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Valuation of growing stock using multisource GIS data, a stem quality database, and bucking simulation

Sanz, Blanca, Malinen, Jukka, Leppänen, Vesa, Valbuena, Rubén, Kauranne, Tuomo, Tokola, Timo
Canadian journal of forest research 2018 v.48 no.8 pp. 888-897
databases, forest stands, geographic information systems, lidar, multispectral imagery, prices, pulpwood, raw materials, sawlogs, sawmills, statistics, system optimization, trees
Customer-oriented production as a sawmill strategy requires up-to-date information on the available raw material resources. Bucking is a process in which the tree stem is divided into products based on the roundwood user’s needs regarding products and their quality and dimensions. Optimization methods are employed in bucking to recover the highest value of the stem for a given product price matrix and requested length–diameter distribution. A method is presented here for assessing the value of harvestable timber stands based on their product yield. Airborne laser scanning, multispectral imagery, and field plots were used to produce timber statistics for a grid covering the target area. The statistics for the plots were generated from this grid. The value of the estimated tree list was assessed using a bucking-to-value simulator together with a stem quality database. Different product yield simulations in terms of volumes, timber assortment recoveries, wood paying capabilities (WPC) and value estimations based on the presented method, and extensive field measurements were compared. As a conclusion, this method can estimate WPC for pulpwood and sawlogs with root mean squared errors of 32.7% and 38.5%, respectively, relative to extensive field measurements.