Estimating potential logging residues by airborne laser scanning

During the last 10 – 15 years there has been growing interest towards the use of forest biomass for energy purposes. In most countries only the tree stems have traditionally been harvested and processed by sawmills and the pulp and paper industries, while branches and tree tops have been left in the forest. Several studies emphasize however forest residuals as a potential exploitable resource (Malinen et al., 2001; Gan and Smith, 2006; Mabee and Saddler, 2010), and the practice of not utilizing it is currently about to change. The European Commission reports a recent annual growth in the use of renewable energy in Europe that exceeds all other energy types, with bioenergy constituting a large part of the renewable energy (Eurostat, 2010). In some countries, such as Finland, harvesting of logging residues is already operational (Heinimö and Alakangas, 2006). This suggests that changes are taking place, which will give forest owners commercial incentives to harvest also logging residues. Logging residues might be defined in several ways. In the Nordic countries usually the branches and the tree tops are included, but also stumps and roots may be considered to be logging residues. In the present study branches and tops was considered. An increase in commercial harvesting of biomass components – such as tops and branches – should spark the demand for estimates of biomass components in forest inventories and resource management plans. The objective of this study was to describe methods to meet this demand, and more specifically to derived methods for estimation of potential logging residues using airborne laser scanner data. The estimated logging residues are considered “potential” in the sense that these are components of the standing tree that will become logging residues when the tree is harvested. Regression models relating variables derived from airborne laser scanning to the amount of biomass of potential logging residues were estimated for 147 sample plots (200 m2) measured in mature boreal forest in Norway. The base regression model explained 86% of the variation, and when the data were stratified into two strata according to site quality, the stratum-specific models accounted for 88% and 77% of the variation in PLR on poor sites and on good sites, respectively. Effect of tree species composition were assessed by including the proportion of Norway spruce as potential explanatory variable and this extended model explained 87% of the variability of potential logging residues . The estimated models were validated using two datasets, one comprising 120 sample plots (200 m2), and the other consisting of 25 measured stands. The ground observations in the stand dataset were based on data collected by a harvester. The validation of the overall model gave an RMSE value of 27.6% and 22.2% of the mean value in the plot and stand data, respectively. The stratum-specific models gave RMSE values of 25.3 and 22.5% in the plot validation, and 24.3 and 17.6% in the stand validation. Including proportion of spruce in the model gave an RMSE of 23.2% in the plot validation and 21.3% in the stand validation. The study has shown that PLR in boreal forests can be estimated by ALS with accuracies in line with those obtained for other forest characteristics, such as volume and basal area, using operational procedures. The proposed procedure utilizes the same field and ALS data as are collected in many operational forest inventories, and can therefore easily be implemented with low or no additional costs.

References in post

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