FOrest RESource Estimation for Energy – FORESEE

Four types of approaches have been investigated to characterize the resource in a fine geographical scale:
1. Post-stratify or classify to refine the estimates. The objective is to improve inventory accuracy at the sub regional level by incorporating auxiliary variables to the processing chain (topography, ...)
2. K-nn or “who looks like an image feels similar in kind”. The objective is to associate with an image pixel of known forest characteristics, other pixels that are similar in the image;
3. Model Forest management variables from Lidar: the «tree« method identifies trees from point clouds. The «surface« method finds empirical correlations between the Lidar data and field data.
4. Measure tree growth over a long period to estimate fertility: Photogrammetry applied to past images opens up an innovative scope.
Furthermore, forest roads network required for resource mobilization was detected by analysis of 3D terrain model derived from Lidar data, even in dense tree cover. Finally, to use these results, GIS integrators tools and web platform were created.

FORESEE has made significant advances in the development of the workflow for a continuous mapping of forest resources, suitable for complex forests and varied topography. The mapping of forest management variables, with his estimation errors, for an area of 1,360 km² is new. Applications developed are prefiguring a decision support system useful for various actors in the forestry sector. FORESEE has shown that research can meet the expectations of stakeholders in the forest management sector.

Remote sensing in forestry is a «breakthrough technology« because it allows the mapping of forest characteristics in very large areas with a resolution of a few hundred square meters. In addition, a pool of services who are today fragmented and expensive, could be tomorrow accessed from remote sensing data. However, the implementation of these results requires the collaboration of all stakeholders to find ways of synergies. Scientific perspectives are related to original concepts developed: fusion of different remote sensing data (Lidar, aerial photo ...) and specific methods (Hyper spectral ...).

The project generated 18 scientific articles for peer-reviewed journals and book chapters, including 8 international which meet the objective of the French research visibility. They mainly concern assessments of forest characteristics by modeling from Lidar data. To address the complexity of the French forest, they bring more elements on the «tree« and «surface« approaches.

A good knowledge of the biomass location, its characteristics (quantity and quality) and its mobilization conditions (exploitability, service roads, mobilization costs) is essential to the development of the forest biomass industry. This knowledge is currently insufficient to provide at reasonable costs, the required guarantees on the wood supply and on its sustainability. The demand is however increasing due to a large number of new projects requiring increasingly large biomass volumes.

Indeed, the only consistent data available today are those of the National Forest Inventory (NFI), which are nationwide statistics that do not lend themselves to mapping the resource at sub-regional levels such as a supply basin.

Up to now, the use of remote sensing data did not meet the precision requirements to take into account stand structure, which is an essential parameter for the quantification and qualification of the forest resource. Recent developments in LiDAR technology (new sensors, GPS and inertial central), combined to other data sources already available (high resolution satellite images, aerial photographs), are now allowing a precise and fine forest description, in terms of both resource characterisation and mobilization conditions. Integrating this technology will provide an innovative response to the challenges of wood mobilization, including that of wood energy.

The Foresee project aims to provide new tools for assessing the characteristics and dynamics of the forest resource biomass and the conditions of its mobilization at the supply basins level.