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.

LARGE-SCALE high-resolution yearly modeling of forest growing stock volume and above-ground carbon pool / Vangi E.; D'Amico G.; Francini S.; Borghi C.; Giannetti F.; Corona P.; Marchetti M.; Travaglini D.; Pellis G.; Vitullo M.; Chirici G.. - In: ENVIRONMENTAL MODELLING & SOFTWARE. - ISSN 1364-8152. - ELETTRONICO. - 159:(2023), pp. 105580.0-105580.0. [10.1016/j.envsoft.2022.105580]

LARGE-SCALE high-resolution yearly modeling of forest growing stock volume and above-ground carbon pool

D'Amico G.
;
Francini S.;Borghi C.;Giannetti F.;Travaglini D.;Chirici G.
2023

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.
2023
159
0
0
Vangi E.; D'Amico G.; Francini S.; Borghi C.; Giannetti F.; Corona P.; Marchetti M.; Travaglini D.; Pellis G.; Vitullo M.; Chirici G.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1296177
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