Forest Structure From Terrestrial Laser Scanning
DOWNLOAD
Download Forest Structure From Terrestrial Laser Scanning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Forest Structure From Terrestrial Laser Scanning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Forest Structure From Terrestrial Laser Scanning
DOWNLOAD
Author : David Kelbe
language : en
Publisher:
Release Date : 2015
Forest Structure From Terrestrial Laser Scanning written by David Kelbe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Forests and forestry categories.
"Forests are an important part of the natural ecosystem, providing resources such as timber and fuel, performing services such as energy exchange and carbon storage, and presenting risks, such as fire damage and invasive species impacts. Improved characterization of forest structural attributes is desirable, as it could improve our understanding and management of these natural resources. However, the traditional, systematic collection of forest information -- dubbed 'forest inventory' -- is time-consuming, expensive, and coarse when compared to novel 3-D measurement technologies. Remote sensing estimates, on the other hand, provide synoptic coverage, but often fail to capture the fine-scale structural variation of the forest environment. Terrestrial laser scanning (TLS) has demonstrated a potential to address these limitations, but its operational use has remained limited due to unsatisfactory performance characteristics vs. budgetary constraints of many end-users. To address this gap, my dissertation advanced affordable mobile laser scanning capabilities for operational forest structure assessment. We developed geometric reconstruction of forest structure from rapid-scan, low-resolution point cloud data, providing for automatic extraction of standard forest inventory metrics. To augment these results over larger areas, we designed a view-invariant feature descriptor to enable marker-free registration of TLS data pairs, without knowledge of the initial sensor pose. Finally, a graph-theory framework was integrated to perform multi-view registration between a network of disconnected scans, which provided improved assessment of forest inventory variables. This work addresses a major limitation related to the inability of TLS to assess forest structure at an operational scale, and may facilitate improved understanding of the phenomenology of airborne sensing systems, by providing fine-scale reference data with which to interpret the active or passive electromagnetic radiation interactions with forest structure. Outputs are being utilized to provide antecedent science data for NASA's HyspIRI mission and to support the National Ecological Observatory Network's (NEON) long-term environmental monitoring initiatives."--Abstract.
On The Use Of Rapid Scan Low Point Density Terrestrial Laser Scanning Tls For Structural Assessment Of Complex Forest Environments
DOWNLOAD
Author : Ali Rouzbeh Kargar
language : en
Publisher:
Release Date : 2020
On The Use Of Rapid Scan Low Point Density Terrestrial Laser Scanning Tls For Structural Assessment Of Complex Forest Environments written by Ali Rouzbeh Kargar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Forests and forestry categories.
"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."--Abstract.
Characterising Forest Structure By Airborne Laser Scanning
DOWNLOAD
Author : R A. Hill
language : en
Publisher:
Release Date : 2002
Characterising Forest Structure By Airborne Laser Scanning written by R A. Hill and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with categories.
Quantifying Vertical And Horizontal Stand Structure Using Terrestrial Lidar In Pacific Northwest Forests
DOWNLOAD
Author : Alexandra N. Kazakova
language : en
Publisher:
Release Date : 2013
Quantifying Vertical And Horizontal Stand Structure Using Terrestrial Lidar In Pacific Northwest Forests written by Alexandra N. Kazakova and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Forest canopies categories.
Stand level spatial distribution is a fundamental part of forest structure that influences many ecological processes and ecosystem functions. Vertical and horizontal spatial structure provides key information for forest management. Although horizontal stand complexity can be measured through stem mapping and spatial analysis, vertical complexity within the stand remains a mostly visual and highly subjective process. Tools and techniques in remote sensing, specifically LiDAR, provide three dimensional datasets that can help get at three dimensional forest stand structure. Although aerial LiDAR (ALS) is the most widespread form of remote sensing for measuring forest structure, it has a high omission rate in dense and structurally complex forests. In this study we used terrestrial LiDAR (TLS) to obtain high resolution three dimensional point clouds of plots from stands that vary by density and composition in the second-growth Pacific Northwest forest ecosystem. We used point cloud slicing techniques and object-based image analysis (OBIA) to produce canopy profiles at multiple points of vertical gradient. At each height point we produced segments that represented canopies or parts of canopies for each tree within the dataset. The resulting canopy segments were further analyzed using landscape metrics to quantify vertical canopy complexity within a single stand. Based on the developed method, we have successfully created a tool that utilizes three dimensional spatial information to accurately quantify the vertical structure of forest stands. Results show significant differences in the number and the total area of the canopy segments and gap fraction between each vertical slice within and between individual forest management plots. We found a significant relationship between the stand density and composition and the vertical canopy complexity. The methods described in this research make it possible to create horizontal stand profiles at any point along the vertical gradient of forest stands with high frequency, therefore providing ecologists with measures of horizontal and vertical stand structure. Key Words: Terrestrial laser scanning, canopy structure, landscape metrics, aerial laser scanning, lidar, calibration, Pacific Northwest
Biomass And Stem Volume Equations For Tree Species In Europe
DOWNLOAD
Author : Dimitris Zianis
language : en
Publisher:
Release Date : 2005
Biomass And Stem Volume Equations For Tree Species In Europe written by Dimitris Zianis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Biomass energy categories.
A review of stem volume and biomass equations for tree species growing in Europe is presented. The mathematical forms of the empirical models, the associated statistical parameters and information about the size of the trees and the country of origin were collated from scientific articles and from technical reports. The collected information provides a basic tool for estimation of carbon stocks and nutrient balance of forest ecosystems across Europe as well as for validation of theoretical models of biomass allocation.
Multi Temporal Terrestrial Lidar For Estimating Individual Tree Dimensions And Biomass Change
DOWNLOAD
Author : Shruthi Srinivasan
language : en
Publisher:
Release Date : 2014
Multi Temporal Terrestrial Lidar For Estimating Individual Tree Dimensions And Biomass Change written by Shruthi Srinivasan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Accurate measures of forest structural parameters are essential to forest inventory and growth models, managing wildfires, and modeling of carbon cycle. Terrestrial laser scanning (TLS) provides accurate understory information rapidly through non-destructive methods. This study developed algorithms to extract individual tree height, diameter at breast height (DBH), and crown width in plots at Ecosystem Science and Management (ESSM) research area and Huntsville, Texas. Further, the influence of scan settings and processing choices on the accuracy of deriving tree measurements was also investigated. The study also developed models to estimate aboveground biomass (AGB) and investigate different conceptual approaches to study tree level growth in forest structural parameters and AGB using multi-temporal TLS datasets. DBH was retrieved by cylinder fitting at different height bins. Individual trees were extracted from the TLS point cloud to determine tree heights and crown widths. The R-squared value ranged from 0.91 to 0.97 when field measured DBH was validated against TLS derived DBH using different methods. An accuracy of 92% was obtained for predicting tree heights. The R-squared value was 0.84 and RMSE was 1.08 m when TLS derived crown widths were validated using field measured crown widths. Examples of underestimations of field measured forest structural parameters due to tree shadowing have also been discussed in this study. Correction factors should be applied or multiple high resolution scans should be conducted to reduce the errors in estimation of forest structural parameters. TLS geometric and statistical parameters were derived for individual trees and used as explanatory variables to estimate AGB. An extensive literature review reveals that this is the first study to model the change in AGB using different innovative and conceptual approaches with multi-temporal TLS data. Tree level AGB growth was studied over a period of three years using three different approaches. Results showed that TLS derived geometric parameters were better correlated to field measured AGB. Promising results for AGB change were obtained using the direct modeling approach; hence forest growth could be studied independent of any field measurements when biomass models are available. However, the models could be improved by incorporating more trees with a wide range of DBH and tree heights. The results from this study will benefit foresters, planners, and other remote sensing studies from airborne and spaceborne platforms, for map upscaling, data fusion, or calibration purposes. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/151740
Same Viewpoint Different Perspectives A Comparison Of Expert Ratings With A Tls Derived Forest Stand Structural Complexity Index
DOWNLOAD
Author : Julian Frey
language : en
Publisher:
Release Date : 2019
Same Viewpoint Different Perspectives A Comparison Of Expert Ratings With A Tls Derived Forest Stand Structural Complexity Index written by Julian Frey and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Abstract: Forests are one of the most important terrestrial ecosystems for the protection of biodiversity, but at the same time they are under heavy production pressures. In many cases, management optimized for timber production leads to a simplification of forest structures, which is associated with species loss. In recent decades, the concept of retention forestry has been implemented in many parts of the world to mitigate this loss, by increasing structure in managed stands. Although this concept is widely adapted, our understanding what forest structure is and how to reliably measure and quantify it is still lacking. Thus, more insights into the assessment of biodiversity-relevant structures are needed, when aiming to implement retention practices in forest management to reach ambitious conservation goals. In this study we compare expert ratings on forest structural richness with a modern light detection and ranging (LiDAR) -based index, based on 52 research sites, where terrestrial laser scanning (TLS) data and 360° photos have been taken. Using an online survey (n = 444) with interactive 360° panoramic image viewers, we sought to investigate expert opinions on forest structure and learn to what degree measures of structure from terrestrial laser scans mirror experts' estimates. We found that the experts' ratings have large standard deviance and therefore little agreement. Nevertheless, when averaging the large number of participants, they distinguish stands according to their structural richness significantly. The stand structural complexity index (SSCI) was computed for each site from the LiDAR scan data, and this was shown to reflect some of the variation of expert ratings (p = 0.02). Together with covariates describing participants' personal background, image properties and terrain variables, we reached a conditional R2 of 0.44 using a linear mixed effect model. The education of the participants had no influence on their ratings, but practical experience showed a clear effect. Because the SSCI and expert opinion align to a significant degree, we conclude that the SSCI is a valuable tool to support forest managers in the selection of retention patches
Evaluating Close Range Remote Sensing Techniques For The Retention Of Biodiversity Related Forest Structures
DOWNLOAD
Author : Julian Frey
language : en
Publisher:
Release Date : 2019
Evaluating Close Range Remote Sensing Techniques For The Retention Of Biodiversity Related Forest Structures written by Julian Frey and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Abstract: Forest management alters the spatial structure of forests, which directly shapes the biodiversity, processes and functioning of such ecosystems. While forest structure is commonly quantified using field-based forest management metrics, close range (distances 150m) remote sensing techniques are able to describe the distribution of material in 3D space with very high detail and precision. Since manual inventories of forest structure are labor intensive and suffer from observer biases remote sensing techniques offer new possibilities for efficient and objective quantifications of forest structure. To investigate the impacts of forest management on stand structure, new indices and metrics based on 3D point clouds have been developed and validated. This dissertation is structured in three publications (Chapters 4 - 6), starting with a methods paper on UAV flight planning optimization, followed by a comparison of tree related microhabitat inventories and close range remote sensing indices for stand structure quantification, and ending with a validation of a remote sensing index based on a forest expert survey.brFor the first paper, a technical flight planning optimization was conducted for unmanned aerial vehicles structure from motion as a basis for optimal data acquisition (Chapter 4). The image forward overlap and ground sampling distance were varied, and the parameters of the resulting geometric model completeness in 2D and 3D space that differed could be independently quantified. While finer resolutions led to a better representation of smaller forest details and a better representation of the understory, the model completeness suffered from it. A higher forward image overlap can compensate for this if the overlap is very high (95%). Tree related microhabitat (TreM) inventories are unlike remote sensing based indices but have a similar aim of quantifying forest structures, which can be accumulated to a stand level. TreMs themselves are special tree level structures such as forks, cavities and fungi. While both approaches have different perspectives on forest structure, their common goal of forest stand structure quantification make the respective insights from these methods worth comparing (Chapter 5). A significant correlation with a weak R2 (0.30) indicate that these two measures are linked but that their representation of stand structure is complementary. Foresters and other forest experts are of major relevance to the topic of implementation of retention management and the selection of retention patches, since they are the decision makers and practitioners in this sector. The judgement of those experts could be biased by additional objectives and individual preferences. In an extensive online survey (n=444), experts were asked to quantify stand structure on 360 degree panoramic images in an interactive viewer. The expert responses were compared to stand structural complexity metrics derived from terrestrial laser scans, which were taken at the same location from the same viewpoint. The standard deviation of the expert judgements were high, which indicates the necessity of objective measurements. The laser scanning based index significantly correlated with the expert judgements, which shows that neither the expert ratings nor the scanning index are random and that laser scanning is an option for more objective decision making processes. However, experts in the field might take a variety of additionally relevant criteria (e.g. rareness of the habitat in landscape, special tree features, breeding places) into account, which should not been overlooked. In summary, this dissertation validated and adapted several indices based on close range remote sensing techniques and showed their potential as monitoring tools and assistance for the forest management decision-making processes
Lidar Principles Processing And Applications In Forest Ecology
DOWNLOAD
Author : Qinghua Guo
language : en
Publisher: Academic Press
Release Date : 2023-03-10
Lidar Principles Processing And Applications In Forest Ecology written by Qinghua Guo and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-10 with Technology & Engineering categories.
LiDAR Principles, Processing and Applications in Forest Ecology introduces the principles of LiDAR technology and explains how to collect and process LiDAR data from different platforms based on real-world experience. The book provides state-of the-art algorithms on how to extract forest parameters from LiDAR and explains how to use them in forest ecology. It gives an interdisciplinary view, from the perspective of remote sensing and forest ecology. Because LiDAR is still rapidly developing, researchers must use programming languages to understand and process LiDAR data instead of established software. In response, this book provides Python code examples and sample data. Sections give a brief history and introduce the principles of LiDAR, as well as three commonly seen LiDAR platforms. The book lays out step-by-step coverage of LiDAR data processing and forest structure parameter extraction, complete with Python examples. Given the increasing usefulness of LiDAR in forest ecology, this volume represents an important resource for researchers, students and forest managers to better understand LiDAR technology and its use in forest ecology across the world. The title contains over 15 years of research, as well as contributions from scientists across the world. Presents LiDAR applications for forest ecology based in real-world experience Lays out the principles of LiDAR technology in forest ecology in a systematic and clear way Provides readers with state-of the-art algorithms on how to extract forest parameters from LiDAR Offers Python code examples and sample data to assist researchers in understanding and processing LiDAR data Contains over 15 years of research on LiDAR in forest ecology and contributions from scientists working in this field across the world
Marker Free Registration Of Forest Terrestrial Laser Scanner Data Pairs With Embedded Confidence Metrics
DOWNLOAD
Author :
language : en
Publisher:
Release Date : 2016
Marker Free Registration Of Forest Terrestrial Laser Scanner Data Pairs With Embedded Confidence Metrics written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate marker-free registration approaches have not been developed. The majority of studies instead rely on the utilization of artificial tie points (e.g., reflective tooling balls) placed within a scene to aid in coordinate transformation. We present a technique for generating view-invariant feature descriptors that are intrinsic to the point cloud data and, thus, enable blind marker-free registration in forest environments. To overcome the limitation of initial pose estimation, we employ a voting method to blindly determine the optimal pairwise transformation parameters, without an a priori estimate of the initial sensor pose. To provide embedded error metrics, we developed a set theory framework in which a circular transformation is traversed between disjoint tie point subsets. This provides an upper estimate of the Root Mean Square Error (RMSE) confidence associated with each pairwise transformation. Output RMSE errors are commensurate with the RMSE of input tie points locations. Thus, while the mean output RMSE=16.3cm, improved results could be achieved with a more precise laser scanning system. This study 1) quantifies the RMSE of the proposed marker-free registration approach, 2) assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and 3) informs optimal sample spacing considerations for TLS data collection in New England forests. Furthermore, while the implications for rapid, accurate, and precise forest inventory are obvious, the conceptual framework outlined here could potentially be extended to built environments.