Large-scale high-resolution yearly modeling of forest growing stock volume and above-ground carbon pool. (January 2023)

• Record Type: Journal Article
• Authors: Vangi, Elia; D'Amico, Giovanni; Francini, Saverio; Borghi, Costanza; Giannetti, Francesca; Corona, Piermaria; Marchetti, Marco; Travaglini, Davide; Pellis, Guido; Vitullo, Marina; Chirici, Gherardo
• Is Part Of: Environmental modelling & software. Volume 159(2023)
• Publication Date: 2023-01
• Subjects: National forest inventory -- GSV -- Carbon stock -- Forest modeling -- Spatial modeling -- Italy; 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.2022.105580
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Within the Paris Agreement's Enhanced Transparency Framework, consistent data collections are the prerequisite for a successful reporting of GHG emissions. For such purposes, NFIs are usually the primary source of information, even if they are frequently not designed for producing estimations on a yearly basis and in the form of wall-to-wall high-resolution maps. In this framework, we present a new spatial model to produce yearly growing stock volume (GSV), above-ground biomass (AGB), and carbon stock wall-to-wall estimates. We tested the model in Italy for the period 2005–2018, obtaining a time-series of yearly maps at 23 m spatial resolution. Results were validated against the 2015 Italian NFI reaching an average RMSE% of 19% for aggregated areas. Results were also compared against data reported by the Italian GHG inventory, reaching an RMSE% of 28% and 20% for GSV and carbon stock respectively. We demonstrated that the modeling approach can be successfully used for setting up a forest monitoring system to meet the interests of governments in inventories of GHG emissions and private entities in carbon offset investments. Highlights: A spatial approach for multitemporal estimation of carbon stock is presented. The approach is consistent with the IPCC's best guidance and practices. The aboveground carbon stock of forests in Italy exceeded 566 million tons in 2018. Results are consistent with official Greenhouse gasses and national forest inventories.