Deriving Fuel Mass by Size Class in Douglas-fir (Pseudotsuga menziesii) Using Terrestrial Laser Scanning

Requirements for describing coniferous forests are changing in response to wildfire concerns, bio-energy needs, and climate change interests. At the same time, technology advancements are transforming how forest properties can be measured. Terrestrial Laser Scanning (TLS) is yielding promising results for measuring tree biomass parameters that, historically, have required costly destructive sampling and resulted in small sample sizes. Here we investigate whether TLS intensity data can be used to distinguish foliage and small branches (less than or equal to 0.635 cm diameter; coincident with the one-hour timelag fuel size class) from larger branchwood (\u3e0.635 cm) in Douglas-fir (Pseudotsuga menziesii) branch specimens. We also consider the use of laser density for predicting biomass by size class. Measurements are addressed across multiple ranges and scan angles. Results show TLS capable of distinguishing fine fuels from branches at a threshold of one standard deviation above mean intensity. Additionally, the relationship between return density and biomass is linear by fuel type for fine fuels (r2 = .0898; SE 22.7%) and branchwood (r2 = 0.937; SE 28.9%), as well as for total mass (r2 = 0.940; SE 25.5%). Intensity decays predictably as scan distances increase; however, the range-intensity relationship is best described by an exponential model rather than 1/d2. Scan angle appears to have no systematic effect on fine fuel discrimination, while some differences are observed in density-mass relationships with changing angles due to shadowing

Characterizing Crown Struture of Three Interior Northwest Conifer Species Using Terrestrial Laser Scanning

Emerging interests in wildland fire behavior and risk, bioenergy utilization, carbon sequestration, and wildlife conservation increasingly rely on accurate assessments of the amount and location of biomass within the dominant plants on the landscape, often at finer scales than traditional methods have provided. At the tree scale, current studies often distribute biomass uniformly through simple volumes (e.g., cones and cylinders). However, biomass is heterogeneous at a variety of scales from needle clusters to groups of trees. This thesis presents techniques for using terrestrial laser scanning data to define crown profiles and describe within-crown heterogeneity in Pseudotusga menziesii, Pinus ponderosa, and Abies lasiocarpa of the Interior Northwest. Crown profiles were modeled using parametric curves applied to crown-length normalized laser point clouds, dimensioned by height above ground and distance from bole-centroids. A crown-base metric was derived from the laser data and compared to conventional field measurements. For all species, a modified Weibull curve fit crown points with significantly smaller error than a beta curve, cone, or cylinder; crown profile Weibull curves were species-specific and not interchangeable without producing signifcantly greater error. Within-crown patterning was described using a 3-D form of the Ripley’s K function. Ripley’s K analysis detected maximum clustering occuring at scales of 1.25 – 2.50 percent of crown length (e.g., 25-50 cm radius clusters in a 20 meter crown). P. ponderosa demonstrated clustering over the largest range of scales and to the greatest degree, while A. lasiocarpa exhibited clustering over the smallest range of scales. The scale of clustering did not change when points roughly corresponding to branchwood were excluded from the analysis. This study provides groundwork for predicting the spatial distribution of biomass with tree crowns. Limitations of the work include uncertainty regarding the impacts of occlusion of inner crowns and the relationships between laser points and foliage-branch elements, and the lack of spatial explicitness inherent to Ripley’s K. Future work should examine these issues with an eye toward refinement of predictive models linking traditional biomass allometry with spatial arrangement of canopy material

Innovative surveying methodologies through Handheld Terrestrial LIDAR Scanner technologies for forest resource assessment

Precision Forestry is an innovative sector that is currently of great importance for forest and spatial planning. It enables complex analyses of forest data to be carried out in a simple and economical way and facilitates collaboration between technicians, industry operators and stakeholders, thus ensuring transparency in forestry interventions (Corona et al., 2017). The principles of "Precision Forestry" are to use modern tools and technologies with the aim to obtain as much real information as possible, to improve decision-making, and to ensure the current objectives of forest management. Thanks to the rapid technological developments in remote sensing during the last few decades, there have been remarkable improvements in measurement accuracy, and consequentially improvements in the quality of technical elaborations supporting planning decisions. During this period, several scientific publications have demonstrated the potential of the LIDAR system for measuring and mapping forests, geology, and topography in large-scale forest areas. The LIDAR scans obtained from the TLS and HLS systems provide detailed information about the internal characteristics of tree canopys, making them an essential tool for studying stem allometry, volume, light environments, photosynthesis, and production models. In light of these considerations, this thesis aims to expand the current knowledge on the terrestrial LIDAR system applications for monitoring forest ecosystems and dynamics by providing insight on the feasibility and effectiveness of these systems for forest planning. In particular, this study fills a gap in the literature regarding practical examples of the use of innovative technologies in forestry. The main themes of this work are: A) The strengths and weaknesses of the mobile LIDAR system for a forest company; B) The applicability and versatility of the LIDAR HLS tool for sustainable forest management applications; C) Single tree analysis from HLS LIDAR data.   To investigate these themes, we analyzed six cases studies: 1) An investigation of the feasibility and efficiency of LIDAR HLS scanning for an accurate estimation of forest structural attributes by comparing scans using the LIDAR HLS survey method (Handheld Mobile Laser Scanner) to traditional instruments; 2) An examination of walking scan path density’s influence on single-tree attribute estimation by HMLS, taking into account the structural biodiversity of two forest ecosystems under examination, and an estimation of the cost-effectiveness of each type of laser survey based on the path scheme considered; 3) A study of how LIDAR HLS surveys can contribute to fire prevention interventions by providing a quantitative classification of fuels and a preliminary description of the structural and spatial development of the forest in question; 4) An application of a method for assessing and rating stem straightness in tree posture using LIDAR HLS surveys to quantify differences between stands of different log qualities; 5) The identification of features of a Mediterranean old-growth forest using LIDAR HLS surveys according to the criteria established in the literature; 6) The extrapolation of dimensional information for Ficus macrophylla subsp. columnaris to identify the monumental character of the tree by comparing the most appropriate LIDAR HLS point cloud processing methodologies and estimating the total volume of individual trees. In conclusion, the results of these cases studies are useful to determine new research aspects within the system in the forest environment by applying recently published analysis methodologies and indications of relevant terrestrial LIDAR methodologies

SENSITIVITY OF LIDAR DERIVED FUEL CELLS TO FIRE MODELING AT LABORATORY SCALE

Computational models of wildfires are an important tool for fire managers and scientists. However, fuel inputs to wildfire models can be difficult to represent with sufficient detail to be both computationally efficient and representative of observations. Recent advances in fuel mapping with airborne and terrestrial laser scanning (LIDAR) techniques present new opportunities to capture variation in fuels within a tree canopy and on a landscape. In this paper, we develop a technique for building 3D representations of vegetation from point clouds created by Terrestrial Laser Scans (TLS). Our voxel based approach can be extended to represent heterogeneous crown fuels as collections of fuel cells in modern 3D Computational Fluid Dynamics wildfire models such as FDS, QUIC-Fire, or FIRETEC. We evaluated the effectiveness of our technique at different fuel cell resolutions by using the DAKOTA optimization toolkit to compare simulated fire behavior in FDS with observed burn data collected during a series of experiments at the Missoula Fire Sciences Laboratory. The primary finding was that within the search space of point cloud derived fuel cells, we find accurate descriptions of observed fire behavior with the FDS model. We also find that within our search space, regions of global minima are consistent across fuel cells at different resolutions. This finding suggests that while new techniques are capable of characterizing fuel models with a high degree of fidelity, high resolution 3D fuel models do not improve parity with observed fire behavior in the FDS fire model. The results of this paper offer fire modelers guidelines for translating LIDAR data to 3D fire models, and what fuel cell resolution can best capture accurate fire behavior

Laser scanning in forests

Peer reviewe

Measuring and Modeling the Structure of Coniferous Trees with Point Clouds Data

Coniferous trees are a major North American crop that has been intensively managed for its commercial value, while also serving as critical habitat for abundant wildlife and as carbon sinks. Having diverse functions, North American temperate coniferous forests have become a research hotspot for numerous scientific studies aiming to integrate ecological and economic objectives, such as examining the contribution of the conifer crown architecture to long-term forest management schemes. Point clouds have become an important source of forest inventory data and forest ecological studies, as provide accurate and comprehensive estimates of many structural variables. The present thesis aims to improve the understanding of conifer crown structure by estimating crown variables and developing stem and crown models using point clouds derived from images or laser scanning. The utilizations of point clouds were tested on loblolly pine plantations and mature Douglas-fir trees in a natural stand. Various types of 3D models were constructed for tree stems and branch attributes using point clouds. The 3D models provide direct volume estimates, as well as estimates of tree structural variables including tree height, stem diameter, branch basal diameter, length, insertion angle, and azimuth. The variable extractions were executed with semi-automatic methods, which combine human interpretation with an automatic estimation algorithm. The accuracy and reliability of point-clouds-based estimates were assessed with ground measurements and estimates from existing equations through simulations. Stem taper equations were developed using point-clouds-based stem diameter estimates. Nonlinear models of branch variables, as well as systematic crown models, were developed using lidar-based estimates by considering neighboring competition effects. The results demonstrate the reliability and efficiency of using point clouds data as alternatives or complements to traditional fieldwork. Stem and branch variables estimated nondestructively from lidar and photogrammetry point clouds agreed with ground measurements and fit in the range of observations from existing equations. Workflows developed and presented in this thesis can be employed by forestry practitioners and researchers to acquire fast and accurate tree structural variables, while models of stem and branch attributes can guide forest inventory and silvicultural practices as well as advance ecological research

An intensity recovery algorithm (IRA) for minimizing the edge effect of LIDAR data

The terrestrial laser scanner is an equipment developed for surveying applications and is also used for many other purposes due to its ability to acquire 3D data quickly. However, before intensity data can be analyzed, it must be processed in order to minimize the edge or border effect, one of the most serious problems of LIDAR’s intensity data. Our research has focused on characterizing the edge effect behavior as well as to develop an algorithm to minimize edge effect distortion automatically (IRA). The IRA showed to be effective recovering 35.71% of points distorted by the edge effect, providing significant improvements and promising results for the development of applications based on TLS data intensity to many studies

Complexity and Dynamics of Semi-Arid Vegetation Structure, Function and Diversity Across Spatial Scales from Full Waveform Lidar

Semi-arid ecosystems cover approximately 40% of the earth’s terrestrial landscape and show high dynamicity in ecosystem structure and function. These ecosystems play a critical role in global carbon dynamics, productivity, and habitat quality. Semi-arid ecosystems experience a high degree of disturbance that can severely alter ecosystem services and processes. Understanding the structure-function relationships across spatial extents are critical in order to assess their demography, response to disturbance, and for conservation management. In this research, using state-of-the-art full waveform lidar (airborne and spaceborne) and field observations, I developed a framework to assess the complexity and dynamics of vegetation structure, function and diversity across spatial scales in a semi-arid ecosystem. Difficulty in differentiating low stature vegetation from bare ground is the key remote sensing challenge in semi-arid ecosystems. In this study, I developed a workflow to differentiate key plant functional types (PFTs) using both structural and biophysical variables derived from the full waveform lidar and an ensemble random forest technique. The results revealed that waveform lidar pulse width can clearly distinguish shrubs from bare ground. The models showed PFT classification accuracy of 0.81–0.86% and 0.60–0.70% at 10 m and 1 m spatial resolutions, respectively. I found that structural variables were more important than the biophysical variables to differentiate the PFTs in this study area. The study further revealed an overlap between the structural features of different PFTs (e.g. shrubs from trees). Using structural features, I derived three main functional traits (canopy height, plant area index and foliage height diversity) of shrubs and trees that describe canopy architecture and light use efficiency of the ecosystem. I evaluated the trends and patterns of functional diversity and their relationship with non-climatic abiotic factors and fire disturbance. In addition to the fine resolution airborne lidar, I used simulated large footprint spaceborne lidar representing the newly launched Global Ecosystem Dynamics Investigation system (GEDI, a lidar sensor on the International Space Station) to evaluate the potential of capturing functional diversity trends of semi-arid ecosystems at global scales. The consistency of diversity trends between the airborne lidar and GEDI confirmed GEDI’s potential to capture functional diversity. I found that the functional diversity in this ecosystem is mainly governed by the local elevation gradient, soil type, and slope. All three functional diversity indices (functional richness, functional evenness and functional divergence) showed a diversity breakpoint near elevations of 1500 m – 1700 m. Functional diversity of fire-disturbed areas revealed that the fires in our study area resulted in a more even and less divergent ecosystem state. Finally, I quantified aboveground biomass using the structural features derived from both the airborne lidar and GEDI data. Regional estimates of biomass can indicate whether an ecosystem is a net carbon sink or source as well as the ecosystem’s health (e.g. biodiversity). Further, the potential of large footprint lidar data to estimate biomass in semi-arid ecosystems are not yet fully explored due to the inherent overlapping vegetation responses in the ground signals that can be affected by the ground slope. With a correction to the slope effect, I found that large footprint lidar can explain 42% of variance of biomass with a RMSE of 351 kg/ha (16% RMSE). The model estimated 82% of the study area with less than 50% uncertainty in biomass estimates. The cultivated areas and the areas with high functional richness showed the highest uncertainties. Overall, this dissertation establishes a novel framework to assess the complexity and dynamics of vegetation structure and function of a semi-arid ecosystem from space. This work enhances our understanding of the present state of an ecosystem and provides a foundation for using full waveform lidar to understand the impact of these changes to ecosystem productivity, biodiversity and habitat quality in the coming decades. The methods and algorithms in this dissertation can be directly applied to similar ecosystems with relevant corrections for the appropriate sensor. In addition, this study provides insights to related NASA missions such as ICESat-2 and future NASA missions such as NISAR for deriving vegetation structure and dynamics related to disturbance

Spectral and spatial information from a novel dual-wavelength full-waveform terrestrial laser scanner for forest ecology

The Salford Advanced Laser Canopy Analyser (SALCA) is an experimental terrestrial laser scanner designed and built specifically to measure the structural and biophysical properties of forest canopies. SALCA is a pulsed dual-wavelength instrument with co-aligned laser beams recording backscattered energy at 1063 and 1545 nm; it records full-waveform data by sampling the backscattered energy at 1 GHz giving a range resolution of 150 mm. The finest angular sampling resolution is 1 mrad and around 9 million waveforms are recorded over a hemisphere above the tripod-mounted scanner in around 110 minutes. Starting in 2010, data pre-processing and calibration approaches, data analysis, and information extraction methods, were developed and a wide range of field experiments conducted. The overall objective is to exploit the spatial, spectral and temporal characteristics of the data to produce ecologically useful information on forest and woodland canopies including leaf area index, plant area volume density and leaf biomass, and to explore the potential for tree species identification and classification. This paper outlines the key challenges in instrument development, highlights the potential applications for providing new data for forest ecology, and describes new avenues for exploring information-rich data from the next generation of TLS instruments like SALCA