Earth observation in support of malaria control and epidemiology: MALAREO monitoring approaches

Malaria affects about half of the world's population, with the vast majority of cases occuring in Africa. National malaria control programmes aim to reduce the burden of malaria and its negative, socioeconomic effects by using various control strategies (e.g. vector control, environmental management and case tracking). Vector control is the most effective transmission prevention strategy, while environmental factors are the key parameters affecting transmission. Geographic information systems (GIS), earth observation (EO) and spatial modelling are increasingly being recognised as valuable tools for effective management and malaria vector control. Issues previously inhibiting the use of EO in epidemiology and malaria control such as poor satellite sensor performance, high costs and long turnaround times, have since been resolved through modern technology. The core goal of this study was to develop and implement the capabilities of EO data for national malaria control programmes in South Africa, Swaziland and Mozambique. High-and very high resolution (HR and VHR) land cover and wetland maps were generated for the identification of potential vector habitats and human activities, as well as geoinformation on distance to wetlands for malaria risk modelling, population density maps, habitat foci maps and VHR household maps. These products were further used for modelling malaria incidence and the analysis of environmental factors that favour vector breeding. Geoproducts were also transferred to the staff of national malaria control programmes in seven African countries to demonstrate how EO data and GIS can support vector control strategy planning and monitoring. The transferred EO products support better epidemiological understanding of environmental factors related to malaria transmission, and allow for spatio-temporal targeting of malaria control interventions, thereby improving the cost-effectiveness of interventions

Towards development of fuzzy spatial datacubes : fundamental concepts with example for multidimensional coastal erosion risk assessment and representation

Les systèmes actuels de base de données géodécisionnels (GeoBI) ne tiennent généralement pas compte de l'incertitude liée à l'imprécision et le flou des objets; ils supposent que les objets ont une sémantique, une géométrie et une temporalité bien définies et précises. Un exemple de cela est la représentation des zones à risque par des polygones avec des limites bien définies. Ces polygones sont créés en utilisant des agrégations d'un ensemble d'unités spatiales définies sur soit des intérêts des organismes responsables ou les divisions de recensement national. Malgré la variation spatio-temporelle des multiples critères impliqués dans l’analyse du risque, chaque polygone a une valeur unique de risque attribué de façon homogène sur l'étendue du territoire. En réalité, la valeur du risque change progressivement d'un polygone à l'autre. Le passage d'une zone à l'autre n'est donc pas bien représenté avec les modèles d’objets bien définis (crisp). Cette thèse propose des concepts fondamentaux pour le développement d'une approche combinant le paradigme GeoBI et le concept flou de considérer la présence de l’incertitude spatiale dans la représentation des zones à risque. En fin de compte, nous supposons cela devrait améliorer l’analyse du risque. Pour ce faire, un cadre conceptuel est développé pour créer un model conceptuel d’une base de donnée multidimensionnelle avec une application pour l’analyse du risque d’érosion côtier. Ensuite, une approche de la représentation des risques fondée sur la logique floue est développée pour traiter l'incertitude spatiale inhérente liée à l'imprécision et le flou des objets. Pour cela, les fonctions d'appartenance floues sont définies en basant sur l’indice de vulnérabilité qui est un composant important du risque. Au lieu de déterminer les limites bien définies entre les zones à risque, l'approche proposée permet une transition en douceur d'une zone à une autre. Les valeurs d'appartenance de plusieurs indicateurs sont ensuite agrégées basées sur la formule des risques et les règles SI-ALORS de la logique floue pour représenter les zones à risque. Ensuite, les éléments clés d'un cube de données spatiales floues sont formalisés en combinant la théorie des ensembles flous et le paradigme de GeoBI. En plus, certains opérateurs d'agrégation spatiale floue sont présentés. En résumé, la principale contribution de cette thèse se réfère de la combinaison de la théorie des ensembles flous et le paradigme de GeoBI. Cela permet l’extraction de connaissances plus compréhensibles et appropriées avec le raisonnement humain à partir de données spatiales et non-spatiales. Pour ce faire, un cadre conceptuel a été proposé sur la base de paradigme GéoBI afin de développer un cube de données spatiale floue dans le system de Spatial Online Analytical Processing (SOLAP) pour évaluer le risque de l'érosion côtière. Cela nécessite d'abord d'élaborer un cadre pour concevoir le modèle conceptuel basé sur les paramètres de risque, d'autre part, de mettre en œuvre l’objet spatial flou dans une base de données spatiales multidimensionnelle, puis l'agrégation des objets spatiaux flous pour envisager à la représentation multi-échelle des zones à risque. Pour valider l'approche proposée, elle est appliquée à la région Perce (Est du Québec, Canada) comme une étude de cas.Current Geospatial Business Intelligence (GeoBI) systems typically do not take into account the uncertainty related to vagueness and fuzziness of objects; they assume that the objects have well-defined and exact semantics, geometry, and temporality. Representation of fuzzy zones by polygons with well-defined boundaries is an example of such approximation. This thesis uses an application in Coastal Erosion Risk Analysis (CERA) to illustrate the problems. CERA polygons are created using aggregations of a set of spatial units defined by either the stakeholders’ interests or national census divisions. Despite spatiotemporal variation of the multiple criteria involved in estimating the extent of coastal erosion risk, each polygon typically has a unique value of risk attributed homogeneously across its spatial extent. In reality, risk value changes gradually within polygons and when going from one polygon to another. Therefore, the transition from one zone to another is not properly represented with crisp object models. The main objective of the present thesis is to develop a new approach combining GeoBI paradigm and fuzzy concept to consider the presence of the spatial uncertainty in the representation of risk zones. Ultimately, we assume this should improve coastal erosion risk assessment. To do so, a comprehensive GeoBI-based conceptual framework is developed with an application for Coastal Erosion Risk Assessment (CERA). Then, a fuzzy-based risk representation approach is developed to handle the inherent spatial uncertainty related to vagueness and fuzziness of objects. Fuzzy membership functions are defined by an expert-based vulnerability index. Instead of determining well-defined boundaries between risk zones, the proposed approach permits a smooth transition from one zone to another. The membership values of multiple indicators (e.g. slop and elevation of region under study, infrastructures, houses, hydrology network and so on) are then aggregated based on risk formula and Fuzzy IF-THEN rules to represent risk zones. Also, the key elements of a fuzzy spatial datacube are formally defined by combining fuzzy set theory and GeoBI paradigm. In this regard, some operators of fuzzy spatial aggregation are also formally defined. The main contribution of this study is combining fuzzy set theory and GeoBI. This makes spatial knowledge discovery more understandable with human reasoning and perception. Hence, an analytical conceptual framework was proposed based on GeoBI paradigm to develop a fuzzy spatial datacube within Spatial Online Analytical Processing (SOLAP) to assess coastal erosion risk. This necessitates developing a framework to design a conceptual model based on risk parameters, implementing fuzzy spatial objects in a spatial multi-dimensional database, and aggregating fuzzy spatial objects to deal with multi-scale representation of risk zones. To validate the proposed approach, it is applied to Perce region (Eastern Quebec, Canada) as a case study

Spatial distribution of marine ecosystem service capacity in the European seas

Practitioners and policy makers at European Union (EU) and Member States level are increasingly seeking spatially-explicit ecosystem service information to use in decision-making and the implementation of the EU Biodiversity Strategy to 2020. Whilst under the MAES Action, land-cover data has already been used to map the distribution of several ecosystem services provided over the European land surface, a similar exercise exploiting existing seabed habitat data is still lacking for the European Seas. In this work we map the distribution of seabed-associated ecosystem services capacity by using a methodology that brings together (i) a geospatial dataset representing the broadscale distribution of permanently-submerged seabed habitats with (ii) information on each habitat capacity to provide ecosystem services. A compilation of EUNIS-harmonized broadscale seabed habitat maps based on EMODNET Seabed Habitats and UNEP GSGFM is exploited as the pan-European cartographic basis. The exercise extends out to the limits of the Extended Continental Shelf claims, achieving an areal coverage of approximately 8.7 million km2, i.e., more than 90% of the EU seafloor area in the Northeast Atlantic and adjacent seas. Alongside, expert-based assessments of each marine EUNIS habitat's capacity to provide CICES-harmonized Ecosystem Services are compiled from a literature review into a presence-only lookup table. Overall, the new seabed habitats versus ecosystem services lookup tables relate 33 ecosystem services to 67 EUNIS and 24 non-EUNIS seabed habitats. These results suggest that out of all marine habitats (n=974) in the EUNIS classification (EUNIS A1 to A7), only 14% (n=141) have so far been related to at least one ecosystem service. When all potential connections between the existing seabed EUNIS classes and CICES services are considered (n=104,218), results further show that only 2% (i.e., n=2,241) of the have been addressed qualitatively or semi-quantitatively. Based on this information, a total of 30 CICES ecosystem service categories are mapped: 3 at level 1 (CICES Sections), 5 at level 2 (CICES Divisions), 10 at level 3 (CICES Groups) and 12 at level 4 (CICES Classes). From these maps, area-based indicators of ecosystem service capacity (i.e., extent where each service is potentially provided) are extracted per MSFD region/subregion, Ecoregion, Fishing Area and an approximation of EU Member States (MS) maritime areas in the Northeast Atlantic and Adjacent Seas. Along with the maps, the study presents also some spatial statistics based on the extent over which each service is potentially provided. Different segmentations of the European Seas are used to aggregate these statistics including MSFD region/subregion, Ecological Region, FAO Fishing Area and an approximation of the Member State maritime area. Overall, continental shelves and oceanic elevations (islands, seamounts and ridges) were highlighted as ecosystem services hotspots where a larger number of services could be potentially held. When maps were segmented using MSFD region/subregion limits, the Extended Continental Shelf areas claimed by the EU MS in the Northeast Atlantic, together with the Celtic Seas and the Greater North Sea sub-regions stood as the regions holding most ecosystem service capacity. An ecoregion-based segmentation of the maps emphasized the Atlantic Deep Sea as the major ecosystem service capacity holder, followed by ecoregions containing large shelves, notably the Boreal Proper, the Boreal-Lusitanean and the Western Mediterranean. A disaggregation of the results per Fishing Area highlighted the Northeast Atlantic, namely areas around the British Isles and Macaronesia, as well as the western Mediterranean. When an approximation of EU Member States (MS) maritime areas was used, MS with larger EEZs (namely, UK, IT, PT and ES) came up as holding most of the marine ecosystem service capacity. The new maps and associated area-based indicators provide a first spatially-explicit baseline concerning the EU-wide distribution of marine ecosystem services. They contribute to the marine component of MAES and fulfil key objectives of the JRC’s SEACOAST and BES projects. Options to develop this research line and eventually make it more quantitative are expounded in the discussion and summarized in the conclusions. The new information is of value to practitioners, managers and policy makers, at European or Member State level, seeking spatially-explicit ecosystem service information for marine spatial planning and environmental management. Researchers initiating and developing marine ecosystem service mapping studies are also expected users.JRC.H.1-Water Resource

Object Tracking in Distributed Video Networks Using Multi-Dimentional Signatures

From being an expensive toy in the hands of governmental agencies, computers have evolved a long way from the huge vacuum tube-based machines to today\u27s small but more than thousand times powerful personal computers. Computers have long been investigated as the foundation for an artificial vision system. The computer vision discipline has seen a rapid development over the past few decades from rudimentary motion detection systems to complex modekbased object motion analyzing algorithms. Our work is one such improvement over previous algorithms developed for the purpose of object motion analysis in video feeds. Our work is based on the principle of multi-dimensional object signatures. Object signatures are constructed from individual attributes extracted through video processing. While past work has proceeded on similar lines, the lack of a comprehensive object definition model severely restricts the application of such algorithms to controlled situations. In conditions with varying external factors, such algorithms perform less efficiently due to inherent assumptions of constancy of attribute values. Our approach assumes a variable environment where the attribute values recorded of an object are deemed prone to variability. The variations in the accuracy in object attribute values has been addressed by incorporating weights for each attribute that vary according to local conditions at a sensor location. This ensures that attribute values with higher accuracy can be accorded more credibility in the object matching process. Variations in attribute values (such as surface color of the object) were also addressed by means of applying error corrections such as shadow elimination from the detected object profile. Experiments were conducted to verify our hypothesis. The results established the validity of our approach as higher matching accuracy was obtained with our multi-dimensional approach than with a single-attribute based comparison

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach

Predicting spawning habitat for coho salmon (Oncorhynchus kisutch), Chinook salmon (Oncorhynchus tshawytscha), and steelhead (Oncorhynchus mykiss) using geospatially constructed stream morphology from high-resolution lidar-derived digital elevation model and field survey data in the Indian Creek watershed, Mendocino County, California

Restoration of anadromous salmonid habitat is of primary importance to the economic, historical, and cultural geography of the Pacific Northwest. Derivation and use of geospatial habitat models as guides to pinpoint key areas where limited restoration funding can be cost-effectively employed is of great importance. To this purpose, 1 meter resolution lidar-derived Digital Elevation Model data was acquired for the Indian Creek and neighboring watersheds in Mendocino County, California, and used together with field-acquired geomorphic stream data to geospatially model stream widths, depths, and streambank morphology. These geospatial covariates were field-verified in selected locations and then used in conjunction with field surveyed habitat presence data and substrate data to model potential anadromous salmonid species spawning habitat. Probability surfaces, each comprising the areal extent of the Indian Creek stream system and representing the probability for spawning habitat occurrence, were developed for each of the species of interest. The mean area under the curve (AUC) for 100 model replications for Chinook, Coho, and Steelhead were 0.954, 0.951, and 0.958, with standard deviations of 0.036, 0.034, and 0.036, respectively. In contrast to other models that solely use linear lengths of stream, the models developed in this work incorporate modeled stream bankfull widths and modeled stream corridor morphology, thus allowing additional interpretation and prediction involving the amount of species’ use of specific streams and watersheds. Models were field-verified by California Department of Fish and Wildlife fisheries biologist staff and Pacific Watershed Associates engineering geologists and field scientist staff as being representative of actual field conditions, thus assuring the value of modeling results and methodology in future projects and research

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Remote Sensing of Riparian Areas and Invasive Species

Riparian areas are critical landscape features situated between terrestrial and aquatic environments, which provide a host of ecosystem functions and services. Although important to the environmental health of an ecosystem, riparian areas have been degraded by anthropogenic disturbances. These routine disturbances have decreased the resiliency of riparian areas and increased their vulnerability to invasive plant species. Invasive plant species are non-native species which cause harm to the ecosystem and thrive in riparian areas due to the access to optimal growing conditions.Remote sensing provides an opportunity to manage riparian habitats at a regional and local level with imagery collected by satellites and unmanned aerial systems (UAS). The aim of this study was two-fold: firstly, to investigate riparian delineation methods using moderate resolution satellite imagery; and secondly, the feasibility of UAS to detect the invasive plant Fallopia japonica (Japanese Knotweed) within the defined areas. I gathered imagery from the Landsat 8 OLI and Sentinel-2 satellites to complete the regional level study and collected UAS imagery at a study site in northern New Hampshire for the local level portion. I obtained a modest overall accuracy from the regional riparian classification of 59% using the Sentinel-2 imagery. The local invasive species classification yielded thematic maps with overall accuracies of up to 70%, which is comparable to other studies with the same focus species. Remote sensing is a valuable tool in the management of riparian habitat and invasive plant species