Applicability of personal laser scanning in forestry inventory

Cao

et al. 2019

Forests

Rubber trees in southern China are often impacted by natural disturbances that can result in a tilted tree body. Accurate crown segmentation for individual rubber trees from scanned point clouds is an essential prerequisite for accurate tree parameter retrieval. In this paper, three plots of different rubber tree clones, PR107, CATAS 7-20-59, and CATAS 8-7-9, were taken as the study subjects. Through data collection using ground-based mobile light detection and ranging (LiDAR), a voxelisation method based on the scanned tree trunk data was proposed, and deep images (i.e., images normally used for deep learning) were generated through frontal and lateral projection transform of point clouds in each voxel with a length of 8 m and a width of 3 m. These images provided the training and testing samples for the faster region-based convolutional neural network (Faster R-CNN) of deep learning. Consequently, the Faster R-CNN combined with the generated training samples comprising 802 deep images with pre-marked trunk locations was trained to automatically recognize the trunk locations in the testing samples, which comprised 359 deep images. Finally, the point clouds for the lower parts of each trunk were extracted through back-projection transform from the recognized trunk locations in the testing samples and used as the seed points for the region’s growing algorithm to accomplish individual rubber tree crown segmentation. Compared with the visual inspection results, the recognition rate of our method reached 100% for the deep images of the testing samples when the images contained one or two trunks or the trunk information was slightly occluded by leaves. For the complicated cases, i.e., multiple trunks or overlapping trunks in one deep image or a trunk appearing in two adjacent deep images, the recognition accuracy of our method was greater than 90%. Our work represents a new method that combines a deep learning framework with point cloud processing for individual rubber tree crown segmentation based on ground-based mobile LiDAR scanned data.

Section: The Advantages Of Our Approachmentioning

confidence: 99%

Section: The Advantages Of Our Approachmentioning

confidence: 99%

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

Wang

Cao

et al. 2019

Forests

“…Of all the forest parameters that can be obtained through nondestructive in-situ measurements, the diameter of the breast height (DBH, 1.30 m height) not only helps researchers understand the structure of a forest but also reflects forest growth state. It contributes to aboveground forest biomass estimation and bridges empirical and process-based models, which directly inform global carbon cycling models [3,4].…”

Section: Introductionmentioning

confidence: 99%

Annular Neighboring Points Distribution Analysis: A Novel PLS Stem Point Cloud Preprocessing Algorithm for DBH Estimation

Duanmu

Xing

2020

Remote Sensing

Personal laser scanning (PLS) has significant potential for estimating the in-situ diameter of breast height (DBH) with high efficiency and precision, which improves the understanding of forest structure and aids in building carbon cycle models in the big data era. PLS collects more complete stem point cloud data compared with the present laser scanning technology. However, there is still no significant advantage of DBH estimation accuracy. Because the error caused by merging different point cloud fragments has not yet been eliminated, overlapping and inaccurate co-registered point cloud fragments are often inevitable, which are usually the leading error sources of PLS-based DBH estimation. In this study, a novel pre-processing algorithm named annular neighboring points distribution analysis (ANPDA) was developed to improve PLS-based DBH estimation accuracy. To reduce the impact of inaccurately co-registered point cloud fragments, ANPDA identified outliers through iterative removal of outermost points and analyzing the distribution of annular neighboring points. Six plots containing 247 trees under different forest conditions were selected to evaluate the ANPDA. Results showed that in the six plots, error reductions of 53.80–87.13% for bias, 38.82–57.30% for mean absolute error (MAE), and 27.17–56.02% for root mean squared error (RMSE) were achieved after applying ANPDA. These results confirmed that ANPDA was generally effective for improving PLS-based DBH estimation accuracy. It appeared that ANPDA could be conveniently fused with an automatic PLS-based DBH estimation process as a preprocessing algorithm. Furthermore, it has the potential to predict and warn operators of potential large errors during hierarchical semi-automatic DBH estimation.

“…This technology has the ability to generate high spatial resolution and accurate three-dimensional (3D) point cloud data. Consequently, it has been widely applied in forestry surveys to acquire basic tree parameters [16], as well as estimate AGB and carbon storage [17,18]. ALS can produce large-scale 3D point cloud data in a short time, from which tree height, DBH, canopy height and density metrics can be obtained, and then the AGB of trees can be evaluated.…”

mentioning

confidence: 99%

“…However, this method of assessing the biomass by ALS is prone to problems, including large estimation uncertainties, large costs, and limited information [21,22]. The system's performance is compromised in forest areas with weak GNSS signals or large variations of topography [16,23]. TLS can generate detailed and accurate parameter information of the 3D structure of trees by calculating the time difference between the emission and return of laser pulses and analyzing the energy of the returned laser pulses, which is not affected by GNSS signals and offers opportunities for a consistent and robust framework to support AGB estimates [3,24].Terrestrial laser scanning (TLS) has shown great potential for accurately assessing forest biomass with greater precision than inferred from the nationwide allometric biomass models [8,25].…”

mentioning

confidence: 99%

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

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...