Prognostics of forest recovery with r.recovery GRASS-GIS module: an open-source forest growth simulation model based on the diffusive-logistic equation. (January 2019)

• Record Type: Journal Article
• Authors: Richit, L.A.; Bonatto, C.; da Silva, R.V.; Grzybowski, J.M.V.
• Is Part Of: Environmental modelling & software. Volume 111(2019)
• Page Start: 108
• Page End: 120
• Publication Date: 2019-01
• Subjects: Forest growth -- Diffusive-logistic growth model -- Environmental conservation -- GRASS module; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.10.002
• Languages: English
• ISSNs: 1364-8152

Abstract

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/ .