTRACT FROM AUTHOR]