scholarly journals Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana

Forests ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 527 ◽  
Author(s):  
Alvaro Lau ◽  
Kim Calders ◽  
Harm Bartholomeus ◽  
Christopher Martius ◽  
Pasi Raumonen ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Tree Height ◽  
Point Clouds ◽  
Validation Data ◽  
Terrestrial Lidar ◽  
Allometric Models ◽  
Crown Diameter ◽  
National Monitoring ◽  
Large Trees

Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates ( R 2 = 0.92–0.93) than traditional pantropical models ( R 2 = 0.85–0.89), and were especially accurate for large trees (diameter > 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested ( R 2 = 0.89) and predicted AGB accurately across all size classes—which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees.

scholarly journals Toward a Novel Laser-Based Approach for Estimating Snow Interception

2020 ◽  
Vol 12 (7) ◽  
pp. 1146 ◽  
Author(s):  
Micah Russell ◽  
Jan U. H. Eitel ◽  
Andrew J. Maguire ◽  
Timothy E. Link
Keyword(s):  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Primary Objective ◽  
Snow Accumulation ◽  
Future Research ◽  
Snow Density ◽  
Canopy Interception ◽  
Terrestrial Lidar ◽  
Large Trees

Forests reduce snow accumulation on the ground through canopy interception and subsequent evaporative losses. To understand snow interception and associated hydrological processes, studies have typically relied on resource-intensive point scale measurements derived from weighed trees or indirect measurements that compared snow accumulation between forested sites and nearby clearings. Weighed trees are limited to small or medium-sized trees, and indirect comparisons can be confounded by wind redistribution of snow, branch unloading, and clearing size. A potential alternative method could use terrestrial lidar (light detection and ranging) because three-dimensional lidar point clouds can be generated for any size tree and can be utilized to calculate volume of the intercepted snow. The primary objective of this study was to provide a feasibility assessment for estimating snow interception volume with terrestrial laser scanning (TLS), providing information on challenges and opportunities for future research. During the winters of 2017 and 2018, intercepted snow masses were continuously measured for two model trees suspended from load-cells. Simultaneously, autonomous terrestrial lidar scanning (ATLS) was used to develop volumetric estimates of intercepted snow. Multiplying ATLS volume estimates by snow density estimates (derived from empirical models based on air temperature) enabled the comparison of predicted vs. measured snow mass. Results indicate agreement between predicted and measured values (R2 ≥ 0.69, RMSE ≥ 0.91 kg, slope ≥ 0.97, intercept ≥ −1.39) when multiplying TLS snow interception volume with a constant snow density estimate. These results suggest that TLS might be a viable alternative to traditional approaches for mapping snow interception, potentially useful for estimating snow loads on large trees, collecting data in difficult to access terrain, and calibrating snow interception models to new forest types around the globe.

scholarly journals Augmentation of Traditional Forest Inventory and Airborne Laser Scanning with Unmanned Aerial Systems and Photogrammetry for Forest Monitoring

2018 ◽  
Vol 10 (10) ◽  
pp. 1562 ◽  
Author(s):  
Kathryn Fankhauser ◽  
Nikolay Strigul ◽  
Demetrios Gatziolis
Keyword(s):  
Laser Scanning ◽  
Canopy Cover ◽  
Tree Height ◽  
Forest Monitoring ◽  
Unmanned Aerial Systems ◽  
Aerial Laser Scanning ◽  
Forest Inventories ◽  
Airborne Laser ◽  
Aerial Systems

Forest inventories are constrained by resource-intensive fieldwork, while unmanned aerial systems (UASs) offer rapid, reliable, and replicable data collection and processing. This research leverages advancements in photogrammetry and market sensors and platforms to incorporate a UAS-based approach into existing forestry monitoring schemes. Digital imagery from a UAS was collected, photogrammetrically processed, and compared to in situ and aerial laser scanning (ALS)-derived plot tree counts and heights on a subsample of national forest plots in Oregon. UAS- and ALS-estimated tree counts agreed with each other (r2 = 0.96) and with field data (ALS r2 = 0.93, UAS r2 = 0.84). UAS photogrammetry also reasonably approximated mean plot tree height achieved by the field inventory (r2 = 0.82, RMSE = 2.92 m) and by ALS (r2 = 0.97, RMSE = 1.04 m). The use of both nadir-oriented and oblique UAS imagery as well as the availability of ALS-derived terrain descriptions likely sustain a robust performance of our approach across classes of canopy cover and tree height. It is possible to draw similar conclusions from any of the methods, suggesting that the efficient and responsive UAS method can enhance field measurement and ALS in longitudinal inventories. Additionally, advancing UAS technology and photogrammetry allows diverse users access to forest data and integrates updated methodologies with traditional forest monitoring.

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.

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 TLS and SfM Approach for Bulk Density Determination of Excavated Heterogeneous Raw Materials

Minerals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 174 ◽  
Author(s):  
Peter Blistan ◽  
Stanislav Jacko ◽  
Ľudovít Kovanič ◽  
Julián Kondela ◽  
Katarína Pukanská ◽  
...  
Keyword(s):  
Bulk Density ◽  
Laser Scanning ◽  
Large Scale ◽  
Raw Materials ◽  
Terrestrial Laser Scanning ◽  
Mineral Resources ◽  
Point Clouds ◽  
Raw Material

A frequently recurring problem in the extraction of mineral resources (especially heterogeneous mineral resources) is the rapid operative determination of the extracted quantity of raw material in a surface quarry. This paper deals with testing and analyzing the possibility of using unconventional methods such as digital close-range photogrammetry and terrestrial laser scanning in the process of determining the bulk density of raw material under in situ conditions. A model example of a heterogeneous deposit is the perlite deposit Lehôtka pod Brehmi (Slovakia). Classical laboratory methods for determining bulk density were used to verify the results of the in situ method of bulk density determination. Two large-scale samples (probes) with an approximate volume of 7 m3 and 9 m3 were realized in situ. 6 point samples (LITH) were taken for laboratory determination. By terrestrial laser scanning (TLS) measurement from 2 scanning stations, point clouds with approximately 163,000/143,000 points were obtained for each probe. For Structure-from-Motion (SfM) photogrammetry, 49/55 images were acquired for both probes, with final point clouds containing approximately 155,000/141,000 points. Subsequently, the bulk densities of the bulk samples were determined by the calculation from in situ measurements by TLS and SfM photogrammetry. Comparison of results of the field in situ measurements (1841 kg∙m−3) and laboratory measurements (1756 kg∙m−3) showed only a 4.5% difference in results between the two methods for determining the density of heterogeneous raw materials, confirming the accuracy of the used in situ methods. For the determination of the loosening coefficient, the material from both large-scale samples was transferred on a horizontal surface. Their volumes were determined by TLS. The loosening coefficient for the raw material of 1.38 was calculated from the resulting values.

Terrestrial laser scanning and structure-from-motion photogrammetry concordance analysis for describing the surface layer of gravel beds

2019 ◽  
Vol 43 (2) ◽  
pp. 260-281 ◽  
Author(s):  
Andrew J Neverman ◽  
Ian C Fuller ◽  
Jon N Procter ◽  
Russell G Death
Keyword(s):  
Surface Layer ◽  
Structure From Motion ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Survey Method ◽  
Gravel Beds ◽  
Non Invasive ◽  
Sampling Biases

Terrestrial laser scanning (TLS) and structure-from-motion photogrammetry (SfMp) offer rapid, non-invasive surveying of in situ gravels. Numerous studies have used the point clouds derived from TLS or SfMp to quantify surface layer characteristics, but direct comparison of the methods for grain-scale analysis has received relatively little attention to date. Comparing equivalent products of different data capture methods is critical as differences in errors and sampling biases between the two methods may produce different outputs, effecting further analysis. The sampling biases and errors related to SfMp and TLS lead to differences in the point clouds produced by each method. The metrics derived from the point clouds are therefore likely to differ, potentially leading to different inputs for entrainment threshold models, different trends in surface layer development being identified and different trajectories for physical processes and habitat quality being predicted. This paper provides a direct comparison between TLS and SfMp surveys of an exposed gravel bar for three different survey periods following inundation and reworking of the bar surface during high flow events. The point clouds derived from the two methods are used to describe changes in the character of the surface layer between bar inundation events, and comparisons are made with descriptions derived from conventional pebble counts. The results found differences in the metrics derived using each method do exist, but the grid resolution used to detrend the surfaces and identify spatial variations in surface layer characteristics had a greater impact than survey method. Further research is required to understand the significance of these variations for quantifying surface texture and structure and for predicting entrainment thresholds and transport rates.

scholarly journals ALGORITHM FOR THE AUTOMATIC ESTIMATION OF AGRICULTURAL TREE GEOMETRIC PARAMETERS USING AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 629-632 ◽  
Author(s):  
E. Hadaś ◽  
A. Borkowski ◽  
J. Estornell
Keyword(s):  
Laser Scanning ◽  
Tree Height ◽  
Field Measurements ◽  
Point Clouds ◽  
Average Density ◽  
Geometric Parameters ◽  
Airborne Laser Scanning ◽  
Maximum Height ◽  
Airborne Laser ◽  
Crown Base Height

The estimation of dendrometric parameters has become an important issue for the agricultural planning and management. Since the classical field measurements are time consuming and inefficient, Airborne Laser Scanning (ALS) data can be used for this purpose. Point clouds acquired for orchard areas allow to determine orchard structures and geometric parameters of individual trees. In this research we propose an automatic method that allows to determine geometric parameters of individual olive trees using ALS data. The method is based on the α-shape algorithm applied for normalized point clouds. The algorithm returns polygons representing crown shapes. For points located inside each polygon, we select the maximum height and the minimum height and then we estimate the tree height and the crown base height. We use the first two components of the Principal Component Analysis (PCA) as the estimators for crown diameters. The α-shape algorithm requires to define the radius parameter <i>R</i>. In this study we investigated how sensitive are the results to the radius size, by comparing the results obtained with various settings of the R with reference values of estimated parameters from field measurements. Our study area was the olive orchard located in the Castellon Province, Spain. We used a set of ALS data with an average density of 4 points&thinsp;m<sip>&minus;2</sup>. We noticed, that there was a narrow range of the <i>R</i> parameter, from 0.48&thinsp;m to 0.80&thinsp;m, for which all trees were detected and for which we obtained a high correlation coefficient (>&thinsp;0.9) between estimated and measured values. We compared our estimates with field measurements. The RMSE of differences was 0.8&thinsp;m for the tree height, 0.5&thinsp;m for the crown base height, 0.6&thinsp;m and 0.4&thinsp;m for the longest and shorter crown diameter, respectively. The accuracy obtained with the method is thus sufficient for agricultural applications.

scholarly journals SIMULATING VARIOUS TERRESTRIAL AND UAV LIDAR SCANNING CONFIGURATIONS FOR UNDERSTORY FOREST STRUCTURE MODELLING

2017 ◽  
Vol IV-2/W4 ◽  
pp. 59-65 ◽  
Author(s):  
M. Hämmerle ◽  
N. Lukač ◽  
K.-C. Chen ◽  
Zs. Koma ◽  
C.-K. Wang ◽  
...  
Keyword(s):  
Laser Scanning ◽  
3D Structure ◽  
Tree Height ◽  
Point Clouds ◽  
Understory Vegetation ◽  
Forest Plot ◽  
Triangle Mesh ◽  
Vegetation Modelling ◽  
Efficient Planning ◽  
Scanning Strategies

Information about the 3D structure of understory vegetation is of high relevance in forestry research and management (e.g., for complete biomass estimations). However, it has been hardly investigated systematically with state-of-the-art methods such as static terrestrial laser scanning (TLS) or laser scanning from unmanned aerial vehicle platforms (ULS). A prominent challenge for scanning forests is posed by occlusion, calling for proper TLS scan position or ULS flight line configurations in order to achieve an accurate representation of understory vegetation. The aim of our study is to examine the effect of TLS or ULS scanning strategies on (1) the height of individual understory trees and (2) understory canopy height raster models. We simulate full-waveform TLS and ULS point clouds of a virtual forest plot captured from various combinations of max. 12 TLS scan positions or 3 ULS flight lines. The accuracy of the respective datasets is evaluated with reference values given by the virtually scanned 3D triangle mesh tree models. TLS tree height underestimations range up to 1.84&amp;thinsp;m (15.30&amp;thinsp;% of tree height) for single TLS scan positions, but combining three scan positions reduces the underestimation to maximum 0.31&amp;thinsp;m (2.41&amp;thinsp;%). Combining ULS flight lines also results in improved tree height representation, with a maximum underestimation of 0.24&amp;thinsp;m (2.15&amp;thinsp;%). The presented simulation approach offers a complementary source of information for efficient planning of field campaigns aiming at understory vegetation modelling.

scholarly journals Influence of Scan Density on the Estimation of Single-Tree Attributes by Hand-Held Mobile Laser Scanning

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 277 ◽  
Author(s):  
Barbara Del Perugia ◽  
Francesca Giannetti ◽  
Gherardo Chirici ◽  
Davide Travaglini
Keyword(s):  
Data Acquisition ◽  
Laser Scanning ◽  
Reference Data ◽  
Central Italy ◽  
Castanea Sativa ◽  
Tree Height ◽  
Point Clouds ◽  
Single Tree ◽  
Breast Height ◽  
Scan Path

Nowadays, forest inventories are frequently carried out using a combination of field measurements and remote sensing data, often acquired with light detection and ranging (LiDAR) sensors. Several studies have investigated how three-dimensional laser scanning point clouds from different platforms can be used to acquire information traditionally collected with forest instruments, such as hypsometers and callipers to detect single-tree attributes like tree height and diameter at the breast height. The present study has tested the performances of the ZEB1 instrument, a type of hand-held mobile laser scanner, for single-tree attributes estimation in pure Castanea sativa Mill. stands cultivated for fruit production in Central Italy. In particular, the influence of walking scan path density on single-tree attributes estimation (number of trees, tree position, diameter at breast height, tree height, and crown base height) was investigated to test the efficiency of field measures. The point clouds were acquired by walking along straight lines drawn with different spacing: 10 and 15 m apart. A single-tree scan approach, which included walking with the instrument around each tree, was used as reference data. In order to evaluate the efficiency of the survey, the influence of the walking scan path was discussed in relation to the accuracy of single-tree attributes estimation, as well as the time and cost needed for data acquisition, pre-processing, and analysis. Our results show that the 10 m scan path provided the best results, with an omission error of 6%; the assessment of single-tree attributes was successful, with values of the coefficient of determination and the relative root mean square error similar to other studies. The 10 m scan path has also proved to decrease the costs by about €14 for data pre-processing, and a saving of time for data acquisition and data analysis of about 37 min compared to the reference data.

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