Extraction of Sample Plot Parameters from 3D Point Cloud Reconstruction Based on Combined RTK and CCD Continuous Photography

Estimating Tree Defects with Point Clouds Developed from Active and Passive Sensors

Traditional inventories require large investments of resources and a trained workforce to measure tree sizes and characteristics that affect wood quality and value, such as the presence of defects and damages. Handheld light detection and ranging (LiDAR) and photogrammetric point clouds developed using Structure from Motion (SfM) algorithms achieved promising results in tree detection and dimensional measurements. However, few studies have utilized handheld LiDAR or SfM to assess tree defects or damages. We used a Samsung Galaxy S7 smartphone camera to photograph trees and create digital models using SfM, and a handheld GeoSLAM Zeb Horizon to create LiDAR point cloud models of some of the main tree species from the Pacific Northwest. We compared measurements of damage count and damage length obtained from handheld LiDAR, SfM photogrammetry, and traditional field methods using linear mixed-effects models. The field method recorded nearly twice as many damages per tree as the handheld LiDAR and SfM methods, but there was no evidence that damage length measurements varied between the three survey methods. Lower damage counts derived from LiDAR and SfM were likely driven by the limited point cloud reconstructions of the upper stems, as usable tree heights were achieved, on average, at 13.6 m for LiDAR and 9.3 m for SfM, even though mean field-measured tree heights was 31.2 m. Our results suggest that handheld LiDAR and SfM approaches show potential for detection and measurement of tree damages, at least on the lower stem.

Unimodal and Multimodal Perception for Forest Management: Review and Dataset

Robotics navigation and perception for forest management are challenging due to the existence of many obstacles to detect and avoid and the sharp illumination changes. Advanced perception systems are needed because they can enable the development of robotic and machinery solutions to accomplish a smarter, more precise, and sustainable forestry. This article presents a state-of-the-art review about unimodal and multimodal perception in forests, detailing the current developed work about perception using a single type of sensors (unimodal) and by combining data from different kinds of sensors (multimodal). This work also makes a comparison between existing perception datasets in the literature and presents a new multimodal dataset, composed by images and laser scanning data, as a contribution for this research field. Lastly, a critical analysis of the works collected is conducted by identifying strengths and research trends in this domain.

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots.

Measurement of Forest Inventory Parameters with Apple iPad Pro and Integrated LiDAR Technology

The estimation of single tree and complete stand information is one of the central tasks of forest inventory. In recent years, automatic algorithms have been successfully developed for the detection and measurement of trees with laser scanning technology. Nevertheless, most of the forest inventories are nowadays carried out with manual tree measurements using traditional instruments. This is due to the high investment costs for modern laser scanner equipment and, in particular, the time-consuming and incomplete nature of data acquisition with stationary terrestrial laser scanners. Traditionally, forest inventory data are collected through manual surveys with calipers or tapes. Practically, this is both labor and time-consuming. In 2020, Apple implemented a Light Detection and Ranging (LiDAR) sensor in the new Apple iPad Pro (4th Gen) and iPhone Pro 12. Since then, access to LiDAR-generated 3D point clouds has become possible with consumer-level devices. In this study, an Apple iPad Pro was tested to produce 3D point clouds, and its performance was compared with a personal laser scanning (PLS) approach to estimate individual tree parameters in different forest types and structures. Reference data were obtained by traditional measurements on 21 circular forest inventory sample plots with a 7 m radius. The tree mapping with the iPad showed a detection rate of 97.3% compared to 99.5% with the PLS scans for trees with a lower diameter at a breast height (dbh) threshold of 10 cm. The root mean square error (RMSE) of the best dbh measurement out of five different dbh modeling approaches was 3.13 cm with the iPad and 1.59 cm with PLS. The data acquisition time with the iPad was approximately 7.51 min per sample plot; this is twice as long as that with PLS but 2.5 times shorter than that with traditional forest inventory equipment. In conclusion, the proposed forest inventory with the iPad is generally feasible and achieves accurate and precise stem counts and dbh measurements with efficient labor effort compared to traditional approaches. Along with future technological developments, it is expected that other consumer-level handheld devices with integrated laser scanners will also be developed beyond the iPad, which will serve as an accurate and cost-efficient alternative solution to the approved but relatively expensive TLS and PLS systems. Such a development would be mandatory to broadly establish digital technology and fully automated routines in forest inventory practice. Finally, high-level progress is generally expected for the broader scientific community in forest ecosystem monitoring, as the collection of highly precise 3D point cloud data is no longer hindered by financial burdens.

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

The Comparison of Stem Curve Accuracy Determined from Point Clouds Acquired by Different Terrestrial Remote Sensing Methods

The knowledge of tree characteristics, especially the shape of standing trees, is important for living tree volume estimation, the computation of a wide range of forest stand features, and the evaluation of stand stability. Nowadays, nondestructive and accurate approaches to data collection in the forest environment are required. Therefore, the implementation of accurate point cloud-based information in the field of forest inventory has become increasingly required. We evaluated the stem curves of the lower part of standing trees (diameters at heights of 0.3 m to 8 m). The experimental data were acquired from three point cloud datasets, which were created through different approaches to three-dimensional (3D) environment modeling (varying in terms of data acquisition and processing time, acquisition costs, and processing complexity): terrestrial laser scanning (TLS), close-range photogrammetry (CRP), and handheld mobile laser scanning (HMLS) with a simultaneous localization and mapping algorithm (SLAM). Diameter estimation errors varied across heights of cross sections and methods. The average root mean squared error (RMSE) of all cross sections for the specific methods was 1.03 cm (TLS), 1.26 cm (HMLS), and 1.90 cm (CRP). TLS and CRP reached the lowest RMSE at a height of 1.3 m, while for HMLS, it was at the height of 8 m. Our findings demonstrated that the accuracy of measurements of the standing tree stem curve was comparable for the usability of all three devices in forestry practices.

Forest Inventory with Long Range and High-Speed Personal Laser Scanning (PLS) and Simultaneous Localization and Mapping (SLAM) Technology

The use of new and modern sensors in forest inventory has become increasingly efficient. Nevertheless, the majority of forest inventory data are still collected manually, as part of field surveys. The reason for this is the sometimes time-consuming and incomplete data acquisition with static terrestrial laser scanning (TLS). The use of personal laser scanning (PLS) can reduce these disadvantages. In this study, we assess a new personal laser scanner and compare it with a TLS approach for the estimation of tree position and diameter in a wide range of forest types and structures. Traditionally collected forest inventory data are used as reference. A new density-based algorithm for position finding and diameter estimation is developed. In addition, several methods for diameter fitting are compared. For circular sample plots with a maximum radius of 20 m and lower diameter at breast height (dbh) threshold of 5 cm, tree mapping showed a detection of 96% for PLS and 78.5% for TLS. Using plot radii of 20 m, 15 m, and 10 m, as well as a lower dbh threshold of 10 cm, the respective detection rates for PLS were 98.76%, 98.95%, and 99.48%, while those for TLS were considerably lower (86.32%, 93.81%, and 98.35%, respectively), especially for larger sample plots. The root mean square error (RMSE) of the best dbh measurement was 2.32 cm (12.01%) for PLS and 2.55 cm (13.19%) for TLS. The highest precision of PLS and TLS, in terms of bias, were 0.21 cm (1.09%) and −0.74 cm (−3.83%), respectively. The data acquisition time for PLS took approximately 10.96 min per sample plot, 4.7 times faster than that for TLS. We conclude that the proposed PLS method is capable of efficient data capture and can detect the largest number of trees with a sufficient dbh accuracy.

Image Data Acquisition for Estimating Individual Trees Metrics: Closer Is Better

Background and Objectives: The recent use of Structure-from-Motion with Multi-View Stereo photogrammetry (SfM-MVS) in forestry has underscored its robustness in tree mensuration. This study evaluated the differences in tree metrics resulting from various related SfM-MVS photogrammetric image acquisition scenarios. Materials and Methods: Scaled tri-dimensional models of 30 savanna trees belonging to five species were built from photographs acquired in a factorial design with shooting distance (d = 1, 2, 3, 4 and 5 m away from tree) and angular shift (α = 15°, 30°, 45° and 60°; nested in d). Tree stem circumference at 1.3 m and bole volume were estimated using models resulting from each of the 20 scenarios/tree. Mean absolute percent error (MAPE) was computed for both metrics in order to compare the performance of each scenario in relation to reference data collected using a measuring tape. Results: An assessment of the effect of species identity (s), shooting distance and angular shift showed that photographic point cloud density was dependent on α and s, and optimal for 15° and 30°. MAPEs calculated on stem circumferences and volumes significantly differed with d and α, respectively. There was a significant interaction between α and s for both circumference and volume MAPEs, which varied widely (1.6 ± 0.4%–20.8 ± 23.7% and 2.0 ± 0.6%–36.5 ± 48.7% respectively), and were consistently lower for smaller values of d and α. Conclusion: The accuracy of photogrammetric estimation of individual tree attributes depended on image-capture approach. Acquiring images 2 m away and with 30° intervals around trees produced reliable estimates of stem circumference and bole volume. Research Highlights: This study indicates that the accuracy of photogrammetric estimations of individual tree attributes is species-dependent. Camera positions in relation to the subject substantially influence the level of uncertainty in measurements.

Development and Testing of a New Ground Measurement Tool to Assist in Forest GIS Surveys

In present forest surveys, some problems occur because of the cost and time required when using external tools to acquire tree measurement. Therefore, it is of great importance to develop a new cost-saving and time-saving ground measurement method implemented in a forest geographic information system (GIS) survey. To obtain a better solution, this paper presents the design and implementation of a new ground measurement tool in which mobile devices play a very important role. Based on terrestrial photogrammetry, location-based services (LBS), and computer vision, the tool assists forest GIS surveys in obtaining important forest structure factors such as tree position, diameter at breast height (DBH), tree height, and tree species. This paper selected two plots to verify the accuracy of the ground measurement tool. Experiments show that the root mean square error (RMSE) of the position coordinates of the trees was 0.222 m and 0.229 m, respectively, and the relative root mean square error (rRMSE) was close to 0. The rRMSE of the DBH measurement was 10.17% and 13.38%, and the relative Bias (rBias) of the DBH measurement was −0.88% and −2.41%. The rRMSE of tree height measurement was 6.74% and 6.69%, and the rBias of tree height measurement was −1.69% and −1.27%, which conforms to the forest investigation requirements. In addition, workers usually make visual observations of trees and then combine their personal knowledge or experience to identify tree species, which may lead to the situations when they cannot distinguish tree species due to insufficient knowledge or experience. Based on MobileNets, a lightweight convolutional neural network designed for mobile phone, a model was trained to assist workers in identifying tree species. The dataset was collected from some forest parks in Beijing. The accuracy of the tree species recognition model was 94.02% on a test dataset and 93.21% on a test dataset in the mobile phone. This provides an effective reference for workers to identify tree species and can assist in artificial identification of tree species. Experiments show that this solution using the ground measurement tool saves time and cost for forest resources GIS surveys.

Terrestrial Structure from Motion Photogrammetry for Deriving Forest Inventory Data

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.

Comparing Non-Destructive Methods to Estimate Volume of Three Tree Taxa in Beijing, China

Establishing the tree volume table is an important aspect of forest inventory for managing the forest ecosystem. The traditional volume models used to build tree volume tables are time consuming and expensive, demanding huge labor and material resources. Aiming at the improvement of the current destructive, costly and time-consuming volume model, we propose a new non-destructive, low-cost and efficient method for calculating the tree volume model with high precision by using the electronic theodolite. For testing and comparing the accuracy of our model with the traditional model, we collected data of three main tree taxa including Platycladus orientalis (L.) Franco, Larix principis-rupprechtii Mayr and Populus spp. L. from different districts and counties of Beijing, China. We collected a total of 1750 tree samples (250 Platycladus orientalis, 300 Larix principis-rupprechtii, and 1200 Populus spp.) to establish our models; 721 pieces of accurate data (94 Platycladus orientalis, 149 Larix principis-rupprechtii, and 478 Populus spp.) were used as test samples, to evaluate the accuracy of the newly established volume models of three tree species (group). After that, the established volume models (unary/binary models) were compared and analyzed with the corresponding ministerial models for applicability and accuracy. The results showed that the difference between the data observed by the new method and the measurement data of parsing trees was not significant. The total relative error (TRE) and the mean system error (MSE) of the newly established unary/binary volume models were all within ±3%, satisfying the accuracy standard specified by the technical indicators. Compared with the ministerial models applied to the same data, our models’ performance and accuracy were higher (close to the field measurements). Our results also showed that the accuracy of ministerial models was lower than the required standards. It is a promising methodology to use the electronic theodolite non-destructive observation method to establish tree volume tables in the future, especially in areas where cutting is prohibited or restricted and there is a lack of tree volume tables. In addition, this method has also shown a great potential of applicability in forest ecology and environmental protection.