The prediction of single-tree biomass, logging recoveries and quality attributes with laser scanning techniques

The precise knowledge of forest structural attributes, such as biomass, logging recoveries and quality of the available timber, play an essential role in decision-making, forest management procedure planning and in wood supply chain optimization. Remote sensing-aided mapping applications are used intensively to acquire required forest resource information. Laser scanning (LS) is one of the most promising remote sensing techniques, which can be used to estimate forest attributes at all levels, from single trees to global applications. The main objectives of the present thesis were to develop LS-based methodologies for mapping and measuring single trees. More specifically, new high-density LS-based models and methodologies were developed for the prediction of aboveground biomass (AGB), logging recovery, stem curve and external tree quality estimation. Multisource remote sensing methodologies were additionally introduced for the detailed next generation forest-inventory process. Substudies I and II concentrated on developing LS-based biomass models. Total AGB was estimated with the relative root mean squared errors (RMSE%) of 12.9% and 11.9% for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) H.Karst.), respectively using terrestrial LS (TLS) -derived predictors in multiple regression modelling. TLS-based AGB models significantly improved the estimation accuracy of AGB components compared to state-of-the-art allometric biomass models. Airborne LS (ALS) resulted in slightly higher RMSE% values of 26.3% and 36.8% for Scots pine and Norway spruce compared to results obtained with TLS. The goal of substudies III and IV was to predict timber assortment and tree quality information using high-density LS data. Sawlog volumes were estimated with RMSE% of 17.5% and 16.8% with TLS and a combination of TLS and ALS, respectively. Tree quality is an important factor for accurate and successful timber assortment estimation. The use of TLS data showed high potential for tree quality assessment. Results in IV showed that trees could be successfully classified in different quality classes based on TLS-measured attributes with accuracies between 76.4% and 83.6% depending on the amount of quality classes. Substudies V and VI presented new automatic processing tools for TLS data and a multisource approach for the more detailed prediction of diameter distribution. Automatic processing of TLS data was demonstrated to be effective and accurate and could be utilized to make future TLS measurements more efficient. Accuracies of ~1 cm were achieved using the automatic stem curve procedure. The multisource single-tree inventory approach combined accurate treemaps produced automatically from the TLS data, and ALS individual tree detection technique for predicting forest preharvest information. Results from diverse forest conditions were promising, resulting in diameter prediction accuracies between 1.4 cm and 4.7 cm depending on tree density and main tree species. Each substudy (I–VI) presented new methods and results for single-tree AGB modelling, external tree quality classification, automatic stem reconstruction and multisource approaches.