Kartotrak software allows optimal waste classification and avoids unnecessary remediation. It has been designed for those - site owners, safety authorities or contractors, involved in environmental site characterization projects - who need to locate and estimate contaminated soil volumes confidently.

Die Kartierung und Lokalisierung eines mobilen Roboters in seiner Umgebung ist eine wichtige Voraussetzung für dessen Autonomie. In dieser Arbeit wird der Einsatz eines 3D-Laserscanners zur Erfüllung dieser Aufgaben untersucht. Durch die optimierte Anordnung eines rotierenden 2D-Laserscanners werden hochauflösende Bereiche vorgegeben. Zudem wird mit Hilfe von ICP die Kartierung und Lokalisierung im Stillstand durchgeführt. Bei der Betrachtung zur Verbesserung der Bewegungsschätzung wird auch eine Möglichkeit zur Lokalisierung während der Bewegung mit 3D-Scans vorgestellt. Die vorgestellten Algorithmen werden durch Experimente mit realer Hardware evaluiert.

Die Identifizierung des albostrians Gens erfolgte mittels Karten-basiertem Klonieren. Begonnen wurde mit der Kartierung in zwei kleinen F2-Kartierungspopulationen, MM4205 und BM4205, die zur Lokalisierung des Genes auf dem langen Arm von Gerstenchromosom 7H führte. Durch Kartierung mit hoher Auflösung in Verbindung mit extensiver Markersättigung konnte der betreffende DNA-Bereich schrittweise von anfangs 14,29 cM auf schließlich 0,06 cM eingeschränkt werden, wobei insgesamt 1344 F2-Pflanzen analysiert wurden. Zwischen den nächsten flankierenden genetischen Markern konnte in einem Bereich von 46 Kbp ein einzelnes Gen identifiziert werden. Durch Sequenzvergleich des abgeleiteten Genprodukts mit Einträgen in Datenbanken konnte das Protein der CMF-Genfamilie putativer Transkritionsregulatoren mit DNA-bindenden oder Protein-Protein-Wechselwirkungs-Eigenschaften zugeordnet werden. Eine erste Bestätigung der Identität des Kandidatengens mit dem albostrians-Gen konnte durch Analyse einer EMS-induzierten TILLING-Population (abgeleitet von der Gerstensorte ‚Barke’) erreicht werden. Unter den 42 gefundenen induzierten Mutationen gab es eine Mutation, die zu einem vorzeitigen Stopcodon und damit nach der Translation potenziell zu einem verkürztem Protein führt. Die Nachkommenschaft dieser heterozygoten Mutante spaltete in grüne und albino Pflanzen auf. Der albino-Phänotyp war perfekt mit dem homozygoten Status der nonsense-Mutation in den untersuchten 245 M4-Nachkommen von fünf heterozygoten M3-Pflanzen der Mutantenfamilie verbunden. Nach transienter Transformation von Gerstenblatt-Epidermiszellen mittels biolistischem Cobombardement von ALBOSTRIANS::GFP-Fusionsprotein mit dem mCherry-markierten Organellenmarker pt-rk-CD3-999 konnte die Lokalisation des ALBOSTRIANS-Proteins in den Plastiden und im Kern beobachtet werden. / Map-based cloning was employed for identification of the albostrians gene. Starting with mapping in two small F2 mapping populations, MM4205 and BM4205, the locus could be assigned to the long arm of barley chromosome 7H. High-resolution genetic mapping in conjunction with extensive marker saturation allowed to reduce the genetic target interval iteratively from initially 14.29 cM to finally 0.06 cM by analyzing a total of 1344 F2 plants. A single gene could be identified in a physical distance of 46 Kbp between the closest flanking genetic markers. Functional annotation of the deduced protein revealed it to represent a member of the CMF gene family of putative transcriptional regulators comprising DNA binding or protein-protein interaction properties. The identified candidate gene was first confirmed by screening an EMS-induced TILLING population derived from barley cv. ‘Barke’. Among the 42 identified induced mutations a single mutation introduced a premature stop codon potentially resulting in a shorter protein upon translation. Progeny of this heterozygous mutant segregated for green and albino plants. The albino phenotype was perfectly linked with the homozygous state of the stop codon mutation in 245 M4 offspring of five heterozygous M3 plants of the mutant family. Transient transformation by biolistic co-bombardment of barley epidermal cells with an ALBOSTRIANS::GFP fusion protein and an mCherry labelled organelle marker pt-rk-CD3-999 revealed the ALBOSTRIANS protein is targeting to plastids and nucleus.

No description available.

No description available.

Biomass is an important ecological variable for understanding the responses of vegetation to the currently observed global change. The impact of changes in vegetation biomass on the global ecosystem is also of high relevance. The vegetation in the arid and semi-arid environments of Kazakhstan is expected to be affected particularly strongly by future climate change. Therefore, it is of great interest to observe large-scale vegetation dynamics and biomass distribution in Kazakhstan. At the beginning of this dissertation, previous research activities and remote-sensing-based methods for biomass estimation in semi-arid regions have been comprehensively reviewed for the first time. The review revealed that the biggest challenge is the transferability of methods in time and space. Empirical approaches, which are predominantly applied, proved to be hardly transferable. Remote-sensing-based Net Primary Productivity (NPP) models, on the other hand, allow for regional to continental modelling of NPP time-series and are potentially transferable to new regions. This thesis thus deals with modelling and analysis of NPP time-series for Kazakhstan and presents a methodological concept for derivation of above-ground biomass estimates based on NPP data. For validation of the results, biomass field data were collected in three study areas in Kazakhstan. For the selection of an appropriate model, two remote-sensing-based NPP models were applied to a study area in Central Kazakhstan. The first is the Regional Biomass Model (RBM). The second is the Biosphere Energy Transfer Hydrology Model (BETHY/DLR). Both models were applied to Kazakhstan for the first time in this dissertation. Differences in the modelling approaches, intermediate products, and calculated NPP, as well as their temporal characteristics were analysed and discussed. The model BETHY/DLR was then used to calculate NPP for Kazakhstan for 2003–2011. The results were analysed regarding spatial, intra-annual, and inter-annual variations. In addition, the correlation between NPP and meteorological parameters was analysed. In the last part of this dissertation, a methodological concept for derivation of above-ground biomass estimates of natural vegetation from NPP time-series has been developed. The concept is based on the NPP time-series, information about fractional cover of herbaceous and woody vegetation, and plants’ relative growth rates (RGRs). It has been the first time that these parameters are combined for biomass estimation in semi-arid regions. The developed approach was finally applied to estimate biomass for the three study areas in Kazakhstan and validated with field data. The results of this dissertation provide information about the vegetation dynamics in Kazakhstan for 2003–2011. This is valuable information for a sustainable land management and the identification of regions that are potentially affected by a changing climate. Furthermore, a methodological concept for the estimation of biomass based on NPP time-series is presented. The developed method is potentially transferable. Providing that the required information regarding vegetation distribution and fractional cover is available, the method will allow for repeated and large-area biomass estimation for natural vegetation in Kazakhstan and other semi-arid environments.

Mithilfe des Modells EROSION-3D wurden für sämtliche Ackerflächen Sachsens Erosionsszenarienkarten erstellt. Der vorliegende Band der Schriftenreihe dient der Dokumentation der Modellierungen. Fachnutzer und interessierte Privatpersonen erhalten so die Möglichkeit, sich mit der Methodik und den zugrundeliegenden Modellannahmen vertraut zu machen. Die Szenarienkarten selbst sind im Portal iDA abrufbar in der Rubrik „Landwirtschaft“ > „Erosionsgefährdung Landwirtschaftliche Nutzflächen“ > „Erosionsszenarienkarten Modell EROSION 3D“. Redaktionsschluss: 31.08.2022

Undichte Abwassersysteme sind in unserer urbanisierten Welt allgegenwärtig, und aufgrund ihrer versteckten Infrastruktur und der schwierigen Überwachung bleiben ihre Leckagen oft in der Anfangsphase unbemerkt. Trotz der umfangreichen technologischen Entwicklung bei den Kanalinspektionsmethoden und den dazugehörigen Techniken ist die Überwachung von Abwasserkanälen auf städtischer Ebene nach wie vor kostspielig und schwierig. Daher werden ein Empfehlungsverfahren und eine Methodenklassifizierung benötigt, um einen nachhaltigen und kosteneffizienten Kanalinspektionsplan auf Stadtebene zu erstellen. In diesem Zusammenhang kann diese Studie im Wesentlichen in drei Teile gegliedert werden. Zunächst wurde eine umfassende Literaturstudie zu den verfügbaren Kanalinspektionsmethoden durchgeführt, um ein umfassenderes Verständnis für deren Wirkungsbereich und technischen Grad zu erhalten. Darüber hinaus wurden diese Inspektionsmethoden auf der Grundlage ihres Wirkungsbereichs in drei Stufen eingeteilt, wobei Stufe 1 die Methoden mit dem größten Wirkungsbereich umfasst, wie z. B. die Verschlechterungsmodellierung, die ein umfassendes und dennoch zuverlässiges Verständnis der Integrität des Abwassersystems ermöglicht. Stufe 2 bietet intermediäre Inspektionsmethoden wie Wärmebildaufnahmen aus der Luft und geoelektrische Inspektionstechniken, die eine zerstörungsfreie Inspektion, der von Stufe 1 vorgeschlagenen Bereiche ermöglichen. Bei den Methoden der Stufe 3 handelt es sich in erster Linie um Inspektionstechniken in der Rohrleitung, die häufig eine Rohrentwässerung erfordern und im Gegenzug für eine hohe Erkennungsgenauigkeit kostspielig zu implementieren sind. Zweitens wurde als Beitrag zu den Tier-1-Methoden das Vulnerability Hotspot Mapping entwickelt, ein GIS-gestütztes Modell, das die am häufigsten von den Entleerungsmodellen verwendeten Faktoren berücksichtigt und Bereiche des Abwassersystems anbietet, die besonders anfällig für Leckagen sind. Die Validierungs- und Sensitivitätsanalysen ergaben, dass die Fließgeschwindigkeit, das Rohralter und die Oberflächenvegetation die sinnvollsten Faktoren für das Modell sind. Darüber hinaus ergab das lineare Modell einen Wirkungsgrad von 76 % und einen mittleren quadratischen Fehler von 0,918, während es durch den Random-Forest-Algorithmus mit 400 Bäumen verbessert wurde, was auf das Potenzial der Schwachstellen-Kartierung als frühzeitige Methode zur Kanalinspektion auf Stadtebene hinweist. Drittens wurden die Tier-2-Methoden aktualisiert, indem das Potenzial der elektrischen Widerstandstomographie und der Mise-la-masse-Techniken als geoelektrische und zerstörungsfreie Methoden hervorgehoben wurde, die experimentell in einem Holzrahmen mit einer Matrix aus Sensoren und Elektroden getestet wurden. Der Versuchsbehälter besteht aus drei Schichten von Elektroden in gesättigten und ungesättigten Zonen, in denen verschiedene Leckageszenarien durchgeführt wurden, um die Sichtbarkeit von Leckagen mit diesen Methoden zu untersuchen. Trotz der Fähigkeit dieser Methoden zur Leckageerkennung wurde festgestellt, dass die elektrische Widerstandstomographie eine höhere Leckageerkennungsempfindlichkeit als die Mise à la masse hat, während sie eine geringere Flexibilität bietet, was ein wichtiger Punkt bei der Methodenauswahl ist. Darüber hinaus wurde festgestellt, dass Mise à-la-masse empfindlicher auf das Vorhandensein von Leckagen reagiert als auf Feuchtigkeits- und Temperaturschwankungen, was zu einem Pearson's r und R2 von 0,8 bzw. 0,7 im Vergleich zu den während der Leckageszenarien gesammelten Daten führte. Insgesamt schlägt diese Studie vor, dass mindestens zwei (vorzugsweise drei) Inspektionstechniken, die zu verschiedenen Ebenen gehören, eingesetzt werden sollten, um einen nachhaltigen Inspektionsplan auf Stadtebene zu haben. Der vorgeschlagene Ansatz hilft dabei, ein Gleichgewicht zwischen Kosten und Präzision sowie ein Gleichgewicht zwischen Zeit und Einwirkungsbereich herzustellen, was einen dezentralisierten und nachhaltigen Inspektionsplan ermöglicht.:List of Abbreviations .......................................................................................... IX List of Peer-Reviewed Publications on the Ph.D. Topic .................................. X List of Co-authored Peer-Reviewed Publications on the Ph.D. Topic ............ X 1 General Introduction........................................................................... 1 1.1 Background ....................................................................................................... 1 1.2 Aim and Objectives .......................................................................................... 3 1.3 Structure of the Document ............................................................................. 3 2 Towards a Decentralized Solution for Sewer Leakage Detection .............................................................................................. 8 2.1 Introduction ...................................................................................................... 10 2.2 Sewer inspection methods (SIMs) overview ................................................. 11 2.2.1 Tier-one (T-I) ................................................................................................................. 11 Deterioration models ....................................................................................................... 12 Hotspot mapping .............................................................................................................. 14 2.2.2 Tier-two (T-II) methods ............................................................................................... 15 Aerial thermal imaging (ATI) ............................................................................................ 15 Ground penetration radar (GPR) .................................................................................... 16 Electrical resistivity tomography (ERT) ........................................................................... 17 Mise-à-la-masse method (MLM)...................................................................................... 18 Soil Sampling ..................................................................................................................... 18 2.2.3 Tier-three (T-III) methods ........................................................................................... 20 General approaches ......................................................................................................... 20 Laser scanning ................................................................................................................... 21 Visual inspection ............................................................................................................... 21 Acoustic methods ............................................................................................................. 22 Ultrasonic inspection ........................................................................................................ 24 Multi-sensor robots .......................................................................................................... 24 Electromagnetic Inspection ............................................................................................. 26 Thermography Inspection ............................................................................................... 26 Tracer Test ......................................................................................................................... 27 VII 2.3 Discussion.......................................................................................................... 30 2.4 Conclusion and outlook ................................................................................... 33 2.5 References ......................................................................................................... 34 3 Vulnerability Hotspot Mapping (VHM) of Sewer Pipes based on Deterioration Factors .................................................................... 42 3.1 Introduction ...................................................................................................... 43 3.2 Materials and Methods.................................................................................... 44 3.2.1 Overview of the sewer deterioration factors. .......................................................... 45 Pipe Age .............................................................................................................................. 46 Pipe Material ...................................................................................................................... 47 Sewer Type ......................................................................................................................... 48 Flow Velocity ...................................................................................................................... 48 Node Degree...................................................................................................................... 49 Surface Vegetation ............................................................................................................ 50 Criticality class and weighting matrix ............................................................................. 50 3.3 Case study ......................................................................................................... 52 3.4 Results and discussions ................................................................................... 54 3.4.1 Network assessment .................................................................................................. 54 3.4.2 Validation and sensitivity analysis ............................................................................ 56 3.5 Summary and conclusion ................................................................................ 61 3.6 Reference........................................................................................................... 63 4 Laboratory Application of the Mise-à-la-Masse (MALM) for Sewer Leakage Detection as an intermediary inspection method. ................................................................................................ 67 4.1 Introduction ...................................................................................................... 68 4.2 Methodology ..................................................................................................... 70 4.2.1 Mise-à-la-Masse method (MALM) .............................................................................. 70 4.2.2 Experimental setup ..................................................................................................... 70 4.2.3 Measurement principles ............................................................................................ 72 4.2.4 Assessed Scenarios ..................................................................................................... 73 4.3 Results and discussions ................................................................................... 74 VIII Inhaltsverzeichnis 4.3.1 Contour Visualization ................................................................................................. 74 First Leakage scenario ...................................................................................................... 74 Other leakage scenarios .................................................................................................. 75 4.3.2 Trend Analyses ............................................................................................................ 77 Leakage proximity ............................................................................................................. 77 Vertical Assessment .......................................................................................................... 78 4.3.3 Data Validation and Sensitivity Analyses ................................................................. 79 Data Validation .................................................................................................................. 79 Sensitivity Analyses ........................................................................................................... 80 4.3.4 Application in practice ................................................................................................ 82 4.4 Summary and Conclusion ............................................................................... 83 4.5 References ......................................................................................................... 85 5 Conclusions and Outlooks .................................................................. 88 5.1 Discussion and Conclusions ............................................................................ 88 5.2 Outlooks ............................................................................................................ 89 6 Supplementary Information ............................................................... 92 / Leaky sewer systems are present in our urbanized world and due to their hidden infrastructure and monitoring challenges, their leakages tend to remain unnoticed often at initial stages. Despite an extensive technological development in sewer inspection methods and their implemented techniques, sewer monitoring at the city scale remains costly and challenging. Therefore, a recommendation procedure and method classification are needed to have a sustainable and cost-effective sewer inspection plan at the city scale. In this context, this study can be mainly divided into three parts. First, an extensive study literature was conducted on available sewer inspection methods to have a wider understanding on their area of impacts and technicality levels, Furthermore, these inspection methods were categorized into three tiers based on their area of impact where Tier-1 consists of largest area of impact methods such as deterioration modelling, which provide a vast yet reliable understanding of the sewer system integrity. Tier-2 offers intermediatory inspection methods such as aerial thermal imagery and geo-electrical inspection techniques, which can provide a non-destructive inspection on areas suggested from Tier-1 techniques. Following the area of impact, Tier-3 methods are mostly in-pipe inspection techniques, which often demand pipe dewatering and are costly to implement in returns of a high detection precision. Second, as a contribution to Tier-1 methods, Vulnerability Hotspot Mapping was developed, which is a GIS-based model according to the most frequently used factors by deterioration models and offers areas of the sewer system more prone to leakage. The validation and sensitivity analyses revealed that flow velocity, pipe age, and surface vegetation are the most sensible factors to the model respectively. Furthermore, the linear model resulted in 76% of efficiency and mean squared error of 0,918 while it was improved with random forest algorithm with 400 trees, which points out the vulnerability mapping potential as an early sewer inspection method at the city scale. Third, Tier-2 methods were updated by emphasizing on the potential of Electrical Resistivity Tomography and Mise à-la-masse techniques as geo-electrical and non-destructive methods, which were experimentally tested within a wooden frame with a matrix of sensors and electrodes implemented. The experimental tank consists of three layers of electrodes in saturated and unsaturated zones, when various leakage scenarios were conducted to investigate on leakage visibility by these methods. Despite the capability of these methods for leakage detection, it was assessed that Electrical Resistivity Tomography has higher leakage detection sensibility than Mise à-la-masse while offering less mobility, which is a considerable point in method selection process. Moreover, it was observed that Mise à-la-masse is more sensitive to leakage presence rather than humidity and temperature variations and resulted in 0.8 and 0.7 in Pearson’s r and R2 respectively in comparison to sampled data during the leakage scenarios. All over, this study suggests that at least two (preferably 3) inspection techniques belonging to different tiers should be implemented to have a sustainable inspection plan at the city scale. The proposed approach helps to have a balance between cost and precision as well as an equilibrium between time and area of impact, which provides a decentralized and sustainable inspection plan.:List of Abbreviations .......................................................................................... IX List of Peer-Reviewed Publications on the Ph.D. Topic .................................. X List of Co-authored Peer-Reviewed Publications on the Ph.D. Topic ............ X 1 General Introduction........................................................................... 1 1.1 Background ....................................................................................................... 1 1.2 Aim and Objectives .......................................................................................... 3 1.3 Structure of the Document ............................................................................. 3 2 Towards a Decentralized Solution for Sewer Leakage Detection .............................................................................................. 8 2.1 Introduction ...................................................................................................... 10 2.2 Sewer inspection methods (SIMs) overview ................................................. 11 2.2.1 Tier-one (T-I) ................................................................................................................. 11 Deterioration models ....................................................................................................... 12 Hotspot mapping .............................................................................................................. 14 2.2.2 Tier-two (T-II) methods ............................................................................................... 15 Aerial thermal imaging (ATI) ............................................................................................ 15 Ground penetration radar (GPR) .................................................................................... 16 Electrical resistivity tomography (ERT) ........................................................................... 17 Mise-à-la-masse method (MLM)...................................................................................... 18 Soil Sampling ..................................................................................................................... 18 2.2.3 Tier-three (T-III) methods ........................................................................................... 20 General approaches ......................................................................................................... 20 Laser scanning ................................................................................................................... 21 Visual inspection ............................................................................................................... 21 Acoustic methods ............................................................................................................. 22 Ultrasonic inspection ........................................................................................................ 24 Multi-sensor robots .......................................................................................................... 24 Electromagnetic Inspection ............................................................................................. 26 Thermography Inspection ............................................................................................... 26 Tracer Test ......................................................................................................................... 27 VII 2.3 Discussion.......................................................................................................... 30 2.4 Conclusion and outlook ................................................................................... 33 2.5 References ......................................................................................................... 34 3 Vulnerability Hotspot Mapping (VHM) of Sewer Pipes based on Deterioration Factors .................................................................... 42 3.1 Introduction ...................................................................................................... 43 3.2 Materials and Methods.................................................................................... 44 3.2.1 Overview of the sewer deterioration factors. .......................................................... 45 Pipe Age .............................................................................................................................. 46 Pipe Material ...................................................................................................................... 47 Sewer Type ......................................................................................................................... 48 Flow Velocity ...................................................................................................................... 48 Node Degree...................................................................................................................... 49 Surface Vegetation ............................................................................................................ 50 Criticality class and weighting matrix ............................................................................. 50 3.3 Case study ......................................................................................................... 52 3.4 Results and discussions ................................................................................... 54 3.4.1 Network assessment .................................................................................................. 54 3.4.2 Validation and sensitivity analysis ............................................................................ 56 3.5 Summary and conclusion ................................................................................ 61 3.6 Reference........................................................................................................... 63 4 Laboratory Application of the Mise-à-la-Masse (MALM) for Sewer Leakage Detection as an intermediary inspection method. ................................................................................................ 67 4.1 Introduction ...................................................................................................... 68 4.2 Methodology ..................................................................................................... 70 4.2.1 Mise-à-la-Masse method (MALM) .............................................................................. 70 4.2.2 Experimental setup ..................................................................................................... 70 4.2.3 Measurement principles ............................................................................................ 72 4.2.4 Assessed Scenarios ..................................................................................................... 73 4.3 Results and discussions ................................................................................... 74 VIII Inhaltsverzeichnis 4.3.1 Contour Visualization ................................................................................................. 74 First Leakage scenario ...................................................................................................... 74 Other leakage scenarios .................................................................................................. 75 4.3.2 Trend Analyses ............................................................................................................ 77 Leakage proximity ............................................................................................................. 77 Vertical Assessment .......................................................................................................... 78 4.3.3 Data Validation and Sensitivity Analyses ................................................................. 79 Data Validation .................................................................................................................. 79 Sensitivity Analyses ........................................................................................................... 80 4.3.4 Application in practice ................................................................................................ 82 4.4 Summary and Conclusion ............................................................................... 83 4.5 References ......................................................................................................... 85 5 Conclusions and Outlooks .................................................................. 88 5.1 Discussion and Conclusions ............................................................................ 88 5.2 Outlooks ............................................................................................................ 89 6 Supplementary Information ............................................................... 92

SLAM (Simultaneous Localization And Mapping) has been a very active and almost ubiquitous problem in the field of mobile and autonomous robotics for over two decades. For many years, filter-based methods have dominated the SLAM literature, but a change of paradigms could be observed recently. Current state of the art solutions of the SLAM problem are based on efficient sparse least squares optimization techniques. However, it is commonly known that least squares methods are by default not robust against outliers. In SLAM, such outliers arise mostly from data association errors like false positive loop closures. Since the optimizers in current SLAM systems are not robust against outliers, they have to rely heavily on certain preprocessing steps to prevent or reject all data association errors. Especially false positive loop closures will lead to catastrophically wrong solutions with current solvers. The problem is commonly accepted in the literature, but no concise solution has been proposed so far. The main focus of this work is to develop a novel formulation of the optimization-based SLAM problem that is robust against such outliers. The developed approach allows the back-end part of the SLAM system to change parts of the topological structure of the problem\'s factor graph representation during the optimization process. The back-end can thereby discard individual constraints and converge towards correct solutions even in the presence of many false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. The approach is evaluated on both large scale synthetic and real-world datasets. This work furthermore shows that the developed approach is versatile and can be applied beyond SLAM, in other domains where least squares optimization problems are solved and outliers have to be expected. This is successfully demonstrated in the domain of GPS-based vehicle localization in urban areas where multipath satellite observations often impede high-precision position estimates.

No description available.