Decision making on forest resources relies on the precise information that is collected using inventory. There are many different kinds of forest inventory techniques that can be applied depending on the goal, scale, resources and the required accuracy. Most of the forest inventories are based on field sample. Therefore, the accuracy of the forest inventories depends on the quality and quantity of the field sample. Conventionally, field sample has been measured using simple tools. When map is required, remote sensing materials are needed. Terrestrial laser scanning (TLS) provides a measurement technique that can acquire millimeter-level of detail from the surrounding area, which allows rapid, automatic and periodical estimates of many important forest inventory attributes. It is expected that TLS will be operationally used in forest inventories as soon as the appropriate software becomes available, best practices become known and general knowledge of these findings becomes more wide spread. Meanwhile, mobile laser scanning, personal laser scanning, and image-based point clouds became capable of capturing similar terrestrial point cloud data as TLS. This paper reviews the advances of applying TLS in forest inventories, discusses its properties with reference to other related techniques and discusses the future prospects of this technique. (C) 2016 The Authors. Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licensesiby-nc-nd/11.0/).Peer reviewe

The demand for detailed ground reference data in quantitative forest inventories is growing rapidly, e.g., to improve the calibration of the developed models of airborne-laser-scanning-based inventories. The application of terrestrial laser scanning (TLS) in the forest has shown great potential for improving the accuracy and efficiency of field data collection. This paper presents a fully automatic stem-mapping algorithm using single-scan TLS data for collecting individual tree information from forest plots. In this method, the stem points are identified by the spatial distribution properties of the laser points, the stem model is built up of a series of cylinders, and the location of the stem is estimated by the model. The experiment was performed on nine plots with 10-m radius. The stem-location maps measured in the field by traditional methods were used as the ground truth. The overall stem-mapping accuracy was 73%. The result shows that, in a relatively dense managed forest, the majority of stems can be located by the automatic algorithm. The proposed method is a general solution for stem locating where particular plot knowledge and data format are not required.

This paper reports on a study of measuring stem curves of standing trees of different species and in different growth stages using terrestrial laser scanning (TLS). Pine and spruce trees are scanned using the multiscan approach in the field, and trees are felled to measure them destructively for the purpose of obtaining reference values. The stem curves are automatically retrieved from laser point clouds, resulting in an accuracy of ~i1 cm. The corresponding manual measurements yield similar accuracy but fewer measurements at the upper parts of tree stems, compared with the automated measurements. The stem volumes based on stem curve data and field measurements and the best Finnish national allometric volume equations (using tree species, height, and diameters at heights of 1.3 and 6 m as predictors) result in similar accuracy. The measurement accuracy of the stem curves and stem volumes is similar for both pine and spruce trees. The results of this paper confirm the feasibility of using TLS to produce stem curve data in an automated, accurate and noninvasive way and indicate that the point cloud provides adequate information to accurately derive stem volumes from standing trees. The stem curves and volumes retrieved from point clouds can be employed in various forest management activities, such as the calibration of national or regional allometric curve functions and the prediction of profits in preharvest inventories.