Cartographie de la biomasse forestière par télédétection Lidar, analyse géographique de l'accessibilité et modélisation économique : étude de cas dans le canton du Valais (Suisse)

Abstract

International audienceIn mountainous areas, forest resources are spatially heterogeneous, difficult to quantify and harvest because of topographical constraints. In the framework of the PlanEter project, we implemented a case study with the objective to map the quantity and mobilisation cost of fuelwood in six communes (600 km 2 ), thanks to LiDAR remote sensing data, a GIS-based accessibility model and an economical model. The main challenge was to design an easily reproducible methodology for larger application, thanks to the use of available public data as inputs. Wood growing stock was mapped at 20 m resolution with models calibrated with national LiDAR and forest inventory data. The technical accessibility of forests to common machinery was mapped with the GIS-based model Sylvaccess using national digital terrain and landscape models. Local forest statistics and cover maps were used to derive the annual timber and fuelwood potential. The results are used at two different levels. For strategic planning, a simplified model of forest management is used to map the annual timber and fuelwood harvest and the corresponding costs, depending on the technical accessibility and forest structure. For operational planning, resource and accessibility information are used as inputs along with expert information, into the detailed model CalCouFor for cost calculation of harvesting, yarding, transport, storage and chipping operations. This case study shows that it is possible to bridge the gap between regional forest statistics and field level analysis through the use of existing datasets. Additional remote sensing information could provide information about annual forest increment and species which would improve the model of forest management, taking into account other socio-economic services provided by forests. It would then be possible to map the effects of various factors (energy price, labour costs, public subsidies) on forest biomass mobilisation