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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (20 April 2019) | Viewed by 79938

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Special Issue Editor

Prof. Dr. Peter Krzystek

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Head of Photogrammetry and Remote Sensing Laboratory, Department of Geoinformatics, Munich University of Applied Sciences, 34, 80335 München, Germany
Interests: LiDAR; remote sensing; computer vision; machine learning; forestry; UAV
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

the advent of LiDAR enabled the acquisition of 3D point clouds in forests and a detailed 3D analysis of forest structures. Depending on the point density, methods operating on the stand and plot level have become operational providing valuable forest parameters for inventories. Moreover, methods from computer vision and machine learning help to detect single forest objects like trees, stems, dead wood, and regeneration, paving the way to precision inventories on the tree level. From gaining a good understanding of ecological health, to protecting and preserving biodiversity, and to monitoring entire forests in the case of wildfires—gaining accurate information on the status and distribution of forest structures over various time scales is vital. This information is used by forest managers, researchers and governmental and inter-governmental institutions. Besides the conventional LiDAR and optical sensors, new instruments—operating with higher point density in extended radiometric ranges—are available as aerial, terrestrial and mobile tools, allowing for new approaches and applications.

The purpose of this Special Issue is to present the state-of-the-art of 3D point cloud processing in forests and to highlight new methods, techniques and applications for the 3D mapping of forest structures, which takes advantage of the inherent high geometric and radiometric 3D information of point clouds and create fused data sets by sensor integration. Both review papers and research contributions will be accepted. The scope of topics to be discussed includes, but is not limited to:

Detection of single trees, tree stems and dead wood
Mapping of understory vegetation
New approaches from machine learning for classifying forest objects
Co-registration of point clouds from different sources
Precise methods for multi-scale forest structural parameters extracted from point clouds
Forest applications of tools for processing point clouds
Integrating and fusing data sets from multiple platforms
New sensors for highly dense data acquisition

Prof. Dr. Peter Krzystek
Guest Editor

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30 pages, 8557 KiB

Open AccessArticle

Influence of Scanner Position and Plot Size on the Accuracy of Tree Detection and Diameter Estimation Using Terrestrial Laser Scanning on Forest Inventory Plots

by Christoph Gollob, Tim Ritter, Clemens Wassermann and Arne Nothdurft

Remote Sens. 2019, 11(13), 1602; https://doi.org/10.3390/rs11131602 - 5 Jul 2019

Cited by 51 | Viewed by 5970

Abstract

This research tested how different scanner positions and sample plot sizes affect the tree detection and diameter measurement in forest inventories. For this, a multistage density-based clustering approach was further developed for the automatic mapping of tree positions and simultaneously applied with automatic measurements of tree diameters. This further development of the algorithm reduced the proportion of falsely detected tree locations by about 64%. The algorithms were tested in different settings with respect to the number and spatial alignment of scanner positions and under manifold forest conditions, covering different age classes and a mixture of scenarios, and representing a broad gradient of structural complexity. For circular sample plots with a maximum radius of 20 m, the tree mapping algorithm showed a detection rate of 82.4% with seven scanner positions at the vertices of a hexagon plus the center coordinates, and 68.3% with four scanner positions aligned in a triangle plus the center. Detection rates were significantly increased with smaller maximum radii. Thus, with a maximum radius of 10 m, the hexagon setting yielded a detection rate of 90.5% and the triangle 92%. Other alignments of scanner positions were also tested, but proved to be either unfavorable or too labor-intensive. The commission rates were on average less than 3%. The root mean square error (RMSE) of the dbh (diameter at breast height) measurement was between 2.66 cm and 4.18 cm for the hexagon and between 3.0 cm and 4.7 cm for the triangle design. The robustness of the algorithm was also demonstrated via tests by means of an international benchmark dataset. It has been shown that the number of stems per hectare had a significant impact on the detection rate. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forests)

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24 pages, 5014 KiB

Open AccessArticle

Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds

by Frederic Brieger, Ulrike Herzschuh, Luidmila A. Pestryakova, Bodo Bookhagen, Evgenii S. Zakharov and Stefan Kruse

Remote Sens. 2019, 11(12), 1447; https://doi.org/10.3390/rs11121447 - 18 Jun 2019

Cited by 24 | Viewed by 6008

Abstract

Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R²) and lower relative root mean square percentage error (RMSE%) for tree heights (mean R² = 0.77, mean RMSE% = 18.46%) than for crown diameters (mean R² = 0.46, mean RMSE% = 24.9%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees <2 m. Our results show that plot sizes for vegetation surveys in the tundra–taiga ecotone should be adapted to the forest structure and have a radius of >15–20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest’s stand structure. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forests)

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15 pages, 2437 KiB

Open AccessArticle

A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests

by Ivan Sačkov, Ladislav Kulla and Tomáš Bucha

Remote Sens. 2019, 11(12), 1431; https://doi.org/10.3390/rs11121431 - 16 Jun 2019

Cited by 18 | Viewed by 4898

Abstract

Estimation of biophysical variables based on airborne laser scanning (ALS) data using tree detection methods concentrates mainly on delineation of single trees and extraction of their attributes. This study provides new insight regarding the potential and limits of two detection methods and underlines some key aspects regarding the choice of the more appropriate alternative. First, we applied the multisource-based method implemented in reFLex software (National Forest Centre, Slovakia), which uses the information contained in the point cloud and a priori information. Second, we applied the raster-based method implemented in OPALS software (Vienna University of Technology, Austria), which extracts information from several ALS-derived height models. A comparative study was conducted for a part of the university forest in Zvolen (Slovakia, Central Europe). ALS-estimated variables of both methods were compared (1) to the ground reference data within four heterogonous stands with an area size of 7.5 ha as well as (2) to each other within a comprehensive forest unit with an area size of 62 ha. We concluded that both methods can be used to evaluate forest stand and ecological variables. The overall performance of both methods achieved a matching rate within the interval of 52%–64%. The raster-based method provided faster and slightly more accurate estimate of most variables, while the total volume was more precisely estimated using the multisource-based method. Specifically, the relative root mean square errors did not exceed 7.2% for mean height, 8.6% for mean diameter, 21.4% for total volume, 29.0% for stand density index, and 7.2% for Shannon’s diversity index. Both methods provided estimations with differences that were statistically significant, relative to the ground data as well as to each other (p < 0.05). Full article

(This article belongs to the Special Issue 3D Point Clouds in Forests)

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21 pages, 6006 KiB

Open AccessArticle

Tree Height Estimation of Forest Plantation in Mountainous Terrain from Bare-Earth Points Using a DoG-Coupled Radial Basis Function Neural Network

by Haiqing He, Yeli Yan, Ting Chen and Penggen Cheng

Remote Sens. 2019, 11(11), 1271; https://doi.org/10.3390/rs11111271 - 29 May 2019

Cited by 32 | Viewed by 3893

Abstract

Tree heights are the principal variables for forest plantation inventory. The increasing availability of high-resolution three-dimensional (3D) point clouds derived from low-cost Unmanned Aerial Vehicle (UAV) and modern photogrammetry offers an opportunity to generate a Canopy Height Model (CHM) in the mountainous areas. In this paper, we assessed the capabilities of tree height estimation using UAV-based Structure-from-Motion (SfM) photogrammetry and Semi-Global Matching (SGM). The former is utilized to generate 3D geometry, while the latter is used to generate dense point clouds from UAV imagery. The two algorithms were coupled with a Radial Basis Function (RBF) neural network to acquire CHMs in mountainous areas. This study focused on the performance of Digital Terrain Model (DTM) interpolation over complex terrains. With the UAV-based image acquisition and image-derived point clouds, we constructed a 5 cm-resolution Digital Surface Model (DSM), which was assessed against 14 independent checkpoints measured by a Real-Time Kinematic Global Positioning System RTK GPS. Results showed that the Root Mean Square Errors (RMSEs) of horizontal and vertical accuracies are approximately 5 cm and 10 cm, respectively. Bare-earth Index (BEI) and Shadow Index (SI) were used to separate ground points from the image-derived point clouds. The RBF neural network coupled with the Difference of Gaussian (DoG) was exploited to provide a favorable generalization for the DTM from 3D ground points with noisy data. CHMs were generated using the height value in each pixel of the DSM and by subtracting the corresponding DTM value. Individual tree heights were estimated using local maxima algorithm under a contour-surround constraint. Two forest plantations in mountainous areas were selected to evaluate the accuracy of estimating tree heights, rather than field measurements. Results indicated that the proposed method can construct a highly accurate DTM and effectively remove nontreetop maxima. Furthermore, the proposed method has been confirmed to be acceptable for tree height estimation in mountainous areas given the strong linear correlation of the measured and estimated tree heights and the acceptable t-test values. Overall, the low-cost UAV-based photogrammetry and RBF neural network can yield a highly accurate DTM over mountainous terrain, thereby making them particularly suitable for rapid and cost-effective estimation of tree heights of forest plantation in mountainous areas. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forests)

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19 pages, 10539 KiB

Open AccessArticle

Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data

by Wen Xiao, Aleksandra Zaforemska, Magdalena Smigaj, Yunsheng Wang and Rachel Gaulton

Remote Sens. 2019, 11(11), 1263; https://doi.org/10.3390/rs11111263 - 28 May 2019

Cited by 51 | Viewed by 8329

Abstract

Airborne lidar has been widely used for forest characterization to facilitate forest ecological and management studies. With the availability of increasingly higher point density, individual tree delineation (ITD) from airborne lidar point clouds has become a popular yet challenging topic, due to the complexity and diversity of forests. One important step of ITD is segmentation, for which various methodologies have been studied. Among them, a long proven image segmentation method, mean shift, has been applied directly onto 3D points, and has shown promising results. However, there are variations among those who implemented the algorithm in terms of the kernel shape, adaptiveness and weighting. This paper provides a detailed assessment of the mean shift algorithm for the segmentation of airborne lidar data, and the effect of crown top detection upon the validation of segmentation results. The results from three different datasets revealed that a crown-shaped kernel consistently generates better results (up to 7 percent) than other variants, whereas weighting and adaptiveness do not warrant improvements. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forests)

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23 pages, 6918 KiB

Open AccessArticle

Terrestrial Structure from Motion Photogrammetry for Deriving Forest Inventory Data

by Livia Piermattei, Wilfried Karel, Di Wang, Martin Wieser, Martin Mokroš, Peter Surový, Milan Koreň, Julián Tomaštík, Norbert Pfeifer and Markus Hollaus

Remote Sens. 2019, 11(8), 950; https://doi.org/10.3390/rs11080950 - 20 Apr 2019

Cited by 88 | Viewed by 10039

Abstract

The measurements of tree attributes required for forest monitoring and management planning, e.g., National Forest Inventories, are derived by rather time-consuming field measurements on sample plots, using calipers and measurement tapes. Therefore, forest managers and researchers are looking for alternative methods. Currently, terrestrial laser scanning (TLS) is the remote sensing method that provides the most accurate point clouds at the plot-level to derive these attributes from. However, the demand for even more efficient and effective solutions triggers further developments to lower the acquisition time, costs, and the expertise needed to acquire and process 3D point clouds, while maintaining the quality of extracted tree parameters. In this context, photogrammetry is considered a potential solution. Despite a variety of studies, much uncertainty still exists about the quality of photogrammetry-based methods for deriving plot-level forest attributes in natural forests. Therefore, the overall goal of this study is to evaluate the competitiveness of terrestrial photogrammetry based on structure from motion (SfM) and dense image matching for deriving tree positions, diameters at breast height (DBHs), and stem curves of forest plots by means of a consumer grade camera. We define an image capture method and we assess the accuracy of the photogrammetric results on four forest plots located in Austria and Slovakia, two in each country, selected to cover a wide range of conditions such as terrain slope, undergrowth vegetation, and tree density, age, and species. For each forest plot, the reference data of the forest parameters were obtained by conducting field surveys and TLS measurements almost simultaneously with the photogrammetric acquisitions. The TLS data were also used to estimate the accuracy of the photogrammetric ground height, which is a necessary product to derive DBHs and tree heights. For each plot, we automatically derived tree counts, tree positions, DBHs, and part of the stem curve from both TLS and SfM using a software developed at TU Wien (Forest Analysis and Inventory Tool, FAIT), and the results were compared. The images were oriented with errors of a few millimetres only, according to checkpoint residuals. The automatic tree detection rate for the SfM reconstruction ranges between 65% and 98%, where the missing trees have average DBHs of less than 12 cm. For each plot, the mean error of SfM and TLS DBH estimates is −1.13 cm and −0.77 cm with respect to the caliper measurements. The resulting stem curves show that the mean differences between SfM and TLS stem diameters is at maximum −2.45 cm up to 3 m above ground, which increases to almost +4 cm for higher elevations. This study shows that with the adopted image capture method, terrestrial SfM photogrammetry, is an accurate solution to support forest inventory for estimating the number of trees and their location, the DBHs and stem curve up to 3 m above ground. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forests)

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18 pages, 4521 KiB

Open AccessArticle

Method to Reduce the Bias on Digital Terrain Model and Canopy Height Model from LiDAR Data

by Marie-Soleil Fradette, Antoine Leboeuf, Martin Riopel and Jean Bégin

Remote Sens. 2019, 11(7), 863; https://doi.org/10.3390/rs11070863 - 10 Apr 2019

Cited by 10 | Viewed by 3679

Abstract

Underestimation of LiDAR heights is widely known but has never been evaluated for several sensors and for diverse types of ecological conditions. This underestimation is mainly linked to the probability of the pulse to reach the ground and the top of vegetation. Main causes of this underestimation are pulse density, pattern of scan (sensors), scan angles, specific contract parameters (flying altitude, pulse repetition frequency) and characteristics of the territory (slopes, stand density and species composition). This study, carried out at a resolution of 1 × 1 m, first assessed the possibility of making an adjustment model to correct the bias of the digital terrain model (DTM), and then proposed a global adjustment model to correct the bias on the canopy height model (CHM). For this study, the bias of both DTM and CHM were calculated by subtracting two LiDAR datasets: high-density pixels with 21 pulses/m² (first return) and more (DTM or CHM reference value pixels) and low-density pixels (DTM or CHM value to correct). After preliminary analyses, it was concluded that the DTM did not need specific adjustment. In contrast, the CHM needed adjustments. Among the variables studied, three were selected for the final CHM adjustment model: the maximum height of the pixel (H2Corr); the density of first returns by m² (D_first); and the standard deviation of nine maximum heights of the neighborhood cells (H_STD9). The modeling occurred in three steps. The first two steps enabled the determination of significant variables and the shape of the equation to be defined (linear mixed model and non-linear model). The third step made it possible to propose an empirical equation using a non-linear mixed model that can be applied to a 1 × 1 m CHM. The CHM underestimation correction could be used for a preliminary step to several uses of the CHM such as volume calculation, forest growth models or multi-temporal analysis. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forests)

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25 pages, 8852 KiB

Open AccessArticle

Processing Chain for Estimation of Tree Diameter from GNSS-IMU-Based Mobile Laser Scanning Data

by Juraj Čerňava, Martin Mokroš, Ján Tuček, Michal Antal and Zuzana Slatkovská

Remote Sens. 2019, 11(6), 615; https://doi.org/10.3390/rs11060615 - 13 Mar 2019

Cited by 28 | Viewed by 4946

Abstract

Mobile laser scanning (MLS) is a progressive technology that has already demonstrated its ability to provide highly accurate measurements of road networks. Mobile innovation of the laser scanning has also found its use in forest mapping over the last decade. In most cases, existing methods for forest data acquisition using MLS result in misaligned scenes of the forest, scanned from different views appearing in one point cloud. These difficulties are caused mainly by forest canopy blocking the global navigation satellite system (GNSS) signal and limited access to the forest. In this study, we propose an approach to the processing of MLS data of forest scanned from different views with two mobile laser scanners under heavy canopy. Data from two scanners, as part of the mobile mapping system (MMS) Riegl VMX-250, were acquired by scanning from five parallel skid trails that are connected to the forest road. Misaligned scenes of the forest acquired from different views were successfully extracted from the raw MLS point cloud using GNSS time based clustering. At first, point clouds with correctly aligned sets of ground points were generated using this method. The loss of points after the clustering amounted to 33.48%. Extracted point clouds were then reduced to 1.15 m thick horizontal slices, and tree stems were detected. Point clusters from individual stems were grouped based on the diameter and mean GNSS time of the cluster acquisition. Horizontal overlap was calculated for the clusters from individual stems, and sufficiently overlapping clusters were aligned using the OPALS ICP module. An average misalignment of 7.2 mm was observed for the aligned point clusters. A 5-cm thick horizontal slice of the aligned point cloud was used for estimation of the stem diameter at breast height (DBH). DBH was estimated using a simple circle-fitting method with a root-mean-square error of 3.06 cm. The methods presented in this study have the potential to process MLS data acquired under heavy forest canopy with any commercial MMS. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forests)

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15 pages, 2937 KiB

Open AccessArticle

Use of UAV Photogrammetric Data for Estimation of Biophysical Properties in Forest Stands Under Regeneration

by Stefano Puliti, Svein Solberg and Aksel Granhus

Remote Sens. 2019, 11(3), 233; https://doi.org/10.3390/rs11030233 - 23 Jan 2019

Cited by 54 | Viewed by 7036

Abstract

The objective of this study was to assess the use of unmanned aerial vehicle (UAV) data for modelling tree density and canopy height in young boreal forests stands. The use of UAV data for such tasks can be beneficial thanks to the high resolution and reduction of the time spent in the field. This study included 29 forest stands, within which 580 clustered plots were measured in the field. An area-based approach was adopted to which random forest models were fitted using the plot data and the corresponding UAV data and then applied and validated at plot and stand level. The results were compared to those of models based on airborne laser scanning (ALS) data and those from a traditional field-assessment. The models based on UAV data showed the smallest stand-level

R M S E

values for mean height (0.56 m) and tree density (1175 trees ha⁻¹). The

R M S E

of the tree density using UAV data was 50% smaller than what was obtained using ALS data (2355 trees ha⁻¹). Overall, this study highlighted that the use of UAVs for the inventory of forest stands under regeneration can be beneficial both because of the high accuracy of the derived data analytics and the time saving compared to traditional field assessments. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forests)

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30 pages, 43448 KiB

Open AccessEditor’s ChoiceArticle

Adaptive Framework for the Delineation of Homogeneous Forest Areas Based on LiDAR Points

by Moritz Bruggisser, Markus Hollaus, Di Wang and Norbert Pfeifer

Remote Sens. 2019, 11(2), 189; https://doi.org/10.3390/rs11020189 - 18 Jan 2019

Cited by 9 | Viewed by 5224

Abstract

We propose a flexible framework for automated forest patch delineations that exploits a set of canopy structure features computed from airborne laser scanning (ALS) point clouds. The approach is based on an iterative subdivision of the point cloud using k-means clustering followed by an iterative merging step to tackle oversegmentation. The framework can be adapted for different applications by selecting relevant input features that best measure the intended homogeneity. In our study, the performance of the segmentation framework was tested for the delineation of forest patches with a homogeneous canopy height structure on the one hand and with similar water cycle conditions on the other. For the latter delineation, canopy components that impact interception and evapotranspiration were used, and the delineation was mainly driven by leaf area, tree functional type, and foliage density. The framework was further tested on two scenes covering a variety of forest conditions and topographies. We demonstrate that the delineated patches capture well the spatial distributions of relevant canopy features that are used for defining the homogeneity. The consistencies range from

R^{2} = 0.84

R^{2} = 0.86

and from

R^{2} = 0.80

R^{2} = 0.91

for the most relevant features in the delineation of patches with similar height structure and water cycle conditions, respectively. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forests)

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15 pages, 4272 KiB

Open AccessArticle

Optimizing the Remote Detection of Tropical Rainforest Structure with Airborne Lidar: Leaf Area Profile Sensitivity to Pulse Density and Spatial Sampling

by Danilo Roberti Alves de Almeida, Scott C. Stark, Gang Shao, Juliana Schietti, Bruce Walker Nelson, Carlos Alberto Silva, Eric Bastos Gorgens, Ruben Valbuena, Daniel de Almeida Papa and Pedro Henrique Santin Brancalion

Remote Sens. 2019, 11(1), 92; https://doi.org/10.3390/rs11010092 - 7 Jan 2019

Cited by 76 | Viewed by 11927

Abstract

Airborne Laser Scanning (ALS) has been considered as a primary source to model the structure and function of a forest canopy through the indicators leaf area index (LAI) and vertical canopy profiles of leaf area density (LAD). However, little is known about the effects of the laser pulse density and the grain size (horizontal binning resolution) of the laser point cloud on the estimation of LAD profiles and their associated LAIs. Our objective was to determine the optimal values for reliable and stable estimates of LAD profiles from ALS data obtained over a dense tropical forest. Profiles were compared using three methods: Destructive field sampling, Portable Canopy profiling Lidar (PCL) and ALS. Stable LAD profiles from ALS, concordant with the other two analytical methods, were obtained when the grain size was less than 10 m and pulse density was high (>15 pulses m⁻²). Lower pulse densities also provided stable and reliable LAD profiles when using an appropriate adjustment (coefficient K). We also discuss how LAD profiles might be corrected throughout the landscape when using ALS surveys of lower density, by calibrating with LAI measurements in the field or from PCL. Appropriate choices of grain size, pulse density and K provide reliable estimates of LAD and associated tree plot demography and biomass in dense forest ecosystems. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forests)

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23 pages, 6988 KiB

Open AccessArticle

Identifying Tree-Related Microhabitats in TLS Point Clouds Using Machine Learning

by Nataliia Rehush, Meinrad Abegg, Lars T. Waser and Urs-Beat Brändli

Remote Sens. 2018, 10(11), 1735; https://doi.org/10.3390/rs10111735 - 3 Nov 2018

Cited by 32 | Viewed by 5709

Abstract

Tree-related microhabitats (TreMs) play an important role in maintaining forest biodiversity and have recently received more attention in ecosystem conservation, forest management and research. However, TreMs have until now only been assessed by experts during field surveys, which are time-consuming and difficult to reproduce. In this study, we evaluate the potential of close-range terrestrial laser scanning (TLS) for semi-automated identification of different TreMs (bark, bark pockets, cavities, fungi, ivy and mosses) in dense TLS point clouds using machine learning algorithms, including deep learning. To classify the TreMs, we applied: (1) the Random Forest (RF) classifier, incorporating frequently used local geometric features and two additional self-developed orientation features, and (2) a deep Convolutional Neural Network (CNN) trained using rasterized multiview orthographic projections (MVOPs) containing top view, front view and side view of the point’s local 3D neighborhood. The results confirmed that using local geometric features is beneficial for identifying the six groups of TreMs in dense tree-stem point clouds, but the rasterized MVOPs are even more suitable. Whereas the overall accuracy of the RF was 70%, that of the deep CNN was substantially higher (83%). This study reveals that close-range TLS is promising for the semi-automated identification of TreMs for forest monitoring purposes, in particular when applying deep learning techniques. Full article

(This article belongs to the Special Issue 3D Point Clouds in Forests)

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