A Density-Based Approach for Leaf Area Index Assessment in a Complex Forest Environment Using a Terrestrial Laser Scanner

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

Accuracy of tree geometric parameters depending on the LiDAR data density

[EN] The aim of this study was to compare geometric parameters of olive trees (tree height, crown base height, crown diameters, crown area), using LiDAR data of different densities: 0.5, 3.5 and 9 points m(-2). Two strategies were proposed and verified with a focus on raster and raw data analysis. Statistical tests have shown, that for the tree height and crown base height estimation, the choice of strategy was irrelevant, but denser LiDAR data provided more accurate results. The raster analysis strategy applied for sparse and dense LiDAR datasets allowed crown shape to be determined with a similar accuracy which means raster data are useful for estimating other indirect tree parameters. The quality of results was independent from the tree size.The authors appreciate the financial support provided by the Vice-Rectorate for Research of the Universitat Politecnica de Valencia [Grant PAID-06-12-3297; SP20120534].Hadás, E.; Estornell Cremades, J. (2016). Accuracy of tree geometric parameters depending on the LiDAR data density. European Journal of Remote Sensing. 49:73-92. https://doi.org/10.5721/EuJRS20164905S73924

On the use of rapid-scan, low point density terrestrial laser scanning (TLS) for structural assessment of complex forest environments

Forests fulfill an important role in natural ecosystems, e.g., they provide food, fiber, habitat, and biodiversity, all of which contribute to stable ecosystems. Assessing and modeling the structure and characteristics in forests can lead to a better understanding and management of these resources. Traditional methods for collecting forest traits, known as “forest inventory”, is achieved using rough proxies, such as stem diameter, tree height, and foliar coverage; such parameters are limited in their ability to capture fine-scale structural variation in forest environments. It is in this context that terrestrial laser scanning (TLS) has come to the fore as a tool for addressing the limitations of traditional forest structure evaluation methods. However, there is a need for improving TLS data processing methods. In this work, we developed algorithms to assess the structure of complex forest environments – defined by their stem density, intricate root and stem structures, uneven-aged nature, and variable understory - using data collected by a low-cost, portable TLS system, the Compact Biomass Lidar (CBL). The objectives of this work are listed as follow: 1. Assess the utility of terrestrial lidar scanning (TLS) to accurately map elevation changes (sediment accretion rates) in mangrove forest; 2. Evaluate forest structural attributes, e.g., stems and roots, in complex forest environments toward biophysical characterization of such forests; and 3. Assess canopy-level structural traits (leaf area index; leaf area density) in complex forest environments to estimate biomass in rapidly changing environments. The low-cost system used in this research provides lower-resolution data, in terms of scan angular resolution and resulting point density, when compared to higher-cost commercial systems. As a result, the algorithms developed for evaluating the data collected by such systems should be robust to issues caused by low-resolution 3D point cloud data. The data used in various parts of this work were collected from three mangrove forests on the western Pacific island of Pohnpei in the Federated States of Micronesia, as well as tropical forests in Hawai’i, USA. Mangrove forests underscore the economy of this region, where more than half of the annual household income is derived from these forests. However, these mangrove forests are endangered by sea level rise, which necessitates an evaluation of the resilience of mangrove forests to climate change in order to better protect and manage these ecosystems. This includes the preservation of positive sediment accretion rates, and stimulating the process of root growth, sedimentation, and peat development, all of which are influenced by the forest floor elevation, relative to sea level. Currently, accretion rates are measured using surface elevation tables (SETs), which are posts permanently placed in mangrove sediments. The forest floor is measured annually with respect to the height of the SETs to evaluate changes in elevation (Cahoon et al. 2002). In this work, we evaluated the ability of the CBL system for measuring such elevation changes, to address objective #1. Digital Elevation Models (DEMs) were produced for plots, based on the point cloud resulted from co-registering eight scans, spaced 45 degree, per plot. DEMs are refined and produced using Cloth Simulation Filtering (CSF) and kriging interpolation. CSF was used because it minimizes the user input parameters, and kriging was chosen for this study due its consideration of the overall spatial arrangement of the points using semivariogram analysis, which results in a more robust model. The average consistency of the TLS-derived elevation change was 72%, with and RMSE value of 1.36 mm. However, what truly makes the TLS method more tenable, is the lower standard error (SE) values when compared to manual methods (10-70x lower). In order to achieve our second objective, we assessed structural characteristics of the above-mentioned mangrove forest and also for tropical forests in Hawaii, collected with the same CBL scanner. The same eight scans per plot (20 plots) were co-registered using pairwise registration and the Iterative Closest Point (ICP). We then removed the higher canopy using a normal change rate assessment algorithm. We used a combination of geometric classification techniques, based on the angular orientation of the planes fitted to points (facets), and machine learning 3D segmentation algorithms to detect tree stems and above-ground roots. Mangrove forests are complex forest environments, containing above-ground root mass, which can create confusion for both ground detection and structural assessment algorithms. As a result, we needed to train a supporting classifier on the roots to detect which root lidar returns were classified as stems. The accuracy and precision values for this classifier were assessed via manual investigation of the classification results in all 20 plots. The accuracy and precision for stem classification were found to be 82% and 77%, respectively. The same values for root detection were 76% and 68%, respectively. We simulated the stems using alpha shapes in order to assess their volume in the final step. The consistency of the volume evaluation was found to be 85%. This was obtained by comparing the mean stem volume (m3/ha) from field data and the TLS data in each plot. The reported accuracy is the average value for all 20 plots. Additionally, we compared the diameter-at-breast-height (DBH), recorded in the field, with the TLS-derived DBH to obtain a direct measure of the precision of our stem models. DBH evaluation resulted in an accuracy of 74% and RMSE equaled 7.52 cm. This approach can be used for automatic stem detection and structural assessment in a complex forest environment, and could contribute to biomass assessment in these rapidly changing environments. These stem and root structural assessment efforts were complemented by efforts to estimate canopy-level structural attributes of the tropical Hawai’i forest environment; we specifically estimated the leaf area index (LAI), by implementing a density-based approach. 242 scans were collected using the portable low-cost TLS (CBL), in a Hawaii Volcano National Park (HAVO) flux tower site. LAI was measured for all the plots in the site, using an AccuPAR LP-80 Instrument. The first step in this work involved detection of the higher canopy, using normal change rate assessment. After segmenting the higher canopy from the lidar point clouds, we needed to measure Leaf Area Density (LAD), using a voxel-based approach. We divided the canopy point cloud into five layers in the Z direction, after which each of these five layers were divided into voxels in the X direction. The sizes of these voxels were constrained based on interquartile analysis and the number of points in each voxel. We hypothesized that the power returned to the lidar system from woody materials, like branches, exceeds that from leaves, due to the liquid water absorption of the leaves and higher reflectivity for woody material at the 905 nm lidar wavelength. We evaluated leafy and woody materials using images from projected point clouds and determined the density of these regions to support our hypothesis. The density of points in a 3D grid size of 0.1 m, which was determined by investigating the size of the branches in the lower portion of the higher canopy, was calculated in each of the voxels. Note that “density” in this work is defined as the total number of points per grid cell, divided by the volume of that cell. Subsequently, we fitted a kernel density estimator to these values. The threshold was set based on half of the area under the curve in each of the distributions. The grid cells with a density below the threshold were labeled as leaves, while those cells with a density above the threshold were set as non-leaves. We then modeled the LAI using the point densities derived from TLS point clouds, achieving a R2 value of 0.88. We also estimated the LAI directly from lidar data by using the point densities and calculating leaf area density (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 found to be 90%. Since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed a 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, where each of the plots were 30 meter spaced for each subset. LAI model R2 values for these subsets ranged between 0.84 - 0.96. The results bode well for using this method for automatic estimation of LAI values in complex forest environments, using a low-cost, low point density, rapid-scan TLS

Terrestrial laser scanning for vegetation analyses with a special focus on savannas

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome

3d Scanning And The Impact Of The Digital Thread On Manufacturing And Re-Manufacturing Applications

3D laser line scanners are becoming a powerful technology for capturing point cloud datasets and collecting dimensional information for many objects. However, the use of point cloud is limited due to many factors. These include the lack of on deep understanding of the effect of point cloud parameters on scan quality. This knowledge is critical to gaining an understanding of the measurement in point cloud. Currently, there are no adequate measurement procedures for 3D scanners. There is a need for standardized measurement procedures to evaluate 3D scanner accuracy due to uncertainties in 3D scanning, such as surface quality, surface orientation and scan depth [6]. The lack of standardized procedures does not allow the technology to be fully automated and used in manufacturing facilities that would allow 100% in-line inspection. In this dissertation I worked on accomplishing four tasks that will achieve the objective of having a standardized measurement procedure that is critical to develop an automated laser scanning system to avoid variations and have consistent data capable of identifying defects. The four tasks are: (1) linking the robot workspace with the scanner workspace; (2) studying the effect of the scanning speed and the resolution on point cloud quality by conducting an experiment with systematically varied scan parameters on scan quality; (3) studying the overall error of that is associated with the transformation of the point cloud in a remanufacturing facility using additive manufacturing. The parameters that were tested are the effect of view angle, standoff distance, speed, and resolution. Knowing the effect of these parameters is important in order to generate the scan path that provides the best coverage and quality of points collected. There is also a need to know the impact of all the scanning parameters especially the speed and the resolution; (4) modeling a machine learning tool to optimize the parameters of different scanning techniques after collecting the scanning results to select the optimal ones that provide the best scan quality. With the success of this work, the advancement and practice of automated quality monitoring in manufacturing will increase

Estimation of gap fraction and clumping index with Terrestrial and Airborne Laser Scanner data

El dosel forestal es una zona de intercambio de flujos y energía entre la superficie de la tierra y la atmósfera. Su estructura está representada por la organización espacial de todos los elementos vegetales que se encuentran sobre la superficie. La estructura del dosel condiciona una serie de variables microclimáticas en el interior de este espacio, las que influyen en la disponibilidad de los recursos y el comportamiento de las especies que cohabitan en él. Existe una serie de variables que permiten describir la estructura del dosel. Entre las más importantes se encuentran el índice de área foliar, cuyo cálculo y corrección depende de otros parámetros como la fracción de huecos (gap fraction, GF) y el índice de agrupamiento foliar (clumping index, CI). En este documento se estudian y desarrollan métodos para la estimación de GF y CI a partir de escáneres láser terrestres y aerotransportados (Terrestrial (TLS) and Airborne (ALS) Laser Scanners). Para lograr esto, se llevaron a cabo mediciones con TLS en Las Majadas del Tiétar (Cáceres, España) en el año 2009 y con ALS en Jasper Ridge (California, EE.UU.) en el 2007. En el caso de la estimación de GF a partir TLS, se desarrolló un nuevo método que calculaba la proporción entre píxeles vacíos y su totalidad a partir de imágenes angulares, una vez que se conocía su resolución. La validación del método fue realizada mediante simulaciones de datos con diversas resoluciones angulares y patrones de huecos en el dosel. El método se comparó también con los resultados de GF a partir de fotografías hemisféricas (hemispherical photography, HP), una vez que los datos TLS se reproyectaron para simular HP (TLS-SHP). La estimación del CI se llevó a cabo aplicando la teoría de la distribución del tamaño de los huecos de Chen y Cihlar (1995) sobre las TLS-SHP, que se contrastó con los valores de CI de las HP. En la zona de Jasper Ridge las estimaciones de GF se realizaron empleando métricas basadas en la ley de transmisividad de Beer-Lambert que miden el porcentaje de retornos láser que llegan al suelo, considerando parcelas circulares de datos ALS con diferentes tamaños de radio, para compararlas con la GF estimado de las HP. Del mismo modo, se probó también con la relación entre las intensidades de los retornos del suelo y las de todos ellos al interior de las parcelas. El CI se estimó a partir de métricas ALS derivadas de la altura de la vegetación y se relacionaron con el CI de las HP. Además, se adaptó con el mismo propósito el índice de segregación espacial de Pielou (1962), que se aplicó sobre imágenes de GF generadas para parcelas de datos ALS con distintos tamaños de radio y que fueron comparadas con el CI generado desde las HP. Para los experimentos llevados a cabo con los datos TLS, la GF fue sobreestimada en un 14% respecto a las HP, siendo las correlaciones estadísticamente significativas. El algoritmo desarrollado es operativo siempre y cuando el ruido en los datos angulares sea inferior al 6% de la resolución angular. Por encima de este umbral el método presentó un alto error, especialmente en los datos simulados con una estructura de huecos agrupados (cluster). El CI se subestimó en 27% respecto a los valores obtenidos por las HP. Los principales problemas vienen dados por la diferencia en la distribución del tamaño de los huecos registrados por las HP y las TLS-SHP. Por otra parte, la GF derivada de los datos ALS subestimó en un 3% y sobrestimó en un 43% comparado con las HP, para las parcelas de bosque y matorral, respectivamente. La GF obtenida presentó una clara dependencia del radio de los datos ALS considerados, que varió según el tipo de vegetación. Respecto a las estimaciones del CI, las métricas ALS de las alturas de la vegetación no mostraron buenos resultados. Esta circunstancia es contraria a estudios previos, lo que parece indicar que estas relaciones empíricas sólo funcionarían para el tipo de vegetación y sitio para el que fueron desarrolladas. Sin embargo, la modificación del algoritmo de Pielou subestimó el CI en sólo 6% y 4% para las parcelas de bosques y matorrales, respectivamente. Las posibles causas de estas diferencias radican en las distintas perspectivas y resolución espacial que poseen los datos ALS y HP

Estimation of gap fraction and clumping index with Terrestrial and Airborne Laser Scanner data

El dosel forestal es una zona de intercambio de flujos y energía entre la superficie de la tierra y la atmósfera. Su estructura está representada por la organización espacial de todos los elementos vegetales que se encuentran sobre la superficie. La estructura del dosel condiciona una serie de variables microclimáticas en el interior de este espacio, las que influyen en la disponibilidad de los recursos y el comportamiento de las especies que cohabitan en él. Existe una serie de variables que permiten describir la estructura del dosel. Entre las más importantes se encuentran el índice de área foliar, cuyo cálculo y corrección depende de otros parámetros como la fracción de huecos (gap fraction, GF) y el índice de agrupamiento foliar (clumping index, CI). En este documento se estudian y desarrollan métodos para la estimación de GF y CI a partir de escáneres láser terrestres y aerotransportados (Terrestrial (TLS) and Airborne (ALS) Laser Scanners). Para lograr esto, se llevaron a cabo mediciones con TLS en Las Majadas del Tiétar (Cáceres, España) en el año 2009 y con ALS en Jasper Ridge (California, EE.UU.) en el 2007. En el caso de la estimación de GF a partir TLS, se desarrolló un nuevo método que calculaba la proporción entre píxeles vacíos y su totalidad a partir de imágenes angulares, una vez que se conocía su resolución. La validación del método fue realizada mediante simulaciones de datos con diversas resoluciones angulares y patrones de huecos en el dosel. El método se comparó también con los resultados de GF a partir de fotografías hemisféricas (hemispherical photography, HP), una vez que los datos TLS se reproyectaron para simular HP (TLS-SHP). La estimación del CI se llevó a cabo aplicando la teoría de la distribución del tamaño de los huecos de Chen y Cihlar (1995) sobre las TLS-SHP, que se contrastó con los valores de CI de las HP. En la zona de Jasper Ridge las estimaciones de GF se realizaron empleando métricas basadas en la ley de transmisividad de Beer-Lambert que miden el porcentaje de retornos láser que llegan al suelo, considerando parcelas circulares de datos ALS con diferentes tamaños de radio, para compararlas con la GF estimado de las HP. Del mismo modo, se probó también con la relación entre las intensidades de los retornos del suelo y las de todos ellos al interior de las parcelas. El CI se estimó a partir de métricas ALS derivadas de la altura de la vegetación y se relacionaron con el CI de las HP. Además, se adaptó con el mismo propósito el índice de segregación espacial de Pielou (1962), que se aplicó sobre imágenes de GF generadas para parcelas de datos ALS con distintos tamaños de radio y que fueron comparadas con el CI generado desde las HP. Para los experimentos llevados a cabo con los datos TLS, la GF fue sobreestimada en un 14% respecto a las HP, siendo las correlaciones estadísticamente significativas. El algoritmo desarrollado es operativo siempre y cuando el ruido en los datos angulares sea inferior al 6% de la resolución angular. Por encima de este umbral el método presentó un alto error, especialmente en los datos simulados con una estructura de huecos agrupados (cluster). El CI se subestimó en 27% respecto a los valores obtenidos por las HP. Los principales problemas vienen dados por la diferencia en la distribución del tamaño de los huecos registrados por las HP y las TLS-SHP. Por otra parte, la GF derivada de los datos ALS subestimó en un 3% y sobrestimó en un 43% comparado con las HP, para las parcelas de bosque y matorral, respectivamente. La GF obtenida presentó una clara dependencia del radio de los datos ALS considerados, que varió según el tipo de vegetación. Respecto a las estimaciones del CI, las métricas ALS de las alturas de la vegetación no mostraron buenos resultados. Esta circunstancia es contraria a estudios previos, lo que parece indicar que estas relaciones empíricas sólo funcionarían para el tipo de vegetación y sitio para el que fueron desarrolladas. Sin embargo, la modificación del algoritmo de Pielou subestimó el CI en sólo 6% y 4% para las parcelas de bosques y matorrales, respectivamente. Las posibles causas de estas diferencias radican en las distintas perspectivas y resolución espacial que poseen los datos ALS y HP

Leaf area index and aboveground biomass estimation of Populus and its hybrids using terrestrial LiDAR

Short rotation woody crops (SRWC) eastern cottonwood (Populus deltoides) and hybrid poplar plantations were established in 2021 in Pontotoc and Oktibbeha counties of Mississippi to study the biomass potential of SRWC for biofuel production. We used a novel backpack LiDAR system to measure forest metrics and harvested sample trees to build aboveground biomass (AGB) and leaf area index (LAI) equations. The results showed that LiDAR-derived variables accurately estimated aboveground biomass (R2 =0.81 and 29.22 % RMSE). However, the LAI estimation results showed that the LiDAR metrics moderately explained field measurements of LAI (R2 =0.31 and 18.05% RMSE) for individual-trees and poorly explained plot-level LAI measured with the LAI-2200C (R2 =0.11 and 66% RMSE). The backpack LiDAR system can be valuable for forest managers and researchers, enabling non-destructive AGB and LAI estimation. However, further research is required to overcome its limitations and achieve precise measurements of AGB and LAI

Modeling and Simulation in Engineering

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results