Large-scale high-resolution yearly modeling of forest growing stock volume and above-ground carbon pool
Author(s)
Date Issued
2023
Type
article
Volume
159
Start Page
105580
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.