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Franke J.,Remote Sensing Solutions GmbH | Gebreslasie M.,University of KwaZulu - Natal | Bauwens I.,Nazka mapps bvba | Deleu J.,EUROSENSE | And 2 more authors.
Geospatial Health | Year: 2015

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. © Copyright M. Eckardt et al. Source


Deleu J.,EUROSENSE | Franke J.,Remote Sensing Solutions GmbH | Gebreslasie M.,University of KwaZulu - Natal | Linard C.,Free University of Colombia
Geospatial Health | Year: 2015

For modelling the spatial distribution of malaria incidence, accurate and detailed information on population size and distribution are of significant importance. Different, global, spatial, standard datasets of population distribution have been developed and are widely used. However, most of them are not up-to-date and the low spatial resolution of the input census data has limitations for contemporary, national- scale analyses. The AfriPop project, launched in July 2009, was initiated with the aim of producing detailed, contemporary and easily updatable population distribution datasets for the whole of Africa. High-resolution satellite sensors can help to further improve this dataset through the generation of high-resolution settlement layers at greater spatial details. In the present study, the settlement extents included in the MALAREO land use classification were used to generate an enhanced and updated version of the AfriPop dataset for the study area covering southern Mozambique, eastern Swaziland and the malarious part of KwaZulu-Natal in South Africa. Results show that it is possible to easily produce a detailed and updated population distribution dataset applying the AfriPop modelling approach with the use of high-resolution settlement layers and population growth rates. The 2007 and 2011 population datasets are freely available as a product of the MALAREO project and can be downloaded from the project website. ©J. Deleu et al., 201 Source


Zillmann E.,BlackBridge | Gonzalez A.,BlackBridge | Montero Herrero E.J.,European Satellite Center | Van Wolvelaer J.,EUROSENSE | And 4 more authors.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2014

Grasslands cover approximately 40% of the Earth's surface. Low-cost tools for inventory, management, and monitoring are needed because of their great expanse, diversity, and the importance for environmental processes. Remote sensing is a useful technique for providing accurate and reliable information for land use planning and large-scale grassland management. In the context of "GIO land" (Copernicus Initial Operations land program), which is currently contracted by the European Environment Agency, a high-resolution grassland layer of 39 European countries is being created with an overall classification accuracy of better than 80%. Since grassland canopy density, chlorophyll status, and ground cover (GC) are highly dynamic throughout the growing season, no unique spectral signature can be used to map grasslands. Therefore, it is necessary to use image time series to characterize the phenological dynamics of grasslands throughout the year in order to discriminate between grasslands and other vegetation with similar spectral responses. This paper describes an operational approach based on a multisensor concept that employs optical multitemporal and multiscale satellite imagery to generate the high-resolution pan-European grassland layer. The approach is based on the supervised decision tree classifier C5.0 in combination with previous image segmentation and seasonal statistics for various vegetation indices (VIs). Results from the grassland classification for Hungary are presented. The accuracy assessment for this classification was carried out using 328 independent sample points derived from a ground-based European field survey program (LUCAS) and current CORINE Land Cover data. The grassland classification approach is explained in detail on the example of Hungary where an overall accuracy of 92.2% has been reached. © 2014 IEEE. Source


Bauwens I.,EUROSENSE | Franke J.,Remote Sensing Solutions GmbH | Gebreslasie M.,University of KwaZulu - Natal
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2012

In 2008, the Roll Back Malaria (RBM) partnership prepared a Global Malaria Action Plan (GMAP) in line with the 2010 targets of the UN Secretary General. A global strategy outlined the goal to have a substantial and sustained reduction in the burden of malaria in the near and mid-term (2015), and the eventual global eradication of malaria in the long term. Geographic Information Systems (GIS), Earth Observation (EO), Global Positioning Systems (GPS) and spatial statistics play a crucial role to monitor, control and plan malaria vector control. MALAREO aims for the development and implementation of EO products and capacities within malaria vector control and management programmes in southern Africa, including South Africa, Swaziland, and Mozambique. End-user surveys conducted in MALAREO have shown high interest for EO epidemiology applications as well as EO products directly supporting the Malaria Control Programmes (MCP) in their daily work. © 2012 IEEE. Source


Zillmann E.,RapidEye AG | Weichelt H.,RapidEye AG | Herrero E.M.,Indra | Esch T.,German Aerospace Center | And 2 more authors.
MultiTemp 2013 - 7th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: "Our Dynamic Environment", Proceedings | Year: 2013

Grasslands cover about 40 % of the earth's surface [1]. Due to its great expanse and diversity, low-cost tools for inventory, management and monitoring are needed. Remote sensing is a useful technique for providing accurate and reliable information for land use planning and to support large scale grassland management. In the context of 'GIO land' (Copernicus initial operations land), which is currently implemented by the European Environment Agency (EEA) the permanent grasslands of 39 countries in Europe has to be mapped with an overall classification accuracy of more than 80 % [2]. Since grassland canopy density, chlorophyll status and ground cover is highly dynamic throughout the growing season, no unique spectral signature can be used to map grasslands. Therefore, it is necessary to use time series to characterize the phenological dynamics of grasslands throughout the year to be able to discriminate among them and other vegetation which shows similar spectral response such as crops. The article outlines the adopted classification method using multi-temporal, multi-scale and multi-source remotely sensed data. The approach is based on the supervised decision Tree (DT) classifier C5 in combination with previous image segmentation and seasonal statistics of bio-physical parameters. In this paper the results of entire Hungary are presented. The accuracy assessment of the grassland classification was carried out using 340 sample points derived from a ground-based European field survey program. The multi-temporal grassland classification of Hungary reached an overall accuracy of 92.2 %. © 2013 IEEE. Source

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