The compromises of rewilding

The purpose of this thesis is to explore how rewilding has emerged as a new alternative to classic nature conservation to reconcile humans with wild nature. The study will explore what are the compromises required for rewilding in a Swedish context. It will focus on the expectations and the processes leading to the rewilding projects and how human expectations for landscapes, animals and nature have to compromise. Most of our modern landscapes being tamed and domesticated, they correspond more to an idea of beautiful, in opposition with the sublime which can designate the wilderness, which is, in Kant’s terms, untamed, wild, and sometimes ugly and terrifying. If Kant considers that the only way for humans to enjoy this sublime nature is pure disinterest, we will look at how rewilding can be a way to restore autonomy and ecological integrity to ecosystems while offering to humans an experience of the sublime nature and letting them benefit from it. This paper will look at Rewilding Lapland as a case study, it proposes to rewild a large area in Northern Sweden by supporting some keystone species like the beaver and restoring key areas of the landscape like rivers and grazing lands. The stated aim of this Rewilding Lapland is to develop a naturebased economy where entrepreneurship and economical activities are combined with nature conservation. Thus, public awareness, local communities approval and nature-based economies around rewilding will be studied as compromises between nature and people

Machine Learning for Materials Science

Machine learning is a branch of artificial intelligence that uses data to automatically build inferences and models designed to generalise and make predictions. In this thesis, the use of machine learning in materials science is explored, for two different problems: the optimisation of gallium nitride optoelectronic devices, and the prediction of material failure in the setting of laboratory earthquakes. Light emitting diodes based on III-nitrides quantum wells have become ubiquitous as a light source, owing to their direct band-gap that covers UV, visible and infra-red light, and their very high quantum efficiency. This efficiency originates from most electronic transitions across the band-gap leading to the emission of a photon. At high currents however this efficiency sharply drops. In chapters 3 and 4 simulations are shown to provide an explanation for experimental results, shedding a new light on this drop of efficiency. Chapter 3 provides a simple and yet accurate model that explains the experimentally observed beneficial effect that silicon doping has on light emitting diodes. Chapter 4 provides a model for the experimentally observed detrimental effect that certain V-shaped defects have on light emitting diodes. These results pave the way for the association of simulations to detailed multi-microscopy. In the following chapters 5 to 7, it is shown that machine learning can leverage the use of device simulations, by replacing in a targeted and efficient way the very labour intensive tasks of making sure the numerical parameters of the simulations lead to convergence, and that the physical parameters reproduce experimental results. It is then shown that machine learning coupled with simulations can find optimal light emitting diodes structures, that have a greatly enhanced theoretical efficiency. These results demonstrate the power of machine learning for leveraging and automatising the exploration of device structures in simulations. Material failure is a very broad problem encountered in a variety of fields, ranging from engineering to Earth sciences. The phenomenon stems from complex and multi-scale physics, and failure experiments can provide a wealth of data that can be exploited by machine learning. In chapter 8 it is shown that by recording the acoustic waves emitted during the failure of a laboratory fault, an accurate predictive model can be built. The machine learning algorithm that is used retains the link with the physics of the experiment, and a new signal is thus discovered in the sound emitted by the fault. This new signal announces an upcoming laboratory earthquake, and is a signature of the stress state of the material. These results show that machine learning can help discover new signals in experiments where the amount of data is very large, and demonstrate a new method for the prediction of material failure.Funding: CHESS and Caius & Gonville award; European Union ALIGHT grant; Los Alamos National Laboratory LDRD

Diffusion Models for Interferometric Satellite Aperture Radar

Probabilistic Diffusion Models (PDMs) have recently emerged as a very promising class of generative models, achieving high performance in natural image generation. However, their performance relative to non-natural images, like radar-based satellite data, remains largely unknown. Generating large amounts of synthetic (and especially labelled) satellite data is crucial to implement deep-learning approaches for the processing and analysis of (interferometric) satellite aperture radar data. Here, we leverage PDMs to generate several radar-based satellite image datasets. We show that PDMs succeed in generating images with complex and realistic structures, but that sampling time remains an issue. Indeed, accelerated sampling strategies, which work well on simple image datasets like MNIST, fail on our radar datasets. We provide a simple and versatile open-source https://github.com/thomaskerdreux/PDM_SAR_InSAR_generation to train, sample and evaluate PDMs using any dataset on a single GPU

Autonomous Detection of Methane Emissions in Multispectral Satellite Data Using Deep Learning

Methane is one of the most potent greenhouse gases, and its short atmospheric half-life makes it a prime target to rapidly curb global warming. However, current methane emission monitoring techniques primarily rely on approximate emission factors or self-reporting, which have been shown to often dramatically underestimate emissions. Although initially designed to monitor surface properties, satellite multispectral data has recently emerged as a powerful method to analyze atmospheric content. However, the spectral resolution of multispectral instruments is poor, and methane measurements are typically very noisy. Methane data products are also sensitive to absorption by the surface and other atmospheric gases (water vapor in particular) and therefore provide noisy maps of potential methane plumes, that typically require extensive human analysis. Here, we show that the image recognition capabilities of deep learning methods can be leveraged to automatize the detection of methane leaks in Sentinel-2 satellite multispectral data, with dramatically reduced false positive rates compared with state-of-the-art multispectral methane data products, and without the need for a priori knowledge of potential leak sites. Our proposed approach paves the way for the automated, high-definition and high-frequency monitoring of point-source methane emissions across the world

Autonomous Extraction of Millimeter-scale Deformation in InSAR Time Series Using Deep Learning

Systematic characterization of slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. Here we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault's location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California

Domestic Livestock and Rewilding: Are They Mutually Exclusive?

Human influence extends across the globe, fromthe tallestmountains to the deep bottom of the oceans. There is a growing call for nature to be protected from the negative impacts of human activity (particularly intensive agriculture); so-called “land sparing”. A relatively new approach is “rewilding”, defined as the restoration of self-sustaining and complex ecosystems, with interlinked ecological processes that promote and support one another while minimising or gradually reducing human intervention. The key theoretical basis of rewilding is to return ecosystems to a “natural” or “self-willed” state with trophic complexity, dispersal (and connectivity) and stochastic disturbance in place. However, this is constrained by context-specific factors whereby it may not be possible to restore the native species that formed part of the trophic structure of the ecosystem if they are extinct (e.g., mammoths, Mammuthus spp., aurochs, Bos primigenius); and, populations/communities of native herbivores/predators may not be able to survive or be acceptable to the public in small scale rewilding projects close to areas of high human density. Therefore, the restoration of natural trophic complexity and disturbance regimes within rewilding projects requires careful consideration if the broader conservation needs of society are to be met. In some circumstances, managers will require a more flexible deliberate approach to intervening in rewilding projects using the range of tools in their toolbox (e.g., controlled burning regimes; using domestic livestock to replicate the impacts of extinct herbivore species), even if this is only in the early stages of the rewilding process. If this approach is adopted, then larger areas can be given over to conservation, because of the potential broader benefits to society from these spaces and the engagement of farmers in practises that are closer to their traditions. We provide examples, primarily European, where domestic and semi-domestic livestock are used by managers as part of their rewilding toolbox. Here managers have looked at the broader phenotype of livestock species as to their suitability in different rewilding systems. We assess whether there are ways of using livestock in these systems for conservation, economic (e.g., branded or certified livestock products) and cultural gains