scholarly journals Detection, Segmentation, and Model Fitting of Individual Tree Stems from Airborne Laser Scanning of Forests Using Deep Learning

2020 ◽  
Vol 12 (9) ◽  
pp. 1469 ◽  
Author(s):  
Lloyd Windrim ◽  
Mitch Bryson
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Laser Scanning ◽  
Structural Characteristics ◽  
Model Fitting ◽  
Tree Height ◽  
Radiata Pine ◽  
Supervised Machine Learning ◽  
Individual Tree ◽  
Tree Stem

Accurate measurements of the structural characteristics of trees such as height, diameter, sweep and taper are an important part of forest inventories in managed forests and commercial plantations. Both terrestrial and aerial LiDAR are currently employed to produce pointcloud data from which inventory metrics can be determined. Terrestrial/ground-based scanning typically provides pointclouds resolutions of many thousands of points per m 2 from which tree stems can be observed and inventory measurements made directly, whereas typical resolutions from aerial scanning (tens of points per m 2 ) require inventory metrics to be regressed from LiDAR variables using inventory reference data collected from the ground. Recent developments in miniaturised LiDAR sensors are enabling aerial capture of pointclouds from low-flying aircraft at high-resolutions (hundreds of points per m 2 ) from which tree stem information starts to become directly visible, enabling the possibility for plot-scale inventories that do not require access to the ground. In this paper, we develop new approaches to automated tree detection, segmentation and stem reconstruction using algorithms based on deep supervised machine learning which are designed for use with aerially acquired high-resolution LiDAR pointclouds. Our approach is able to isolate individual trees, determine tree stem points and further build a segmented model of the main tree stem that encompasses tree height, diameter, taper, and sweep. Through the use of deep learning models, our approach is able to adapt to variations in pointcloud densities and partial occlusions that are particularly prevalent when data is captured from the air. We present results of our algorithms using high-resolution LiDAR pointclouds captured from a helicopter over two Radiata pine forests in NSW, Australia.

scholarly journals THE EFFECT OF WIND ON TREE STEM PARAMETER ESTIMATION USING TERRESTRIAL LASER SCANNING

2016 ◽  
Vol III-8 ◽  
pp. 117-122 ◽  
Author(s):  
M. T. Vaaja ◽  
J.-P. Virtanen ◽  
M. Kurkela ◽  
V. Lehtola ◽  
J. Hyyppä ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Tree Height ◽  
Individual Tree ◽  
Finnish Meteorological Institute ◽  
Wind Speeds ◽  
Forest Inventories ◽  
Level Data ◽  
Tree Stem

The 3D measurement technique of terrestrial laser scanning (TLS) in forest inventories has shown great potential for improving the accuracy and efficiency of both individual tree and plot level data collection. However, the effect of wind has been poorly estimated in the error analysis of TLS tree measurements although it causes varying deformations to the trees. In this paper, we evaluated the effect of wind on tree stem parameter estimation at different heights using TLS. The data consists of one measured Scots pine captured from three different scanning directions with two different scanning resolutions, 6.3 mm and 3.1 mm at 10 m. The measurements were conducted under two different wind speeds, approximately 3 m/s and 9 m/s, as recorded by a nearby weather station of the Finnish Meteorological Institute. Our results show that the wind may cause both the underestimation and overestimation of tree diameter when using TLS. The duration of the scanning is found to have an impact for the measured shape of the tree stem under 9 m/s wind conditions. The results also indicate that a 9 m/s wind does not have a significant effect on the stem parameters of the lower part of a tree (<28% of the tree height). However, as the results imply, the wind conditions should be taken into account more comprehensively in analysis of TLS tree measurements, especially if multiple scans from different positions are registered together. In addition, TLS could potentially be applied to indirectly measure wind speed by observing the tree stem movement.

scholarly journals THE EFFECT OF WIND ON TREE STEM PARAMETER ESTIMATION USING TERRESTRIAL LASER SCANNING

2016 ◽  
Vol III-8 ◽  
pp. 117-122 ◽  
Author(s):  
M. T. Vaaja ◽  
J.-P. Virtanen ◽  
M. Kurkela ◽  
V. Lehtola ◽  
J. Hyyppä ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Tree Height ◽  
Individual Tree ◽  
Finnish Meteorological Institute ◽  
Wind Speeds ◽  
Forest Inventories ◽  
Level Data ◽  
Tree Stem

The 3D measurement technique of terrestrial laser scanning (TLS) in forest inventories has shown great potential for improving the accuracy and efficiency of both individual tree and plot level data collection. However, the effect of wind has been poorly estimated in the error analysis of TLS tree measurements although it causes varying deformations to the trees. In this paper, we evaluated the effect of wind on tree stem parameter estimation at different heights using TLS. The data consists of one measured Scots pine captured from three different scanning directions with two different scanning resolutions, 6.3 mm and 3.1 mm at 10 m. The measurements were conducted under two different wind speeds, approximately 3 m/s and 9 m/s, as recorded by a nearby weather station of the Finnish Meteorological Institute. Our results show that the wind may cause both the underestimation and overestimation of tree diameter when using TLS. The duration of the scanning is found to have an impact for the measured shape of the tree stem under 9 m/s wind conditions. The results also indicate that a 9 m/s wind does not have a significant effect on the stem parameters of the lower part of a tree (<28% of the tree height). However, as the results imply, the wind conditions should be taken into account more comprehensively in analysis of TLS tree measurements, especially if multiple scans from different positions are registered together. In addition, TLS could potentially be applied to indirectly measure wind speed by observing the tree stem movement.

scholarly journals Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

2021 ◽  
Vol 13 (12) ◽  
pp. 2297
Author(s):  
Jonathon J. Donager ◽  
Andrew J. Sánchez Meador ◽  
Ryan C. Blackburn
Keyword(s):  
Ponderosa Pine ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Basal Area ◽  
Tree Height ◽  
Absolute Error ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Structural Configurations ◽  
Individual Trees

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

scholarly journals Comparison and Evaluation of Different Pit-Filling Methods for Generating High Resolution Canopy Height Model Using UAV Laser Scanning Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2239
Author(s):  
Ying Quan ◽  
Mingze Li ◽  
Yuanshuo Hao ◽  
Bin Wang
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Evaluation Framework ◽  
Canopy Height ◽  
Filter Method ◽  
Individual Tree ◽  
Significant Difference ◽  
Point Interpolation ◽  
Canopy Height Model ◽  
Height Model

As a common form of light detection and ranging (LiDAR) in forestry applications, the canopy height model (CHM) provides the elevation distribution of aboveground vegetation. A CHM is traditionally generated by interpolating all the first LiDAR echoes. However, the first echo cannot accurately represent the canopy surface, and the resulting large amount of noise (data pits) also reduce the CHM quality. Although previous studies concentrate on many pit-filling methods, the applicability of these methods in high-resolution unmanned aerial vehicle laser scanning (UAVLS)-derived CHMs has not been revealed. This study selected eight widely used, recently developed, representative pit-filling methods, namely first-echo interpolation, smooth filtering (mean, medium and Gaussian), highest point interpolation, pit-free algorithm, spike-free algorithm and graph-based progressive morphological filtering (GPMF). A comprehensive evaluation framework was implemented, including a quantitative evaluation using simulation data and an additional application evaluation using UAVLS data. The results indicated that the spike-free algorithm and GPMF had excellent visual performances and were closest to the real canopy surface (root mean square error (RMSE) of simulated data were 0.1578 m and 0.1093 m, respectively; RMSE of UAVLS data were 0.3179 m and 0.4379 m, respectively). Compared with the first-echo method, the accuracies of the spike-free algorithm and GPMF improved by approximately 23% and 22%, respectively. The pit-free algorithm and highest point interpolation method also have advantages in high-resolution CHM generation. The global smooth filter method based on the first-echo CHM reduced the average canopy height by approximately 7.73%. Coniferous forests require more pit-filling than broad-leaved forests and mixed forests. Although the results of individual tree applications indicated that there was no significant difference between these methods except the median filter method, pit-filling is still of great significance for generating high-resolution CHMs. This study provides guidance for using high-resolution UAVLS in forestry applications.

scholarly journals Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 905 ◽  
Author(s):  
Guerra-Hernández ◽  
Cosenza ◽  
Cardil ◽  
Silva ◽  
Botequim ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Basal Area ◽  
Remote Sensing Data ◽  
Tree Height ◽  
Point Clouds ◽  
Volume Estimation ◽  
P Value ◽  
Test Statistic ◽  
Individual Tree ◽  
Significant Difference

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired from Unmanned Aerial Vehicles (UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model Efficiency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant difference was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations.

Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

2020 ◽  
Vol 50 (10) ◽  
pp. 1012-1024
Author(s):  
Meimei Wang ◽  
Jiayuan Lin
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Tree Height ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Surface Model ◽  
Individual Tree ◽  
Aerial Vehicle ◽  
The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

Individual Tree Measurement in Tropical Environment using Terrestrial Laser Scanning

2015 ◽  
Vol 73 (5) ◽  
Author(s):  
Muhammad Zulkarnain Abdul Rahman ◽  
Zulkepli Majid ◽  
Md Afif Abu Bakar ◽  
Abd Wahid Rasib ◽  
Wan Hazli Wan Kadir
Keyword(s):  
Laser Scanning ◽  
Mean Squared Error ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Crown ◽  
Individual Tree ◽  
Branch Diameter ◽  
Reference Tree ◽  
Crown Volume ◽  
Tree Branch

Detailed forest inventory and mensuration of individual trees have drawn attention of research society mainly to support sustainable forest management. This study aims at estimating individual tree attributes from high density point cloud obtained by terrestrial laser scanner (TLS). The point clouds were obtained over single reference tree and group of trees in forest area. The reference tree is treated as benchmark since detailed measurements of branch diameter were made on selected branches with different sizes and locations. Diameter at breast height (DBH) was measured for trees in forest. Furthermore tree height, height to crown base, crown volume and tree branch volume were also estimated for each tree. Branch diameter is estimated directly from the point clouds based on semi-automatic approach of model fitting i.e. sphere, ellipse and cylinder. Tree branch volume is estimated based on the volume of the fitted models. Tree height and height to crown base are computed using histogram analysis of the point clouds elevation. Tree crown volume is estimated by fitting a convex-hull on the tree crown. The results show that the Root Mean Squared Error (RMSE) of the estimated tree branch diameter does not have a specific trend with branch sizes and number of points used for fitting process. This explains complicated distribution of point clouds over the branches. Overall cylinder model produces good results with most branch sizes and number of point clouds for fitting. The cylinder fitting approach shows significantly better estimation results compared to sphere and ellipse fitting models.   

scholarly journals Very High Density Point Clouds from UAV Laser Scanning for Automatic Tree Stem Detection and Direct Diameter Measurement

2020 ◽  
Vol 12 (8) ◽  
pp. 1236 ◽  
Author(s):  
Karel Kuželka ◽  
Martin Slavík ◽  
Peter Surový
Keyword(s):  
Cross Sections ◽  
Hough Transform ◽  
Laser Scanning ◽  
Point Clouds ◽  
Mean Bias Error ◽  
Bias Error ◽  
Least Trimmed Squares ◽  
Remotely Sensed Data ◽  
Individual Tree ◽  
Tree Stem

Three-dimensional light detection and ranging (LiDAR) point clouds acquired from unmanned aerial vehicles (UAVs) represent a relatively new type of remotely sensed data. Point cloud density of thousands of points per square meter with survey-grade accuracy makes the UAV laser scanning (ULS) a very suitable tool for detailed mapping of forest environment. We used RIEGL VUX-SYS to scan forest stands of Norway spruce and Scots pine, the two most important economic species of central European forests, and evaluated the suitability of point clouds for individual tree stem detection and stem diameter estimation in a fully automated workflow. We segmented tree stems based on point densities in voxels in subcanopy space and applied three methods of robust circle fitting to fit cross-sections along the stems: (1) Hough transform; (2) random sample consensus (RANSAC); and (3) robust least trimmed squares (RLTS). We detected correctly 99% and 100% of all trees in research plots for spruce and pine, respectively, and were able to estimate diameters for 99% of spruces and 98% of pines with mean bias error of −0.1 cm (−1%) and RMSE of 6.0 cm (19%), using the best performing method, RTLS. Hough transform was not able to fit perimeters in unfiltered and often incomplete point representations of cross-sections. In general, RLTS performed slightly better than RANSAC, having both higher stem detection success rate and lower error in diameter estimation. Better performance of RLTS was more pronounced in complicated situations, such as incomplete and noisy point structures, while for high-quality point representations, RANSAC provided slightly better results.

ESTIMATING SINGLE TREE STEM AND BRANCH BIOMASS USING TERRESTRIAL LASER SCANNING

2015 ◽  
Vol 77 (26) ◽  
Author(s):  
Nurliyana Izzati Ishak ◽  
Md Afif Abu Bakar ◽  
Muhammad Zulkarnain Abdul Rahman ◽  
Abd Wahid Rasib ◽  
Kasturi Devi Kanniah ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Stem Volume ◽  
Biomass Estimation ◽  
Forest Plot ◽  
Individual Tree ◽  
Matching Process ◽  
Branch Biomass ◽  
Individual Trees ◽  
Tree Stem

This paper presents a novel non-destructive approach for individual tree stem and branch biomass estimation using terrestrial laser scanning data. The study area is located at the Royal Belum Reserved Forest area, Gerik, Perak. Each forest plot was designed with a circular shape and contains several scanning locations to ensure good visibility of each tree. Unique tree signage was located on trees with diameter at breast height (DBH) of 10cm and above.  Extractions of individual trees were done manually and the matching process with the field collected tree properties were relied on the tree signage and tree location as collected by total station. Individual tree stems were reconstructed based on cylinder models from which the total stem volume was calculated. Biomass of individual tree stems was calculated by multiplying stem volume with specific wood density. Biomass of individual was estimated using similar concept of tree stem with the volume estimated from alpha-hull shape. The root mean squared errors (RMSE) of estimated biomass are 50.22kg and 27.20kg for stem and branch respectively. 

Influence of ULS data acquisition characteristics on the achievable stem reconstruction accuracies

2020 ◽  
Author(s):  
Moritz Bruggisser ◽  
Johannes Otepka ◽  
Norbert Pfeifer ◽  
Markus Hollaus
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Stem Diameter ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Detection Rates ◽  
Point Count ◽  
Tree Stem

<p>Unmanned aerial vehicles-borne laser scanning (ULS) allows time-efficient acquisition of high-resolution point clouds on regional extents at moderate costs. The quality of ULS-point clouds facilitates the 3D modelling of individual tree stems, what opens new possibilities in the context of forest monitoring and management. In our study, we developed and tested an algorithm which allows for i) the autonomous detection of potential stem locations within the point clouds, ii) the estimation of the diameter at breast height (DBH) and iii) the reconstruction of the tree stem. In our experiments on point clouds from both, a RIEGL miniVUX-1DL and a VUX-1UAV, respectively, we could detect 91.0 % and 77.6 % of the stems within our study area automatically. The DBH could be modelled with biases of 3.1 cm and 1.1 cm, respectively, from the two point cloud sets with respective detection rates of 80.6 % and 61.2 % of the trees present in the field inventory. The lowest 12 m of the tree stem could be reconstructed with absolute stem diameter differences below 5 cm and 2 cm, respectively, compared to stem diameters from a point cloud from terrestrial laser scanning. The accuracy of larger tree stems thereby was higher in general than the accuracy for smaller trees. Furthermore, we recognized a small influence only of the completeness with which a stem is covered with points, as long as half of the stem circumference was captured. Likewise, the absolute point count did not impact the accuracy, but, in contrast, was critical to the completeness with which a scene could be reconstructed. The precision of the laser scanner, on the other hand, was a key factor for the accuracy of the stem diameter estimation. <br>The findings of this study are highly relevant for the flight planning and the sensor selection of future ULS acquisition missions in the context of forest inventories.</p>

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