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Human activities have the potential to enhance carbon sequestration by the world's forests and contribute to climate change mitigation. Voluntary carbon trading is currently the only option to pursue and reward carbon sequestration by forestry activities. Carbon credits for enhanced sequestration can be sold to partners wishing to offset their own emissions. Here we illustrate the steps taken to design guidelines for the generation of voluntary carbon credits by improved forest management in Piemonte, Italy. The guidelines have been developed in a joint effort by academia, regional administrations, forest owners and professional consultants. In particular, we show how to compute the baseline and the additionality of credit-generating forest management activities, and how to reconcile the generation of forest carbon credits with law requirements, technical limitations, and the provision of other ecosystem services. To illustrate the profitability of carbon credit generation, we simulated the application of carbon credit guidelines to two forest-rich mountain watersheds in the southern part of the Piemonte region. The two dominating tree species are beech (Fagus sylvatica L.) and chestnut (Castanea sativa Mill.). We computed current forest carbon stock and carbon credits generated in 20 years under business as usual and an alternative biomass retention scenario. The IFM resulted in an avoided harvest of 39,362 m³ for a net total of 64,014 MgCO2e after subtracting harvest emissions, or 38 Mg ha-1 throughout the permanence period of 20 years. These steps can be replicated in other mountain regions where there is interest in promoting this ecosystem service as an alternative or an addition to production-oriented forest management. [ABSTRACT FROM AUTHOR]

Aerial LiDAR (Light Detection and Ranging) derived data are widely adopted for the study and characterization of forests. In particular, LiDAR derived-CHM (Canopy Height Model) has proved essential in identifying tree height variability and estimating many forest features such as biomass and wood volume. However, CHM quality may be affected by internal limits and anomalies caused by raw data (point cloud) processing (i.e., vertical errors), which are quite often disregarded by users, thus generating potentially erroneous results in their applications. In this work, an auto-consistent procedure for the fast evaluation of CHM accuracy has been developed based on the assessment of internal anomalies affecting CHM data obtained by differencing gridded DSM (Digital Surface Model) and DTM (Digital Terrain Model). To this purpose, a CHM was generated using the gridded DTMs and DSMs provided by the Cartographic Office of the Piemonte Region (north-western Italy). We estimated the local potential CHM error over the whole region, and demonstrated its strictly dependence on the terrain morphometry, particularly slope. The relationship between potential CHM error and slope was modeled separately for mountain, hill and flat terrain contexts, and used to produce a potential error map over the whole region. Our results showed that approximately 20% of the regional territory suffers from CHM uncertainty (in particular high elevation areas, including the treeline), though the majority of regional forest categories was affected by negligible CHM error. The potential consequences of CHM error in forest applications were evaluated, concluding that the tested LiDAR dataset provide a reliable basis for forest applications in most of the regional territory. [ABSTRACT FROM AUTHOR]