Multi-sensor model-based quality control of mountain forest production
WP4 aims at exploiting advantages of combined diagnostics techniques and multivariate data analysis for more reliable and rational assessment of the harvested material. Multi-sensor analysis aims at estimating both extrinsic and intrinsic quality indicators (related to the external characteristics of the tree and the internal wood structure, respectively). A scope of WP4 is the definition of threshold values and variability models of the selected quality indicators for the different end-uses (i.e. wood processing industries, bioenergy production).
The final objective is to combine all obtained “quality-related information” provided by machines/operators during on-field survey (T2.3), harvesting and processing (WP3). The aim is to benefit from the system’s ability for tracking (assure propagation of information about material characteristics along the value chain) and in consequence improving the grading system reliability.
Due to its distribution, and economical relevance (especially in the alpine region, where demonstrations are planned) the proposed quality control procedures will be implemented for Norway spruce (Picea abies) wood. The development of an integrated system for quality control of mountain forest production will be based on multi-sensor approach principle, and will include combination of several “quality indexes” computed on a base of different measurements.
Both, technologies commercially available and newly developed (within framework of SLOPE project) will be evaluated and possibly implemented also considering harsh operating conditions. The first set of techniques will include measurement of the log dimension (both diameters and length) and weight (of the whole tree, cut-to-length logs as well as tree residues). It will be performed by means of already available equipment installed on the forest machinery, eventually optimized for use on steep terrain, according to T3.4. The innovative set of measurement techniques (as described in the following tasks) will be validated in a dedicated research campaign located in the Italian Alps (Trentino region). All the activities of WP4 will be coordinated, in order to generate corresponding results from different techniques on the base of identical samples.
|T.4.1||Data mining and model integration of stand quality indicators from on-field survey for the determination of the tree “3D quality index”||TREEMETRICS|
|T.4.2||Evaluation of near infrared (NIR) spectroscopy as a tool for determination of log/biomass quality index in mountain forests||CNR|
|T.4.3||Evaluation of hyperspectral imaging (HI) for the determination of log/biomass “HI quality index”||BOKU|
|T.4.4||Data mining and model integration of log/biomass quality indicators from stress-wave (SW) measurements, for the determination of the “SW quality index”||CNR|
|T.4.5||Evaluation of cutting process (CP) for the determination of log/biomass “CP quality index”||CNR|
|T.4.6||Implementation of the log/biomass grading system||CNR|