Hyyppä Juha, D.Sc. (Tech.)

The emergence of hand-held Personal Laser Scanning (H-PLS) systems in recent years resulted in initial research on the possibility of its application in forest inventory, primarily for the estimation of the main tree attributes (e.g. tree detection, stem position, DBH, tree height, etc.). Research knowledge acquired so far can help to direct further research and eventually include H-PLS into operational forest inventory in the future. The main aims of this review are:

Þ   to present the current state of the art for H-PLS systems

Þ   briefly describe the fundamental concept and methods for H-PLS application in forest inventory

Þ   provide an overview of the results of previous studies

Þ   emphasize pros and cons for H-PLS application in forest inventory in relation to conventional field measurements and other similar laser scanning systems

Þ   highlight the main issues that should be covered by further H-PLS-based forest inventory studies.

Prior tree marking is used to guide loggers or forest machine operators on which trees to cut to achieve the desirable silvicultural quality of a thinning treatment. In the future, this beneficial but expensive human work could be automated with advanced driver assistance systems. This study aimed to investigate the effect of conventional prior tree marking on cutting productivity and harvesting quality of the first and later thinnings. A comparative time study was conducted with four experienced harvester operators. The operators thinned 4825 stems with the cut-to-length (CTL) harvesting method in eight thinning stands. The time consumption of the different time elements of cutting work was measured to model the cutting productivity with average values or regress these values against the stem volume or density of removal. Prior tree marking increased the cutting productivity by an average of 2.8% in the first thinnings and 2.7% in later thinnings by reducing the time consumption of boom-out (positioning the harvester head for cut) and moving. The operator effect was notable, even though only experienced operators participated in the study. For some operators, prior tree marking did not make cutting work more efficient, and sometimes hampered it. Prior tree marking improved the quality of the remaining stands in thinnings by producing a more accurate density of remaining trees after the harvesting operation in relation to thinning guidelines. When the stands were not marked, the operators chose trees of poor quality with almost the same accuracy as the forester. These findings lay the foundation for the next-generation operators’ guidance and decision support systems, which could detect trees around the harvester and guide the operator in tree selection and managing better thinning intensity in cutting work. Although prior tree marking increased productivity only marginally, the improvement in the quality of harvesting operations must be acknowledged.

Advances in sensor technology and computing performance has brought us into an era of digital forestry where a forest environment can be digitally replicated. At the same time, an increasing interest in the use of unmanned vehicles and other autonomous mobile systems (AMSs) in forest mapping and operations has emerged. However, a forest is an unstructured and rather complex environment for AMSs to operate in, and usually some kind of a priori information of traversability is required. The aim of this study was to assess forest traversability for AMSs using high-density airborne laser scanning (ALS) point clouds. It was assumed that such point clouds acquired from a helicopter flying at a low altitude can be used to characterise vegetation obstacles affecting forest traversability. A voxel-based vegetation occupancy analysis was carried out with the aim to detect open space to define traversable three-dimensional space. The experimental setup included seven sample plots (32×32 m) representing diverse boreal forest structures. Terrestrial laser scanning (TLS) was used for obtaining reference for vegetation occupancy. Comparison between ALS and TLS revealed an overall accuracy of 0.85–0.94 with a recall of 0.78–0.91 and a precision of 0.62–0.74 for ALS-based voxel classification for vegetation occupancy depending on forest structure. This implies that up to 91% of voxels assigned a classification »occupied« based on the TLS could be correctly classified using the ALS, while up to 74% of voxels assigned a classification »occupied« using the ALS were occupied based on the TLS. Density of low vegetation accounted for 83% of the variation in accuracy and precision. The feasibility of vegetation occupancy information to be used by an AMS for navigation was also demonstrated. It was assumed that the ALS data convey as sufficient information of AMS path planning as does the TLS data. The experiments showed that out of 1393 randomly generated paths based on empty space detected by the ALS, 72% were considered feasible when validated with the TLS data. The success rate in path planning varied from 0.54 to 0.92 between the sample plots and was seemingly affected by vegetation density that accounted for 53% of variation in success rate. Altogether, the demonstrated possibility to predefine forest traversability using remote sensing will support the use of AMSs in forestry.