An Integrated Method for Coding Trees, Measuring Tree Diameter, and Estimating Tree Positions

Accurately measuring tree diameter at breast height (DBH) and estimating tree positions in a sample plot are important in tree mensuration. The main aims of this paper include (1) developing a new, integrated device that can identify trees using the quick response (QR) code technique to record tree identifications, measure DBH, and estimate tree positions concurrently; (2) designing an innovative algorithm to measure DBH using only two angle sensors, which is simple and can reduce the impact of eccentric stems on DBH measures; and (3) designing an algorithm to estimate the position of the tree by combining ultra-wide band (UWB) technology and altitude sensors, which is based on the received signal strength indication (RSSI) algorithm and quadrilateral localization algorithm. This novel device was applied to measure ten 10 × 10 m square plots of diversified environments and various tree species to test its accuracy. Before measuring a plot, a coded sticker was fixed at a height of 1.3 m on each individual tree stem, and four UWB module anchors were set up at the four corners of the plot. All individual trees\u27 DBHs and positions within the plot were then measured. Tree DBH, measured using a tree caliper, and the values of tree positions, measured using tape, angle ruler, and inclinometer, were used as the respective reference values for comparison. Across the plots, the decode rate of QR codes was 100%, with an average response time less than two seconds. The DBH values had a bias of 1.89 mm (1.88% in relative terms) and a root mean square error (RMSE) of 5.38 mm (4.53% in relative terms). The tree positions were accurately estimated; the biases on the x-axis and the y-axis of the tree position were -8.55-14.88 cm and -12.07-24.49 cm, respectively, and the corresponding RMSEs were 12.94-33.96 cm and 17.78-28.43 cm. The average error between the estimated and reference distances was 30.06 cm, with a standard deviation of 13.53 cm. The device is cheap and friendly to use in addition to its high accuracy. Although further studies are needed, our method provides a great alternative to conventional tools for improving the efficiency and accuracy of tree mensuration

Consequences of vertical basic wood density variation on the estimation of aboveground biomass with terrestrial laser scanning

Terrestrial laser scanning (TLS) is used to generate realistic 3D tree models that enable a non-destructive way of quantifying tree volume. An accurate value for basic wood density is required to convert tree volume into aboveground biomass (AGB) for forest carbon assessments. However, basic density is characterised by high inter-, intra-species and within-tree variability and a likely source of error in TLS-derived biomass estimates. Here, 31 adult trees of 4 important European timber species (Fagus sylvatica, Larix decidua, Pinus sylvestris, Fraxinus excelsior) were scanned using TLS and then felled for several basic wood density measurements. We derived a reference volume-weighted basic density (ρw) by combining volume from 3D tree models with destructively assessed vertical density profiles. We compared this to basic density retrieved from a single basal disc over bark (ρbd), two perpendicular pith-to-bark increment cores at breast height (ρic), and sourcing the best available local basic wood density from publications. Stump-to-tip trends in basic wood density caused site-average woody AGB estimation biases ranging from −3.3 to + 7.8% when using ρbd and from −4.1 to + 11.8% when using ρic. Basic wood density from publications was in general a bad predictor for ρw as the bias ranged from −3.2 to + 17.2%, with little consistency across different density repositories. Overall, our density-attributed biases were similar to several recently reported biases in TLS-derived tree volume, leading to potentially large compound errors in biomass assessments with TLS if patterns of vertical basic wood density variation are not properly accounted for

Forestry and Arboriculture Applications Using High-Resolution Imagery from Unmanned Aerial Vehicles (UAV)

Forests cover over one-third of the planet and provide unmeasurable benefits to the ecosystem. Forest managers have collected and processed countless amounts of data for use in studying, planning, and management of these forests. Data collection has evolved from completely manual operations to the incorporation of technology that has increased the efficiency of data collection and decreased overall costs. Many technological advances have been made that can be incorporated into natural resources disciplines. Laser measuring devices, handheld data collectors and more recently, unmanned aerial vehicles, are just a few items that are playing a major role in the way data is managed and collected. Field hardware has also been aided with new and improved mobile and computer software. Over the course of this study, field technology along with computer advancements have been utilized to aid in forestry and arboricultural applications. Three-dimensional point cloud data that represent tree shape and height were extracted and examined for accuracy. Traditional fieldwork collection (tree height, tree diameter and canopy metrics) was derived from remotely sensed data by using new modeling techniques which will result in time and cost savings. Using high resolution aerial photography, individual tree species are classified to support tree inventory development. Point clouds were used to create digital elevation models (DEM) which can further be used in hydrology analysis, slope, aspect, and hillshades. Digital terrain models (DTM) are in geographic information system (GIS), and along with DEMs, used to create canopy height models (CHM). The results of this study can enhance how the data are utilized and prompt further research and new initiatives that will improve and garner new insight for the use of remotely sensed data in forest management

Interconnection of a Forest Growth Model and a Structural Model for Young Poplar Trees (Populus spp.)

Beim Anbau von schnellwachsenden Baumarten wie Pappel und Weide auf landwirtschaftlichen Flächen in Kurzumtriebsplantagen stellt die Standortwahl und die daran gebundene Ertragsprognose eine zentrale Entscheidung für den Bewirtschafter dar. In Verbindung mit dem Sortenaspekt besteht hier Forschungsbedarf zur Wechselwirkung von Standort und Genotyp hinsichtlich der Wuchsleistung. Ziel dieser Arbeit ist es, diese Fragestellungen auf mehreren Ebenen zu betrachten. Dazu wurde ein Multiskalen-Ansatz gewählt, in dessen Rahmen zwei Modellkomplexe entwickelt werden, um sie anschließend durch eine Schnittstelle zu verbinden. Der erste Komplex sieht dabei die Implementierung eines Ertragssimulators vor, der das einzelbaumbasierte Wachstum und die Mortalität in Abhängigkeit von Konkurrenz und Standortbedingungen abbildet. Die Datengrundlage hierfür stellen Zuwachsdaten aus dem vom BMEL geförderten ProLoc Verbundvorhaben dar. Dazu wird auf 18 Versuchsflächen zurückgegriffen, die auf einer breiten Amplitude standörtlicher Eigenschaften angelegt wurden. Nach einem einheitlichem Versuchsdesign wurden monoklonale Versuchsparzellen mit drei Pappel- und zwei Weidenklonen (interspezifisch gekreuzte Hybride) in zwei je dreijährigen Rotationen versuchstechnisch betreut und nach dem dritten Jahr auf den Stock gesetzt. Basierend auf der Vorlage des Waldwachstumssimulators BWINPro und der zugehörigen Programmbibliothek TreeGross werden mehrere Modelle parametrisiert, die neben den Überlebensraten nach der Pflanzung und dem Rückschnitt die Höhenzuwächse in der ersten und zweiten Rotation schätzen. Mit dem distanzunabhängigen Konkurrenzparameter “basal area of larger trees'' kann die Entwicklung innerhalb der Bestände abgebildet werden. Hinsichtlich der Wuchsleistung auf Standortebene stellen sich im Zuge der Variablenselektion die Parameter Pflanzdatum, nutzbare Feldkapazität, Bodenzahl, Niederschlagssumme im Mai und Juni und Mitteltemperatur im Juni und Juli als entscheidend heraus. Zur Schätzung des Höhenzuwachses und der Überlebensrate nach Rückschnitt wird die Baumhöhe vor der Ernte als unabhängige Variable genutzt. Der Faktor Klon deutet innerhalb der Modelle zwar auf Unterschiede in den Wachstumsvorgängen hin, Wechselwirkungen mit Standortvariablen können jedoch nicht festgestellt werden. Fehlende Variablen wie der durchschnittliche Gesamtzuwachs des Ertrags der Trockenmasse in t_atro ha^-1 a^-1 werden über zusätzliche am Datensatz parametrisierte Funktionen geschätzt. Die Einzelmodelle werden zu einem Simulationsablauf verbunden und die Gesamtschätzgüte überprüft. In der ersten Rotation können gute Ergebnisse erzielt werden mit quadrierten Korrelationen der beobachteten und geschätzten Bestandesmittelhöhen von 0.79. In der zweiten Rotation nimmt die Schätzgüte jedoch auf 0.53 ab. Es finden sich vereinzelte Standorte mit starken Abweichungen, als problematisch werden die Tiefe der Bodenbeprobung und fehlende erweiterte Informationen über den Wasserhaushalt vermutet. Der zweite Modellkomplex beinhaltet ein Strukturmodell, für das sich auf die Pappel-Genotpyen und die zweite Rotation beschränkt wird. Zunächst wurden mehrere Messmethoden identifiziert, die geeignet sind, die Baumarchitektur in Form von Geometrie und Topologie der oberirdischen holzigen Biomasse sowie die Morphologie der Belaubung hinsichtlich der Blattarchitektur und Blattform zu bestimmen. Für die Verzweigungsarchitektur wurden ein manuelles Verfahren und ein semi-automatisches Verfahren mit einem elektromagnetischen Digitizer zur Bestimmung der Astkrümmung gewählt und angewandt. Die Blattarchitektur wurde mit einem manuellen Verfahren gemessen. Die Blattform konnte per Digitalisierung von eingesammelten Blättern bestimmt werden. Im Zuge der Analyse der gewonnenen Daten werden mehrere Modelle parametrisiert. Hierdurch können für Apikal- und Lateralknospen die Austriebswahrscheinlichkeiten sowie die Dimension und Orientierung im Raum von sich bildenden Trieben geschätzt werden. Innerhalb der Modelle wird nach Haupt- und Nebenstämmen, Verlängerungs- und Seitentrieben, Lang- und Kurztrieben und innerhalb der Seitentriebe nach sylleptischen sowie regulären Trieben differenziert. Der Ausgangspunkt ist hier die Schätzung die Internodienanzahl je Trieb, die über die Trieblänge wiederum andere Parameter wie den Verzweigungswinkel und die Krümmung beeinflusst. Weitere Faktoren, die mehreren Modellen zugrunde liegen, sind das Alter und die Verzweigungsordnung sowie der genotypische Einfluss. Parameter wie die Belaubung und die Blattgröße lassen sich mitunter durch die relative Höhe am Baum schätzen. Die Blattform wiederum wird durch Konturpunkte bestimmt, deren Koordinaten in Abhängigkeit von der Blattlänge berechnet werden. Im Rahmen der Analyse dieser Modelle stellen sich geringe Unterschiede in der Struktur zwischen den Klonen heraus. Ausnahmen stellen die Krümmung und Verzweigungswinkel der Seitentriebe für einen der Klone dar, bei dem die Modelle den beobachtbaren schlankeren Habitus gut reproduzieren. Deutliche Unterschiede ergeben sich auch bei den Blattformen, die die Blattformen der zugrundeliegenden Elternspezies der Hybride wiedergeben. Die einzelnen Modellfunktionen werden anschließend als Gesamt-Strukturmodell in der Modellplattform GroIMP implementiert. Das erhaltene Modell kann in Jahresschritten die Entwicklung der Baumstruktur für jeden der drei Klone abbilden. Wahlweise können beliebig große Bestände simuliert werden, die durch stochastische Komponenten im Modell über eine realitätsnahe Variabilität der Baumgrößen verfügen. Die Verbindung der beiden Modellkomplexe wird durch eine Schnittstelle realisiert, die den Import von Einzelbaumdaten aus dem Ertragsmodell in das Strukturmodell vorsieht. Zwei weitere Modelle werden parametrisiert, um für das Strukturmodell die Internodienanzahl aus der Trieblänge als jährliche Höhenzuwächse des Ertragsmodells ermitteln zu können und das Wachstum der Nebenstämme an den Hauptstamm anzupassen. Darüber hinaus können die vom Ertragssimulator erzeugten Ausfälle in den Beständen berücksichtigt werden. Zukünftige Forschungsarbeiten werden zeigen, inwiefern das hier entwickelte Ertragsmodell durch eine Validierung mit Daten aus anderen Versuchen weiterentwickelt werden kann, um auch tiefere Bodenschichten mit einzubeziehen. Das Strukturmodell könnte durch Einbau eines Physiologiemoduls zu einem vollständigen Funktions-Struktur-Pflanzenmodell ausgebaut werden. Durch die Erweiterung der Schnittstelle zur Rückgabe von Daten vom Strukturmodell zum Ertragsmodell wäre auch eine Verbesserung der Schätzgüte z.B. durch erweiterte Möglichkeiten zur Modellierung der Konkurrenzverhältnisse vorstellbar.When planting fast-growing tree species such as poplars and willows on agricultural land in short rotation coppice plantations, site selection and the associated yield potential pose a central decision for the practitioner. In connection with the cultivar aspect there has been a need for research on the interaction between site and genotype in terms of growth performance. The aim of this work is to examine these questions on several levels. For this purpose, a multi-scale approach was chosen in the framework of which two model complexes are developed which are then connected by an interface. The first model complex incorporates the implementation of a yield simulator which depicts single tree based growth and mortality as a function of competition and site conditions. The data basis for this is growth data from the joint research project ProLoc funded by the BMEL. For this purpose, 18 trial sites are chosen which were initiated on a broad amplitude of environmental conditions. Following a uniform experimental design, monoclonal trial plots with three poplar and two willow clones (interspecific crossed hybrids) were supervised in two tri-annual rotations and cut back after the third year. Based on the model of the forest growth simulator BWINPro and the associated TreeGross program library, several models are parameterized which, in addition to the survival rates after planting and harvest, estimate the height increment in the first and second rotation. With the distance-independent competition index ``basal area of larger trees'' the development within the stands can be predicted. Regarding the growth performance on the site level, the parameters of planting date, available water capacity, German agricultural soil quality rating, sum of precipitation in May and June and mean temperature in June and July are identified as influential by variable selection. To estimate the height increment and survival after pruning, tree height before harvest is regarded as an independent variable. The factor clone indicates differences in the growth processes within the models but interactions with site variables can not be determined as significant. Missing variables such as the mean annual increment in dry matter yield in oven-dry tons ha^-1 a^-1 are estimated by additional functions parameterized with the dataset. The individual models are connected to a simulation procedure and the overall predictive power is assessed. Good results can be achieved for the first rotation with squared correlations of the observed and estimated mean stand height of 0.79. However, in the second rotation the estimation quality decreases to 0.53. There are single sites with considerable deviations. The depth of the soil sampling and missing extended information on the water supply are suspected as problematic here. The second model complex includes a structural model focused on the poplar genotypes and the second rotation. First, several measuring methods were identified which are deemed suitable for determining the tree architecture in terms of geometry and topology of the above-ground woody biomass, as well as the morphology of foliage in terms of leaf architecture and leaf shape. For the branch architecture, a manual method and a semi-automatic method with an electromagnetic digitizer for determining branch curvature have been selected and employed. The leaf architecture was measured by a manual method. The leaf shape could be determined by digitizing collected leaves. After analyzing the obtained data, several models are parameterized. As a result, the probability of bud growth and the dimensions and orientation in space of developing shoots can be estimated for apical and lateral buds. The models differentiate between main and minor stems, prolongation and lateral shoots, long and short shoots and, within the lateral shoots, sylleptic and regular shoots. The starting point here is the estimation of the number of internodes per shoot which in turn influences other parameters such as the branch angle and the curvature through the shoot length. Other factors underlying several models are the age, branch order and the genotypic influence. Parameters such as foliage and leaf size can mainly be estimated by the relative height with regard to the absolute tree height. The leaf shape in turn is determined by contour points whose coordinates are calculated as a function of the leaf blade length. As part of the analysis of these models, only slight differences in the structure between the clones are found. Exceptions are the curvature and branching angles of the lateral shoots for one of the clones, for which the models reproduce the observable slender habitus. Significant differences also occur in the leaf shape which reflect the leaf shapes of the underlying parent species of the hybrids. The individual model functions are then implemented into a structural model in the model platform GroIMP. The resulting model can simulate the development of the tree structure for each of the three clones in annual steps. Arbitrarily large stands can be simulated that have realistically varying tree sizes through stochastic components in the model. The interconnection of the two model complexes is realized through the import of single tree data from the yield model into the structural model. Two further models are parameterized to determine the number of internodes from the shoot length as annual height increment of the yield model for the structural model and to modify the growth of the minor stems in dependence of the main stem growth. Additionally, the single tree mortality generated by the yield simulator is incorporated into the structural model. Further research will show whether it is possible to improve the yield model by validation with data from other experiments to include deeper soil layers here. The structural model could be extended to a complete functional structural plant model by incorporating a physiology module. By extending the interconnection to return data from the structural model to the yield model, the predictive power could be improved, for example by means of extended possibilities for modeling the within-stand competition dynamics

Computational virtual measurement for trees

National forest inventory (NFI) is a systematic sampling method to collect forest information, including tree parameters, site conditions, and auxiliary data. The sample plot measurement is the key work in NFI. However, compared to the techniques 100 years ago, measuring methods and data-processing (modeling) approaches for NFI sample plots have been improved to a minor extent. The limit was that the newly-developed methods introduced additional validation workflows and would increase the workload in NFI. That was due to that these methods were usually developed based on species-specific and site-specific strategies. In order to overcome these obstacles, the integration of the novel measuring instruments is in urgent need, e.g., light detection and ranging (LiDAR) and the corresponding data processing methods with NFI. Given these situations, this thesis proposed a novel computational virtual measurement (CVM) method for the determination of tree parameters without the need for validation. Primarily, CVM is a physical simulation method and works as a virtual measuring instrument. CVM measures raw data, e.g., LiDAR point clouds and tree models, by the simulation of the physical mechanism of measuring instruments and natural phenomena. Based on the theory of CVM, this thesis is a systematic description of how to develop virtual measuring instruments. The first work is to introduce the CVM theory. CVM is a conceptual and general methodology, which is different from a specific measurement of tree parameters. Then, the feasibility of CVM was tested using a conceptual implementation, i.e., virtual ruler. The development of virtual ruler demonstrated the two key differences between CVM and conventional modeling methods. Firstly, the research focus of CVM is to build an appropriate physical scenario instead of finding a mathematical relationship between modeling results and true values. Secondly, the CVM outputs can approach true values, whereas the modeling results could not. Consequently, in a virtual space, tree parameters are determined by a measuring process without mathematical predictions. Accordingly, the result is free of validation and can be regarded as true values, at least in virtual spaces. With the knowledge from the virtual ruler development, two exceptional implementations are further developed. They are the virtual water displacement (VWD) method and sunlight analysis method. Both of them employ the same CVM workflow, which is firstly measured in reality and secondly measured in virtual space. The VWD aims to virtually measure the point clouds using the simulation of water displacement methods in reality. There are two stages in this method. The first stage is to apply the simulation of water displacement using massive virtual water molecules (VWMs). Some empirical regressions have to be employed in this stage, due to the limitation of computer performance. In the second stage, a single (or few) VWM (or VWMs) is developed to remove those empirical processes in VWD. Finally, VWD can function as a fully automatic method to measure point clouds.The sunlight analysis method aims to virtually measure the tree models using the simulation of solar illumination during daylight. There are also two stages in this method. The first stage is to develop sunlight analysis for a single tree. The second stage is to analyze the interference from neighboring trees. The results include default tree attributes, which can be collected in the future NFI. The successful developments of CVM, along with implementations of VWD and sunlight analysis methods, prove the initial assumptions in this thesis. It is the conversion of mathematical processing of data into virtual measurements. Accordingly, this is a different philosophy, i.e., the role of data is extended to the digital representative of trees. It opens an avenue of data processing using a more natural approach and is expected to be employed in the near future as a standard measuring instrument, such as a diameter tape, in NFI.Die Nationale Waldinventur (NFI) ist eine systematische Stichprobenmethode zur Erfassung von Waldinformationen, einschließlich Baumparameter, Standortbedingungen und Hilfsdaten. Die Messung von Stichprobenparzellen ist die Schlüsselarbeit der NFI. Im Vergleich zu den Techniken vor 100 Jahren wurden die Messmethoden und Datenverarbeitungsansätze (Modellierung) für NFI-Stichprobenparzellen jedoch in geringem Umfang verbessert. Die Grenze lag darin, dass die neu entwickelten Methoden zusätzliche Validierungsabläufe einführten und den Arbeitsaufwand in der NFI erhöhen würden. Dies war darauf zurückzuführen, dass diese Methoden in der Regel auf der Grundlage art- und standortspezifischer Strategien entwickelt wurden. Um diese Hindernisse zu überwinden, ist die Integration der neuartigen Messinstrumente dringend erforderlich, z.B. Light Detection and Ranging (LiDAR) und die entsprechenden Datenverarbeitungsmethoden mit NFI. Vor diesem Hintergrund wird in dieser Arbeit ein neuartiges rechnergestütztes virtuelles Messverfahren (CVM) zur Bestimmung von Baumparametern ohne Validierungsbedarf vorgeschlagen. CVM ist in erster Linie eine physikalische Simulationsmethode und arbeitet als virtuelles Messinstrument. CVM misst Rohdaten, z.B. LiDAR-Punktwolken und Baummodelle, durch die Simulation des physikalischen Mechanismus von Messinstrumenten und Naturphänomenen. Basierend auf der Theorie des CVM ist diese Arbeit eine systematische Beschreibung, wie virtuelle Messinstrumente entwickelt werden können. Die erste Arbeit dient der Einführung in die Theorie des CVM. CVM ist eine konzeptuelle und allgemeine Methodik, die sich von einer spezifischen Messung von Baumparametern unterscheidet. Anschliessend wird die Durchführbarkeit des CVM anhand einer konzeptuellen Implementierung, d.h. eines virtuellen Lineals, getestet. Die Entwicklung des virtuellen Lineals zeigte die beiden Hauptunterschiede zwischen CVM und konventionellen Modellierungsmethoden auf. Erstens besteht der Forschungsschwerpunkt von CVM darin, ein geeignetes physisches Szenario zu erstellen, anstatt eine mathematische Beziehung zwischen Modellierungsergebnissen und wahren Werten zu finden. Zweitens können sich die Ergebnisse des CVM den wahren Werten annähern, während die Modellierungsergebnisse dies nicht konnten. Folglich werden in einem virtuellen Raum die Baumparameter durch einen Messprozess ohne mathematische Vorhersagen bestimmt. Dementsprechend ist das Ergebnis frei von Validierung und kann, zumindest in virtuellen Räumen, als wahre Werte betrachtet werden. Mit dem Wissen aus der Entwicklung des virtuellen Lineals werden zwei aussergewöhnliche Implementierungen weiterentwickelt. Es handelt sich um die Methode der virtuellen Wasserverdrängung (VWD) und die Methode der Sonnenlichtanalyse. Beide verwenden den gleichen CVM-Workflow, der erstens in der Realität und zweitens im virtuellen Raum gemessen wird. Das VWD zielt darauf ab, die Punktwolken virtuell zu messen, wobei die Simulation von Wasserverdrängungsmethoden in der Realität verwendet wird. Diese Methode besteht aus zwei Stufen. Die erste Stufe besteht in der Anwendung der Simulation der Wasserverdrängung unter Verwendung massiver virtueller Wassermoleküle (VWMs). Aufgrund der begrenzten Computerleistung müssen in dieser Phase einige empirische Regressionen angewandt werden. In der zweiten Stufe wird ein einzelnes (oder wenige) VWM (oder VWMs) entwickelt, um diese empirischen Prozesse im VWD zu entfernen. Schließlich kann VWD als vollautomatische Methode zur Messung von Punktwolken fungieren. Die Methode der Sonnenlichtanalyse zielt darauf ab, die Baummodelle virtuell zu messen, indem die Simulation der Sonneneinstrahlung bei Tageslicht verwendet wird. Auch bei dieser Methode gibt es zwei Stufen. In der ersten Stufe wird die Sonnenlichtanalyse für einen einzelnen Baum entwickelt. Die zweite Stufe ist die Analyse der Interferenz von benachbarten Bäumen. Die Ergebnisse umfassen Standard-Baumattribute, die in der zukünftigen NFI gesammelt werden können. Die erfolgreichen Entwicklungen von CVM, zusammen mit Implementierungen von VWD- und Sonnenlichtanalysemethoden, beweisen die anfänglichen Annahmen in dieser Arbeit. Es handelt sich um die Umsetzung der mathematischen Verarbeitung von Daten in virtuelle Messungen. Dementsprechend handelt es sich um eine andere Philosophie, d.h. die Rolle der Daten wird auf die digitale Darstellung von Bäumen ausgedehnt. Sie eröffnet einen Weg der Datenverarbeitung unter Verwendung eines natürlicheren Ansatzes und wird voraussichtlich in naher Zukunft als Standard-Messinstrument, wie z.B. ein Durchmesser-Band, in der NFI eingesetzt werden

Estimates of tree root water uptake from soil moisture profile dynamics

Root water uptake (RWU), as an important process in the terrestrial water cycle, can help us to better understand the interactions in the soil–plant–atmosphere continuum. We conducted a field study monitoring soil moisture profiles in the rhizosphere of beech trees at two sites with different soil conditions. We present an algorithm to infer RWU from step-shaped, diurnal changes in soil moisture. While this approach is a feasible, easily implemented method for moderately moist and homogeneously textured soil conditions, limitations were identified during drier states and for more heterogeneous soil settings. A comparison with the time series of xylem sap velocity underlines that RWU and sap flow (SF) are complementary measures in the transpiration process. The high correlation between the SF time series of the two sites, but lower correlation between the RWU time series, suggests that soil characteristics affect RWU of the trees but not SF

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

Artificial intelligence-based software (AID-FOREST) for tree detection: A new framework for fast and accurate forest inventorying using LiDAR point clouds

Forest inventories are essential to accurately estimate different dendrometric and forest stand parameters. However, classical forest inventories are time consuming, slow to conduct, sometimes inaccurate and costly. To address this problem, an efficient alternative approach has been sought and designed that will make this type of field work cheaper, faster, more accurate, and easier to complete. The implementation of this concept has required the development of a specifically designed software called "Artificial Intelligence for Digital Forest (AID-FOREST)", which is able to process point clouds obtained via mobile terrestrial laser scanning (MTLS) and then, to provide an array of multiple useful and accurate dendrometric and forest stand parameters. Singular characteristics of this approach are: No data pre-processing is required either pre-treatment of forest stand; fully automatic process once launched; no limitations by the size of the point cloud file and fast computations.To validate AID-FOREST, results provided by this software were compared against the obtained from in-situ classical forest inventories. To guaranty the soundness and generality of the comparison, different tree spe-cies, plot sizes, and tree densities were measured and analysed. A total of 76 plots (10,887 trees) were selected to conduct both a classic forest inventory reference method and a MTLS (ZEB-HORIZON, Geoslam, ltd.) scanning to obtain point clouds for AID-FOREST processing, known as the MTLS-AIDFOREST method. Thus, we compared the data collected by both methods estimating the average number of trees and diameter at breast height (DBH) for each plot. Moreover, 71 additional individual trees were scanned with MTLS and processed by AID-FOREST and were then felled and divided into logs measuring 1 m in length. This allowed us to accurately measure the DBH, total height, and total volume of the stems.When we compared the results obtained with each methodology, the mean detectability was 97% and ranged from 81.3 to 100%, with a bias (underestimation by MTLS-AIDFOREST method) in the number of trees per plot of 2.8% and a relative root-mean-square error (RMSE) of 9.2%. Species, plot size, and tree density did not significantly affect detectability. However, this parameter was significantly affected by the ecosystem visual complexity index (EVCI). The average DBH per plot was underestimated (but was not significantly different from 0) by the MTLS-AIDFOREST, with the average bias for pooled data being 1.8% with a RMSE of 7.5%. Similarly, there was no statistically significant differences between the two distribution functions of the DBH at the 95.0% confidence level.Regarding the individual tree parameters, MTLS-AIDFOREST underestimated DBH by 0.16 % (RMSE = 5.2 %) and overestimated the stem volume (Vt) by 1.37 % (RMSE = 14.3 %, although the BIAS was not statistically significantly different from 0). However, the MTLS-AIDFOREST method overestimated the total height (Ht) of the trees by a mean 1.33 m (5.1 %; relative RMSE = 11.5 %), because of the different height concepts measured by both methodological approaches. Finally, AID-FOREST required 30 to 66 min per ha-1 to fully automatically process the point cloud data from the *.las file corresponding to a given hectare plot. Thus, applying our MTLS-AIDFOREST methodology to make full forest inventories, required a 57.3 % of the time required to perform classical plot forest inventories (excluding the data postprocessing time in the latter case). A free trial of AID -FOREST can be requested at [email protected]