Abstract
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 m3 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 (ρ) 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 ρ = 0.986 and ρ = 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.
Highlights
IntroductionInformation about forest cover and its characteristics are essential in national and international forest inventories, monitoring programs, and reporting activities [1,2] such as in the context of international agreements (e.g., Kyoto protocol), and restoration programs (e.g., Reducing emissions from deforestation and forest degradation projects, REDD+) [3].Two of the most common forest variables needed to estimate sustainable forest management indicators as required by the national and international framework and agreements relate to forest cover area (generally according to the international definition adopted by the Food and Agriculture Organization (FAO) and the total growing stock volume (GSV, m3 ) [4,5].These data are usually provided by national forest inventory (NFI) programs which use probability-based approaches to infer the estimates for large areas such as countries and regions within countries. [4,6,7]
Two of the most common forest variables needed to estimate sustainable forest management indicators as required by the national and international framework and agreements relate to forest cover area (generally according to the international definition adopted by the Food and Agriculture Organization (FAO) and the total growing stock volume (GSV, m3 ) [4,5]
The Corine Land Cover 2006 (CLC06) achieved similar results, with the major exception of Sardegna and in general in the southern regions, where, as we reported before, the minimum mapping unit (MMU) of the CORINE Land Cover (CLC) project is not fine enough to discern the complex patchwork in the landscape of a rural region
Summary
Information about forest cover and its characteristics are essential in national and international forest inventories, monitoring programs, and reporting activities [1,2] such as in the context of international agreements (e.g., Kyoto protocol), and restoration programs (e.g., Reducing emissions from deforestation and forest degradation projects, REDD+) [3].Two of the most common forest variables needed to estimate sustainable forest management indicators as required by the national and international framework and agreements relate to forest cover area (generally according to the international definition adopted by the Food and Agriculture Organization (FAO) and the total growing stock volume (GSV, m3 ) [4,5].These data are usually provided by national forest inventory (NFI) programs which use probability-based approaches to infer the estimates for large areas such as countries and regions within countries. [4,6,7]. Two of the most common forest variables needed to estimate sustainable forest management indicators as required by the national and international framework and agreements relate to forest cover area (generally according to the international definition adopted by the Food and Agriculture Organization (FAO) and the total growing stock volume (GSV, m3 ) [4,5]. These data are usually provided by national forest inventory (NFI) programs which use probability-based approaches to infer the estimates for large areas such as countries and regions within countries. The latter is considered asymptotically unbiased in the sense that the mean of estimates obtained using the estimator for all possible samples approaches the true value as the sample size increases [23]
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