429 research outputs found

    Particulate organic matter in the Lena River and its delta: from the permafrost catchment to the Arctic Ocean

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    Rapid Arctic warming accelerates permafrost thaw, causing an additional release of terrestrial organic matter (OM) into rivers and, ultimately, after transport via deltas and estuaries, to the Arctic Ocean nearshore. The majority of our understanding of nearshore OM dynamics and fate has been developed from freshwater rivers despite the likely impact of highly dynamic estuarine and deltaic environments on the transformation, storage, and age of OM delivered to coastal waters. Here, we studied particulate organic carbon (POC) dynamics in the Lena River delta and compared them with POC dynamics in the Lena River main stem along a ∼ 1600 km long transect from Yakutsk, downstream to the delta. We measured POC, total suspended matter (TSM), and carbon isotopes (δ13C and Δ14C) in POC to compare riverine and deltaic OM composition and changes in OM source and fate during transport offshore. We found that TSM and POC concentrations decreased by 70 % during transit from the main stem to the delta and Arctic Ocean. We found deltaic POC to be strongly depleted in 13C relative to fluvial POC. Dual-carbon (Δ14C and δ13C) isotope mixing model analyses indicated a significant phytoplankton contribution to deltaic POC (∼ 68 ± 6 %) and suggested an additional input of permafrost-derived OM into deltaic waters (∼ 18 ± 4 % of deltaic POC originates from Pleistocene deposits vs. ∼ 5 ± 4 % in the river main stem). Despite the lower concentration of POC in the delta than in the main stem (0.41 ± 0.10 vs. 0.79 ± 0.30 mg L−1, respectively), the amount of POC derived from Yedoma deposits in deltaic waters was almost twice as large as the amount of POC of Yedoma origin in the main stem (0.07 ± 0.02 and 0.04 ± 0.02 mg L−1, respectively). We assert that estuarine and deltaic processes require consideration in order to correctly understand OM dynamics throughout Arctic nearshore coastal zones and how these processes may evolve under future climate-driven change.</p

    Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize

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    This thesis presents an adapted method to derive coastal vulnerability through the application of Earth Observation (EO) data in the quantification of forcing variables. A modelled assessment for vulnerability has been produced using the Coastal Vulnerability Index (CVI) approach developed by Gornitz (1991) and enhanced using Machine learning (ML) clustering. ML has been employed to divide the coastline based on the geotechnical conditions observed to establish relative vulnerability. This has been demonstrated to alleviate bias and enhanced the scalability of the approach – especially in areas with poor data coverage – a known hinderance to the CVI approach (Koroglu et al., 2019).Belize provides a demonstrator for this novel methodology due to limited existing data coverage and the recent removal of the Mesoamerican Reef from the International Union for Conservation of Nature (IUCN) List of World Heritage In Danger. A strong characterization of the coastal zone and associated pressures is paramount to support effective management and enhance resilience to ensure this status is retained.Areas of consistent vulnerability have been identified using the KMeans classifier; predominantly Caye Caulker and San Pedro. The ability to automatically scale to conditions in Belize has demonstrated disparities to vulnerability along the coastline and has provided more realistic estimates than the traditional CVI groups. Resulting vulnerability assessments have indicated that 19% of the coastline at the highest risk with a seaward distribution to high risk observed. Using data derived using Sentinel-2, this study has also increased the accuracy of existing habitat maps and enhanced survey coverage of uncharted areas.Results from this investigation have been situated within the ability to enhance community resilience through supporting regional policies. Further research should be completed to test the robust nature of this model through an application in regions with different geographic conditions and with higher resolution input datasets

    Aerial Drone-based System for Wildfire Monitoring and Suppression

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    Wildfire, also known as forest fire or bushfire, being an uncontrolled fire crossing an area of combustible vegetation, has become an inherent natural feature of the landscape in many regions of the world. From local to global scales, wildfire has caused substantial social, economic and environmental consequences. Given the hazardous nature of wildfire, developing automated and safe means to monitor and fight the wildfire is of special interest. Unmanned aerial vehicles (UAVs), equipped with appropriate sensors and fire retardants, are available to remotely monitor and fight the area undergoing wildfires, thus helping fire brigades in mitigating the influence of wildfires. This thesis is dedicated to utilizing UAVs to provide automated surveillance, tracking and fire suppression services on an active wildfire event. Considering the requirement of collecting the latest information of a region prone to wildfires, we presented a strategy to deploy the estimated minimum number of UAVs over the target space with nonuniform importance, such that they can persistently monitor the target space to provide a complete area coverage whilst keeping a desired frequency of visits to areas of interest within a predefined time period. Considering the existence of occlusions on partial segments of the sensed wildfire boundary, we processed both contour and flame surface features of wildfires with a proposed numerical algorithm to quickly estimate the occluded wildfire boundary. To provide real-time situational awareness of the propagated wildfire boundary, according to the prior knowledge of the whole wildfire boundary is available or not, we used the principle of vector field to design a model-based guidance law and a model-free guidance law. The former is derived from the radial basis function approximated wildfire boundary while the later is based on the distance between the UAV and the sensed wildfire boundary. Both vector field based guidance laws can drive the UAV to converge to and patrol along the dynamic wildfire boundary. To effectively mitigate the impacts of wildfires, we analyzed the advancement based activeness of the wildfire boundary with a signal prominence based algorithm, and designed a preferential firefighting strategy to guide the UAV to suppress fires along the highly active segments of the wildfire boundary

    Investigating the role of UAVs and convolutional neural networks in the identification of invasive plant species in the Albany Thicket

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    The study aimed to determine whether plant species could be classified by using high resolution aerial imagery and a convolutional neural network (CNN). The full capabilities of a CNN were examined including testing whether the platform could be used for land cover and the evaluation of land change over time. A drone or unmanned aerial vehicle (UAV) was used to collect the aerial data of the study area, and 45 subplots were used for the image analysis. The CNN was coded and operated in RStudio, and digitised data from the input imagery were used as training and validation data by the programme to learn features. Four classifications were performed using various quantities of input data to access the performance of the neural network. In addition, tests were performed to understand whether the CNN could be used as a land cover and land change detection tool. Accuracy assessments were done on the results to test reliability and accuracy. The best-performing classification achieved an average user and producer accuracy of above 90%, while the overall accuracy was 93%, and the kappa coefficient score was 0.86. The CNN was also able to predict the land coverage area of Opuntia to be within 4% of the ground truthing data area. A change in land cover over time was detected by the programme after the manual clearing of the invasive plant had been undertaken. This research has determined that the use of a CNN in remote sensing is a very powerful tool for supervised image classifications and that it can be used for monitoring land cover by accurately estimating the spatial distribution of plant species and by monitoring the species' growth or decline over time. A CNN could also be used as a tool for landowners to prove that they are making efforts to clear invasive species from their land.Thesis (MSc) -- Faculty of Science, School of Environmental Sciences, 202

    An uncertainty prediction approach for active learning - application to earth observation

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    Mapping land cover and land usage dynamics are crucial in remote sensing since farmers are encouraged to either intensify or extend crop use due to the ongoing rise in the world’s population. A major issue in this area is interpreting and classifying a scene captured in high-resolution satellite imagery. Several methods have been put forth, including neural networks which generate data-dependent models (i.e. model is biased toward data) and static rule-based approaches with thresholds which are limited in terms of diversity(i.e. model lacks diversity in terms of rules). However, the problem of having a machine learning model that, given a large amount of training data, can classify multiple classes over different geographic Sentinel-2 imagery that out scales existing approaches remains open. On the other hand, supervised machine learning has evolved into an essential part of many areas due to the increasing number of labeled datasets. Examples include creating classifiers for applications that recognize images and voices, anticipate traffic, propose products, act as a virtual personal assistant and detect online fraud, among many more. Since these classifiers are highly dependent from the training datasets, without human interaction or accurate labels, the performance of these generated classifiers with unseen observations is uncertain. Thus, researchers attempted to evaluate a number of independent models using a statistical distance. However, the problem of, given a train-test split and classifiers modeled over the train set, identifying a prediction error using the relation between train and test sets remains open. Moreover, while some training data is essential for supervised machine learning, what happens if there is insufficient labeled data? After all, assigning labels to unlabeled datasets is a time-consuming process that may need significant expert human involvement. When there aren’t enough expert manual labels accessible for the vast amount of openly available data, active learning becomes crucial. However, given a large amount of training and unlabeled datasets, having an active learning model that can reduce the training cost of the classifier and at the same time assist in labeling new data points remains an open problem. From the experimental approaches and findings, the main research contributions, which concentrate on the issue of optical satellite image scene classification include: building labeled Sentinel-2 datasets with surface reflectance values; proposal of machine learning models for pixel-based image scene classification; proposal of a statistical distance based Evidence Function Model (EFM) to detect ML models misclassification; and proposal of a generalised sampling approach for active learning that, together with the EFM enables a way of determining the most informative examples. Firstly, using a manually annotated Sentinel-2 dataset, Machine Learning (ML) models for scene classification were developed and their performance was compared to Sen2Cor the reference package from the European Space Agency – a micro-F1 value of 84% was attained by the ML model, which is a significant improvement over the corresponding Sen2Cor performance of 59%. Secondly, to quantify the misclassification of the ML models, the Mahalanobis distance-based EFM was devised. This model achieved, for the labeled Sentinel-2 dataset, a micro-F1 of 67.89% for misclassification detection. Lastly, EFM was engineered as a sampling strategy for active learning leading to an approach that attains the same level of accuracy with only 0.02% of the total training samples when compared to a classifier trained with the full training set. With the help of the above-mentioned research contributions, we were able to provide an open-source Sentinel-2 image scene classification package which consists of ready-touse Python scripts and a ML model that classifies Sentinel-2 L1C images generating a 20m-resolution RGB image with the six studied classes (Cloud, Cirrus, Shadow, Snow, Water, and Other) giving academics a straightforward method for rapidly and effectively classifying Sentinel-2 scene images. Additionally, an active learning approach that uses, as sampling strategy, the observed prediction uncertainty given by EFM, will allow labeling only the most informative points to be used as input to build classifiers; Sumário: Uma Abordagem de Previsão de Incerteza para Aprendizagem Ativa – Aplicação à Observação da Terra O mapeamento da cobertura do solo e a dinâmica da utilização do solo são cruciais na deteção remota uma vez que os agricultores são incentivados a intensificar ou estender as culturas devido ao aumento contínuo da população mundial. Uma questão importante nesta área é interpretar e classificar cenas capturadas em imagens de satélite de alta resolução. Várias aproximações têm sido propostas incluindo a utilização de redes neuronais que produzem modelos dependentes dos dados (ou seja, o modelo é tendencioso em relação aos dados) e aproximações baseadas em regras que apresentam restrições de diversidade (ou seja, o modelo carece de diversidade em termos de regras). No entanto, a criação de um modelo de aprendizagem automática que, dada uma uma grande quantidade de dados de treino, é capaz de classificar, com desempenho superior, as imagens do Sentinel-2 em diferentes áreas geográficas permanece um problema em aberto. Por outro lado, têm sido utilizadas técnicas de aprendizagem supervisionada na resolução de problemas nas mais diversas áreas de devido à proliferação de conjuntos de dados etiquetados. Exemplos disto incluem classificadores para aplicações que reconhecem imagem e voz, antecipam tráfego, propõem produtos, atuam como assistentes pessoais virtuais e detetam fraudes online, entre muitos outros. Uma vez que estes classificadores são fortemente dependente do conjunto de dados de treino, sem interação humana ou etiquetas precisas, o seu desempenho sobre novos dados é incerta. Neste sentido existem propostas para avaliar modelos independentes usando uma distância estatística. No entanto, o problema de, dada uma divisão de treino-teste e um classificador, identificar o erro de previsão usando a relação entre aqueles conjuntos, permanece aberto. Mais ainda, embora alguns dados de treino sejam essenciais para a aprendizagem supervisionada, o que acontece quando a quantidade de dados etiquetados é insuficiente? Afinal, atribuir etiquetas é um processo demorado e que exige perícia, o que se traduz num envolvimento humano significativo. Quando a quantidade de dados etiquetados manualmente por peritos é insuficiente a aprendizagem ativa torna-se crucial. No entanto, dada uma grande quantidade dados de treino não etiquetados, ter um modelo de aprendizagem ativa que reduz o custo de treino do classificador e, ao mesmo tempo, auxilia a etiquetagem de novas observações permanece um problema em aberto. A partir das abordagens e estudos experimentais, as principais contribuições deste trabalho, que se concentra na classificação de cenas de imagens de satélite óptico incluem: criação de conjuntos de dados Sentinel-2 etiquetados, com valores de refletância de superfície; proposta de modelos de aprendizagem automática baseados em pixels para classificação de cenas de imagens de satétite; proposta de um Modelo de Função de Evidência (EFM) baseado numa distância estatística para detetar erros de classificação de modelos de aprendizagem; e proposta de uma abordagem de amostragem generalizada para aprendizagem ativa que, em conjunto com o EFM, possibilita uma forma de determinar os exemplos mais informativos. Em primeiro lugar, usando um conjunto de dados Sentinel-2 etiquetado manualmente, foram desenvolvidos modelos de Aprendizagem Automática (AA) para classificação de cenas e seu desempenho foi comparado com o do Sen2Cor – o produto de referência da Agência Espacial Europeia – tendo sido alcançado um valor de micro-F1 de 84% pelo classificador, o que representa uma melhoria significativa em relação ao desempenho Sen2Cor correspondente, de 59%. Em segundo lugar, para quantificar o erro de classificação dos modelos de AA, foi concebido o Modelo de Função de Evidência baseado na distância de Mahalanobis. Este modelo conseguiu, para o conjunto de dados etiquetado do Sentinel-2 um micro-F1 de 67,89% na deteção de classificação incorreta. Por fim, o EFM foi utilizado como uma estratégia de amostragem para a aprendizagem ativa, uma abordagem que permitiu atingir o mesmo nível de desempenho com apenas 0,02% do total de exemplos de treino quando comparado com um classificador treinado com o conjunto de treino completo. Com a ajuda das contribuições acima mencionadas, foi possível desenvolver um pacote de código aberto para classificação de cenas de imagens Sentinel-2 que, utilizando num conjunto de scripts Python, um modelo de classificação, e uma imagem Sentinel-2 L1C, gera a imagem RGB correspondente (com resolução de 20m) com as seis classes estudadas (Cloud, Cirrus, Shadow, Snow, Water e Other), disponibilizando à academia um método direto para a classificação de cenas de imagens do Sentinel-2 rápida e eficaz. Além disso, a abordagem de aprendizagem ativa que usa, como estratégia de amostragem, a deteção de classificacão incorreta dada pelo EFM, permite etiquetar apenas os pontos mais informativos a serem usados como entrada na construção de classificadores

    Abstracts of the 1st GeoDays, 14th–17th March 2023, Helsinki, Finland

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    Změny na krajinné škále v období okolo přelomu pleistocén-holocén a v antropocénu ve střední Evropě

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    Práce se zabývá dynamikou středoevropské krajiny. Zahrnuty jsou čtyři případové studie zaměřené na dvě klíčová období environmentální transformace: pozdní glaciál a antropocén. Všechny případové studie spojuje krajinná škála jako prostorové měřítko zkoumaných jevů, tedy nejen jako prostorový rozsah výběru vzorků, jak je rámcově popsáno v úvodu. Případové studie využívají disparátní kontexty a metody, což napomáhá přiblížení se tak komplexnímu fenoménu - krajině. Zahrnuté studie se zabývají krajinou a vegetací posledního glaciálu, a to (1) srovnáváním pylových záznamů napříč ČR s využitím moderních analogií (zde z Jakutska), které ukázalo, že změna na přechodu pozdního glaciálu a holocénu nemusela být tak velká, jak se dosud předpokládalo. Alespoň někde mohly již během posledního glaciálu existovat lesy podporované táním permafrostu. Navazující studie (2) zkoumá, jak tání permafrostu, tzv. termokrasové procesy vedly ke genezi celé jezerní krajiny, jejíž dědictví na Třeboňsku nečekaně přetrvalo až do současnosti. Na to navazuje studie (3) využívající podrobného paleoenvironmentálního záznamu sedimentů objevených jezer s využitím především geochemických sedimentologických metod. Dynamika eroze a pedogeneze během klimatických výkyvů v pozdním glaciálu odhalila dalekosáhlé změny krajiny v časovém...This thesis investigates the dynamics of the central European landscape. Four case studies, exploring two key periods of environmental transformation: Late Glacial and the Anthropocene, are included. All case studies are connected by the spatial scale of interest: the landscape scale. This scale is targeted not only by the spatial extent of the sampling, but by the essence of the issues investigated, as broadly described in the introduction. The studies use disparate methods and different contexts, which helps to approach such a complex phenomenon - the landscape and its formation. The included studies are dealing with the Last Glacial landscape and vegetation by (1) comparing pollen records using modern analogues (here from Yakutia) and argues that the change at the Late Glacial/Holocene transition may not have been as great as previously thought, because at least somewhere forests may had existed during the Last Glacial being supported by permafrost melting. A follow-up study (2) explores how permafrost melting, i.e., thermokarst processes, generated an entire lake landscape whose remnants unexpectedly largely persist in the Třeboň region (southern Czech Republic) to recent times. This is followed by (3) the use of a detailed palaeoenvironmental record of the discovered lakes and their contexts...Katedra botanikyDepartment of BotanyPřírodovědecká fakultaFaculty of Scienc

    Advancing large-scale analysis of human settlements and their dynamics

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    Due to the importance for a range of sustainability challenges, it is important to understand the spatial dynamics of human settlements. The rapid expansion of built-up land is among the most extensive global land changes, even though built-up land occupies only a small fraction of the terrestrial biosphere. Moreover, the different ways in which human settlements are manifested are crucially important for their environmental and socioeconomic impacts. Yet, current analysis of human settlements heavily relies on land cover datasets, which typically have only one class to represent human settlements. Consequently, the analysis of human settlements does often not account for the heterogeneity within urban environment or their subtle changes. This simplistic representation severely limits our understanding of change processes in human settlements, as well as our capacity to assess socioeconomic and environmental impacts. This thesis aims to advance large-scale analysis of human settlements and their dynamics through the lens of land systems, with a specific focus on the role of land-use intensity. Chapter 2 explores the use of human settlement systems as an approach to understanding their variation in space and changes over time. Results show that settlement systems exist along a density gradient, and their change trajectories are typically gradual and incremental. In addition, results indicate that the total increase in built-up land in village landscapes outweighs that of dense urban regions. This chapter suggests that we should characterize human settlements more comprehensively to advance the analysis of human settlements, going beyond the emergence of new built-up land in a few mega-cities only. In Chapter 3, urban land-use intensity is operationalized by the horizontal and vertical spatial patterns of buildings. Particularly, I trained three random forest models to estimate building footprint, height, and volume, respectively, at a 1-km resolution for Europe, the US, and China. The models yield R2 values of 0.90, 0.81, and 0.88 for building footprint, height, and volume, respectively. The correlation between building footprint and building height at a pixel level was 0.66, illustrating the relevance of mapping these properties independently. Chapter 4 builds on the methodological approach presented in chapter 3. Specifically, it presents an improved approach to mapping 3D built-up patterns (i.e., 3D building structure), and applies this to map building footprint, height, and volume at a global scale. The methodological improvement includes an optimized model structure, additional explanatory variables, and updated input data. I find distance decay functions from the centre of the city to its outskirts for all three properties for major cities in all continents. Yet, again, the height, footprint (density), and volume differ drastically across these cities. Chapter 5 uses built-up land per person as an operationalization for urban land-use intensity, in order to investigate its temporal dynamics at a global scale. Results suggest that the decrease of urban land-use intensity relates to 38.3%, 49.6%, and 37.5% of the built-up land expansion in the three periods during 1975-2015, but with large local variations. In the Global South, densification often happens in regions where human settlements are already used intensively, suggesting potential trade-offs with other living standards. These chapters represent the recent advancements in large-scale analysis of human settlements by revealing a large variation in urban fabric. Urban densification is widely acknowledged as one of the tangible solutions to satisfy the increased land demand for human settlement while conserving other land, suggesting the relevance of these findings to inform sustainable development. Nevertheless, local settlement trajectories towards intensive forms should also be guided in a large-scale context with broad considerations, including the quality of life for inhabitants, because these trade-offs and synergies remain largely unexplored in this analysis

    Dissolved organic carbon concentration and character in northern hardwood-dominated headwater catchments: A paired-catchment investigation of legacy harvesting impacts

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    The water quality of forested source water regions can be degraded by natural and anthropogenic landscape disturbances such as wildfires and forest harvesting, the latter an economically important primary industry in Canada and a proposed wildfire mitigation strategy. Harvesting practices can alter the chemistry and hydrologic connectivity of hillslope solute pools, thereby enhancing hillslope-stream transport and the downstream propagation of sediments and solutes, including those relevant to drinking water treatment operations such as dissolved organic carbon (DOC). Although many studies have evaluated the sub-decadal impacts of forest harvesting on the concentration, export, and character of stream DOC, less is known about the legacy (decadal-scale) impacts. The purpose of this thesis was to evaluate the legacy impacts of clearcut harvesting on the variability of stream DOC concentrations, export, and character at the Turkey Lakes experimental Watershed (TLW). Using a paired-catchment approach (unharvested reference vs. legacy (24 years post-) clearcut), inter- and intra-catchment variability in stream DOC concentrations and export was evaluated under a range of flow conditions. Stream DOC variability was related to the concentrations, spatial distribution, and hydrologic connectivity of hillslope solute pool DOC. Additionally, a subset of event-scale stream and hillslope solute pool samples were analyzed for DOC character using Liquid-Chromatography Organic Carbon Detection (LC-OCD). DOC character was expressed in terms of the specific UV absorbance at 254 nm (SUVA) and the relative contributions of LC-OCD-defined DOC fractions. Whereas stream DOC concentrations in the legacy clearcut catchment exceeded (+1.21 mg L-1) and differed significantly (p ≤ 0.05) from the unharvested reference catchment, inter-catchment differences in stream DOC export were inconsistent. No inter-catchment differences were observed in the DOC concentrations or hydrologic connectivity of the hillslope solute pools, despite the common association of these mechanisms with post-harvest increases in stream DOC concentrations. Significant (p ≤ 0.05) inter-catchment differences in the fractional composition of stream DOC were observed at the event-scale but may be related to the presence of a wetland near the outlet of the unharvested reference catchment, rather than a harvesting impact. Wetland position was identified as a key factor in the variability of both DOC concentration and character in the unharvested reference catchment. Overall, the results of this thesis suggest that while forest harvesting practices may result in long-term increases in stream DOC concentration in northern hardwood-dominated headwater catchments, the effects may be limited at decadal-scales and likely do not pose a reasonable threat to downstream drinking water treatment operations
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