Revealing microhabitat requirements of an endangered specialist lizard with LiDAR

A central principle of threatened species management is the requirement for detailed understanding of species habitat requirements. Difficult terrain or cryptic behaviour can, however, make the study of habitat or microhabitat requirements difficult, calling for innovative data collection techniques. We used high-resolution terrestrial LiDAR imaging to develop three-dimensional models of log piles, quantifying the structural characteristics linked with occupancy of an endangered cryptic reptile, the western spiny-tailed skink (Egernia stokesii badia). Inhabited log piles were generally taller with smaller entrance hollows and a wider main log, had more high-hanging branches, fewer low-hanging branches, more mid- and understorey cover, and lower maximum canopy height. Significant characteristics linked with occupancy were longer log piles, an average of three logs, less canopy cover, and the presence of overhanging vegetation, likely relating to colony segregation, thermoregulatory requirements, and foraging opportunities. In addition to optimising translocation site selection, understanding microhabitat specificity of E. s. badia will help inform a range of management objectives, such as targeted monitoring and invasive predator control. There are also diverse opportunities for the application of this technology to a wide variety of future ecological studies and wildlife management initiatives pertaining to a range of cryptic, understudied taxa

Revealing microhabitat requirements of an endangered specialist lizard with LiDAR

A central principle of threatened species management is the requirement for detailed understanding of species habitat requirements. Difcult terrain or cryptic behaviour can, however, make the study of habitat or microhabitat requirements difcult, calling for innovative data collection techniques. We used high-resolution terrestrial LiDAR imaging to develop three-dimensional models of log piles, quantifying the structural characteristics linked with occupancy of an endangered cryptic reptile, the western spiny-tailed skink (Egernia stokesii badia). Inhabited log piles were generally taller with smaller entrance hollows and a wider main log, had more high-hanging branches, fewer low-hanging branches, more mid- and understorey cover, and lower maximum canopy height. Signifcant characteristics linked with occupancy were longer log piles, an average of three logs, less canopy cover, and the presence of overhanging vegetation, likely relating to colony segregation, thermoregulatory requirements, and foraging opportunities. In addition to optimising translocation site selection, understanding microhabitat specifcity of E. s. badia will help inform a range of management objectives, such as targeted monitoring and invasive predator control. There are also diverse opportunities for the application of this technology to a wide variety of future ecological studies and wildlife management initiatives pertaining to a range of cryptic, understudied taxa.Holly S. Bradley, Michael D. Craig, Adam T. Cross, SeanTomlinson, Michael J. Bamford, Philip W. Batema

Applicazione dei laser scanner terrestri in campo agro-forestale e ambientale

Le tecniche di laser scanning terrestre suscitano un interesse sempre più crescente sia in campo ambientale sia in campo agro-forestale in quanto consentono un’acquisizione rapida e affidabile di nuvole di punti 3D in grado di rappresentare tridimensionalmente gli oggetti rilevati. In un lasso di tempo relativamente breve, tali tecniche hanno aperto la strada ad un ampio spettro di applicazioni in pieno campo. Al di là della misura interattiva di nuvole georeferenziate di punti, le tecniche per il rilevamento automatizzato di oggetti, la determinazione delle loro caratteristiche geometriche e l’analisi della risposta del segnale laser rappresentano temi di ricerca prioritari. L’elevata qualità delle nuvole di punti 3D generate dai sistemi di laser scanning insieme alla possibilità di automatizzare le successive post-elaborazioni dei dati hanno esteso il potenziale applicativo dei laser scanner terrestri anche ai settori ambientali e agro-forestali. Questo contributo illustra innanzitutto lo stato dell’arte dei sistemi laser scanner terrestri dal punto di vista tecnologico e discute le diverse soluzioni tecnologiche e i diversi parametri di sistema in relazione alle possibili applicazioni in campo ambientale e forestale. Nella seconda parte saranno invece presentati alcuni casi di studio esplicativi delle potenzialità e delle limitazioni operative della tecnica di laser scanning terrestre in campo ambientale e agro-forestale. Terrestrial laser scanning techniques find rapidly growing interest in both environmental and agro-forestry fields as efficient tools for fast and reliable 3D point data acquisition and 3D representation of the object viewed by the scanners. They have opened a wide range of application fields within a rather short period of time. Beyond interactive measurement in 3D point clouds, techniques for the automatic detection of objects, the determination of geometric parameters, and the analysis of the reflectance signal form high priority research issues. The high quality of 3D point clouds generated by laser scanners and the data process automation potential make terrestrial laser scanning also an interesting tool for environmental and agro-forestry applications. The paper will first review current laser scanner systems from a technological point of view and discuss different scanner technologies and system parameters regarding their suitability for environmental and agro-forestry applications. In the second part of the paper, results of case studies on potentials and limitations of terrestrial laser scanners in environmental and agro-forestry fields will be presented

Revisión de la teledetección activa y la fusión de datos para la caracterización de bosques en modelos especie-hábitat

Revista oficial de la Asociación Española de Teledetección[EN] Spatially explicit maps of wildlife habitat relationships have proven to be valuable tools for conservation and management applications including evaluating how and which species may be impacted by large scale climate change, ongoing fragmentation of habitat, and local land-use practices. Studies have turned to remote sensing datasets as a way to characterize vegetation for the examination of habitat selection and for mapping realized relationships across the landscape. Potentially one of the more difficult habitat types to try to characterize with remote sensing are the vertically and horizontally complex forest systems. Characterizing this complexity is needed to explore which aspects may represent driving and/or limiting factors for wildlife species. Active remote sensing data from lidar and radar sensors has thus caught the attention of the forest wildlife research and management community in its potential to represent three dimensional habitat features. The purpose of this review was to examine the applications of active remote sensing for characterizing forest in wildlife habitat studies through a keyword search within Web of Science. We present commonly used active remote sensing metrics and methods, discuss recent advances in characterizing aspects of forest habitat, and provide suggestions for future research in the area of new remote sensing data/techniques that could benefit forest wildlife studies that are currently not represented or may be underutilized within the wildlife literature. We also highlight the potential value in data fusion of active and passive sensor data for representing multiple dimensions and scales of forest habitat. While the use of remote sensing has increased in recent years within wildlife habitat studies, continued communication between the remote sensing, forest management, and wildlife communities is vital to ensure appropriate data sources and methods are understood and utilized, and so that creators of mapping products may better realize the needs of secondary users.[ES] Se ha probado que los mapas que muestran explícitamente las relaciones especie-hábitat constituyen herramientas valiosas en aplicaciones de conservación y gestión, incluyendo la evaluación sobre qué especies y de qué forma se pueden ver afectadas por el cambio climático a gran escala, la fragmentación progresiva del hábitat y los usos del suelo a nivel local. Diversos estudios se han centrado en utilizar la teledetección como herramienta que permite caracterizar la vegetación para el análisis de la selección del hábitat y para cartografiar las relaciones con el entorno natural. Uno de los tipos de hábitats más difíciles de caracterizar mediante teledetección son los sistemas forestales verticales y horizontales complejos. Su caracterización es necesaria para estudiar los aspectos determinantes y/o limitantes para las especies. El uso de la teledetección activa mediante sensores LiDAR y RADAR ha suscitado gran interés en el ámbito de la investigación de especies de fauna silvestre en áreas forestales así como su gestión, dado el potencial de esta tecnología para representar características tridimensionales de estos hábitats. El objetivo de este artículo de revisión es analizar las aplicaciones de teledetección activa en los estudios de hábitat de fauna silvestre en zonas forestales a través de búsquedas de palabras claves en la WebofScience. Se presentan las métricas y métodos comúnmente utilizados, los avances recientes en la caracterización de hábitats forestales y se recomiendan líneas futuras de investigación en el área de teledetección que podrían beneficiar estudios sobre fauna silvestre en ámbitos forestales que actualmente o no existen o están infrautilizados. También se destaca el valor potencial de la fusión de datos de sensores activos y pasivos para la representación de múltiples dimensiones y escalas del hábitat forestal. Si bien el uso de la teledetección en estudios de hábitat de fauna silvestre se ha incrementado en los últimos años, la comunicación fluida entre las comunidades científicas relacionadas con la teledetección, la gestión forestal y la ecología es vital para garantizar el uso y comprensión adecuados de los datos, permitiendo un mejor conocimiento de las necesidades de los usuarios.Our review was funded by the National Aeronautics and Space Administration, Carbon Cycle Program (NASA Grant 10-CARBON10-45).Vogeler, JC.; Cohen, WB. (2016). A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models. Revista de Teledetección. (Special Issue):1-14. https://doi.org/10.4995/raet.2016.3981SWORD114Special Issu

Using multi-platform LiDAR to assess vegetation structure for woodland forest fauna

Abstract Vegetation structure can support biodiversity by creating a variety of microclimates and microhabitats that contribute to food and shelter for different species. For this reason, biodiversity and wildlife habitat assessments often require accurate measurements of vegetation structure. Traditional methods for measuring the three-dimensional distribution of vegetation are time-consuming and often limited to small areas or a subset of the landscape. Light detection and ranging (LiDAR) is an alternative method for collecting three dimensional information on vegetation structure and other landscape features across wide areas. For the first time, we used multi-platform LiDAR data from a terrestrial sensor (TLS) and an unmanned aerial vehicle (ULS) to investigate the relationship between vegetation structure and the diversity and abundance of birds, reptiles and amphibians in a critically endangered grassy woodland ecosystem. The first Chapter of this thesis involves TLS and ULS data collection methods, post-processing steps and exploratory data analysis. We calculated a number of variables to characterise the three-dimensional structure of vegetation across four structurally different, one hectare sites (high-tree/high-shrub, high-tree/low-shrub, low-tree/high-shrub, and low-tree/low-shrub) and compared the values of the TLS and ULS derived variables. Generally, TLS outperformed ULS by producing higher volumetric and height diversity indices within our landscape. In the Second Chapter, the relationship between TLS and ULS derived vegetation structural variables and overall bird abundance, species richness and diversity were investigated using mixed effects regression models. Models showed strong significant associations between vegetation structural variables including canopy roughness, vegetation volume, vertical complexity and the abundance of individual species and guilds. The best performing models were for individual bird species and guilds, whereas overall diversity and abundance showed less association to LiDAR-derived vegetation structural metrics. TLS and ULS based models performed similarly when identifying vegetation structural associations with bird communities and individual species. In the Third Chapter, coarse woody debris (CWD) from TLS, ULS and the combination of both datasets (Fusion) was extracted. Several topographic variables were calculated as raster imagery from LiDAR point clouds and Random Forest (RF) machine learning algorithms were then utilised to classify CWD. Noise reduction algorithms were applied to reduce noise from the classified imagery. Digital height model (DHM), surface roughness and topographic position index were important variables in classifying CWD with RF. Classification accuracy varied depending on the amount of ground vegetation cover. The impacts of ground vegetation cover on CWD accuracy in a grassy woodland were quantified and discussed. The Fourth Chapter explores the relationship between LiDAR derived vegetation structural metrics and the presence and abundance of reptiles and amphibians. Our models demonstrate that woodland reptile and amphibian populations were significantly associated with a number of vegetation structural characteristics from the selected variables, the most common of which were mean canopy height, canopy skewedness, vertical complexity, volume of vegetation and CWD. Notable relationships between herpetofauna population data and vegetation structural metrics are discussed with reference to existing literature on habitat associations for these animals. We also explore reasons why significant associations between LiDAR derived vegetation structural metrics and animal population data were not consistent across sensors and suggest directions for future research

Applicability of LiDAR Technology in Saltmarshes: Landscape-Scale Predictive Models to Local-Scale Biomass Estimation

The management of saltmarshes requires detailed knowledge of the underlying processes driving their distribution in both time and space to make appropriate management decisions. With most of the world’s population living in the coastal zone and rising sea levels, one of our most important natural resources in the coastal zone faces increasing threat of collapse. This study uses the current state of Light Detection and Ranging (LiDAR) technology to model and predict saltmarsh distribution at a landscape-scale and provide evidence that a terrestrial laser scanner (TLS) can be used to estimate saltmarsh biomass for inclusion into existing models. Land cover classification of the dominant saltmarsh species, S. alterniflora and S. patens, of the Plum Island Estuary in Massachusetts indicate that when augmented by LiDAR, aerial imagery can spectrally discriminate these species allowing for the identification of species elevation range. A spatial ‘bathtub’ model of the estuary indicates that the saltmarshes will survive a 1m sea-level rise but not without a change in the dominant marsh plant species. These changes will occur at different rates along a latitudinal gradient owing to a difference in relative marsh tidal elevation. Although the numerical Marsh Equilibrium Model (MEM) was developed with data from North Inlet, South Carolina and has been coupled with spatial models to predict saltmarsh distribution, no such study exists for North Inlet. A stand-alone python model, MEM3D, was created to couple MEM with a Geographic Information System (GIS) and analyze the future distribution of saltmarshes within North Inlet following a 1m sea-level rise in the next 100 yr. Results indicate that the saltmarshes will not survive sea-level rise of this magnitude, and the system will switch to mudflat dominance by the end of the simulation. A TLS was used to address the need to quickly and non-destructively estimate biomass. Results indicate that there exists an optimal resolution for collecting data in a saltmarsh and that contrary to airborne LiDAR systems, TLS can also penetrate the canopy to ground level. Predictive biomass equations are generated for S. alterniflora and J. roemerianus with R2 = 0

Applications of Airborne and Portable LiDAR in the Structural Determination, Management, and Conservation of Southeastern U.S. Pine Forests

Active remote sensing techniques, such as Light Detection and Ranging (LiDAR), have transformed the field of forestry and natural resource management in the last decade. Intensive assessments of forest resources and detailed structural assessments can now be accomplished faster and at multiple landscape scales. The ecological applications of having this valuable information at-hand are still only being developed. This work explores the use of two active remote sensing techniques, airborne and portable LiDAR for forestry applications in a rapidly changing landscape, Southeastern Coastal Pine woodlands. Understanding the strengths and weaknesses of airborne and portable LiDAR, the tools used to extract structural information, and how to apply these to managing fire regimes are key to conserving unique upland pine ecosystems. Measuring habitat structure remotely and predicting habitat suitability through modeling will allow for the management of specific species of interest, such as threatened and endangered species. Chapter one focuses on the estimation of canopy cover and height measures across a variety of conditions of secondary upland pine and hardwood forests at Tall Timbers Research Station, FL. This study is unique since it uses two independent high resolution small-footprint LiDAR datasets (years 2002 and 2008) and extensive field plot and transect sampling for validation. Chapter One explores different tools available for metric derivation and tree extraction from discrete return airborne LiDAR data, highlighting strengths and weaknesses of each. Field and LiDAR datasets yielded better correlations for stand level comparisons, especially in canopy cover and mean height data extracted. Individual tree crown extraction from airborne LiDAR data significantly under-reported the total number of trees reported in the field datasets using either Fusion/LVD and LiDAR Analyst (Overwatch). Chapter two evaluates stand structure at the site of one of the longest running fire ecology studies in the US, located at Tall Timbers Research Station (TTRS) in the southeastern U.S. Small footprint high resolution discrete return LiDAR was used to provide an understanding of the impact of multiple disturbance regimes on forest structure, especially on the 3-dimensional spatial arrangement of multiple structural elements and structural diversity indices. LiDAR data provided sensitive detection of structural metrics, diversity, and fine-scale vertical changes in the understory and mid-canopy structure. Canopy cover and diversity indices were shown to be statistically higher in fire suppressed and less frequently burned plots than in 1- and 2-year fire interval treated plots, which is in general agreement with the increase from 2- to 3-year fire return interval being considered an ecological threshold for these systems (Masters et al. 2005). The results from this study highlight the value of the use of LiDAR in evaluating disturbance impacts on the three-dimensional structure of pine forest systems, particularly over large landscapes. Chapter three uses an affordable portable LiDAR system, first presented by Parker et al. (2004) and further modified for extra portability, to provide an understanding of structural differences between old-growth and secondary-growth forests in the Red Hills area of southwestern Georgia and North Florida. It also provides insight into the strengths and weaknesses in structural determination of ground-based portable systems in contrast to airborne LiDAR systems. Structural plot metrics obtained from airborne and portable LiDAR systems presented some similarities (i.e. canopy cover), but distinct differences appeared when measuring canopy heights (maximum and mean heights) using these different methods. Both the airborne and portable systems were able to provide gap detection and canopy cover estimation at the plot level. The portable system, when compared to the airborne LiDAR sensor, provides an underestimation of canopy cover in open forest systems ([less than]50% canopy cover), but is more sensitive in detection of cover in hardwood woodland plots ([greater than]60% canopy cover). The strength of the portable LiDAR system lies in the detection of 3-dimensional fine structural changes (i.e. recruitment, encroachment) and with higher sensitivity in detecting lower canopy levels, often missed by airborne systems. Chapter four addresses a very promising application for fine-scale airborne LiDAR data, the creation of habitat suitability models for species of management and conservation concerns. This Chapter uses fine scale LiDAR metrics, such as canopy cover at various height strata, canopy height information, and a measure of horizontal vegetation distribution (clumped versus dispersed) to model the preferences of 10 songbirds of interest in southeast US woodlands. The results from this study highlight the rapidly changing nature of habitat conditions and how these impact songbird occurrence. Furthermore, Chapter four provides insight into the use of airborne LiDAR to provide specific management guidance to enhance the suitable habitat for 10 songbird species. The collection of studies presented here provides applied tools for the use of airborne and portable LiDAR for rapid assessment and responsive management in southeastern pine woodlands. The advantages of detecting small changes in three-dimensional vegetation structure and how these can impact habitat functionality and suitability for species of interest are explored throughout the next four chapters. The research presented here provides an original and important contribution in the application of airborne and portable LiDAR datasets in forest management and ecological studies

Automatic Retrieval of Skeletal Structures of Trees from Terrestrial Laser Scanner Data

Research on forest ecosystems receives high attention, especially nowadays with regard to sustainable management of renewable resources and the climate change. In particular, accurate information on the 3D structure of a tree is important for forest science and bioclimatology, but also in the scope of commercial applications. Conventional methods to measure geometric plant features are labor- and time-intensive. For detailed analysis, trees have to be cut down, which is often undesirable. Here, Terrestrial Laser Scanning (TLS) provides a particularly attractive tool because of its contactless measurement technique. The object geometry is reproduced as a 3D point cloud. The objective of this thesis is the automatic retrieval of the spatial structure of trees from TLS data. We focus on forest scenes with comparably high stand density and with many occlusions resulting from it. The varying level of detail of TLS data poses a big challenge. We present two fully automatic methods to obtain skeletal structures from scanned trees that have complementary properties. First, we explain a method that retrieves the entire tree skeleton from 3D data of co-registered scans. The branching structure is obtained from a voxel space representation by searching paths from branch tips to the trunk. The trunk is determined in advance from the 3D points. The skeleton of a tree is generated as a 3D line graph. Besides 3D coordinates and range, a scan provides 2D indices from the intensity image for each measurement. This is exploited in the second method that processes individual scans. Furthermore, we introduce a novel concept to manage TLS data that facilitated the researchwork. Initially, the range image is segmented into connected components. We describe a procedure to retrieve the boundary of a component that is capable of tracing inner depth discontinuities. A 2D skeleton is generated from the boundary information and used to decompose the component into sub components. A Principal Curve is computed from the 3D point set that is associated with a sub component. The skeletal structure of a connected component is summarized as a set of polylines. Objective evaluation of the results remains an open problem because the task itself is ill-defined: There exists no clear definition of what the true skeleton should be w.r.t. a given point set. Consequently, we are not able to assess the correctness of the methods quantitatively, but have to rely on visual assessment of results and provide a thorough discussion of the particularities of both methods. We present experiment results of both methods. The first method efficiently retrieves full skeletons of trees, which approximate the branching structure. The level of detail is mainly governed by the voxel space and therefore, smaller branches are reproduced inadequately. The second method retrieves partial skeletons of a tree with high reproduction accuracy. The method is sensitive to noise in the boundary, but the results are very promising. There are plenty of possibilities to enhance the method’s robustness. The combination of the strengths of both presented methods needs to be investigated further and may lead to a robust way to obtain complete tree skeletons from TLS data automatically.Die Erforschung des ÖkosystemsWald spielt gerade heutzutage im Hinblick auf den nachhaltigen Umgang mit nachwachsenden Rohstoffen und den Klimawandel eine große Rolle. Insbesondere die exakte Beschreibung der dreidimensionalen Struktur eines Baumes ist wichtig für die Forstwissenschaften und Bioklimatologie, aber auch im Rahmen kommerzieller Anwendungen. Die konventionellen Methoden um geometrische Pflanzenmerkmale zu messen sind arbeitsintensiv und zeitaufwändig. Für eine genaue Analyse müssen Bäume gefällt werden, was oft unerwünscht ist. Hierbei bietet sich das Terrestrische Laserscanning (TLS) als besonders attraktives Werkzeug aufgrund seines kontaktlosen Messprinzips an. Die Objektgeometrie wird als 3D-Punktwolke wiedergegeben. Basierend darauf ist das Ziel der Arbeit die automatische Bestimmung der räumlichen Baumstruktur aus TLS-Daten. Der Fokus liegt dabei auf Waldszenen mit vergleichsweise hoher Bestandesdichte und mit zahlreichen daraus resultierenden Verdeckungen. Die Auswertung dieser TLS-Daten, die einen unterschiedlichen Grad an Detailreichtum aufweisen, stellt eine große Herausforderung dar. Zwei vollautomatische Methoden zur Generierung von Skelettstrukturen von gescannten Bäumen, welche komplementäre Eigenschaften besitzen, werden vorgestellt. Bei der ersten Methode wird das Gesamtskelett eines Baumes aus 3D-Daten von registrierten Scans bestimmt. Die Aststruktur wird von einer Voxelraum-Repräsentation abgeleitet indem Pfade von Astspitzen zum Stamm gesucht werden. Der Stamm wird im Voraus aus den 3D-Punkten rekonstruiert. Das Baumskelett wird als 3D-Liniengraph erzeugt. Für jeden gemessenen Punkt stellt ein Scan neben 3D-Koordinaten und Distanzwerten auch 2D-Indizes zur Verfügung, die sich aus dem Intensitätsbild ergeben. Bei der zweiten Methode, die auf Einzelscans arbeitet, wird dies ausgenutzt. Außerdem wird ein neuartiges Konzept zum Management von TLS-Daten beschrieben, welches die Forschungsarbeit erleichtert hat. Zunächst wird das Tiefenbild in Komponenten aufgeteilt. Es wird eine Prozedur zur Bestimmung von Komponentenkonturen vorgestellt, die in der Lage ist innere Tiefendiskontinuitäten zu verfolgen. Von der Konturinformation wird ein 2D-Skelett generiert, welches benutzt wird um die Komponente in Teilkomponenten zu zerlegen. Von der 3D-Punktmenge, die mit einer Teilkomponente assoziiert ist, wird eine Principal Curve berechnet. Die Skelettstruktur einer Komponente im Tiefenbild wird als Menge von Polylinien zusammengefasst. Die objektive Evaluation der Resultate stellt weiterhin ein ungelöstes Problem dar, weil die Aufgabe selbst nicht klar erfassbar ist: Es existiert keine eindeutige Definition davon was das wahre Skelett in Bezug auf eine gegebene Punktmenge sein sollte. Die Korrektheit der Methoden kann daher nicht quantitativ beschrieben werden. Aus diesem Grund, können die Ergebnisse nur visuell beurteiltwerden. Weiterhinwerden die Charakteristiken beider Methoden eingehend diskutiert. Es werden Experimentresultate beider Methoden vorgestellt. Die erste Methode bestimmt effizient das Skelett eines Baumes, welches die Aststruktur approximiert. Der Detaillierungsgrad wird hauptsächlich durch den Voxelraum bestimmt, weshalb kleinere Äste nicht angemessen reproduziert werden. Die zweite Methode rekonstruiert Teilskelette eines Baums mit hoher Detailtreue. Die Methode reagiert sensibel auf Rauschen in der Kontur, dennoch sind die Ergebnisse vielversprechend. Es gibt eine Vielzahl von Möglichkeiten die Robustheit der Methode zu verbessern. Die Kombination der Stärken von beiden präsentierten Methoden sollte weiter untersucht werden und kann zu einem robusteren Ansatz führen um vollständige Baumskelette automatisch aus TLS-Daten zu generieren

The use of remote sensing for characterizing forests in wildlife habitat modeling

Spatially explicit maps of habitat relationships have proven to be valuable tools for conservation and management applications including evaluating how and which species may be impacted by large scale climate change, ongoing fragmentation of habitat, and local land-use practices. Studies have turned to remote sensing datasets as a way to characterize vegetation for the examination of habitat selection and for mapping realized relationships across the landscape. Although the use of remote sensing in wildlife studies has increased in recent years, the use of these datasets is still limited and some data sources and methods are yet to be explored. The overall goal of this dissertation was to look at the state of the wildlife ecology discipline in the use of geospatial data for habitat mapping, and to advance this area through the fusion of remote sensing tools for the mapping of previously difficult to characterize forest metrics for inclusion in avian cavity-nester habitat models. Chapter 2 reviewed over 60 years of selected wildlife literature to examine the wildlife ecology disciple through historic trends and recent advances in the use of remote sensing for habitat characterization focusing on aspects of scale and the use of available technology. We discuss commonly used remote sensing data sources, point out recent advances in the use of geospatial data for characterizing forest wildlife habitat (the use of lidar data and the creation of spatially explicit habitat prediction maps), and provide future suggestions for increased utilization of available datasets (secondary lidar metrics and time series Landsat data). In chapters 3 and 4 we explored the use of remote sensing for characterizing forest components previously difficult to map across landscapes at scales relevant to local wildlife habitat selection. Chapter 3 found promise in the fusion of lidar structure and Landsat time series disturbance products in the modeling and mapping of post-fire snag and shrub distributions at fine scales and at size/cover thresholds relevant for habitat mapping applications for many wildlife species. The study was conducted within the 2003 B&B Fire Complex in central Oregon. Using 164 field calibration plots and remote sensing predictors, we modeled the presence/absence of snag classes (dbh ≥40cm, ≥50cm, and ≥75cm) and woody shrub cover resulting in 10m output predictive grid maps. Remote sensing predictors included various lidar structure and topography variables and Landsat time series products representing the pre-fire forest, disturbance magnitude, and current forest conditions. We were able to model and map all habitat metrics with acceptable predictive performance and low-moderate errors. The utility of these snag and shrub metrics for representing important nesting habitat features for a cavity-nesting species of conservation concern, the Lewis's Woodpecker (Melanerpes lewis), was demonstrated in Chapter 4. We were able to model nesting habitat with good accuracies according to multiple performance measures and then map realized relationships for this species of conservation concern in an identified source habitat type, providing a potential resource for local scale conservation and management efforts and adding to the regional knowledge of habitat selection for the Lewis's Woodpecker. To our knowledge, these chapters represent first attempts to fuse lidar and time series Landsat disturbance metrics in a post-fire landscape and for the mapping of snag and shrub distributions at scales relevant to avian cavity nesting habitat

Automatische Extraktion von 3D-Baumparametern aus terrestrischen Laserscannerdaten

Ein großes Anwendungsgebiet des Flugzeuglaserscannings ist in Bereichen der Forstwirtschaft und der Forstwissenschaft zu finden. Die Daten dienen flächendeckend zur Ableitung von digitalen Gelände- und Kronenmodellen, aus denen sich die Baumhöhe ableiten lässt. Aufgrund der Aufnahmerichtung aus der Luft lassen sich spezielle bodennahe Baumparameter wie Stammdurchmesser und Kronenansatzhöhe nur durch Modelle schätzen. Der Einsatz terrestrischer Laserscanner bietet auf Grund der hochauflösenden Datenakquisition eine gute Ergänzung zu den Flugzeuglaserscannerdaten. Inventurrelevante Baumparameter wie Brusthöhendurchmesser und Baumhöhe lassen sich ableiten und eine Verdichtung von digitalen Geländemodellen durch die terrestrisch erfassten Daten vornehmen. Aufgrund der dichten, dreidimensionalen Punktwolken ist ein hoher Dokumentationswert gegeben und eine Automatisierung der Ableitung der Geometrieparameter realisierbar. Um den vorhandenen Holzvorrat zu kontrollieren und zu bewirtschaften, werden in periodischen Zeitabständen Forstinventuren auf Stichprobenbasis durchgeführt. Geometrische Baumparameter, wie Baumhöhe, Baumposition und Brusthöhendurchmesser, werden gemessen und dokumentiert. Diese herkömmliche Erfassung ist durch einen hohen Arbeits- und Zeitaufwand gekennzeichnet. Aus diesem Grund wurden im Rahmen dieser Arbeit Algorithmen entwickelt, die eine automatische Ableitung der geometrischen Baumparameter aus terrestrischen Laserscannerpunktwolken ermöglichen. Die Daten haben neben der berührungslosen und lichtunabhängigen Datenaufnahme den Vorteil einer objektiven und schnellen Parameterbestimmung. Letztendlich wurden die Algorithmen in einem Programm zusammengefasst, das neben der Baumdetektion eine Bestimmung der wichtigsten Parameter in einem Schritt realisiert. An Datensätzen von drei verschiedenen Studiengebieten werden die Algorithmen getestet und anhand manuell gewonnener Baumparameter validiert. Aufgrund der natürlich gewachsenen Vegetationsstruktur sind bei Aufnahmen von einem Standpunkt gerade im Kronenraum Abschattungen vorhanden. Durch geeignete Scankonfigurationen können diese Abschattungen minimiert, allerdings nicht vollständig umgangen werden. Zusätzlich ist der Prozess der Registrierung gerade im Wald mit einem zeitlichen Aufwand verbunden. Die größte Schwierigkeit besteht in der effizienten Verteilung der Verknüpfungspunkte bei dichter Bodenvegetation. Deshalb wird ein Ansatz vorgestellt, der eine Registrierung über die berechneten Mittelpunkte der Brusthöhendurchmesser durchführt. Diese Methode verzichtet auf künstliche Verknüpfungspunkte und setzt Mittelpunkte von identischen Stammabschnitten in beiden Datensätzen voraus. Dennoch ist die größte Unsicherheit in der Z-Komponente der Translation zu finden. Eine Methode unter Verwendung der Lage der Baumachsen sowie mit einem identischen Verknüpfungspunkt führt zu besseren Ergebnissen, da die Datensätze an dem homologen Punkt fixiert werden. Anhand eines Studiengebietes werden die Methoden mit den herkömmlichen Registrierungsverfahren über homologe Punkte verglichen und analysiert. Eine Georeferenzierung von terrestrischen Laserscannerpunktwolken von Waldbeständen ist aufgrund der Signalabschattung der Satellitenpositionierungssysteme nur bedingt und mit geringer Genauigkeit möglich. Deshalb wurde ein Ansatz entwickelt, um Flugzeuglaserscannerdaten mit terrestrischen Punktwolken allein über die Kenntnis der Baumposition und des vorliegenden digitalen Geländemodells zu verknüpfen und zusätzlich das Problem der Georeferenzierung zu lösen. Dass ein terrestrischer Laserscanner nicht nur für Forstinventuren gewinnbringend eingesetzt werden kann, wird anhand von drei verschiedenen Beispielen beleuchtet. Neben der Ableitung von statischen Verformungsstrukturen an Einzelbäumen werden beispielsweise auch die Daten zur Bestimmung von Vegetationsmodellen auf Basis von Gitterstrukturen (Voxel) zur Simulation von turbulenten Strömungen in und über Waldbeständen eingesetzt. Das aus Laserscannerdaten abgeleitete Höhenbild einer Rinde führt unter Verwendung von Bildverarbeitungsmethoden (Texturanalyse) zur Klassifizierung der Baumart. Mit dem terrestrischen Laserscanning ist ein interessantes Werkzeug für den Einsatz im Forst gegeben. Bestehende Konzepte der Forstinventur können erweiterte werden und es eröffnen sich neue Felder in forstwirtschaftlichen und forstwissenschaftlichen Anwendungen, wie beispielsweise die Nutzung eines Scanners auf einem Harvester während des Erntevorganges. Mit der stetigen Weiterentwicklung der Laserscannertechnik hinsichtlich Gewicht, Reichweite und Geschwindigkeit wird der Einsatz im Forst immer attraktiver.An important application field of airborne laser scanning is forestry and the science of forestry. The captured data serve as an area-wide determination of digital terrain and canopy models, with a derived tree height. Due to the nadir recording direction, near-ground tree parameters, such as diameter at breast height (dbh) and crown base height, are predicted using forest models. High resolution terrestrial laser scanner data complements the airborne laser scanner data. Forest inventory parameters, such as dbh and tree height can be derived directly and digital terrain models are created. As a result of the dense three dimensional point clouds captured, a high level of detail exists, and a high degree of automation of the determination of the parameters is possible. To control and manage the existing stock of wood, forest inventories are carried out at periodic time intervals, on the base of sample plots. Geometric tree parameters, such as tree height, tree position and dbh are measured and documented. This conventional data acquisition is characterised by a large amount of work and time. Because of this, algorithms are developed to automatically determine geometric tree parameters from terrestrial laser scanner point clouds. The data acquisition enables an objective and fast determination of parameters, remotely, and independent of light conditions. Finally the majority of the algorithms are combined into a single program, allowing tree detection and the determination of relevant parameters in one step. Three different sample plots are used to test the algorithms. Manually measured tree parameters are also used to validate the algorithms. The natural vegetation structure causes occlusions inside the crown when scanning from one position. These scan shadows can be minimized, though not completely avoided, via an appropriate scan configuration. Additional the registration process in forest scenes is time-consuming. The largest problem is to find a suitable distribution of tie points when dense ground vegetation exists. Therefore an approach is introduced that allows data registration with the determined centre points of the dbh. The method removes the need for artificial tie points. However, the centre points of identical stem sections in both datasets are assumed. Nevertheless the biggest uncertainness is found in the Z co-ordinate of the translation. A method using the tree axes and one homologous tie point, which fixes the datasets, shows better results. The methods are compared and analysed with the traditional registration process with tie points, using a single study area. Georeferencing of terrestrial laser scanner data in forest stands is problematic, due to signal shadowing of global navigation satellite systems. Thus an approach was developed to register airborne and terrestrial laser scanner data, taking the tree positions and the available digital terrain model. With the help of three examples the benefits of applying laser scanning to forest applications is shown. Besides the derivation of static deformation structures of single trees, the data is used to determine vegetation models on the basis of a grid structure (voxel space) for simulation of turbulent flows in and over forest stands. In addition, the derived height image of tree bark using image processing methods (texture analysis) can be used to classify the tree species. Terrestrial laser scanning is a valuable tool for forest applications. Existing inventory concepts can be enlarged, and new fields in forestry and the science of forestry are established, e. g. the application of scanners on a harvester. Terrestrial laser scanners are becoming increasingly important for forestry applications, caused by continuous technological enhancements that reduce the weight, whilst increasing the range and the data rate