FogLearn: Leveraging Fog-based Machine Learning for Smart System Big Data Analytics

Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This paper discussed the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This paper proposed and developed fog computing based framework i.e. FogLearn for application of K-means clustering in Ganga River Basin Management and realworld feature data for detecting diabetes patients suffering from diabetes mellitus. Proposed architecture employed machine learning on deep learning framework for analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results showed that fog computing hold an immense promise for analysis of medical and geospatial big data

A remote sensing approach to the quantification of local to global scale social-ecological impacts of anthropogenic landscape changes

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsLanduse and Landcover (LULC) is the common aspect that influences several ecological issues, environmental degradations, changes in Land Surface Temperature (LST), hydrological changes and ecosystem function at regional to global level. Research on the drivers and progressions of LULC change has been key to developing models that can project and predict future LULC extent, level and patterns under different assumptions of socioeconomic, ecological and environmental situations. Rapid and extensive urbanization and Urban Sprawl (US), propelled by rapid population growth leads to the shrinkage of productive agricultural lands, boosting mining, decrease in surface permeability and the emergence of Urban Heat Islands (UHI), and in turn, adversely affects the provision of ecosystem services. Mining for resources extraction may lead to geological and associated environmental changes due to ground movements, collision with mining cavities, and deformation of aquifers. Geological changes may continue in a reclaimed mine area, and the deformed aquifers may entail a breakdown of substrates and an increase in ground water tables, which may cause surface area inundation. Consequently, a reclaimed mine area may experience surface area collapse, i.e., subsidence, and degradation of vegetation productivity. The greater changes in LULC, US, LST and vegetation dynamics due to increasing human population not only affects inland forest and wetland, it also directly influences coastal forest lands such as mangroves, peat swamps and riparian forest and threats to ecosystem services. Mangroves provide valuable provisioning (e.g. aquaculture, fisheries, fuel, medicine, textiles), regulation (e.g. shoreline protection, erosion control, climate regulation), supporting (nutrient cycling, nursery habitat), and cultural (recreation and tourism) ecosystem services with an important impact on human well-being. However, the mangrove forest is highly threatened due to climate changes, and human activities which ignore the ecological and economic value of these habitats, contributing to its degradation. There is an increasing number of studies about mangrove distribution, changes and re-establishment activities, denoting a growing attentiveness on the value of these coastal wetland ecosystems. Most of these studies address mangrove degradation drivers at regional or local levels. However, there has not been yet enough assessment on the drivers of mangrove degradation at global level. Thus, complexity of inland and coastal landscape degradation should be addressed using multidisciplinary methodology and conditions. Therefore, this dissertation aimed to assess the impact of LULC associated with vegetation, temperature and wetland changes. To understand the relation among three different types of landscape changes associated with anthropogenic activities: Urbanization, Geological changes and Forest degradation at local to global level, we have selected thirty-three global regions. In chapter 2, We employed the Random Forest (RF) classification on Landsat imageries from 1991, 2003, and 2016, and computed six landscape metrics to delineate the extent of urban areas within a 10km suburban buffer of Chennai city, Tamilnadu, India. The level of US was then quantified using Renyi’s entropy. A land change model was subsequently used to project land cover for 2027. A 70.35% expansion in urban areas was observed mainly towards the suburban periphery of Chennai between 1991 and 2016. The Renyi’s entropy value for year 2016 was 0.9, exhibiting a two-fold level of US when compared to 1991. The spatial metrics values indicate that the existing urban areas became denser and the suburban agricultural, forests and particularly barren lands were transformed into fragmented urban settlements. The forecasted land cover for 2027 indicates a conversion of 13,670.33 ha (16.57% of the total landscape) of existing forests and agricultural lands into urban areas with an associated increase in the entropy value to 1.7, indicating a tremendous level of US. Our study provides useful metrics for urban planning authorities to address the social-ecological consequences of US and to protect ecosystem services. In chapter 3, We studied landscape dynamics in Kirchheller Heide, Germany, which experienced extensive soil movement due to longwall mining without stowing, using Landsat imageries between 2013 and 2016. A Random Forest image classification technique was applied to analyse landuse and landcover dynamics, and the growth of wetland areas was assessed using a Spectral Mixture Analysis (SMA). We also analyzed the changes in vegetation productivity using a Normalized Difference Vegetation Index (NDVI). We observed a 19.9% growth of wetland area within four years, with 87.2% growth in the coverage of two major waterbodies in the reclaimed mine area. NDVI values indicate that the productivity of 66.5% of vegetation of the Kirchheller Heide was degraded due to changes in ground water tables and surface flooding. Our results inform environmental management and mining reclamation authorities about the subsidence spots and priority mitigation areas from land surface and vegetation degradation in Kirchheller Heide. In chapter 4, We demonstrated the advantage of fusing imageries from multiple sensors for LULC change assessments as well as for assessing surface permeability and temperature and UHI emergence in a fast-growing city, i.e. Tirunelveli, Tamilnadu, India. IRS-LISSIII and Landsat-7 ETM+ imageries were fused for 2007 and 2017, and classified using a Rotation Forest (RF) algorithm. Surface permeability and temperature were then quantified using Soil-Adjusted Vegetation Index (SAVI) and Land Surface Temperature (LST) index, respectively. Finally, we assessed the relationship between SAVI and LST for entire Tirunelveli as well as for each LULC zone, and also detected UHI emergence hot spots using a SAVI-LST combined metric. Our fused images exhibited higher classification accuracies, i.e. overall kappa coefficient values, than non-fused images. We observed an overall increase in the coverage of urban (dry, real estate plots and built-up) areas, while a decrease for vegetated (cropland and forest) areas in Tirunelveli between 2007 and 2017. The SAVI values indicated an extensive decrease in surface permeability for Tirunelveli overall and also for almost all LULC zones. The LST values showed an overall increase of surface temperature in Tirunelveli with the highest increase for urban built-up areas between 2007 and 2017. LST also exhibited a strong negative association with SAVI. South-eastern built-up areas in Tirunelveli were depicted as a potential UHI hotspot, with a caution for the Western riparian zone for UHI emergence in 2017. Our results provide important metrics for surface permeability, temperature and UHI monitoring, and inform urban and zonal planning authorities about the advantages of satellite image fusion. In chapter 5, We identified mangrove degradation drivers at regional and global levels resulted from decades of research data (from 1981 to present) of climate variations (seal-level rising, storms, precipitation, extremely high water events and temperature), and human activities (pollution, wood extraction, aquaculture, agriculture and urban expansion). This information can be useful for future research on mangroves, and to help delineating global planning strategies which consider the correct ecological and economic value of mangroves protecting them from further loss.O uso e a cobertura da Terra (UCT) são o aspeto comum que influencia várias questões ecológicas, degradações ambientais, mudanças na temperatura da superfície terrestre, mudanças hidrológicas, e de funções dos ecossistemas a nível regional e global. A investigação sobre os determinantes e progressão da mudança de UCT tem sido fundamental para o desenvolvimento de modelos que podem projetar e prever a extensão, o nível e os padrões futuros de UCT sob diferentes hipóteses de situações socioeconómicas, ecológicas e ambientais. A rápida e extensa urbanização e expansão urbana impulsionada pelo rápido crescimento populacional, levou ao encolhimento de terras agrícolas produtivas, impulsionando a mineração, a diminuição da permeabilidade da superfície e o surgimento de ilhas urbanas. Por outro lado, tem afetado negativamente a produção de serviços de ecossistemas. A mineração para extração de recursos pode levar a mudanças geológicas e ambientais devido a movimentos do solo, colisão com cavidades de mineração e deformação de aquíferos. As mudanças geológicas podem continuar numa área de mina recuperada, e os aquíferos deformados podem acarretar uma quebra de substratos e um aumento nos lençóis freáticos, causando a inundação na superfície. Consequentemente, uma área de mina recuperada pode sofrer um colapso à superfície, provocando o afundamento e a degradação da produtividade da vegetação. As mudanças na UCT, no crescimento urbano rápido, na temperatura da superfície terrestre e na dinâmica da vegetação devido ao aumento da população humana não afetam apenas a floresta interior e as zonas húmidas. Estas também influenciam diretamente as terras florestais costeiras, tais como mangais, pântanos e florestas ribeirinhas, ameaçando os serviços de ecossistemas. Os mangais proporcionam um aprovisionamento valioso (por exemplo, aquacultura, pesca, combustível, medicamentos, têxteis), a regulação (por exemplo, proteção da linha de costa, controlo da erosão, regulação do clima), os serviços de ecossistema de apoio (ciclo de nutrientes, habitats) e culturais (recreação e turismo) com um impacto importante no bem-estar humano. No entanto, a floresta de mangal é altamente ameaçada devido às mudanças climáticas e às atividades humanas que ignoram o valor ecológico e económico desses habitats, contribuindo para a sua degradação. Há um número crescente de estudos sobre distribuição, mudança e atividades de restabelecimento de mangais, denotando uma crescente atenção sobre o valor desses ecossistemas costeiros de zonas húmidas. A maioria desses estudos aborda os fatores de degradação dos mangais a nível regional ou local. No entanto, ainda não há avaliação suficiente sobre os determinantes da degradação dos mangais a nível global. Assim, a complexidade da degradação da paisagem interior e costeira deve ser abordada usando uma metodologia multidisciplinar. Portanto, esta dissertação teve, também, como objetivo avaliar o impacto do UCT associado à vegetação, temperatura e mudanças de zonas húmidas. Para compreender a relação entre a dinâmica da paisagem associada às atividades antrópicas a nível local e global, selecionámos quatro áreas de estudo, duas da Ásia, uma da Europa e outro estudo a nível global. No capítulo 2, empregamos a classificação Random Forest (RF) nas imagens Landsat de 1991, 2003 e 2016, e computamos seis métricas de paisagem para delinear a extensão das áreas urbanas numa área de influência suburbana de 10 km da cidade de Chennai, Tamil Nadu, Índia. O nível de crescimento urbano rápido foi quantificado usando a entropia de Renyi. Um modelo de UCT foi posteriormente usado para projetar a cobertura de terra para 2027. Uma expansão de 70,35% nas áreas urbanas foi observada principalmente para a periferia suburbana de Chennai entre 1991 e 2016. O valor de entropia do Renyi para 2016 foi de 0,9, exibindo uma duplicação do nível de crescimento urbano rápido quando comparado com 1991. Os valores das métricas espaciais indicam que as áreas urbanas existentes se tornaram mais densas e as terras agrícolas, florestas e terras particularmente áridas foram transformadas em assentamentos urbanos fragmentados. A previsão de cobertura da Terra para 2027 indica uma conversão de 13.670,33 ha (16,57% da paisagem total) de florestas e terras agrícolas existentes em áreas urbanas, com um aumento associado no valor de entropia para 1,7, indicando um tremendo nível de crescimento urbano rápido. O nosso estudo fornece métricas úteis para as autoridades de planeamento urbano para lidarem com as consequências socio-ecológicas do crescimento urbano rápido e para proteger os serviços de ecossistemas. No capítulo 3, estudamos a dinâmica da paisagem em Kirchheller Heide, Alemanha, que experimentou um movimento extensivo do solo devido à mineração, usando imagens Landsat entre 2013 e 2016. Uma técnica de classificação de imagem Random Forest foi aplicada para analisar dinâmicas de UCT e o crescimento das áreas de zonas húmidas foi avaliado usando uma Análise de Mistura Espectral. Também analisámos as mudanças na produtividade da vegetação usando um Índice de Vegetação por Diferença Normalizada (NDVI). Observámos um crescimento de 19,9% da área húmida em quatro anos, com um crescimento de 87,2% de dois principais corpos de água na área de mina recuperada. Valores de NDVI indicam que a produtividade de 66,5% da vegetação de Kirchheller Heide foi degradada devido a mudanças nos lençóis freáticos e inundações superficiais. Os resultados informam as autoridades de gestão ambiental e recuperação de mineração sobre os pontos de subsidência e áreas de mitigação prioritárias da degradação da superfície e da vegetação da terra em Kirchheller Heide. No capítulo 4, demonstramos a vantagem de fusionar imagens de múltiplos sensores para avaliações de mudanças de UCT, bem como para avaliar a permeabilidade, temperatura da superfície e a emergência do ilhas de calor numa cidade em rápido crescimento, Tirunelveli, Tamilnadu, Índia. As imagens IRS-LISSIII e Landsat-7 ETM + foram fusionadas para 2007 e 2017, e classificadas usando um algoritmo de Random Forest (RF). A permeabilidade de superfície e a temperatura foram então quantificadas usando-se o Índice de Vegetação Ajustada pelo Solo (SAVI) e o Índice de Temperatura da Superfície Terrestre (LST), respectivamente. Finalmente, avaliamos a relação entre SAVI e LST para Tirunelveli, bem como para cada zona de UCT, e também detetamos a emergência de pontos quentes de emergência usando uma métrica combinada de SAVI-LST. As nossas imagens fusionadas exibiram precisões de classificação mais altas, ou seja, valores globais do coeficiente kappa, do que as imagens não fusionadas. Observámos um aumento geral na cobertura de áreas urbanas (áreas de terrenos secos e construídas), e uma diminuição de áreas com vegetação (plantações e florestas) em Tirunelveli entre 2007 e 2017. Os valores de SAVI indicaram uma extensa diminuição na superfície de permeabilidade para Tirunelveli e também para quase todas as classes de UCT. Os valores de LST mostraram um aumento global da temperatura da superfície em Tirunelveli, sendo o maior aumento para as áreas urbanas entre 2007 e 2017. O LST também apresentou uma forte associação negativa com o SAVI. As áreas urbanas do Sudeste de Tirunelveli foram representadas como um potencial ponto quente, com uma chamada de atenção para a zona ribeirinha ocidental onde foi verificada a emergência de uma ilha de calor em 2017. Os nossos resultados fornecem métricas importantes sobre a permeabilidade da superfície, temperatura e monitoramento de ilhas de calor e informam as autoridades de planeamento sobre as vantagens da fusão de imagens de satélite. No capítulo 5, identificamos os fatores de degradação dos mangais a nível regional e global resultantes de décadas de dados de investigação (de 1981 até o presente) de variações climáticas (aumento do nível das águas do mar, tempestades, precipitação, eventos extremos de água e temperatura) e atividades humanas (poluição, extração de madeira, aquacultura, agricultura e expansão urbana). Estas informações podem ser úteis para investigações futuras sobre mangais e para ajudar a delinear estratégias de planeamento global que considerem o valor ecológico e económico dos mangais, protegendo-os de novas perdas

Limitations in Global Information on Species Occurrences

Detaillierte Informationen über die Verbreitungsareale von Arten sind essentiell für die Beantwortung zentraler Fragen der Ökologie, Evolutionsbiologie und Biogeographie. Solche Informationen sind auch notwendig, um Naturschutzressourcen kostenwirksam zwischen verschiedenen Regionen und Maßnahmen zu verteilen. Unser Wissen über Artverbreitungen beruht vor allem auf Punktdaten, die das Vorkommen einer bestimmten Art an einem bestimmten Ort zu einem bestimmten Zeitpunkt belegen (nachstehend „Records“). Riesige Mengen solcher Records wurden über internationale Data-Sharing-Netzwerke mobilisiert, allen voran durch die Global Biodiversity Information Facility (GBIF). Auch wenn diese Netzwerke die Zugänglichkeit zu solchen Informationen enorm verbessert haben, ist unser Wissen über globale Artverbreitungen immer noch äußerst lückenhaft und von grober räumlicher Auflösung – der sogenannte Wallace’sche Wissensrückstand. Vorhandene Informationen enthalten zudem zahlreiche Unsicherheiten, Fehler und Daten-‘Biases’. Diese könnten durch Ort-spezifische Faktoren wie Zugänglichkeit oder durch artspezifische Faktoren, wie Entdeckungswahrscheinlichkeit, verursacht werden. Zukünftiges Sammeln und Mobilisieren von Informationen sollte so gestaltet werden, dass der erreichte Nutzen der Records für Forschung und Naturschutz maximiert wird. Hierfür ist ein tiefgehendes Verständnis der Lücken, Unsicherheiten und Biases in den Informationen sowie der sie verursachenden Faktoren notwendig. Bisher wurden diese Mängel in globalen Artverbreitungsinformationen niemals quantitativ untersucht. Mit meiner Dissertation liefere ich die ersten globalen Analysen zu Mängeln von digital verfügbaren Verbreitungsinformationen für terrestrische Wirbeltiere und Landpflanzen. Ich habe >300 Millionen Records für Landpflanzen und drei Gruppen terrestrischer Wirbeltiere (Amphibien, Säugetiere, Vögel) über GBIF abgerufen. Diese Informationen habe ich mit taxonomischen Datenbanken sowie unabhängigen Verbreitungskarten und Checklisten verbunden. Auf Grundlage der erstellten Datensätze habe ich unterschiedliche Formen von Informations-Mängeln für verschiedene taxonomische Gruppen und auf mehreren räumlichen Maßstäben untersucht. In Kapitel I habe Daten-Abdeckung sowie Daten-Unsicherheiten in Informationen zu Pflanzenvorkommen jeweils in Bezug auf Taxonomie, Raum und Zeit quantifiziert. Für diese insgesamt 6 Maße habe in anschließend Variation in den drei Dimensionen (Taxonomie, Raum, Zeit) gemessen. Zudem habe ich mithilfe von paarweisen Spearman-Rang-Korrelationen und Hauptkomponentenanalysen die Zusammenhänge zwischen diesen verschiedenen Formen von Informationsmängeln analysiert. In Kapitel II habe ich anhand von terrestrischen Wirbeltieren zwei spezielle Aspekte von Datenabdeckung zwischen geographischen Regionen verglichen: i) die Datendichte und ii) die Vollständigkeit der abgedeckten Arten. Durch Multi-Modell-Analysen habe ich die Effekte von zwölf potentiellen sozioökonomischen Einflussfaktoren auf Informationsmängel verglichen, und zwar einzeln für jede der drei Wirbeltiergruppen auf jeder von vier verschiedenen räumlichen Auflösungen. In Kapitel III habe ich anhand von Säugetieren drei Aspekte von Datenabdeckung zwischen einzelnen Arten verglichen: i) die Anzahl von Records pro Art, ii) die räumliche Abdeckung der Verbreitungsareale durch Records, und iii) den räumlichen Bias in der Abdeckung verschiedener Teile der Verbreitungsareale. Durch Multi-Modell-Analysen und Variations-Partitionierung habe ich die Effekte von verschiedenen Artmerkmalen, Größe und Form der Verbreitungsareale sowie von sozioökonomischen Faktoren untersucht. Diese Analysen habe ich auf globalem Maßstab sowie einzeln für sechs zoogeographische Gebiete durchgeführt. In meiner Dissertation habe ich in allen untersuchten Aspekten von Artverbreitungsinformationen starke Biases gefunden. Die Anzahl von Records variierte um mehrere Größenordnungen zwischen Arten und zwischen geographischen Gebieten. Verschiedene Maße von Datenabdeckung und Datenunsicherheiten zeigten klare taxonomische, geographische und zeitliche Muster. Ich fand beispielsweise Höchstwerte von taxonomischer Abdeckung in industrialisierten westlichen Ländern, aber auch in einigen tropischen Gebieten wie Mexiko. Im Gegensatz dazu gab es in weiten Teilen Afrikas und Asiens entweder gar keine oder nur sehr veraltete Informationen. Da taxonomische, räumliche und zeitliche Abdeckung jeweils durch die Anzahl der Records numerisch eingeschränkt sind, fand ich zwischen diesen Maßen gemäßigte bis starke positive Korrelationen. Maße von Datenunsicherheiten hingegen korrelierten kaum untereinander oder mit Datenabdeckungsmaßen. In Kapitel II habe ich den Einfluss von zwölf potentiellen sozioökonomischen Einflussfaktoren auf Datendichte und Datenvollständigkeit von geographischen Artgemeinschaften untersucht. Nur vier hatten einen durchweg für alle untersuchten Wirbeltiergruppen und räumlichen Auflösungen starken Einfluss. Dies waren der Endemitenreichtum, die räumliche Nähe zu Daten-beisteuernden Institutionen, politische Mitgliedschaft im GBIF-Netzwerk, sowie lokal verfügbare Forschungsgelder. Andere Faktoren, von denen man oft annimmt, dass sie eine große Rolle spielen würden, hatten einen erstaunlich geringen Einfluss, wie z.B. Verkehrsinfrastruktur oder Größe und Finanzausstattungen westlicher Daten-beisteuernder Institutionen. Meine Analysen in Kapitel III ergaben, dass die vier in Kapitel II identifizierten sozioökonomischen Schlüsselfaktoren ebenfalls einen starken Einfluss auf Artverbreitungsinformationen auf der Ebene von einzelnen Arten hatten. Jedoch unterschied sich ihre relative Wichtigkeit deutlich zwischen geographischen Gebieten. Zwischenartliche Unterschiede in Verbreitungsinformationen waren zudem sehr stark durch Größe und Form der Verbreitungsareale beeinflusst. Dies unterstützt meine Hypothese, dass diese geometrischen Faktoren die Wahrscheinlichkeit beeinflussen, dass sich Verbreitungsgebiete bestimmter Arten mit Untersuchungsgebieten von Feldforschern überschneiden, was wiederum Aufswirkungen auf die Wahrscheinlichkeiten hat, mit denen diese Arten besammelt werden. Entgegen unserer Annahmen hatten Artmerkmale wie etwa Nachtaktivität, die das Entdecken oder Sammeln bestimmter Arten wahrscheinlich machen sollten, kaum einen Einfluss auf zwischenartliche Unterschiede in Verbreitungsinformationen. Die Ergebnisse meiner Dissertation lassen wichtige Schlussfolgerungen darüber zu, wie mobilisierte Artverbreitungsinformationen effizient genutzt und verbessert werden können. Erstens belegen meine Ergebnisse schwerwiegende Mängel in digital verfügbaren Artverbreitungsinformationen, insbesondere für Gebiete und Arten von besonderer Wichtigkeit für den Naturschutz. Zweitens zeigen sie, dass für die allermeisten Arten feiner aufgelöste Informationen nur durch Artverbreitungsmodelle erreicht werden können, die mit geringen Datenmengen auskommen, die starke Datenunsicherheiten und Biases innehaben. Eine vielversprechende Methode, um in solchen Modellen mit Biases umzugeben, ist das explizite Einbeziehen der Bias-verursachenden Faktoren in die Modelle, und meine Ergebnisse bieten hilfreiche Anhaltspunkte für die Auswahl relevanter Faktoren. Drittens schaffen meine Ergebnisse eine empirische Grundlage zur Überwachung von Fortschritten in der Verbesserung weltweiter Artverbreitungsinformationen. Schließlich schafft mein Identifizieren der global wichtigsten Informations-limitierenden Faktoren sowie das Unterscheiden verschiedener Informationsaspekte eine Grundlage dafür, um Aktivitäten zu identifizieren, die Datenmängel effektiv beheben können. Als wichtigste Aktivitäten empfehle ich unter anderem i) das Unterstützen von Bemühungen zur Datenmobilisierung in Institutionen, die in geographischer Nähe zu datenarmen Gebieten liegen, ii) das Fördern von Kooperation zwischen großen Schwellenländern und Data-Sharing-Netzwerken, iii) die Durchführung von neuen Biodiversitäts-Surveys im zentralen Afrika und südlichen Asien, um weitgehend veraltete Informationen zu aktualisieren, und iv) das Verschieben des Fokus von Datensammel- und Datenmobilisierungsbemühungen auf Asien sowie Arten mit begrenzten Verbreitungsarealen.Detailed information on species distributions is crucial to answering central questions in ecology, evolutionary biology and biogeography and for effectively allocating conservation resources among regions. Huge numbers of species occurrence records, the basic data underlying our knowledge of species distributions, have been mobilized via international data-sharing networks, most notably that of the Global Biodiversity Information Facility (GBIF). While these networks have greatly increased accessibility of information, severe knowledge gaps remain, a situation termed the ‘Wallacean shortfall’. Moreover, the available information is rife with uncertainties, gaps and biases caused by site-specific factors like accessibility or species-specific factors like detectability. If we are to effectively prioritize future data collection and mobilization, we must understand the gaps, biases and uncertainties in current distribution information and what causes them. So far, patterns and drivers of the different information limitations have never been analyzed in detail at the global scale. In this thesis, I provide the first global analyses of limitations in digital accessible occurrence information for land plant and terrestrial vertebrates. I retrieved >300 million occurrence records for land plants and three vertebrate groups (amphibians, bird and mammals) from GBIF, and integrated these with taxonomic databases and independent range map and checklist information. I then used these datasets to analyze different types of limitations in occurrence information for different taxonomic groups and spatial scales. In chapter 1, I analyzed taxonomic, geographical and temporal data coverage and uncertainty for land plants. I measured taxonomic, geographical and temporal variation in these aspects of occurrence information and quantified their relationships using pairwise correlations and principal component analysis. In chapter 2, I used terrestrial vertebrates to analyze two aspects of occurrence information at the level of geographical assemblages: i) record density and ii) inventory completeness. I used multi-model inference to compare effects of twelve potential socio-economic drivers across the three vertebrate groups and across four spatial grains. In chapter 3, I focused on terrestrial mammals to analyze three aspects of occurrence information at the species level: i) record count per species, ii) how these records cover individual species’ ranges, and iii) the level of geographical bias in their representation of different parts of their ranges. I used multi-model inference and variation partitioning to test effects of different species attributes, size and shape of their ranges, and socio-economic factors at the global scale and for individual zoogeographical regions.  In my thesis, I found severe biases in all examined aspects of occurrence information. Record counts varied by several orders of magnitude across species and regions. Different coverage and uncertainty measures showed clear taxonomic, geographical and temporal patterns. For instance, taxonomic coverage peaked in Western industrialized countries, but also in several tropical regions. In contrast, information was either antiquated or entirely lacking for many Asian and African regions. As taxonomic, geographical and temporal coverage are all numerically constrained by the number of records, these metrics showed moderate to strong positive correlations. Metrics of data uncertainty generally showed low pairwise correlations with one another and with coverage metrics. In Chapter 2, I found that only four of my twelve hypothesized drivers of assemblage-level record density and inventory completeness received strong support across vertebrate taxa and spatial grains. These were endemism richness, proximity of grid cells to record-contributing institutions, political participation in GBIF, and locally available research funding. Other factors often assumed to strongly constrain information, like transportation infrastructure or size and funding of Western data-contributing institutions, received surprisingly little support. In Chapter 3, I found that the four key socio-economic factors identified in Chapter 2 also had a strong influence on occurrence information at the species-level, but their relative importance differed depending on the geographical focus of the analysis. Interspecific variation in occurrence information was also strongly determined by range size and shape. This supports our hypothesis that while large ranges are bound to overlap with more sampling locations, large, irregular-shaped ranges constrain the detail with which a given number of records can cover a range. Against expectation, species attributes related to detection or collection probabilities had little impact on species-level differences in occurrence information.  The results of my thesis have important implications for the improvement and effective use of mobilized occurrence information. First, my results prove that digital accessible occurrence information is severely limited, particularly for regions and species of conservation concern. Second, success in refining distribution knowledge for these species will depend on distribution modeling techniques that can deal with low record numbers, data biases and data uncertainties. One promising way to account for biases is explicitly incorporating bias-causing factors into models, and my results can help identify meaningful predictor variables. Third, my results create an empirical baseline for monitoring progress in improving the state of global species occurrence data. Finally, my identification of the main factors limiting occurrence information, and the distinction between different information aspects, will help in identifying activities that will remedy data limitations most effectively. I suggest that key activities include supporting mobilization efforts in institutions near data scarce regions, fostering cooperation of large emerging economies with data-sharing networks, conducting novel surveys for Central Africa and Southern Asia as local data are often outdated, and generally increasing the focus of collection and mobilization activities on Asia and on range-restricted species

Empirical and mechanistic approaches to understanding and projecting change in coastal marine communities

This work details the effects of disturbance events on tropical coral reefs and highlights emerging techniques for improved monitoring and assessment of benthic change. The first chapter is in the form of a literature review, which aims to give a broad introduction to reef ecology, the impacts experienced by this system, and the methods used to monitor and assess change. The second chapter highlights a recently developed photogrammetric methodology which can be used to assess change in the marine environment. The methodology is then assessed for accuracy and comparability to standard benthic monitoring techniques. // The proceeding four chapters aim to address a number of ecological and management questions relating to reef community ecology, focussing on physical structure and demonstrating the utility of ‘Structure from Motion’ (SfM) photogrammetry as a monitoring and assessment tool. Chapters three and four more specifically use community managed small-scale Marine Protected Areas (MPAs) in the Philippines as a case study applying SfM, and assess the effectiveness of these MPAs. These chapters further highlight how physical changes can affect the function of the reefs and their associated fisheries. Chapters five and six then investigate how extreme climatic events can affect the structure and growth of reefs in the Indian Ocean, away from the array of confounding anthropogenic factors seen in the Philippines. // The final section looks to bring together these chapters to discuss the benefits of new technology, and the future of reefs under a changing climate

Climate-Smart Forestry in Mountain Regions

This open access book offers a cross-sectoral reference for both managers and scientists interested in climate-smart forestry, focusing on mountain regions. It provides a comprehensive analysis on forest issues, facilitating the implementation of climate objectives. This book includes structured summaries of each chapter. Funded by the EU’s Horizon 2020 programme, CLIMO has brought together scientists and experts in continental and regional focus assessments through a cross-sectoral approach, facilitating the implementation of climate objectives. CLIMO has provided scientific analysis on issues including criteria and indicators, growth dynamics, management prescriptions, long-term perspectives, monitoring technologies, economic impacts, and governance tools

Intellectual Property Management in Health and Agricultural Innovation: A Handbook of Best Practices, Vol. 1

Prepared by and for policy-makers, leaders of public sector research establishments, technology transfer professionals, licensing executives, and scientists, this online resource offers up-to-date information and strategies for utilizing the power of both intellectual property and the public domain. Emphasis is placed on advancing innovation in health and agriculture, though many of the principles outlined here are broadly applicable across technology fields. Eschewing ideological debates and general proclamations, the authors always keep their eye on the practical side of IP management. The site is based on a comprehensive Handbook and Executive Guide that provide substantive discussions and analysis of the opportunities awaiting anyone in the field who wants to put intellectual property to work. This multi-volume work contains 153 chapters on a full range of IP topics and over 50 case studies, composed by over 200 authors from North, South, East, and West. If you are a policymaker, a senior administrator, a technology transfer manager, or a scientist, we invite you to use the companion site guide available at http://www.iphandbook.org/index.html The site guide distills the key points of each IP topic covered by the Handbook into simple language and places it in the context of evolving best practices specific to your professional role within the overall picture of IP management