Quantifying the urban forest environment using dense discrete return LiDAR and aerial color imagery for segmentation and object-level biomass assessment

The urban forest is becoming increasingly important in the contexts of urban green space and recreation, carbon sequestration and emission offsets, and socio-economic impacts. In addition to aesthetic value, these green spaces remove airborne pollutants, preserve natural resources, and mitigate adverse climate changes, among other benefits. A great deal of attention recently has been paid to urban forest management. However, the comprehensive monitoring of urban vegetation for carbon sequestration and storage is an under-explored research area. Such an assessment of carbon stores often requires information at the individual tree level, necessitating the proper masking of vegetation from the built environment, as well as delineation of individual tree crowns. As an alternative to expensive and time-consuming manual surveys, remote sensing can be used effectively in characterizing the urban vegetation and man-made objects. Many studies in this field have made use of aerial and multispectral/hyperspectral imagery over cities. The emergence of light detection and ranging (LiDAR) technology, however, has provided new impetus to the effort of extracting objects and characterizing their 3D attributes - LiDAR has been used successfully to model buildings and urban trees. However, challenges remain when using such structural information only, and researchers have investigated the use of fusion-based approaches that combine LiDAR and aerial imagery to extract objects, thereby allowing the complementary characteristics of the two modalities to be utilized. In this study, a fusion-based classification method was implemented between high spatial resolution aerial color (RGB) imagery and co-registered LiDAR point clouds to classify urban vegetation and buildings from other urban classes/cover types. Structural, as well as spectral features, were used in the classification method. These features included height, flatness, and the distribution of normal surface vectors from LiDAR data, along with a non-calibrated LiDAR-based vegetation index, derived from combining LiDAR intensity at 1064 nm with the red channel of the RGB imagery. This novel index was dubbed the LiDAR-infused difference vegetation index (LDVI). Classification results indicated good separation between buildings and vegetation, with an overall accuracy of 92% and a kappa statistic of 0.85. A multi-tiered delineation algorithm subsequently was developed to extract individual tree crowns from the identified tree clusters, followed by the application of species-independent biomass models based on LiDAR-derived tree attributes in regression analysis. These LiDAR-based biomass assessments were conducted for individual trees, as well as for clusters of trees, in cases where proper delineation of individual trees was impossible. The detection accuracy of the tree delineation algorithm was 70%. The LiDAR-derived biomass estimates were validated against allometry-based biomass estimates that were computed from field-measured tree data. It was found out that LiDAR-derived tree volume, area, and different distribution parameters of height (e.g., maximum height, mean of height) are important to model biomass. The best biomass model for the tree clusters and the individual trees showed an adjusted R-Squared value of 0.93 and 0.58, respectively. The results of this study showed that the developed fusion-based classification approach using LiDAR and aerial color (RGB) imagery is capable of producing good object detection accuracy. It was concluded that the LDVI can be used in vegetation detection and can act as a substitute for the normalized difference vegetation index (NDVI), when near-infrared multiband imagery is not available. Furthermore, the utility of LiDAR for characterizing the urban forest and associated biomass was proven. This work could have significant impact on the rapid and accurate assessment of urban green spaces and associated carbon monitoring and management

Simulation of multi-platform LiDAR for assessing total leaf area in tree crowns

LiDAR (Light Detection and Ranging) technology has been increasingly implemented to assess the biophysical attributes of forest canopies. However, LiDAR-based estimation of tree biophysical attributes remains difficult mainly due to the occlusion of vegetative elements in multi-layered tree crowns. In this study, we developed a new algorithm along with a multiple-scan methodology to analyse the impact of occlusion on LiDAR-based estimates of tree leaf area. We reconstructed five virtual tree models using a computer graphic-based approach based on in situ measurements from multiple tree crowns, for which the position, size, orientation and area of all leaves were measured. Multi-platform LiDAR simulations were performed on these 3D tree models through a point-line intersection algorithm. An approach based on the Delaunay triangulation algorithm with automatic adaptive threshold selection was proposed to construct the scanned leaf surface from the simulated discrete LiDAR point clouds. In addition, the leaf area covered by laser beams in each layer was assessed in combination with the ratio and number of the scanned points. Quantitative comparisons of LiDAR scanning for the occlusion effects among various scanning approaches, including fixed-position scanning, multiple terrestrial LiDAR scanning and airborne-terrestrial LiDAR cross-scanning, were assessed on different target trees. The results showed that one simulated terrestrial LiDAR scan alongside the model tree captured only 25–38% of the leaf area of the tree crown. When scanned data were acquired from three simulated terrestrial LiDAR scans around one tree, the accuracy of the leaf area recovery rate reached 60–73% depending on the leaf area index, tree crown volume and leaf area density. When a supplementary airborne LiDAR scanning was included, occlusion was reduced and the leaf area recovery rate increased to 72–90%. Our study provides an approach for the measurement of total leaf area in tree crowns from simulated multi-platform LiDAR data and enables a quantitative assessment of occlusion metrics for various tree crown attributes under different scanning strategies

ANALYSIS OF LIDAR DATA FOR FLUVIAL GEOMORPHIC CHANGE DETECTION AT A SMALL MARYLAND STREAM

Numerous detailed topographic measurements, which must be periodically repeated, are required to characterize stream bank and channel geometry. Light Detection and Ranging (LiDAR) is becoming more widely used, but its accuracy for change detection in and around small streams is not well quantified. Two LiDAR and one ground-surveyed elevation data sets are compared for a thickly vegetated riparian area in the Maryland Piedmont. Interpolated surfaces (prediction maps) and estimates of their uncertainty (standard error maps) are created from the point data using kriging. The LiDAR 2006 elevations are compared to ground-survey to evaluate accuracy. LiDAR 2002 and 2006 elevations are compared to evaluate the potential for change detection. When the estimated LiDAR system error is included in hypothesis testing, no statistically significant elevation differences are found between 2002 and 2006. Conclusions about geomorphic change based on LiDAR scenes should account for error and uncertainty in the data collection and processing

Automatic Reconstruction of Urban Objects from Mobile Laser Scanner Data

Aktuelle 3D-Stadtmodelle werden immer wichtiger in verschiedenen städtischen Anwendungsbereichen. Im Moment dienen sie als Grundlage bei der Stadtplanung, virtuellem Tourismus und Navigationssystemen. Mittlerweile ist der Bedarf an 3D-Gebäudemodellen dramatisch gestiegen. Der Grund dafür sind hauptsächlich Navigationssysteme und Onlinedienste wie Google Earth. Die Mehrheit der Untersuchungen zur Rekonstruktion von Gebäudemodellen von Luftaufnahmen konzentriert sich ausschließlich auf Dachmodellierung. Jedoch treiben Anwendungen wie Virtuelle Realität und Navigationssysteme die Nachfrage nach detaillieren Gebäudemodellen, die nicht nur die geometrischen Aspekte sondern auch semantische Informationen beinhalten, stark an. Urbanisierung und Industrialisierung beeinflussen das Wachstum von urbaner Vegetation drastisch, welche als ein wesentlicher Teil des Lebensraums angesehen wird. Aus diesem Grund werden Aufgaben wie der Ökosystemüberwachung, der Verbesserung der Planung und des Managements von urbanen Regionen immer mehr Aufmerksamkeit geschenkt. Gleichermaßen hat die Erkennung und Modellierung von Bäumen im Stadtgebiet sowie die kontinuierliche Überprüfung ihrer Inventurparameter an Bedeutung gewonnen. Die steigende Nachfrage nach 3D-Gebäudemodellen, welche durch Fassadeninformation ergänzt wurden, und Informationen über einzelne Bäume im städtischen Raum erfordern effiziente Extraktions- und Rekonstruktionstechniken, die hochgradig automatisiert sind. In diesem Zusammenhang ist das Wissen über die geometrische Form jedes Objektteils ein wichtiger Aspekt. Heutzutage, wird das Mobile Laser Scanning (MLS) vermehrt eingesetzt um Objekte im städtischen Umfeld zu erfassen und es entwickelt sich zur Hauptquelle von Daten für die Modellierung von urbanen Objekten. Eine Vielzahl von Objekten wurde schon mit Daten von MLS rekonstruiert. Außerdem wurden bereits viele Methoden für die Verarbeitung von MLS-Daten mit dem Ziel urbane Objekte zu erkennen und zu rekonstruieren vorgeschlagen. Die 3D-Punkwolke einer städtischen Szene stellt eine große Menge von Messungen dar, die viele Objekte von verschiedener Größe umfasst, komplexe und unvollständige Strukturen sowie Löcher (Rauschen und Datenlücken) enthält und eine inhomogene Punktverteilung aufweist. Aus diesem Grund ist die Verarbeitung von MLS-Punktwolken im Hinblick auf die Extrahierung und Modellierung von wesentlichen und charakteristischen Fassadenstrukturen sowie Bäumen von großer Bedeutung. In der Arbeit werden zwei neue Methoden für die Rekonstruktion von Gebäudefassaden und die Extraktion von Bäumen aus MLS-Punktwolken vorgestellt, sowie ihre Anwendbarkeit in der städtischen Umgebung analysiert. Die erste Methode zielt auf die Rekonstruktion von Gebäudefassaden mit expliziter semantischer Information, wie beispielsweise Fenster, Türen, und Balkone. Die Rekonstruktion läuft vollautomatisch ab. Zu diesem Zweck werden einige Algorithmen vorgestellt, die auf dem Vorwissen über die geometrische Form und das Arrangement von Fassadenmerkmalen beruhen. Die initiale Klassifikation, mit welcher die Punkte in Objektpunkte und Bodenpunkte unterschieden werden, wird über eine lokale Höhenhistogrammanalyse zusammen mit einer planaren Region-Growing-Methode erzielt. Die Punkte, die als zugehörig zu Objekten klassifiziert werden, werden anschließend in Ebenen segmentiert, welche als Basiselemente der Merkmalserkennung angesehen werden können. Information über die Gebäudestruktur kann in Form von Regeln und Bedingungen erfasst werden, welche die wesentlichen Steuerelemente bei der Erkennung der Fassadenmerkmale und der Rekonstruktion des geometrischen Modells darstellen. Um Merkmale wie Fenster oder Türen zu erkennen, die sich an der Gebäudewand befinden, wurde eine löcherbasierte Methode implementiert. Einige Löcher, die durch Verdeckungen entstanden sind, können anschließend durch einen neuen regelbasierten Algorithmus eliminiert werden. Außenlinien der Merkmalsränder werden durch ein Polygon verbunden, welches das geometrische Modell repräsentiert, indem eine Methode angewendet wird, die auf geometrischen Primitiven basiert. Dabei werden die topologischen Relationen unter Beachtung des Vorwissens über die primitiven Formen analysiert. Mögliche Außenlinien können von den Kantenpunkten bestimmt werden, welche mit einer winkelbasierten Methode detektiert werden können. Wiederkehrende Muster und Ähnlichkeiten werden ausgenutzt um geometrische und topologische Ungenauigkeiten des rekonstruierten Modells zu korrigieren. Neben der Entwicklung des Schemas zur Rekonstruktion des 3D-Fassadenmodells, sind die Segmentierung einzelner Bäume und die Ableitung von Attributen der städtischen Bäume im Fokus der Untersuchung. Die zweite Methode zielt auf die Extraktion von individuellen Bäumen aus den Restpunktwolken. Vorwissen über Bäume, welches speziell auf urbane Regionen zugeschnitten ist, wird im Extraktionsprozess verwendet. Der formbasierte Ansatz zur Extraktion von Einzelbäumen besteht aus einer Reihe von Schritten. In jedem Schritt werden Objekte in Abhängigkeit ihrer geometrischen Merkmale gefunden. Stämme werden unter Ausnutzung der Hauptrichtung der Punktverteilung identifiziert. Dafür werden Punktsegmente gesucht, die einen Teil des Baumstamms repräsentieren. Das Ergebnis des Algorithmus sind segmentierte Bäume, welche genutzt werden können um genaue Informationen über die Größe und Position jedes einzelnen Baumes abzuleiten. Einige Beispiele der Ergebnisse werden in der Arbeit angeführt. Die Zuverlässigkeit der Algorithmen und der Methoden im Allgemeinen wurden unter Verwendung von drei Datensätzen, die mit verschiedenen Laserscannersystemen aufgenommen wurden, verifiziert. Die Untersuchung zeigt auch das Potential sowie die Einschränkungen der entwickelten Methoden wenn sie auf verschiedenen Datensätzen angewendet werden. Die Ergebnisse beider Methoden wurden quantitativ bewertet unter Verwendung einer Menge von Maßen, die die Qualität der Fassadenrekonstruktion und Baumextraktion betreffen wie Vollständigkeit und Genauigkeit. Die Genauigkeit der Fassadenrekonstruktion, der Baumstammdetektion, der Erfassung von Baumkronen, sowie ihre Einschränkungen werden diskutiert. Die Ergebnisse zeigen, dass MLS-Punktwolken geeignet sind um städtische Objekte detailreich zu dokumentieren und dass mit automatischen Rekonstruktionsmethoden genaue Messungen der wichtigsten Attribute der Objekte, wie Fensterhöhe und -breite, Flächen, Stammdurchmesser, Baumhöhe und Kronenfläche, erzielt werden können. Der gesamte Ansatz ist geeignet für die Rekonstruktion von Gebäudefassaden und für die korrekte Extraktion von Bäumen sowie ihre Unterscheidung zu anderen urbanen Objekten wie zum Beispiel Straßenschilder oder Leitpfosten. Aus diesem Grund sind die beiden Methoden angemessen um Daten von heterogener Qualität zu verarbeiten. Des Weiteren bieten sie flexible Frameworks für das viele Erweiterungen vorstellbar sind.Up-to-date 3D urban models are becoming increasingly important in various urban application areas, such as urban planning, virtual tourism, and navigation systems. Many of these applications often demand the modelling of 3D buildings, enriched with façade information, and also single trees among other urban objects. Nowadays, Mobile Laser Scanning (MLS) technique is being progressively used to capture objects in urban settings, thus becoming a leading data source for the modelling of these two urban objects. The 3D point clouds of urban scenes consist of large amounts of data representing numerous objects with significant size variability, complex and incomplete structures, and holes (noise and data gaps) or variable point densities. For this reason, novel strategies on processing of mobile laser scanning point clouds, in terms of the extraction and modelling of salient façade structures and trees, are of vital importance. The present study proposes two new methods for the reconstruction of building façades and the extraction of trees from MLS point clouds. The first method aims at the reconstruction of building façades with explicit semantic information such as windows, doors and balconies. It runs automatically during all processing steps. For this purpose, several algorithms are introduced based on the general knowledge on the geometric shape and structural arrangement of façade features. The initial classification has been performed using a local height histogram analysis together with a planar growing method, which allows for classifying points as object and ground points. The point cloud that has been labelled as object points is segmented into planar surfaces that could be regarded as the main entity in the feature recognition process. Knowledge of the building structure is used to define rules and constraints, which provide essential guidance for recognizing façade features and reconstructing their geometric models. In order to recognise features on a wall such as windows and doors, a hole-based method is implemented. Some holes that resulted from occlusion could subsequently be eliminated by means of a new rule-based algorithm. Boundary segments of a feature are connected into a polygon representing the geometric model by introducing a primitive shape based method, in which topological relations are analysed taking into account the prior knowledge about the primitive shapes. Possible outlines are determined from the edge points detected from the angle-based method. The repetitive patterns and similarities are exploited to rectify geometrical and topological inaccuracies of the reconstructed models. Apart from developing the 3D façade model reconstruction scheme, the research focuses on individual tree segmentation and derivation of attributes of urban trees. The second method aims at extracting individual trees from the remaining point clouds. Knowledge about trees specially pertaining to urban areas is used in the process of tree extraction. An innovative shape based approach is developed to transfer this knowledge to machine language. The usage of principal direction for identifying stems is introduced, which consists of searching point segments representing a tree stem. The output of the algorithm is, segmented individual trees that can be used to derive accurate information about the size and locations of each individual tree. The reliability of the two methods is verified against three different data sets obtained from different laser scanner systems. The results of both methods are quantitatively evaluated using a set of measures pertaining to the quality of the façade reconstruction and tree extraction. The performance of the developed algorithms referring to the façade reconstruction, tree stem detection and the delineation of individual tree crowns as well as their limitations are discussed. The results show that MLS point clouds are suited to document urban objects rich in details. From the obtained results, accurate measurements of the most important attributes relevant to the both objects (building façades and trees), such as window height and width, area, stem diameter, tree height, and crown area are obtained acceptably. The entire approach is suitable for the reconstruction of building façades and for the extracting trees correctly from other various urban objects, especially pole-like objects. Therefore, both methods are feasible to cope with data of heterogeneous quality. In addition, they provide flexible frameworks, from which many extensions can be envisioned

Scale challenges in inventory of forests aided by remote sensing

The impact of changing the scale of observation on information derived from forest inventories is the basis of scale-related research in forest inventory and analysis (FIA). Interactions between the scale of observation and observed heterogeneity in studied variables highlight a dependence on scale that affects measurements, estimates, and relationships between inventory data from terrestrial and remote sensing surveys. This doctoral research defines "scale" as the divisions of continuous space over which measurements are made, or hierarchies of discrete units of study/analysis in space. Therefore, the "scale of observation" (also known as support) refers to that integral of space over which statistics are computed and forest inventory variables regionalized. Given the ubiquitous nature of scale issues, a case study approach was undertaken in this research (Articles I-IV) with the goal to provide fundamental understanding of responses to the scale of observation for specific FIA variables. The studied forest inventory variables are; forest stand structural heterogeneity, forest cover proportion and tree species identities. Forest cover proportion (or simply forest area) and tree species are traditional and fundamental forest inventory variables commonly assessed over large areas using both terrestrial samples and remote sensing data whereas, forest stand structural heterogeneity is a contemporary FIA variable that is increasingly demanded in multi-resource inventories to inform management and conservation efforts as it is linked to biodiversity, productivity, ecosystem functioning and productivity, and used as auxiliary data in forest inventory. This research has two overall aims: 1. To improve the understanding of the association between the scale of observation and observed heterogeneity in inventory of forest stand structural heterogeneity, forest-cover proportions, and identification of tree species from a combination of terrestrial samples and remote sensing data. 2. To contribute knowledge to the estimation of scale-dependence in inventory of forest stand structural heterogeneity, forest-cover proportions, and identification of tree species from a combination of terrestrial samples and remote sensing data. Different scales of observation were considered across the four case studies encompassing individual leaf, crown-part or branch, single-tree crown, forest stand, landscape and global levels of analysis. Terrestrial and remote sensing data sets from a variety of temperate forests in Germany and France were utilized across case studies. In cases where no inventory data were available, synthetic data was simulated at different scales of observation. Heterogeneity in FIA variable estimates was monitored across scales of observation using estimators of variance and associated precision. As too much heterogeneity is hardly interpreted due to a low signal to noise ratio, object-based image analysis (OBIA) methods were used to manage heterogeneity in high resolution remote sensing data before evaluating scale dependence or scaling across observed scales. Similarly, ensemble classification techniques were applied to address methodological heterogeneity across classifiers in a case study on classification of two physically and spectrally similar Pinus species. Across case studies, a dependence on the scale of observation was determined by linking estimates of heterogeneity to their respective scales of observation using linear regression and a combination of geo-statistics and Monte-Carlo approaches. In order to address scale-dependence, thresholds to scale domains were identified so as to enable efficient observation of studied FIA variables and scaling approaches proposed to bridge observations across scales. For scaling, this research evaluated the potential of different regression techniques to map forest stand structural heterogeneity and tree species wall-to-wall from remote sensing data. In addition, radiative transfer modelling was evaluated in the transfer between leaf and crown hyperspectra, and a global sampling grid framework proposed to efficiently link different stages of survey sampling. This research shows that the scale of observation affected all studied FIA variables albeit to varying degrees, conditioned on the spatial structure and aggregation properties of the assessed FIA variable (i.e. whether the variable is extensive, intensive or scale-specific) and the method used in aggregation on support (e.g. mean, variance, quantile etc.). The scale of observation affected measurements or estimates of the studied FIA variables as well as relationships between spatially structured FIA variables. The scale of observation determined observed heterogeneity in FIA variables, affected parameter retrieval from radiative transfer models, and affected variable selection and performance of models linking terrestrial and remote sensing data. On the other hand, this research shows that it is possible to determine domains of scale dependence within which to efficiently observe the studied FIA variables and to bridge between scales of observation using various scaling methods. The findings of this doctoral research are relevant for the general understanding of scale issues in FIA. Research in Article I, for example, informs optimization of plot sizes for efficient inventory and mapping of forest structural heterogeneity, as well as for the design of natural resource inventories. Similarly, research in Article II is applicable in large area forest (or general land) cover monitoring from sampling by both visual interpretation of high resolution remote sensing imagery and terrestrial surveys. This research is also useful to determine observation design for efficient inventory of land cover. Research in Article III contributes in many contexts of remote sensing assisted inventory of forests especially in management and conservation planning, pest and diseases control and in the estimation of biomass. Lastly, research in Article IV highlights scale-related effects in passive optical remote sensing of forests currently understudied and can ultimately contribute to sensor calibration and modelling approaches