Terrestrial Laser Scanning to Detect Liana Impact on Forest Structure

Tropical forests are currently experiencing large-scale structural changes, including an increase in liana abundance and biomass. Higher liana abundance results in reduced tree growth and increased tree mortality, possibly playing an important role in the global carbon cycle. Despite the large amount of data currently available on lianas, there are not many quantitative studies on the influence of lianas on the vertical structure of the forest. We study the potential of terrestrial laser scanning (TLS) in detecting and quantifying changes in forest structure after liana cutting using a small scale removal experiment in two plots (removal plot and non-manipulated control plot) in a secondary forest in Panama. We assess the structural changes by comparing the vertical plant profiles and Canopy Height Models (CHMs) between pre-cut and post-cut scans in the removal plot. We show that TLS is able to detect the local structural changes in all the vertical strata of the plot caused by liana removal. Our study demonstrates the reproducibility of the TLS derived metrics for the same location confirming the applicability of TLS for continuous monitoring of liana removal plots to study the long-term impacts of lianas on forest structure. We therefore recommend to use TLS when implementing new large scale liana removal experiments, as the impact of lianas on forest structure will determine the aboveground competition for light between trees and lianas, which has important implications for the global carbon cycle

Comparative effects of logging and wildfire on carbon and fire dynamics in resprouting and non-resprouting eucalypt forests

The tall (\u3e30 m) eucalypt forests of south-eastern Australia are valued for their carbon storage and sequestration. However, they may also act as a carbon source given that they are prone to large wildfires and subject to commercial logging. Logging may reduce carbon stocks, but the relative losses compared to wildfire have not been quantified in many types of these forests. There is also growing evidence that logging may make carbon stocks in affected forests less resistant to fire and increase the risk of wildfire. These dynamics may also vary between eucalypt forest types. Carbon and fire dynamics in forests dominated by eucalypt species that cannot resprout new foliage after fire may be more sensitive to antecedent disturbance than the more widespread resprouting eucalypt forests. Non-resprouting eucalypt forests are often subject to stand replacing wildfires, but such a response is inherently absent in resprouting eucalypt forests. Non-resprouting eucalypt forests are also subject to clearfell logging, while logging practices in resprouting eucalypt forests are often less intense. Hence, a thorough comparative assessment of the effects of logging, wildfire and carbon dynamics across these broad forest types is needed to inform ongoing management of tall eucalypt forests. In this thesis, I compare how logging and wildfire affect forest carbon stocks, carbon stability (the capacity for carbon stocks to persist through, and recover after likely disturbances) and the risk of fire. The effects of logging and wildfire are compared between resprouting and non-resprouting eucalypt forests. I measured above ground carbon stocks and fuel characteristics (using a terrestrial laser scanner) along approximately 80-year chronosequences of logging and wildfire. Most sites in the resprouting forest study area were subsequently burnt by a mixed severity fire during the 2019-2020 fire season, enabling me to measure the change in carbon stock associated with wildfire and how it was affected by antecedent disturbance and fire severity. I also assessed the effects of variations in fuel characteristics on the severity of the 2019-2020 wildfires. To determine the effects of logging and wildfire on fire weather conditions, I measured fire weather conditions below the canopy across approximately 70-year chronosequences of logging and wildfire in the resprouting study area

The Burning Bush: Linking LiDAR-derived Shrub Architecture to Flammability

Light detection and ranging (LiDAR) and terrestrial laser scanning (TLS) sensors are powerful tools for characterizing vegetation structure and for constructing three-dimensional (3D) models of trees, also known as quantitative structural models (QSM). 3D models and structural traits derived from them provide valuable information for biodiversity conservation, forest management, and fire behavior modeling. However, vegetation studies and 3D modeling methodologies often only focus on the forest canopy, with little attention given to understory vegetation. In particular, 3D structural information of shrubs is limited or not included in fire behavior models. Yet, understory vegetation is an important component of forested ecosystems, and has an essential role in determining fire behavior. In this dissertation, I explored the use of TLS data and quantitative structure models to model shrub architecture in three related studies. In the first study, I present a semi-automated methodology for reconstructing architecturally different shrubs from TLS LiDAR. By investigating shrubs with different architectures and point cloud densities, I showed that occlusion, shrub complexity, and shape greatly affect the accuracy of shrub models. In my second study, I assessed the 3D architectural drivers of understory flammability by evaluating the use of architectural metrics derived from the TLS point cloud and 3D reconstructions of the shrubs. I focused on eight species common in the understory of the fire-prone longleaf pine forest ecosystem of the state of Florida, USA. I found a general tendency for each species to be associated with a unique combination of flammability and architectural traits. Novel shrub architectural traits were found to be complementary to the direct use of TLS data and improved flammability predictions. The inherent complexity of shrub architecture and uncertainty in the TLS point cloud make scaling up from an individual shrub to a plot level a challenging task. Therefore, in my third study, I explored the effects of lidar uncertainty on vegetation parameter prediction accuracy. I developed a practical workflow to create synthetic forest stands with varying densities, which were subsequently scanned with simulated terrestrial lidar. This provided data sets quantitatively similar to those created by real-world LiDAR measurements, but with the advantage of exact knowledge of the forest plot parameters, The results showed that the lidar scan location had a large effect on prediction accuracy. Furthermore, occlusion is strongly related to the sampling density and plot complexity. The results of this study illustrate the potential of non-destructive lidar approaches for quantifying shrub architectural traits. TLS, empirical quantitative structural models, and synthetic models provide valuable insights into shrub structure and fire behavior

Terrestrial laser scanning: an operational tool for fuel hazard mapping?

Fuel hazard estimates are vital for the prediction of fire behaviour and planning fuel treatment activities. Previous literature has highlighted the potential of Terrestrial Laser Scanning (TLS) to be used to assess fuel properties. However, operational uptake of these systems has been limited due to a lack of a sampling approach that balances efficiency and data efficacy. This study aims to assess whether an operational approach utilising Terrestrial Laser Scanning (TLS) to capture fuel information over an area commensurate with current fuel hazard assessment protocols implemented in South-Eastern Australia is feasible. TLS data were captured over various plots in South-Eastern Australia, utilising both low- and high-cost TLS sensors. Results indicate that both scanners provided similar overall representation of the ground, vertical distribution of vegetation and fuel hazard estimates. The analysis of fuel information contained within individual scans clipped to 4 m showed similar results to that of the fully co-registered plot (cover estimates of near-surface vegetation were within 10%, elevated vegetation within 15%, and height estimates of near-surface and elevated strata within 0.05 cm). This study recommends that, to capture a plot in an operational environment (balancing efficiency and data completeness), a sufficient number of non-overlapping individual scans can provide reliable estimates of fuel information at the near-surface and elevated strata, without the need for co-registration in the case study environments. The use of TLS within the rigid structure provided by current fuel observation protocols provides incremental benefit to the measurement of fuel hazard. Future research should leverage the full capability of TLS data and combine it with moisture estimates to gain a full realisation of the fuel hazard

Assessing new methods for measuring forest understorey vegetation using terrestrial laser scanning

Forest structure is the complex 3D arrangement of all components within the forest architecture. This includes stems, foliage, branches (the components of trees) but also includes non-tree components such as understorey shrubs and herbs. Understanding the structural components of forests is critical when considering forest ecosystems. The structure of a forest can affect functional and compositional characteristics such as productivity and species richness with structure being an important factor influencing animal-habitat associations. Structural characteristics of forests include the size distribution and spatial organisation of trees, and the horizontal and vertical density of objects within the understorey. Trees are the dominant feature of any forest, but the understorey is also very important when considering forest characteristics. Examining the links between the spatial distribution of understorey material and ecological parameters, such as diversity and productivity, has an important role in ecological studies. There are multiple field survey techniques that can be applied when collecting data for a forest survey. For a technique to be an effective survey tool it should be readily quantifiable, repeatable, cost-effective, easily assessed, ecologically meaningful and where possible not contain observer bias. Traditional methods of forest survey are very common as they offer reliable, low cost estimations of forest structural parameters such as diameter, height and understorey cover. Recent developments within 3D data collection using terrestrial laser scanning (TLS) have allowed foresters and ecologists to reproduce the structural parameters collected during traditional forest surveys. These developments have shown the usefulness of 3D data collection in assessing forest structure, but have focused on replicating existing forest metrics rather than developing new ones. For TLS to reach its full potential within the field of forest ecology, new metrics and indices need to be developed specifically for laser scan analysis. This study developed and tested new methods of forest survey, concentrating on understorey vegetation, using commercially available TLS. Results showed that these new techniques can provide novel structural assessments of the understorey layers of forests for use in forest ecology surveys, not available through traditional methods. Using a new index describing the vertical component of forest understorey, it was shown how the relationship between deer browsing and forest structure can be identified through feature extraction from laser scanning. The method developed required minimal manual processing and was applied to large data sets. The structural changes between high and low deer density sites were also observed through the creation of an understorey density profile. This method, specifically targeted at the lower layers of the understorey, successfully identified structural change at the decimetre level. Using microtopography estimates from understorey point clouds it was shown how understorey complexity corresponded with vegetation surfaces extracted through TLS. This suggests that correlation between understorey structure (and therefore habitat type) and the microtopography of vegetation surfaces may be used for detailed assessment of understorey structural characteristics utilising TLS. In addition to the development of novel analysis methods, new techniques for acquiring TLS data of forest understorey were examined. The use of a standardised methodology for temporal surveying, utilising a common digital terrain model and fixed ground control, as developed here, provides a framework from which further data can be acquired. This approach offers a relatively quick, efficient, non-destructive assessment of temporal change within forests. A novel method of forest survey utilising handheld mobile laser scanning (HMLS) was also tested, showing its potential to complement static TLS surveying by providing increased survey coverage and allowing point cloud processing to be considered for areas which are otherwise difficult to access

Assessing new methods for measuring forest understorey vegetation using terrestrial laser scanning

Forest structure is the complex 3D arrangement of all components within the forest architecture. This includes stems, foliage, branches (the components of trees) but also includes non-tree components such as understorey shrubs and herbs. Understanding the structural components of forests is critical when considering forest ecosystems. The structure of a forest can affect functional and compositional characteristics such as productivity and species richness with structure being an important factor influencing animal-habitat associations. Structural characteristics of forests include the size distribution and spatial organisation of trees, and the horizontal and vertical density of objects within the understorey. Trees are the dominant feature of any forest, but the understorey is also very important when considering forest characteristics. Examining the links between the spatial distribution of understorey material and ecological parameters, such as diversity and productivity, has an important role in ecological studies. There are multiple field survey techniques that can be applied when collecting data for a forest survey. For a technique to be an effective survey tool it should be readily quantifiable, repeatable, cost-effective, easily assessed, ecologically meaningful and where possible not contain observer bias. Traditional methods of forest survey are very common as they offer reliable, low cost estimations of forest structural parameters such as diameter, height and understorey cover. Recent developments within 3D data collection using terrestrial laser scanning (TLS) have allowed foresters and ecologists to reproduce the structural parameters collected during traditional forest surveys. These developments have shown the usefulness of 3D data collection in assessing forest structure, but have focused on replicating existing forest metrics rather than developing new ones. For TLS to reach its full potential within the field of forest ecology, new metrics and indices need to be developed specifically for laser scan analysis. This study developed and tested new methods of forest survey, concentrating on understorey vegetation, using commercially available TLS. Results showed that these new techniques can provide novel structural assessments of the understorey layers of forests for use in forest ecology surveys, not available through traditional methods. Using a new index describing the vertical component of forest understorey, it was shown how the relationship between deer browsing and forest structure can be identified through feature extraction from laser scanning. The method developed required minimal manual processing and was applied to large data sets. The structural changes between high and low deer density sites were also observed through the creation of an understorey density profile. This method, specifically targeted at the lower layers of the understorey, successfully identified structural change at the decimetre level. Using microtopography estimates from understorey point clouds it was shown how understorey complexity corresponded with vegetation surfaces extracted through TLS. This suggests that correlation between understorey structure (and therefore habitat type) and the microtopography of vegetation surfaces may be used for detailed assessment of understorey structural characteristics utilising TLS. In addition to the development of novel analysis methods, new techniques for acquiring TLS data of forest understorey were examined. The use of a standardised methodology for temporal surveying, utilising a common digital terrain model and fixed ground control, as developed here, provides a framework from which further data can be acquired. This approach offers a relatively quick, efficient, non-destructive assessment of temporal change within forests. A novel method of forest survey utilising handheld mobile laser scanning (HMLS) was also tested, showing its potential to complement static TLS surveying by providing increased survey coverage and allowing point cloud processing to be considered for areas which are otherwise difficult to access

Characteriation of Mediterranean Aleppo pine forest using low-density ALS data

Los espacios forestales son una fuente de servicios, tanto ambientales como económicos, de gran importancia para la sociedad. La caracterización de estos ambientes ha requerido tradicionalmente de un laborioso trabajo de campo. La aplicación de técnicas de teledetección ha proporcionado una visión más amplia a escala espacial y temporal, a la par que ha generado una reducción de los costes. La utilización de sensores óptico-pasivo multiespectrales y de sensores radar posibilita la estimación de parámetros forestales, si bien el desarrollo de sensores LiDAR, como el caso de los escáneres láser aeroportados (ALS), ha mejorado la caracterización tridimensional de la estructura de los bosques. La disponibilidad pública de dos coberturas LiDAR, generadas en el marco del Plan Nacional de Ortofotografía Aérea (PNOA), ha abierto nuevas líneas de investigación que permiten proporcionar información útil para la gestión forestal. La presente tesis utiliza datos LiDAR aeroportados de baja densidad para estimar diversas variables forestales, con ayuda de trabajo de campo, en masas forestales de Pino carrasco (Pinus halepensis Miller) en Aragón. La investigación aborda dos cuestiones relevantes como son la exploración de las metodologías más adecuadas para estimar variables forestales considerando escalas locales y regionales, teniendo en cuenta las posibles fuentes de error en el modelado; y, además, analiza la potencialidad de los datos LiDAR del PNOA para el desarrollo de aplicaciones forestales que valoricen las áreas forestales como recursos socio-económicos. La tesis se ha desarrollado según la modalidad de compendio de publicaciones, incluyendo cuatro trabajos que dan respuesta a los objetivos planteados. En primer lugar, se realiza un análisis comparativo de distintos modelos de regresión, paramétricos y no paramétricos, para estimar la pérdida de biomasa y las emisiones de CO2 en un incendio, mediante la utilización de datos LiDAR-PNOA y datos ópticos del satélite Landsat 8. En segundo lugar, se explora la idoneidad de distintos métodos de selección de variables para estimar biomasa total en masas de Pino carrasco utilizando datos LiDAR de baja densidad. En tercer lugar, se cuantificó y cartografió la biomasa residual forestal en el conjunto de masas de Pino carrasco de Aragón y se evaluó el efecto de diversas características de la tecnología LiDAR y de las variables ambientales en la precisión de los modelos. Finalmente, se analiza la transferibilidad temporal de modelos para estimar a escala regional siete variables forestales, utilizando datos LiDAR-PNOA multi-temporales. A este respecto, se compararon dos enfoques que permiten analizar la transferibilidad temporal: en primer lugar, el método directo ajusta un modelo para un determinado punto en el tiempo y estima las variables forestales para otra fecha; por otra parte, el método indirecto ajusta dos modelos diferentes para cada momento en el tiempo, estimando las variables forestales en dos fechas distintas. Los resultados obtenidos y las conclusiones derivadas de la investigación indican que la técnica basada en coeficientes de correlación de Spearman y el método de selección por todos los subconjuntos constituyen los métodos de selección de métricas LiDAR más apropiados para la modelización. El análisis de métodos de regresión para la estimación de variables forestales indicó que su idoneidad variaba de acuerdo con el tamaño y complejidad de la muestra. El método de regresión linear multivariante arrojó mejores resultados que los métodos no-paramétricos en el caso de muestras pequeñas. Por el contrario, el método Support Vector Machine produjo los mejores resultados con muestras grandes. El incremento de la densidad de puntos y de los valores de penetración de los pulsos LiDAR en el dosel, así como la presencia de ángulos de escaneo pequeños, incrementó la exactitud de los modelos. De forma similar, el incremento de la pendiente y la presencia de arbustos en el sotobosque implican una reducción en la exactitud de los modelos. En la estimación de variables forestales utilizando datos LiDAR multi-temporales, aunque la utilización del enfoque indirecto arrojó generalmente una mayor precisión en los modelos, se obtuvieron resultados similares con el enfoque directo, el cual constituye una alternativa óptima para reducir el tiempo de modelado y los costes de realización de trabajo de campo. La fusión de datos LiDAR y datos óptico-pasivos ha evidenciado la conveniencia de los métodos aplicados para cuantificar las emisiones de CO2 a la atmósfera generadas por un incendio. Esta metodología constituye una alternativa adecuada cuando no existen datos multi-temporales LiDAR. La estimación de variables de inventario forestal, así como de diversas fracciones de biomasa, como la biomasa total y la biomasa residual forestal, proporciona información valiosa para caracterizar las masas forestales mediterráneas de Pino carrasco y mejorar la gestión forestalForest ecosystems provide environmental and economic services of great importance to the society. The characterization of these environments has been traditionally accomplished with intense field work. In comparison, the application of remote sensing tools provides a greater overview over large spatial and temporal scales while minimizing costs. Although optical data and Synthetic Aperture Radar (SAR) allow estimating forest stand variables, the development of LiDAR sensors such as Airborne Laser Scanner (ALS) have improved three-dimensional characterization of forest structure. The availability of two ALS public data coverages for the Spanish territory, provided by the National Plan for Aerial Ortophotography (PNOA), opens new research opportunities to generate useful information for forest management. This PhD Thesis used low-density ALS-PNOA data to estimate different forest variables, with support in fieldwork, in the Aleppo pine (Pinus halepensis Miller) forests of Aragón region. The addressed research is relevant mainly for two reasons: first, the examination of suitable methodologies and error sources in forest stand variables prediction at local (small area) and regional scales (large area), and second, the application of ALS data to the characterization of forest areas as a socio-economic reservoir. This PhD Thesis is a compendium of four scientific papers, which sequentially answer the objectives established. Firstly, a comparative analysis of different parametric and non-parametric models was performed to estimate biomass losses and CO2 emissions using low-density ALS and Landsat 8 data in a burnt Aleppo pine forest. Secondly, we assess the suitability of variable selection methods when estimating total biomass in Aleppo pine forest stands using low-density ALS data. In the third manuscript, the quantification and mapping of forest residual biomass in Aleppo pine forest of Aragón region and the assessment of the effect of ALS and environmental variables in model accuracy were accomplished. Finally, the temporal transferability of seven forest stands attributes modelling using multi-temporal ALS-PNOA data in Aleppo pine forest at regional scale was explored. In this case, the temporal transferability was assessed comparing two methodologies; the direct and indirect approach. The first one fits a model for one point in time and estimates the forest variable for another point in time. The indirect approach adjusts two models in different points in time to estimate the forest variables in two different dates. The results derived from this research indicated that Spearman’s rank and All Subset Selection are the most appropriate methods in the ALS metrics selection step commonly applied in modelling. The suitability of the regression methods depends on the sample size and complexity. Thus, multivariate linear regression outperformed non-parametric methods with small samples while support vector machine was the most accurate method with larger samples. Model accuracy increased with higher point density and canopy pulse penetration, while decreasing with wider scan angles. Furthermore, the presence of steep slopes and shrub reduced model performance. In the case of forest stand variables prediction using multi-temporal ALS data, although the indirect approach produced generally a higher precision, the direct approach provided similar results, constituting a suitable alternative to reduce modelling time and fieldwork costs. The fusion of ALS and passive optical data have evidenced the suitability of this information for quantifying wildfire CO2 emissions to atmosphere, constituting a good alternative when multi-temporal ALS data is not available. The estimation of forest inventory variables as well as different biomass fractions, such as total biomass and forest residual biomass, provided valuable information to characterize Mediterranean Aleppo pine forests and improve forest management.<br /

Terrestrial laser scanning for forest monitoring

Remote sensing data worden beschouwd als één van de belangrijkste databronnen voor het observeren van uitgestrekte bosgebieden. Het belangrijkste doel van dit proefschrift is om het potentieel van terrestrische LiDAR voor het monitoren van bossen te onderzoeken en om methoden te ontwikkelen voor het afleiden van accurate in-situ referentiedata voor bos monitoring