Handheld Laser Scanning Detects Spatiotemporal Differences in the Development of Structural Traits among Species in Restoration Plantings

A major challenge in ecological restoration is assessing the success of restoration plantings in producing habitats that provide the desired ecosystem functions and services. Forest structural complexity and biomass accumulation are key measures used to monitor restoration success and are important factors determining animal habitat availability and carbon sequestration. Monitoring their development through time using traditional field measurements can be costly and impractical, particularly at the landscape-scale, which is a common requirement in ecological restoration. We explored the application of proximal sensing technology as an alternative to traditional field surveys to capture the development of key forest structural traits in a restoration planting in the Midlands of Tasmania, Australia. We report the use of a hand-held laser scanner (ZEB1) to measure annual changes in structural traits at the tree-level, in a mixed species common-garden experiment from seven- to nine-years after planting. Using very dense point clouds, we derived estimates of multiple structural traits, including above ground biomass, tree height, stem diameter, crown dimensions, and crown properties. We detected annual increases in most LiDAR-derived traits, with individual crowns becoming increasingly interconnected. Time by species interaction were detected, and were associated with differences in productivity between species. We show the potential for remote sensing technology to monitor temporal changes in forest structural traits, as well as to provide base-line measures from which to assess the restoration trajectory towards a desired state.

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Characterizing Seedling Stands Using Leaf-Off and Leaf-On Photogrammetric Point Clouds and Hyperspectral Imagery Acquired from Unmanned Aerial Vehicle

Forests ◽

10.3390/f10050415 ◽

2019 ◽

Vol 10 (5) ◽

pp. 415 ◽

Cited By ~ 11

Author(s):

Mohammad Imangholiloo ◽

Ninni Saarinen ◽

Lauri Markelin ◽

Tomi Rosnell ◽

Roope Näsi ◽

...

Keyword(s):

Laser Scanning ◽

Hyperspectral Imagery ◽

Digital Terrain Model ◽

Tree Height ◽

Point Clouds ◽

Tree Density ◽

Tree Canopy ◽

Tree Level ◽

Species Classification ◽

Individual Tree

Seedling stands are mainly inventoried through field measurements, which are typically laborious, expensive and time-consuming due to high tree density and small tree size. In addition, operationally used sparse density airborne laser scanning (ALS) and aerial imagery data are not sufficiently accurate for inventorying seedling stands. The use of unmanned aerial vehicles (UAVs) for forestry applications is currently in high attention and in the midst of quick development and this technology could be used to make seedling stand management more efficient. This study was designed to investigate the use of UAV-based photogrammetric point clouds and hyperspectral imagery for characterizing seedling stands in leaf-off and leaf-on conditions. The focus was in retrieving tree density and the height in young seedling stands in the southern boreal forests of Finland. After creating the canopy height model from photogrammetric point clouds using national digital terrain model based on ALS, the watershed segmentation method was applied to delineate the tree canopy boundary at individual tree level. The segments were then used to extract tree heights and spectral information. Optimal bands for calculating vegetation indices were analysed and used for species classification using the random forest method. Tree density and the mean tree height of the total and spruce trees were then estimated at the plot level. The overall tree density was underestimated by 17.5% and 20.2% in leaf-off and leaf-on conditions with the relative root mean square error (relative RMSE) of 33.5% and 26.8%, respectively. Mean tree height was underestimated by 20.8% and 7.4% (relative RMSE of 23.0% and 11.5%, and RMSE of 0.57 m and 0.29 m) in leaf-off and leaf-on conditions, respectively. The leaf-on data outperformed the leaf-off data in the estimations. The results showed that UAV imagery hold potential for reliably characterizing seedling stands and to be used to supplement or replace the laborious field inventory methods.

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Airborne laser scanning applied to eucalyptus stand inventory at individual tree level

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2018001200010 ◽

2018 ◽

Vol 53 (12) ◽

pp. 1373-1382 ◽

Cited By ~ 1

Author(s):

Diogo Nepomuceno Cosenza ◽

Vicente Paulo Soares ◽

Helio Garcia Leite ◽

José Marinaldo Gleriani ◽

Cibele Hummel do Amaral ◽

...

Keyword(s):

Laser Scanning ◽

Large Scale ◽

Tree Height ◽

Point Clouds ◽

Equation System ◽

Airborne Laser Scanning ◽

Tree Level ◽

Individual Tree ◽

Local Maxima ◽

Airborne Laser

Abstract: The objective of this work was to evaluate the application of airborne laser scanning (ALS) to a large-scale eucalyptus stand inventory by the method of individual trees, as well as to propose a new method to estimate tree diameter as a function of the height obtained from point clouds. The study was carried out in a forest area of 1,681 ha, consisting of eight eucalyptus stands with ages varying from four to seven years. After scanning, tree heights were obtained using the local maxima algorithm, and total wood stock by summing up individual volumes. To determine tree diameters, regressions fit using data measured in the inventory plots were used. The results were compared with the estimates obtained from field sampling. The equation system proposed is adequate to be applied to the tree height data derived from ALS point clouds. The tree individualization approach by local maxima filters is efficient to estimate number of trees and wood stock from ALS data, as long as the results are previously calibrated with field data.

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

Forests ◽

10.3390/f10100905 ◽

2019 ◽

Vol 10 (10) ◽

pp. 905 ◽

Cited By ~ 3

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.

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Individual Tree Measurement in Tropical Environment using Terrestrial Laser Scanning

Jurnal Teknologi ◽

10.11113/jt.v73.4329 ◽

2015 ◽

Vol 73 (5) ◽

Cited By ~ 1

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.

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-629-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 629-632 ◽

Cited By ~ 1

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 R. 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 m<sip>−2. We noticed, that there was a narrow range of the R parameter, from 0.48 m to 0.80 m, for which all trees were detected and for which we obtained a high correlation coefficient (> 0.9) between estimated and measured values. We compared our estimates with field measurements. The RMSE of differences was 0.8 m for the tree height, 0.5 m for the crown base height, 0.6 m and 0.4 m for the longest and shorter crown diameter, respectively. The accuracy obtained with the method is thus sufficient for agricultural applications.

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

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w4-59-2017 ◽

2017 ◽

Vol IV-2/W4 ◽

pp. 59-65 ◽

Cited By ~ 2

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&thinsp;m (15.30&thinsp;% of tree height) for single TLS scan positions, but combining three scan positions reduces the underestimation to maximum 0.31&thinsp;m (2.41&thinsp;%). Combining ULS flight lines also results in improved tree height representation, with a maximum underestimation of 0.24&thinsp;m (2.15&thinsp;%). The presented simulation approach offers a complementary source of information for efficient planning of field campaigns aiming at understory vegetation modelling.

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Individual tree extraction from terrestrial laser scanning data via graph pathing

Forest Ecosystems ◽

10.1186/s40663-021-00340-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Di Wang ◽

Xinlian Liang ◽

Gislain II Mofack ◽

Olivier Martin-Ducup

Keyword(s):

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Point Clouds ◽

Tree Level ◽

Forest Types ◽

Computationally Efficient ◽

Large Area ◽

Individual Tree ◽

Single Crown ◽

Tree Extraction

Abstract Background Individual tree extraction from terrestrial laser scanning (TLS) data is a prerequisite for tree-scale estimations of forest biophysical properties. This task currently is undertaken through laborious and time-consuming manual assistance and quality control. This study presents a new fully automatic approach to extract single trees from large-area TLS data. This data-driven method operates exclusively on a point cloud graph by path finding, which makes our method computationally efficient and universally applicable to data from various forest types. Results We demonstrated the proposed method on two openly available datasets. First, we achieved state-of-the-art performance on locating single trees on a benchmark dataset by significantly improving the mean accuracy by over 10% especially for difficult forest plots. Second, we successfully extracted 270 trees from one hectare temperate forest. Quantitative validation resulted in a mean Intersection over Union (mIoU) of 0.82 for single crown segmentation, which further led to a relative root mean square error (RMSE%) of 21.2% and 23.5% for crown area and tree volume estimations, respectively. Conclusions Our method allows automated access to individual tree level information from TLS point clouds. The proposed method is free from restricted assumptions of forest types. It is also computationally efficient with an average processing time of several seconds for one million points. It is expected and hoped that our method would contribute to TLS-enabled wide-area forest qualifications, ranging from stand volume and carbon stocks modelling to derivation of tree functional traits as part of the global ecosystem understanding.

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Reliable Estimates of Merchantable Timber Volume from Terrestrial Laser Scanning

Remote Sensing ◽

10.3390/rs13183610 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3610

Author(s):

Dimitrios Panagiotidis ◽

Azadeh Abdollahnejad

Keyword(s):

Laser Scanning ◽

Accurate Determination ◽

Laser Scanner ◽

Terrestrial Laser Scanning ◽

Tree Height ◽

Tree Level ◽

Consensus Method ◽

Field Methods ◽

Forest Inventories ◽

Random Sampling Consensus

Simple and accurate determination of merchantable tree height is needed for accurate estimations of merchantable volume. Conventional field methods of forest inventory can lead to biased estimates of tree height and diameter, especially in complex forest structures. Terrestrial laser scanner (TLS) data can be used to determine merchantable height and diameter at different heights with high accuracy and detail. This study focuses on the use of the random sampling consensus method (RANSAC) for generating the length and diameter of logs to estimate merchantable volume at the tree level using Huber’s formula. For this study, we used two plots; plot A contained deciduous trees and plot B consisted of conifers. Our results demonstrated that the TLS-based outputs for stem modelling using the RANSAC method performed very well with low bias (0.02 for deciduous and 0.01 for conifers) and a high degree of accuracy (97.73% for deciduous and 96.14% for conifers). We also found a high correlation between the proposed method and log length (−0.814 for plot A and −0.698 for plot B), which is an important finding because this information can be used to determine the optimum log properties required for analyzing stem curvature changes at different heights. Furthermore, the results of this study provide insight into the applicability and ergonomics during data collection from forest inventories solely from terrestrial laser scanning, thus reducing the need for field reference data.

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

Forests ◽

10.3390/f10030277 ◽

2019 ◽

Vol 10 (3) ◽

pp. 277 ◽

Cited By ~ 8

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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Tropical Tree Species 3D Modelling and Classification Based on LiDAR Technology

Recent Advances in 3D Imaging, Modeling, and Reconstruction - Advances in Multimedia and Interactive Technologies ◽

10.4018/978-1-5225-5294-9.ch001 ◽

2020 ◽

pp. 1-22

Author(s):

Panagiotis Barmpoutis ◽

Tania Stathaki ◽

Jonathan Lloyd ◽

Magna Soelma Bessera de Moura

Keyword(s):

Tree Species ◽

Laser Scanning ◽

Tensor Decomposition ◽

Singular Values ◽

Point Clouds ◽

Tropical Tree ◽

Species Classification ◽

Tropical Tree Species ◽

Sensing Technology ◽

3D Information

Over the last decade or so, laser scanning technology has become an increasingly popular and important tool for forestry inventory, enabling accurate capture of 3D information in a fast and environmentally friendly manner. To this end, the authors propose here a system for tropical tree species classification based on 3D scans of LiDAR sensing technology. In order to exploit the interrelated patterns of trees, skeleton representations of tree point clouds are extracted, and their structures are divided into overlapping equal-sized 3D segments. Subsequently, they represent them as third-order sparse structure tensors setting the value of skeleton coordinates equal to one. Based on the higher-order tensor decomposition of each sparse segment, they 1) estimate the mode-n singular values extracting intra-correlations of tree branches and 2) model tropical trees as linear dynamical systems extracting appearance information and dynamics. The proposed methodology was evaluated in tropical tree species and specifically in a dataset consisting of 26 point clouds of common Caatinga dry-forest trees.

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