A forest typology for monitoring sustainable forest management: The case of European Forest Types

Sustainable forest management (SFM) is presently widely accepted as the overriding objective for forest policy and practice. Regional processes are in progress all over the world to develop and implement criteria and indicators of SFM. In continental Europe, a set of 35 Pan-European indicators has been endorsed under the Ministerial Conference on the Protection of Forests in Europe (MCPFE) to measure progress towards SFM in the 44 countries of the region. The formulation of seven indicators (forest area, growing stock, age structure/diameter distribution, deadwood, tree species composition, damaging agents, naturalness) requires national data to be reported by forest types. Within the vast European forest area the values taken by these indicators show a considerable range of variation, due to variable natural conditions and anthropogenic influences. Given this variability, it is very difficult to grasp the meaning of these indicators when taken out of their ecological background. The paper discusses the concepts behind, and the requirements of, a classification more soundly ecologically framed and suitable for MCPFE reporting than the three (un-informative) classes adopted so far: broadleaved forest, coniferous forest, mixed broadleaved and coniferous forest. We propose a European Forest Types scheme structured into a reasonably higher number of classes, that would improve the specificity of the indicators reported under the MCPFE process and its understanding.L'articolo è disponibile sul sito dell'editore www.tandf.co.uk/journals

The use of airborne laser scanning to develop a pixel-based stratification for a verified carbon offset project

Background The voluntary carbon market is a new and growing market that is increasingly important to consider in managing forestland. Monitoring, reporting, and verifying carbon stocks and fluxes at a project level is the single largest direct cost of a forest carbon offset project. There are now many methods for estimating forest stocks with high accuracy that use both Airborne Laser Scanning (ALS) and high-resolution optical remote sensing data. However, many of these methods are not appropriate for use under existing carbon offset standards and most have not been field tested. Results This paper presents a pixel-based forest stratification method that uses both ALS and optical remote sensing data to optimally partition the variability across an ~10,000 ha forest ownership in Mendocino County, CA, USA. This new stratification approach improved the accuracy of the forest inventory, reduced the cost of field-based inventory, and provides a powerful tool for future management planning. This approach also details a method of determining the optimum pixel size to best partition a forest. Conclusions The use of ALS and optical remote sensing data can help reduce the cost of field inventory and can help to locate areas that need the most intensive inventory effort. This pixel-based stratification method may provide a cost-effective approach to reducing inventory costs over larger areas when the remote sensing data acquisition costs can be kept low on a per acre basis