Automatic dendrometry: Tree detection, tree height and diameter estimation using terrestrial laser scanning

Zhong-ke

et al. 2019

Forests

Above-ground biomass (AGB) plays a pivotal role in assessing a forest's resource dynamics, ecological value, carbon storage, and climate change effects. The traditional methods of AGB measurement are destructive, time consuming and laborious, and an efficient, relatively accurate and non-destructive AGB measurement method will provide an effective supplement for biomass calculation. Based on the real biophysical and morphological structures of trees, this paper adopted a non-destructive method based on terrestrial laser scanning (TLS) point cloud data to estimate the AGBs of multiple common tree species in boreal forests of China, and the effects of differences in bark roughness and trunk curvature on the estimation of the diameter at breast height (DBH) from TLS data were quantitatively analyzed. We optimized the quantitative structure model (QSM) algorithm based on 100 trees of multiple tree species, and then used it to estimate the volume of trees directly from the tree model reconstructed from point cloud data, and to calculate the AGBs of trees by using specific basic wood density values. Our results showed that the total DBH and tree height from the TLS data showed a good consistency with the measured data, since the bias, root mean square error (RMSE) and determination coefficient (R 2 ) of the total DBH were −0.8 cm, 1.2 cm and 0.97, respectively. At the same time, the bias, RMSE and determination coefficient of the tree height were −0.4 m, 1.3 m and 0.90, respectively. The differences of bark roughness and trunk curvature had a small effect on DBH estimation from point cloud data. The AGB estimates from the TLS data showed strong agreement with the reference values, with the RMSE, coefficient of variation of root mean square error (CV(RMSE)), and concordance correlation coefficient (CCC) values of 17.4 kg, 13.6% and 0.97, respectively, indicating that this non-destructive method can accurately estimate tree AGBs and effectively calibrate new allometric biomass models. We believe that the results of this study will benefit forest managers in formulating management measures and accurately calculating the economic and ecological benefits of forests, and should promote the use of non-destructive methods to measure AGB of trees in China.2 of 26 limiting deforestation and afforestation, and positively supports and implements the mechanism of Reducing emissions from deforestation and forest degradation in developing countries (REDD+). Accurate assessment of forest biomass plays a pivotal role in afforestation management planning, forest resource monitoring, the assessment of the ecological value of forests, climate change impacts and policy formulation for forest harvesting, conservation and management [4,5]. The assessment of forest biomass includes the estimation of both above-ground biomass (AGB) and underground biomass. However, the underground biomass is not only difficult to quantify, but it is relatively small to the AGB [6]. Therefore, the estimation of AGB has always been the main focus in biomass rese...

Section: Discussioncontrasting

confidence: 86%

Section: Discussioncontrasting

confidence: 71%

Nondestructive Estimation of the Above-Ground Biomass of Multiple Tree Species in Boreal Forests of China Using Terrestrial Laser Scanning

Zhong-ke

et al. 2019

Forests

“…After stem extraction, the DBH can be estimated from the stem points at breast height. There have been many DBH methods proposed, such as linear least square (Landau algorithm) circle fitting [3,10], nonlinear least squares (Gauss Newton) circle fitting [7], crescent moon method proposed by Kiraly and Brolly [47], RANSAC circle detection [39], Hough transform, and random Hough transform [26,31,40]. However, most of them are based on the assumption that the stem section is circular.…”

Section: Robust Least Squares Elliptic Fitting For Dbh Estimationmentioning

confidence: 99%

“…The main task is to estimate the optimal DBH parameters from the point cloud of the trunk at the corresponding height. Many articles have proposed many DBH estimation methods, such as linearized or nonlinear least square circle fitting [3,7,24], Hough-transform [26], cylinder fitting [15,38], random sample consensus (RANSAC) algorithm [8,39], and random Hough transform [31,40]. Most of these methods model the stem profiles as a circle and fit the diameter parameter from the stem points at the breast height.…”

mentioning

confidence: 99%

Improved 3D Stem Mapping Method and Elliptic Hypothesis-Based DBH Estimation from Terrestrial Laser Scanning Data

Qian

Tang

et al. 2020

Remote Sensing

The detailed structure information under the forest canopy is important for forestry surveying. As a high-precision environmental sensing and measurement method, terrestrial laser scanning (TLS) is widely used in high-precision forestry surveying. In TLS-based forestry surveys, stem-mapping, which is focused on detecting and extracting trunks, is one of the core data processing tasks and the basis for the subsequent calculation of tree attributes; one of the most basic attributes is the diameter at breast height (DBH). This article explores and improves the methods for stem mapping and DBH estimation from TLS data. Firstly, an improved 3D stem mapping algorithm considering the growth direction in random sample consistency (RANSAC) cylinder fitting is proposed to extract and fit the individual tree point cloud section. It constructs the hierarchical optimum cylinder of the trunk and introduces the growth direction into the establishment of the backbone buffer in the next layer. Experimental results show that it can effectively remove most of the branches and reduce the interference of the branches to the discrimination of trunks and improve the integrity of stem extraction by about 36%. Secondly, a robust least squares ellipse fitting method based on the elliptic hypothesis is proposed for DBH estimation. Experimental results show that the DBH estimation accuracy of the proposed estimation method is improved compared with other methods. The mean root mean squared error (RMSE) of the proposed estimation method is 1.14 cm, compared with other methods with a mean RMSE of 1.70, 2.03, and 2.14 cm. The mean relative accuracy of the proposed estimation method is 95.2%, compared with other methods with a mean relative accuracy of 92.9%, 91.9%, and 90.9%.

“…Terrestrial laser scanning (TLS) has proven to non-destructively provide three-dimensional (3D) information on tree stems (Liang et al 2014, Kankare et al 2013, Raumonen et al 2013, Saarinen et al 2017 that has not been possible with calipers or measurement tape. Individual trees can be detected from a TLS-based point cloud through identification of circular shapes (Aschoff et al 2004, Maas et al 2008 or clusters of points (Cabo et al 2018, Zhang et al 2019. Points from individual trees can then be utilized in reconstructing the entire architectural structure of a tree (Raumonen et al 2013, Hackenberg et al 2014 or only the stem (Liang et al 2011, Heinzel & Huber 2017.…”

Section: Introductionmentioning

confidence: 99%

Assessing the effects of thinning on stem growth allocation of individual Scots pine trees

Saarinen

Kankare

Yrttimaa

et al. 2020

Preprint

Forest management alters the growing conditions and thus further development of trees. However, quantitative assessment of forest management on tree growth has been demanding as methodologies for capturing changes comprehensively in space and time have been lacking. Terrestrial laser scanning (TLS) has shown to be capable of providing three-dimensional (3D) tree stem reconstructions required for revealing differences between stem forms and growth allocation. In this study, we used TLS-based 3D reconstructions of tree stems to investigate how forest management (i.e., varying thinning treatments and the following growth effects) has affected stem size and form of Scots pine (Pinus sylvestris L.) trees. The results showed that intensive thinning resulted in more stem volume and therefore total biomass allocation and carbon uptake compared to the moderate thinning. Scots pines growing in plots from which suppressed trees were removed (i.e., thinning from below) had larger bottom part of stems (i.e., volume accumulated more below 50% of tree height) compared to Scots pines growing in plots from which part of the dominant trees had been removed (i.e., thinning from above). Furthermore, intensive thinning, especially from below, produced less variation in relative stem attributes characterizing stem size and form. Thus, it can be concluded that thinning intensity, type, and the following growth effects have an impact on post-thinning stem growth as well as stem form of individual Scots pine trees. Our study presented detailed measurements on postthinning growth of Scots pines that have been laborious or impracticable before the emergence of TLS technology. Moreover, TLS-based stem reconstructions provided variety of attributes characterizing stem growth allocation that have not traditionally been feasible to obtain. The study demonstrated that TLS technology can be used to generate new knowledge for supporting forest management and silviculture as well as improving ecological understanding of boreal forests.