Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment

Forests are an important part natural ecosystems, by for example providing food, fiber, habitat, and biodiversity, all of which contribute to stable natural systems. Assessing and modeling the structure and characteristics of forests, e.g., Leaf Area Index (LAI), volume, biomass, etc., can lead to a better understanding and management of these resources. In recent years, Terrestrial Laser Scanning (TLS) has been recognized as a tool that addresses many of the limitations of manual and traditional forest data collection methods. In this study, we propose a density-based approach for estimating the LAI in a structurally-complex forest environment, which contains variable and diverse structural attributes, e.g., non-circular stem forms, dense canopy and below-canopy vegetation cover, and a diverse species composition. In addition, 242 TLS scans were collected using a portable low-cost scanner, the Compact Biomass Lidar (CBL), in the Hawaii Volcanoes National Park (HAVO), Hawaii Island, USA. LAI also was measured for 242 plots in the site, using an AccuPAR LP-80 ceptometer. The first step after cleaning the point cloud involved detecting the higher forest canopy in the light detection and ranging (lidar) point clouds, using normal change rate assessment. We then estimated Leaf Area Density (LAD), using a voxel-based approach, and divided the canopy point cloud into five layers in the Z (vertical) direction. These five layers subsequently were divided into voxels in the X direction, where the size of these voxels were obtained based on inter-quartile analysis and the number of points in each voxel. We hypothesized that the intensity returned to the lidar system from woody materials, like branches, would be higher than from leaves, due to the liquid water absorption feature of the leaves and higher reflectance for woody material at the 905 nm laser wavelength. We also differentiated between foliar and woody materials using edge detection in the images from projected point clouds and evaluated the density of these regions to support our hypothesis. Density of points, or the number of points divided by the volume of a grid, in a 3D grid size of 0.1 m, was calculated for each of the voxels. The grid size was determined by investigating the size of the branches in the lower portion of the canopy. Subsequently, we fitted a Kernel Density Estimator (KDE) to these values, with the threshold set based on half of the area under the curve in each of the density distributions. All the grids with a density below the threshold were labeled as leaves, while those grids above the threshold were identified as non-leaves. Finally, we modeled LAI using the point densities derived from the TLS point clouds and the listed analysis steps. This model resulted in an R 2 value of 0.88. We also estimated the LAI directly from lidar data using the point densities and calculating LAD, which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was 90%, with an RMSE value of 0.31, and an average overestimation of 9 % in TLS-derived LAI, when compared to field-measured LAI. Algorithm performance mainly was affected by the vegetation density and complexity of the canopy structures. It is worth noting that, since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets in which the plots were 30 m spaced. The R 2 values for these subsets, based on modeling of the field-measured LAI using leaf point density values, ranged between 0.84–0.96. The results bode well for using this method for efficient, automatic, and accurate/precise estimation of LAI values in complex forest environments, using a low-cost, rapid-scan TLS.

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Estimation of Northern Hardwood Forest Inventory Attributes Using UAV Laser Scanning (ULS): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-Level

Remote Sensing ◽

10.3390/rs13142796 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2796

Author(s):

Bastien Vandendaele ◽

Richard A. Fournier ◽

Udayalakshmi Vepakomma ◽

Gaetan Pelletier ◽

Philippe Lejeune ◽

...

Keyword(s):

Forest Inventory ◽

Point Cloud ◽

Laser Scanning ◽

High Density ◽

Hardwood Forest ◽

Canopy Layer ◽

Bottom Up ◽

Northern Hardwood ◽

Tree Detection ◽

Dbh Distribution

UAV laser scanning (ULS) has the potential to support forest operations since it provides high-density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven-aged northern hardwood stand. We investigated: (1) the transferability of raster-based and bottom-up point cloud-based individual tree detection (ITD) algorithms to ULS data; and (2) automated approaches to the retrieval of tree-level (i.e., height, crown diameter (CD), DBH) and stand-level (i.e., tree count, basal area (BA), DBH-distribution) forest inventory attributes. These objectives were studied under leaf-on and leaf-off canopy conditions. Results achieved from ULS data were cross-compared with ALS and TLS to better understand the potential and challenges faced by different laser scanning systems and methodological approaches in hardwood forest environments. The best results that characterized individual trees from ULS data were achieved under leaf-off conditions using a point cloud-based bottom-up ITD. The latter outperformed the raster-based ITD, improving the accuracy of tree detection (from 50% to 71%), crown delineation (from R2 = 0.29 to R2 = 0.61), and prediction of tree DBH (from R2 = 0.36 to R2 = 0.67), when compared with values that were estimated from reference TLS data. Major improvements were observed for the detection of trees in the lower canopy layer (from 9% with raster-based ITD to 51% with point cloud-based ITD) and in the intermediate canopy layer (from 24% with raster-based ITD to 59% with point cloud-based ITD). Under leaf-on conditions, LiDAR data from aerial systems include substantial signal occlusion incurred by the upper canopy. Under these conditions, the raster-based ITD was unable to detect low-level canopy trees (from 5% to 15% of trees detected from lower and intermediate canopy layers, respectively), resulting in a tree detection rate of about 40% for both ULS and ALS data. The cylinder-fitting method used to estimate tree DBH under leaf-off conditions did not meet inventory standards when compared to TLS DBH, resulting in RMSE = 7.4 cm, Bias = 3.1 cm, and R2 = 0.75. Yet, it yielded more accurate estimates of the BA (+3.5%) and DBH-distribution of the stand than did allometric models −12.9%), when compared with in situ field measurements. Results suggest that the use of bottom-up ITD on high-density ULS data from leaf-off hardwood forest leads to promising results when estimating trees and stand attributes, which opens up new possibilities for supporting forest inventories and operations.

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Desenvolvimento de capacete inteligente para aplicações de pesquisa de campo ecológico

10.5753/semish.2019.6568 ◽

2019 ◽

Author(s):

Mateus Coelho Silva ◽

Sérvio Pontes Ribeiro ◽

Saul Delabrida ◽

Ricardo Augusto Rabelo Oliveira

Keyword(s):

Embedded System ◽

Environmental Protection ◽

Forest Inventory ◽

Environmental Perception ◽

Environmental Data ◽

Forest Environment ◽

New Applications

Forest inventory and management are important topics to enhance environmental protection initiatives and policies. Thus, sampling processes inside the forest environment are normally manual and limited. These conditions nurture an increasing need for novel solutions to enhance environmental perception, especially in ground-sampling processes. In this work, we present a new solution to augment environmental perception. The proposed appliance is a wearable embedded system based on a helmet and projected to acquire environmental data. It also allows the development of new applications to expand the researcher reality perception.

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DERIVATION OF FOREST INVENTORY PARAMETERS FOR CARBON ESTIMATION USING TERRESTRIAL LIDAR

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

10.5194/isprs-archives-xli-b8-677-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 677-684

Author(s):

Om Prakash Prasad Kalwar ◽

Yousif A. Hussin ◽

Michael J. C. Weir ◽

Yogendra K. Karna

Keyword(s):

Forest Inventory ◽

Point Cloud ◽

Tree Height ◽

Field Measurements ◽

Allometric Equation ◽

Carbon Stocks ◽

Above Ground Biomass ◽

Terrestrial Lidar ◽

Average Value ◽

Tree Inventory

This research was conducted to derive forest sample plot inventory parameters from terrestrial LiDAR (T-LiDAR) for estimating above ground biomass (AGB)/carbon stocks in primary tropical rain forest. Inventory parameters of all sampled trees within circular plots of 500 m2 were collected from field observations while T-LiDAR data were acquired through multiple scanning using Reigl VZ-400 scanner. Pre-processing and registration of multiple scans were done in RSCAN PRO software. Point cloud constructing individual sampled tree was extracted and tree inventory parameters (diameter at breast height-DBH and tree height) were measured manually. AGB/carbon stocks were estimated using Chave et al., (2005) allometric equation. An average 80 % of sampled trees were detected from point cloud of the plots. The average of plots values of R2 and RMSE for manually measured DBHs were 0.95, 2.7 cm respectively. Similarly, the average of plots values of R2 and RMSE for manually measured trees heights were 0.77, 2.96 m respectively. The average value of AGB/carbon stocks estimated from field measurements and T-LiDAR manually derived DBHs and trees heights were 286 Mg ha-1 and 134 Mg ha−1; and 278 M ha-1 and 130 Mg ha−1 respectively. The R2 values for the estimated AGB and AGC were both 0.93 and corresponding RMSE values were 42.4 Mg ha−1 and 19.9 Mg  ha−1 respectively. AGB and AGC were estimated with 14.8 % accuracy.

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Auxiliary information resolution effects on small area estimation in plantation forest inventory

Forestry An International Journal of Forest Research ◽

10.1093/forestry/cpaa012 ◽

2020 ◽

Vol 93 (5) ◽

pp. 685-693

Author(s):

P Corey Green ◽

Harold E Burkhart ◽

John W Coulston ◽

Philip J Radtke ◽

Valerie A Thomas

Keyword(s):

Loblolly Pine ◽

Forest Inventory ◽

Small Area ◽

Point Cloud ◽

Small Area Estimation ◽

Auxiliary Information ◽

Plantation Forest ◽

Area Estimation ◽

Improve Estimate ◽

Elevation Model

Abstract In forest inventory, traditional ground-based resource assessments are often expensive and time-consuming forcing managers to reduce sample sizes to meet budgetary and logistical constraints. Small area estimation (SAE) is a class of statistical estimators that uses a combination of traditional survey data and linearly related auxiliary information to improve estimate precision. These techniques have been shown to improve the precision of stand-level inventory estimates in loblolly pine plantations using lidar height percentiles and thinning status as covariates. In this study, the effects of reduced lidar point-cloud densities and lower digital elevation model (DEM) spatial resolutions were investigated for total planted volume estimates using area-level SAE models. In the managed Piedmont pine plantation conditions evaluated, lower lidar point-cloud densities and DEM spatial resolutions were found to have minimal effects on estimates and precision. The results of this study are promising to those interested in incorporating SAE methods into forest inventory programs.

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Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

Remote Sensing ◽

10.3390/rs12050863 ◽

2020 ◽

Vol 12 (5) ◽

pp. 863 ◽

Cited By ~ 14

Author(s):

Ana Paula Dalla Corte ◽

Franciel Eduardo Rex ◽

Danilo Roberti Alves de Almeida ◽

Carlos Roberto Sanquetta ◽

Carlos A. Silva ◽

...

Keyword(s):

Forest Inventory ◽

Point Cloud ◽

Tree Height ◽

High Density ◽

Forest Plantations ◽

Density Point ◽

Individual Tree ◽

Total Height ◽

Area Of Interest ◽

Mean Square Errors

Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.

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UAV Laser Scans Allow Detection of Morphological Changes in Tree Canopy

Remote Sensing ◽

10.3390/rs12223829 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3829

Author(s):

Martin Slavík ◽

Karel Kuželka ◽

Roman Modlinger ◽

Ivana Tomášková ◽

Peter Surový

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Morphological Changes ◽

Tree Canopy ◽

High Temporal Resolution ◽

Human Observer ◽

Individual Tree ◽

Dead Trees ◽

Multi Temporal ◽

Cloud Metrics

High-resolution laser scans from unmanned aerial vehicles (UAV) provide a highly detailed description of tree structure at the level of fine branches. Apart from ultrahigh spatial resolution, unmanned aerial laser scanning (ULS) can also provide high temporal resolution due to its operability and flexibility during data acquisition. We examined the phenomenon of bending branches of dead trees during one year from ULS multi-temporal data. In a multi-temporal series of three ULS datasets, we detected a synchronized reversible change in the inclination angles of the branches of 43 dead trees in a stand of blue spruce (Picea pungens Engelm.). The observed phenomenon has important consequences for both tree physiology and forest remote sensing (RS). First, the inclination angle of branches plays a crucial role in solar radiation interception and thus influences the total photosynthetic gain. The ability of a tree to change the branch position has important ecophysiological consequences, including better competitiveness across the site. Branch shifting in dead trees could be regarded as evidence of functional mycorrhizal interconnections via roots between live and dead trees. Second, we show that the detected movement results in a significant change in several point cloud metrics often utilized for deriving forest inventory parameters, both in the area-based approach (ABA) and individual tree detection approaches, which can affect the prediction of forest variables. To help quantify its impact, we used point cloud metrics of automatically segmented individual trees to build a generalized linear model to classify trees with and without the observed morphological changes. The model was applied to a validation set and correctly identified 86% of trees that displayed branch movement, as recorded by a human observer. The ULS allows for the study of this phenomenon across large areas, not only at individual tree levels.

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POINT CLOUD METRICS FOR SEPARATING STANDING ARCHAEOLOGICAL REMAINS AND LOW VEGETATION IN ALS DATA

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

10.5194/isprsarchives-xl-5-w2-459-2013 ◽

2013 ◽

Vol XL-5/W2 ◽

pp. 459-464 ◽

Cited By ~ 1

Author(s):

R. Opitz ◽

L. Nuninger

Keyword(s):

Point Cloud ◽

Archaeological Remains ◽

Cloud Metrics

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

Remote Sensing ◽

10.3390/rs10081299 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1299 ◽

Cited By ~ 12

Author(s):

Jincheng Liu ◽

Zhongke Feng ◽

Liyan Yang ◽

Abdul Mannan ◽

Tauheed Khan ◽

...

Keyword(s):

Forest Inventory ◽

Point Cloud ◽

Forest Canopy ◽

Measurement Data ◽

Sample Plot ◽

3D Point Cloud ◽

Automatic Data ◽

3D Point Clouds ◽

Measurement Efficiency ◽

Terrestrial Photogrammetry

Enriching forest resource inventory is important to ensure the sustainable management of forest ecosystems. Obtaining forest inventory data from the field has always been difficult, laborious, time consuming, and expensive. Advances in integrating photogrammetry and computer vision have helped researchers develop some numeric algorithms and methods that can turn 2D (images) into 3D (point clouds) and are highly applicable to forestry. This paper aimed to develop a new, highly accurate methodology that extracts sample plot parameters based on continuous terrestrial photogrammetry. For this purpose, we designed and implemented a terrestrial observation instrument combining real-time kinematic (RTK) and charge-coupled device (CCD) continuous photography. Then, according to the set observation plan, three independent experimental plots were continuously photographed and the 3D point cloud of the plot was generated. From this 3D point cloud, the tree position coordinates, tree DBHs, tree heights, and other plot characteristics of the forest were extracted. The plot characteristics obtained from the 3D point cloud were compared with the measurement data obtained from the field to check the accuracy of our methodology. We obtained the position coordinates of the trees with the positioning accuracy (RMSE) of 0.162 m to 0.201 m. The relative root mean square error (rRMSE) of the trunk diameter measurements was 3.07% to 4.51%, which met the accuracy requirements of traditional forestry surveys. The hypsometrical measurements were due to the occlusion of the forest canopy and the estimated rRMSE was 11.26% to 11.91%, which is still good reference data. Furthermore, these image-based point cloud data also have portable observation instruments, low data collection costs, high field measurement efficiency, automatic data processing, and they can directly extract tree geographic location information, which may be interesting and important for certain applications such as the protection of registered famous trees. For forest inventory, continuous terrestrial photogrammetry with its unique advantages is a solution that deserves future attention in the field of tree detection and ecological construction.

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Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

Remote Sensing ◽

10.3390/rs13010024 ◽

2020 ◽

Vol 13 (1) ◽

pp. 24

Author(s):

Yuanshuo Hao ◽

Faris Rafi Almay Widagdo ◽

Xin Liu ◽

Ying Quan ◽

Lihu Dong ◽

...

Keyword(s):

Unmanned Aerial Vehicle ◽

Random Effects ◽

Forest Inventory ◽

Point Cloud ◽

Laser Scanning ◽

Small Scale ◽

Individual Tree ◽

Scanning Angle ◽

Tree Diameter ◽

Aerial Vehicle

Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.

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