The Effect of Forest Mask Quality in the Wall-to-Wall Estimation of Growing Stock Volume

Information about forest cover and its characteristics are essential in national and international forest inventories, monitoring programs, and reporting activities. Two of the most common forest variables needed to support sustainable forest management practices are forest cover area and growing stock volume (GSV m(3) ha(-1)). Nowadays, national forest inventories (NFI) are complemented by wall-to-wall maps of forest variables which rely on models and auxiliary data. The spatially explicit prediction of GSV is useful for small-scale estimation by aggregating individual pixel predictions in a model-assisted framework. Spatial knowledge of the area of forest land is an essential prerequisite. This information is contained in a forest mask (FM). The number of FMs is increasing exponentially thanks to the wide availability of free auxiliary data, creating doubts about which is best-suited for specific purposes such as forest area and GSV estimation. We compared five FMs available for the entire area of Italy to examine their effects on the estimation of GSV and to clarify which product is best-suited for this purpose. The FMs considered were a mosaic of local forest maps produced by the Italian regional forest authorities; the FM produced from the Copernicus Land Monitoring System; the JAXA global FM; the hybrid global FM produced by Schepaschencko et al., and the FM estimated from the Corine Land Cover 2006. We used the five FMs to mask out non-forest pixels from a national wall-to-wall GSV map constructed using inventory and remotely sensed data. The accuracies of the FMs were first evaluated against an independent dataset of 1,202,818 NFI plots using four accuracy metrics. For each of the five masked GSV maps, the pixel-level predictions for the masked GSV map were used to calculate national and regional-level model-assisted estimates. The masked GSV maps were compared with respect to the coefficient of correlation (rho) between the estimates of GSV they produced (both in terms of mean and total of GSV predictions within the national and regional boundaries) and the official NFI estimates. At the national and regional levels, the model-assisted GSV estimates based on the GSV map masked by the FM constructed as a mosaic of local forest maps were closest to the official NFI estimates with rho = 0.986 and rho = 0.972, for total and mean GSV, respectively. We found a negative correlation between the accuracies of the FMs and the differences between the model-assisted GSV estimates and the NFI estimate, demonstrating that the choice of the FM plays an important role in GSV estimation when using the model-assisted estimator.