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Die Anzahl der unterernährten Menschen in der Welt steigt seit 2017 wieder an. Der Klimawandel wird den Druck auf die Landwirtschaft und die Ernährungssicherheit weiter erhöhen, insbesondere für kleinbäuerliche und von Subsistenzwirtschaft geprägte Agrarsysteme in den Tropen. Um die Widerstandsfähigkeit der Ernährungssysteme und die Ernährungssicherheit zu stärken, bedarf es eines Klimarisikomanagements und Klimaanpassung. Dies kann sowohl die Antizipation als auch die Reaktion auf die Auswirkungen der globalen Erwärmung ermöglichen. Eine zentrale Rolle spielen in dieser Hinsicht landwirtschaftliche Modelle. Sie können die Reaktionen von Pflanzen auf Veränderungen in den Klimabedingungen quantifizieren und damit Risiken identifizieren. Diese Dissertation demonstriert anhand dreier in Peru, in Tansania und in Burkina Faso durchgeführten Fallstudien, wie statistische Ertragsmodelle das Klimarisikomanagement und die Anpassung in der tropischen Landwirtschaft unterstützen können. Während die erste Studie zeigt, wie Klimaanpassungsbestrebungen unterstützt werden können, werden in Studie zwei und drei statistische Modelle genutzt, um Ertrags- und Produktionsvorhersagen zu erstellen. Die Ergebnisse können dazu beitragen, Frühwarnsysteme für Ernährungsunsicherheit zu unterstützen. In den drei Veröffentlichungen werden neue Ansätze statistischer Ertragsmodellierung auf verschiedenen räumlichen Ebenen vorgestellt. Ein besonderer Fokus liegt hierbei auf der Weiterentwicklung von bisherigen Ertragsvorhersagen, insbesondere in Bezug auf unabhängige Modellvalidierungen, eine stärkere Berücksichtigung von Wetterextremen und die Übertragbarkeit der Modelle auf andere Regionen. The number of undernourished people in the world has been increasing since 2017. Climate change will further exacerbate pressure on agriculture and food security, particularly for smallholder and subsistence-based farming systems in the tropics. Anticipating and responding to global warming through climate risk management is needed to increase the resilience of food systems and food security. Crop models play an indispensable role in this regard. They allow quantifying crop responses to changes in climatic conditions and thus identify risks. This dissertation demonstrates how statistical crop modelling can inform climate risk management and adaptation in tropical agriculture in the case studies of Peru, Tanzania and Burkina Faso. While the first study shows how statistical crop models can support climate adaptation, studies two and three provide yield and production forecasts. The results can contribute to supporting early warning systems on food insecurity. The three publications present novel approaches of statistical yield modelling at different spatial scales. A particular focus is on further developing existing yield forecasts, especially with regard to independent rigorous model validations, improved consideration of weather extremes, and the transferability of the models to other regions.

The Arctic Ocean is undergoing a major transition from a year-round sea ice cover to ice-free summers with global consequences. Sea ice thickness is at the center of the ongoing changes because the thickness regulates key processes of the Arctic climate system and in the last six decades, the mean thickness has more than halved. With the most scientific attention on the increased melting and delayed freezing of Arctic sea ice, dynamic thickness change caused by sea ice deformation has remained less studied. Dynamic thickness change alters the sea ice thickness through colliding floes that raft or form pressure ridges or floes breaking apart resulting in leads. Because sea ice grows faster in open water and under thin ice, new ice formation is enhanced in those leads compared to the surrounding ice during the growth season. Because thinner ice is easier to break and move, the ongoing thinning of Arctic sea ice may result in more ridges and leads, which, in turn, could increase ice thickness in winter. However, our limited quantitative understanding of dynamic thickness change has hampered any robust prediction if and to which extent such increased dynamic thickening in winter could mitigate summer thinning in the warming Arctic. To address this gap, we need more robust estimates of the current magnitude as well as a better understanding and representation of the different processes in state-of-the-art sea ice models. Thus, the overarching goal of this thesis is to resolve and quantify dynamic thickness change and to link it to the corresponding sea ice deformation. I focus on the freezing period addressing the following research questions: (1) How large is the dynamic contribution to the mean sea ice thickness in different dynamic regimes? (2) How is deformation shaping the ice thickness distribution? (3) How can high-resolution microwave synthetic aperture radar (SAR) satellite data be used to estimate dynamic thickness change? I answer them in two regional case studies: a unique month-long deformation event during the closing of a polynya north of Greenland and in the Transpolar Drift along the drift track of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The combination of available high-resolution electromagnetic (EM) induction sounding ice thickness data and high-resolution deformation data offer unique research opportunities to study the highly localized and intermittent dynamic thickness changes. My results show that dynamic thickness change plays an important role in both convergent and divergent drift regimes. Studying the polynya closing event reveals that convergence can locally double the thickness of young, thin (<1 m) ice and restore the mean thickness of 2 m of the surrounding multi-year ice within one month. In more divergent regimes like the Transpolar Drift, new ice formation in leads contributes 30% to the sea ice mass balance. There are indicators that this fraction may increase in a more seasonal Arctic sea ice cover. Besides the mean changes, I show how deformation shapes the ice thickness distribution (ITD) with a particular focus on the transfer of observational results into modeling concepts. I identify the ice that participates in ridging, show that the current ridging parameterization in state-of-the-art models is not able to reproduce the observed changes in the shape of the ITD, and suggest an updated parameterization that relates the shape of the ITD proportionally to the observed deformation. Lastly, I demonstrate that SAR-derived deformation can successfully be used to describe sea ice dynamics and to estimate the dynamic contribution to the ice thickness on regional scales. In conclusion, this dissertation substantially advances our understanding of dynamic thickness change with robust and quantitative estimates. The high-resolution EM ice thickness data with simultaneously collected high-resolution deformation data provide an excellent opportunity to deepen our process understanding and to evaluate and improve the modeling of the dynamic processes shaping the ITD. With the increasing availability of SAR data in the Arctic and the presented deformation datasets and methods, new opportunities are opening up to derive dynamic thickness change on Arctic-wide scales and to study the temporal trends in dynamic thickness change over the last decade.

Learning from observing others is a powerful human competence that robots lack. People can learn by analyzing others’ interactions under almost any conditions because of reasoning capabilities, such as inferring causal relationships, predicting, adapting, and imagining. These capabilities allow people to attain causal understanding and harness observations for their benefit, such as anticipating others’ behaviors, rehearsing them under different conditions, and imagining behavior not seen before. Possessing the four inference capabilities is essential for observational learning, but robots do not fully support them and require quality inputs to render inferences feasible. To explore the viability of robots analyzing others’ interactions in natural conditions, in this dissertation, we focus on formalizing human observational learning and then challenge and evaluate its potential, such as inferring hand behavior from everyday activities. The proof of principle comprises the identification of a formalism covering core capabilities of human observational learning, the instantiation of a framework serving as the object of study, the specification of a scenario that challenges its potential, the verification of proper functioning, and the utility determining inferences remain meaningful. The results show inferences can operate outside the formalism’s functional design despite atypical conditions and breaking a foundational assumption. Moreover, under such conditions, inferences manage to find causal relationships which happen to be meaningful. By introducing this proof of principle and value, we know that robots equipped with the inference formalism operating outside the functional design do not necessarily fail and could provide valuable inferences.

It has been demonstrated many times that modern robotic platforms can generate competent bodily behavior comparable to the level of humans. However, the implementation of such behavior requires a lot of programming effort, and is often not feasible for the general case, i.e., regardless of the situational context in which the activity is performed. Furthermore, research and industry have an enormous need for intuitive robot programming. This is due to the high complexity of realizing an integrated robot control system, and adapting it to other robots, tasks and environments. The challenge is how a robot control program can be realized that can generate competent behavior depending on characteristics of the robot, the task it executes, and the environment where it operates. One way to approach this problem is to specialize the control program through the context-specific application of abstract knowledge. In this work, it will be investigated how abstract knowledge, required for flexible and competent robot task execution, can be represented using a formal ontology. To this end, a domain ontology of robot activity context will be proposed. Using this ontology, robots can infer how tasks can be accomplished through movements and interactions with the environment, and how they can improvise to a certain extent to take advantage of action possibilities that objects provide in their environment. Accordingly, it will be shown that parts of the context-specific information required for flexible task execution can be derived from broadly applicable knowledge represented in an ontology. Furthermore, it will be shown that the domain vocabulary yields additional benefits for the representation of knowledge gained through experimentation and simulation. Such knowledge can be leveraged for learning, or be used to inspect the robot's behavior. The latter of which will be demonstrated in this work by means of a case study.

Die zunehmende Verfügbarkeit von Satellitenfernerkundungs- und Wildtier-Telemetriedaten eröffnet neue Möglichkeiten für eine verbesserte Überwachung von Wildtierhabitaten durch Habitatmodelle, doch fehlt es häufig an geeigneten Ansätzen, um dieses Potenzial voll auszuschöpfen. Das übergeordnete Ziel dieser Arbeit bestand in der Konzipierung und Weiterentwicklung von Ansätzen zur Nutzung des Potenzials großer Satellitenbild- und Telemetriedatensätze in Habitatmodellen. Am Beispiel von drei großen Säugetierarten in Europa (Eurasischer Luchs, Rothirsch und Reh) wurden Ansätze entwickelt, um (1) Habitatmodelle mit dem umfangreichsten global und frei verfügbaren Satellitenbildarchiv der Landsat-Satelliten zu verknüpfen und (2) Wildtier-Telemetriedaten über Wildtierpopulationen hinweg in großflächigen Analysen der Habitateignung und -nutzung zu integrieren. Die Ergebnisse dieser Arbeit belegen das enorme Potenzial von Landsat-basierten Variablen als Prädiktoren in Habitatmodellen, die es ermöglichen von statischen Habitatbeschreibungen zu einem kontinuierlichen Monitoring von Habitatdynamiken über Raum und Zeit überzugehen. Die Ergebnisse meiner Forschung zeigen darüber hinaus, wie wichtig es ist, die Kontextabhängigkeit der Lebensraumnutzung von Wildtieren in Habitatmodellen zu berücksichtigen, insbesondere auch bei der Integration von Telemetriedatensätzen über Wildtierpopulationen hinweg. Die Ergebnisse dieser Dissertation liefern neue ökologische Erkenntnisse, welche zum Management und Schutz großer Säugetiere beitragen können. Darüber hinaus zeigt meine Forschung, dass eine bessere Integration von Satellitenbild- und Telemetriedaten eine neue Generation von Habitatmodellen möglich macht, welche genauere Analysen und ein besseres Verständnis von Lebensraumdynamiken erlaubt und so Bemühungen zum Schutz von Wildtieren unterstützen kann. The growing availability of satellite remote sensing and animal tracking data opens new opportunities for an improved monitoring of wildlife habitats based on habitat models, yet suitable approaches for making full use of this potential are commonly lacking. The overarching goal of this thesis was to develop and advance approaches for harnessing the potential of big satellite image and animal tracking data in habitat models. Specifically, using three large mammal species in Europe as an example (Eurasian lynx, red deer, and roe deer), I developed approaches for (1) linking habitat models to the largest global and freely available satellite image record, the Landsat image archive, and (2) for integrating animal tracking datasets across wildlife populations in large-area assessments of habitat suitability and use. The results of this thesis demonstrate the enormous potential of Landsat-based variables as predictors in habitat models, allowing to move from static habitat descriptions to a continuous monitoring of habitat dynamics across space and time. In addition, my research underscores the importance of considering context-dependence in species’ habitat use in habitat models, particularly also when integrating tracking datasets across wildlife populations. The findings of this thesis provide novel ecological insights that help to inform the management and conservation of large mammals and more broadly, demonstrate that a better integration of satellite image and animal tracking data will allow for a new generation of habitat models improving our ability to monitor and understand habitat dynamics, thus supporting efforts to restore and protect wildlife across the globe.

Die Kartierung der städtische Oberflächenmaterialien ist aufgrund der komplexen räumlichen Muster eine Herausforderung. Daten von bildgebenden Spektrometern können hierbei durch die feine und kontinuierliche Abtastung des elektromagnetischen Spektrums detaillierte spektrale Merkmale von Oberflächenmaterialien erkennen, was mit multispektralen oder RGB-Bildern nicht mit der gleichen Genauigkeit erreicht werden kann. Bislang wurden in zahlreichen Studien zur Kartierung von städtischen Oberflächenmaterialien Daten von flugzeuggestützten abbildenden Spektrometern mit hoher räumlicher Auflösung verwendet, die ihr Potenzial unter Beweis stellen und gute Ergebnisse liefern. Im Vergleich zu diesen Sensoren haben weltraumgestützte abbildende Spektrometer eine regionale oder globale Abdeckung, eine hohe Wiederholbarkeit und vermeiden teure, zeit- und arbeitsaufwändige Flugkampagnen. Allerdings liegt die räumliche Auflösung der aktuellen weltraumgestützten abbildenden Spektroskopiedaten bei etwa 30 m, was zu einem Mischpixelproblem führt, welches mit herkömmlichen Kartierungsansätzen nur schwer zu bewältigen ist. Das Hauptziel dieser Studie ist die Kartierung städtischer Materialien mit bildgebenden Spektroskopiedaten in verschiedenen Maßstäben und die gleichzeitige Nutzung des Informationsgehalts dieser Daten, um die chemischen und physikalischen Eigenschaften von Oberflächenmaterialien zu erfassen sowie das Mischpixelproblem zu berücksichtigen. Konkret zielt diese Arbeit darauf ab, (1) photovoltaische Solarmodule mit Hilfe von luftgestützten bildgebenden Spektroskopiedaten auf der Grundlage ihrer spektralen Merkmale zu kartieren; (2) die Robustheit der Stichprobe von städtischen Materialgradienten zu untersuchen; (3) die Übertragbarkeit von städtischen Materialgradienten auf andere Gebiete zu analysieren. Mapping urban surface materials is challenging due to the complex spatial patterns. Data from imaging spectrometers can identify detailed spectral features of surface materials through the fine and continuous sampling of the electromagnetic spectrum, which cannot be achieved with the same accuracy using multispectral or RGB images. To date, numerous studies in urban surface material mapping have been using data from airborne imaging spectrometers with high spatial resolution, demonstrating the potential and providing good results. Compared to these sensors, spaceborne imaging spectrometers have regional or global coverage, high repeatability, and avoid expensive, time-consuming, and labor-intensive flight campaigns. However, the spatial resolution of current spaceborne imaging spectroscopy data (also known as hyperspectral data) is about 30 m, resulting in a mixed pixel problem that is challenging to handle with conventional mapping approaches. The main objective of this study is to perform urban surface material mapping with imaging spectroscopy data at different spatial scales, simultaneously explore the information content of these data to detect the chemical and physical properties of surface materials, and take the mixed-pixel problem into account. Specifically, this thesis aims to (1) map solar photovoltaic modules using airborne imaging spectroscopy data based on their spectral features; (2) investigate the sampling robustness of urban material gradients; (3) analyze the area transferability of urban material gradients.

Aus spektral hoch aufgelösten Messungen im ultravioletten (UV) und infraroten (IR) Spektralbereich können mittels einer Tikhonov Regularisierung vertikale Ozonprofile in der Stratosphäre und Troposphäre invertiert werden. Der neu geschaffene Retrieval-Algorithmus TOPAS (Tikhonov regularized Ozone Profile retrievAl with SCIATRAN) ermöglicht die kombinierte Auswertung beider Spektralbereiche und enthält einen neuen und verbesserten Ansatz der Re-Kalibrierung der UV Messungen. In der Stratosphäre liefern die UV Messungen des Instruments TROPOMI auf dem Satelliten S5P sehr genaue Ozonwerte, aber in der Troposphäre ist ihr Informationsgehalt begrenzt. Daher wurden zusätzliche IR Messungen von CrIS auf Suomi-NPP verwendet. Die Kombination beider Spektralbereiche erhöht den Informationsgehalt des Ozonprofilretrievals in der oberen Troposphäre und in der unteren Stratosphäre. In diese Arbeit werden die Ozonprofile in der Stratosphäre und in der Troposphäre mit anderen satellitengestützten Ozonprofilen, Ozonsonden und troposphärischen und stratopshärischen Lidarsystemen validiert und die Ergebnisse präsentiert.

Wälder spielen global eine entscheidende Rolle bei der Regulierung des Weltklimas, da sie aktiv Kohlenstoff speichern und binden. Trotz der Bemühungen durch internationale Programme nehmen die Waldschäden weiter zu. Entwaldung und Walddegradierung sind zwei unterschiedliche Prozesse, die sich auf die globalen Wälder auswirken. Entwaldung ist eine klar definierte Umwandlung oder Abholzung der Waldflächen, während Degradierung subtiler, vorübergehend und variabel sein kann und daher schwer zu detektieren ist. Walddegradierung wird im Allgemeinen als eine funktionale Verringerung der Fähigkeit von Wäldern Ökosystemleistungen zu erbringen identifiziert. Sie wird nicht als Veränderung der Landbedeckung oder Entwaldung klassifiziert. Daraus folgt keine deutliche Verringerung der Waldfläche, sondern eher eine Abnahme der Qualität und des Zustands. Diese Veränderung kann, wie die Entwaldung dennoch mit einer signifikanten Verringerung der oberirdischen Biomasse und damit miterheblichen Treibhausgasemissionen verbunden sein. Die Schätzungen der Kohlenstoffemissionen aus Waldstörungen liegen zwischen 12 und 20 % aller weltweit emittierten Emissionen. Durch eine fehlende einheitliche Definition oder Methode zur Quantifizierung der Degradation, der Vielzahl an Einflussfaktoren und der Unsicherheit bei der Schätzung der Biomasse variieren die Werte stark. Die von der Walddegradierung betroffene Fläche könnte in der Tat viel größer sein als die der Entwaldung, die ohnehin jedes Jahr auf eine Fläche von etwa der Größe Islands geschätzt wird. Die REDD+-Mechanismen zur Finanzierung von Emissionsreduktionen zur Minderung des Klimawandels erfordern robuste, transparente und skalierbare Methoden zur Quantifizierung der Walddegradierung, zusammen mit der Erfassung der damit verbundenen Treibern. Da die Degradierung oft der Entwaldung vorausgeht, kann ein schnelles Monitoring mit einer Beurteilung der Waldschäden und ihren Treibern ein wichtiges Frühwarnsystem sein. Nur so können Maßnahmen frühzeitig ergriffen werden, die die Wälder schützen und sowohl der Natur und der Biodiversität als auch dem Lebensunterhalt, der Gesundheit und dem Wohlbefinden von Millionen von Menschen auf der ganzen Welt zugute kommen. In dieser Arbeit werden Methoden für konsistente, reproduzierbare, skalierbare und satellitengestützte Indikatoren zur Identifizierung und Quantifizierung verschiedener Arten von Walddegradation um zukünftige Risiko- und Politikszenarien zu unterstützen. Global forests play a crucial role in regulating global climate by actively storing and sequestering carbon. Despite efforts to mitigate climate through international efforts, human-caused forest disturbance and forest-related greenhouse gas emissions continue to rise. Deforestation and forest degradation are two different processes affecting global forests. Deforestation is a clearly defined conversion or removal of forest cover, while degradation can be more subtle, temporary, variable, and therefore difficult to detect. Forest degradation is generally identified as a functional reduction in the capacity of forests to provide ecosystem services, that does not qualify as a change in land cover or forest clearing. That means no clear reduction of the forest area, but rather a decrease in quality and condition. This change, like deforestation can still be associated with significant reductions in above-ground biomass and therefore considerable greenhouse gas emissions. Estimates of carbon emissions from forest degradation and disturbance range anywhere from 12-20% of all emissions emitted globally with values varying widely because of a lack of uniform definition or method for quantifying degradation, the broad number of influencing factors, and uncertainty in biomass estimates. The area affected by forest degradation could in fact be much larger than that of deforestation, which is already estimated to be an area about the size of Iceland every year. The REDD+ mechanisms of financing emissions reductions to mitigate climate change require robust, transparent and scalable methods for quantifying degradation, along with a quantification of associated direct drivers. Furthermore, as degradation often precedes deforestation, timely monitoring and assessment of forest degradation and changes in drivers can provide crucial early warning to engage interventions to keep forests intact, benefitting nature and biodiversity as well as the livelihoods, health and well-being of millions of people around the world. This research proposes methods for consistent, repeatable and scalable satellite-derived indicators for identifying and quantifying different types of forest degradation and its causes to inform future risk and policy scenarios.

Similar to biological systems, robots may need skin-like sensing ability to perceive interactions in complex, changing, and human-involved environments. Current skin-like sensing technologies are still far behind their biological counterparts when considering resolution, dynamics range, robustness, and surface coverage together. One key challenge is the wiring of sensing elements. During my Ph.D. study, I explore how machine learning can enable the design of a new kind of haptic sensors to deal with such a challenge. On the one hand, I propose super-resolution-oriented tactile skins, reducing the number of physical sensing elements while achieving high spatial accuracy. On the other hand, I explore vision-based haptic sensor designs. In this thesis, I present four types of machine-learning-driven haptic sensors that I designed for coarse and fine robotic applications, varying from large surface (robot limbs) to small surface sensing (robot fingers). Moreover, I propose a super-resolution theory to guide sensor designs at all levels ranging from hardware design (material/structure/transduction), data collection (real/simulated), and signal processing methods (analytical/data-driven). I investigate two designs for large-scale coarse-resolution sensing, e.g., robotic limbs. HapDef sparsely attaches a few strain gauges on a large curved surface internally to measure the deformation over the whole surface. ERT-DNN wraps a large surface with a piece of multi-layered conductive fabric, which varies its conductivity upon contacts exerted. I also conceive two approaches for small-scale fine-resolution sensing, e.g., robotic fingertips. BaroDome sparsely embeds a few barometers inside a soft elastomer to measure internal pressure changes caused by external contact. Insight encloses a high-resolution camera to view a soft shell from within. Generically, an inverse problem needs to be solved when trying to obtain high-resolution sensing with a few physical sensing elements. I develop machine-learning frameworks suitable for solving this inverse problem. They process various raw sensor data and extract useful haptic information in practice. Machine learning methods rely on data collected by an automated robotic stimulation device or synthesized using finite element methods. I build several physical testbeds and finite element models to collect copious data. I propose machine learning frameworks to combine data from different sources that are good enough to deal with the noise in real data and generalize well from seen to unseen situations. While developing my prototype sensors, I have faced reoccurring design choices. To help my developments and guide future research, I propose a unified theory with the concept of taxel-value-isolines. It captures the physical effects required for super-resolution, ties them to all parts of the sensor design, and allows us to assess them quantitatively. The theory offers an explanation about physically achievable accuracies for localizing and quantifying contact based on uncertainties introduced by measurement noise in sensor elements. The theoretical analysis aims to predict the best performance before a physical prototype is built and helps to evaluate the hardware design, data collection, and data processing methods during implementation. This thesis presents a new perspective on haptic sensor design. Using machine learning to substitute the entire data-processing pipeline, I present several haptic sensor designs for applications ranging from large-surface skins to high-resolution tactile fingertip sensors. The developed theory for obtaining optimal super-resolution can guide future sensor designs.

Die schulische Bildung befindet sich in einem rasanten Wandel, denn Inklusion und Digitalisierung als gesamtgesellschaftlich anerkannte neue Rahmenbedingungen erfordern von den Lehrkräften Basis-kompetenzen in der Realisierung der inklusiven, digitalen Welt in der Schule (Meier, Kremser, Finger, & Huwer, 2020). Für den Chemieunterricht besteht die Herausforderung, die Strategie der Kultusministerkonferenz KMK (2016) zur „Bildung in der digitalen Welt“, deren cha-rakteristisches Merkmal die Verbindung der Inklusion – die Teilhabe Aller an Zugängen zur Bil-dung – und der Digitalisierung, integrativ zu verwirklichen. Dennoch lassen sich für den internati-onalen wie auch für den deutschsprachigen Raum kaum geeignete Modelle als Instrumente zur Planung von inklusivem Unterricht unter Einbindung digitaler Medien finden. Die Paradigmen-wechsel für den Unterricht von exklusiv zu inklusiv und analog zu digital werden meist isoliert be-trachtet und selten in ihrer Gesamtheit bedacht, aber nur in der synthetischen Betrachtung die-ser Aspekte kann der Inklusionsprozess optimiert werden (Muuß-Merholz, 2020). Naturwissen-schaftliche Erkenntnisgewinnung durch Problemlösen bietet aber eine Möglichkeit, verschiedene Anforderungen eines inklusiven Chemieunterrichts zu berücksichtigen, wobei das Promotions-projekt ausdrücklich auf dem weiter zu fassenden Begriff der Inklusion beruht. Die vorliegende Arbeit widmet sich demzufolge der Entwicklung und Validierung eines Modells für den inklusiven Chemieunterricht (MiC) und darauf aufbauend dem Transfer des Modells in eine praxisnahe, in-klusive Lernumgebung unter Verwendung eines interaktiven Lernbuches. Anhand des erfolgrei-chen Transfers des MiC-Ansatzes in eine inklusive, analog-digitale Lernumgebung konnte gezeigt werden, dass die drei Schritte des MiC-Ansatzes handhabbar und praktisch umsetzbar sind. Zu-gleich konnte die Eignung und das Potenzial des Multitouch-Learning-Books für den inklusiven Chemieunterricht nachgewiesen werden. School education is undergoing rapid change, because inclusion and digitalisation as new frame-work conditions recognised by society as a whole require basic competences from teachers in the realisation of the inclusive, digital world in school (Meier, Kremser, Finger & Huwer, 2020). For chemistry teaching, the challenge is to realise the strategy of the KMK Conference of Ministers of Culture (2016) on "Education in the Digital World", whose characteristic feature is the connection of inclusion - the participation of all in access to education - and digitalisation in an integrative way. Nevertheless, hardly any suitable models can be found for the international as well as for the German-speaking area as instruments for planning inclusive lessons with the integration of digital media. The paradigm shifts for teaching from exclusive to inclusive and analogue to digital are usually considered in isolation and rarely considered in their entirety, but only in the synthetic consideration of these aspects can the inclusion process be optimised (Muuß-Merholz, 2020). However, scientific knowledge acquisition through problem solving offers a possibility to consider different requirements of an inclusive chemistry education, whereby the PhD project is explicitly based on the broader concept of inclusion. Accordingly, this thesis is dedicated to the develop-ment and validation of a model for inclusive chemistry teaching (MiC) and, building on this, the transfer of the model into a practical, inclusive learning environment using an interactive learning book. The successful transfer of the MiC approach into an inclusive, analogue-digital learning en-vironment showed that the three steps of the MiC approach are manageable and can be imple-mented in practice. At the same time, the suitability and potential of the multitouch learning book for inclusive chemistry teaching was demonstrated.