Adigun A.B.,Swiss Tropical and Public Health Institute |
Adigun A.B.,University of Basel |
Adigun A.B.,National Center for Remote Sensing |
Gajere E.N.,National Center for Remote Sensing |
And 3 more authors.
Malaria Journal | Year: 2015
Background: In 2010, the National Malaria Control Programme with the support of Roll Back Malaria partners implemented a nationally representative Malaria Indicator Survey (MIS), which assembled malaria burden and control intervention related data. The MIS data were analysed to produce a contemporary smooth map of malaria risk and evaluate the control interventions effects on parasitaemia risk after controlling for environmental/climatic, demographic and socioeconomic characteristics. Methods: A Bayesian geostatistical logistic regression model was fitted on the observed parasitological prevalence data. Important environmental/climatic risk factors of parasitaemia were identified by applying Bayesian variable selection within geostatistical model. The best model was employed to predict the disease risk over a grid of 4 km2 resolution. Validation was carried out to assess model predictive performance. Various measures of control intervention coverage were derived to estimate the effects of interventions on parasitaemia risk after adjusting for environmental, socioeconomic and demographic factors. Results: Normalized difference vegetation index and rainfall were identified as important environmental/climatic predictors of malaria risk. The population adjusted risk estimates ranges from 6.46% in Lagos state to 43.33% in Borno. Interventions appear to not have important effect on malaria risk. The odds of parasitaemia appears to be on downward trend with improved socioeconomic status and living in rural areas increases the odds of testing positive to malaria parasites. Older children also have elevated risk of malaria infection. Conclusions: The produced maps and estimates of parasitaemic children give an important synoptic view of current parasite prevalence in the country. Control activities will find it a useful tool in identifying priority areas for intervention. © 2015 Adigun et al.; licensee BioMed Central.
Ogunmola J.K.,National Center For Remote Sensing |
Gajere E.N.,National Center For Remote Sensing |
Ayolabi E.A.,University of Lagos |
Olobaniyi S.B.,University of Lagos |
And 2 more authors.
Journal of African Earth Sciences | Year: 2015
Wamba 1:100,000 sheet 210 covers Wamba and Nassarawa Eggon area of North-Central Nigeria and consists of basement rocks, biotite granites and Older Granites in most parts of the northern part and by sedimentary rocks of the Cretaceous Middle Benue Trough in the southern part. High resolution aeromagnetic data was interpreted and the results integrated in a GIS environment with data from NigeriaSat-X image to map out the major structural trends within the area.Reduction-to-the-equator (RTE) operation was carried out on the aeromagnetic data after which several data transforms/derivatives such as horizontal derivative, analytical signal, and tilt derivative were calculated to highlight subsurface boundaries and the major structures within the area. Several digital image enhancement techniques such as general contrast stretching and edge enhancement were applied to the NigeraSat-X image in ERDAS IMAGINE 9.2 after which structures from the interpreted magnetic data and the image were mapped out on-screen using ArcMap 10.The results show that the RTE produced a reasonable geological picture of the area. Also the basement configuration consists of several NE-SW and NW-SE structures that range from 1 km to about 17 km in length with the NE-SW structures being the major trend within the area. The lineaments are mainly within the basement and the Older granites and may be related to the Pan-African Orogeny. This study was also able to map out more accurately the contact between the basement and the sediments hence a modified geological map of the area was produced. © 2015 Elsevier Ltd.
PubMed | National Malaria Control Programme, National Center for Remote Sensing and Swiss Tropical and Public Health Institute
Type: | Journal: Malaria journal | Year: 2015
In 2010, the National Malaria Control Programme with the support of Roll Back Malaria partners implemented a nationally representative Malaria Indicator Survey (MIS), which assembled malaria burden and control intervention related data. The MIS data were analysed to produce a contemporary smooth map of malaria risk and evaluate the control interventions effects on parasitaemia risk after controlling for environmental/climatic, demographic and socioeconomic characteristics.A Bayesian geostatistical logistic regression model was fitted on the observed parasitological prevalence data. Important environmental/climatic risk factors of parasitaemia were identified by applying Bayesian variable selection within geostatistical model. The best model was employed to predict the disease risk over a grid of 4 km(2) resolution. Validation was carried out to assess model predictive performance. Various measures of control intervention coverage were derived to estimate the effects of interventions on parasitaemia risk after adjusting for environmental, socioeconomic and demographic factors.Normalized difference vegetation index and rainfall were identified as important environmental/climatic predictors of malaria risk. The population adjusted risk estimates ranges from 6.46% in Lagos state to 43.33% in Borno. Interventions appear to not have important effect on malaria risk. The odds of parasitaemia appears to be on downward trend with improved socioeconomic status and living in rural areas increases the odds of testing positive to malaria parasites. Older children also have elevated risk of malaria infection.The produced maps and estimates of parasitaemic children give an important synoptic view of current parasite prevalence in the country. Control activities will find it a useful tool in identifying priority areas for intervention.
Arodudu O.,Leibniz Center for Agricultural Landscape Research |
Ibrahim E.,National Center for Remote Sensing |
Voinov A.,University of Twente |
Van Duren I.,University of Twente
Ecological Indicators | Year: 2014
The production of bioenergy is dependent on the supply of biomass. Biomass production for bioenergy may cause large land use conversions, impact agricultural production, food prices, forest conservation, etc. The best solution is to use biomass that does not have agricultural or ecological value. Some of such unconventional sources of biomass are found within urban spaces. We employed Geographic Information System (GIS) and quantitative Life Cycle Assessment (LCA) methodologies to identify and estimate bioenergy potential of green roofs and other bioenergy options within urban areas. Net Energy Gain (NEG) and Energy Return on Energy Invested (EROEI) were used as indicators to assess the bioenergy potential of urban spaces within the Overijssel province of the Netherlands as a case study. Data regarding suitable areas were geometrically extracted from available GIS datasets, and used to estimate the biomass/bioenergy potential of different species with different yields per hectare, growing under different environmental conditions. We found that potential net-energy gain from built-up areas can meet 0.6-7.7% of the 2030 renewable energy targets of the province without conflicting with socio-ecological concerns, while also improving human habitat. © 2014 Elsevier Ltd. All rights reserved.
Mhawej M.,National Center for Remote Sensing |
Mhawej M.,Damascus University |
Faour G.,National Center for Remote Sensing |
Abdallah C.,National Center for Remote Sensing |
Adjizian-Gerard J.,Damascus University
Ecological Informatics | Year: 2016
Wildfire is one of many natural hazards affecting the Mediterranean basin; its consequences could be fatal for individuals and beyond repair for the environment. While factors worldwide included in a fire ignition are unstandardized, in this paper, we built a model from literature-cited factors - fourteen elements were included - to highlight the probability of wildfires' occurrence in the Lebanese forest. It was named Three-Type Model (TTM), where forests were classified into three types: pine, oak and mixed. Validations have been conducted by using thirty percent of datasets versus the other seventy percent; then, by comparing its accuracy to another model that study the forest as one unit only. Accuracy assessment of the model reached above 83%, and it could be portable to other Mediterranean-climate forests.In addition, we produced a wildfire risk map by combining fire ignition-related factors with vulnerability-related variables. Results show that 15.9% of the Lebanese regions and 43.46% of the total amount of wildfires are human-induced wildfires. The majority of human-induced wildfires exists in a medium to high wildfire-ignition probabilities classes and in oak forests, representing approximately 93 and 83% of these wildfires, respectively. We concluded as well that only 1.6% of the Lebanese forest is at high risk of wildfire ignition. The implementation of our methodology in different Mediterranean countries is easy and straightforward, mainly because of the reduction of the ignition parameters as well as the usage of remote sensing datasets. It shall help decision-makers and official authorities in preventing, pre-suppressing and battling this phenomenon. © 2016 Elsevier B.V.
El Hage M.,Laboratoire Of Geodesie Et Geomatique L2G |
El Hage M.,National Center for Remote Sensing |
Simonetto E.,Laboratoire Of Geodesie Et Geomatique L2G |
Faour G.,National Center for Remote Sensing |
Polidori L.,Laboratoire Of Geodesie Et Geomatique L2G
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2010
Topographic indices are extracted from DEMs to describe the terrain geomorphology. Most DEM reconstruction parameters have an important impact on these indices (slope, curvature and hydrographic characteristics) even though they have limited impact on elevations. In this study, we will assess the impact of the DEM mesh size, image matching window size and interpolation methods on these indices. The results show that the degradation of the mesh size does not affect the elevation but it influences the elevation derivatives and the drainage networks. They confirm that elevation is almost scale independent while slope and other topographic indices are strongly scale dependent.
Ati O.F.,National Center for Remote Sensing |
Iguisi E.O.,Ahmadu Bello University |
Mohammed S.O.,National Center for Remote Sensing
African Journal of Agricultural Research | Year: 2010
Annual rainfall amount, onset and termination dates, duration of the rainy season, the number of wet and dry days and August rainfall characteristics in Katsina, Nigeria were compared between the ENSO years, La Niña years and normal years over a period of 31 years (1972 - 2002). In ENSO years annual rainfall amount is lower than the long term mean, onset is later and the termination of the rainy season is earlier than the normal years. Duration is shorter during ENSO years than other years and August rainfall is lower in ENSO years. Generally, precipitation index (PI) indicates that ENSO years particularly 1972/73 and 1982/83 coincided with drought years in the area. Since there is some empirical relationship between ENSO and rainfall in Katsina, Nigeria, it would be possible to predict extreme rainfall events like drought if the periodicity of ENSO can be understood.
Saleh M.,Islamic University of Lebanon |
Faour G.,National Center for Remote Sensing
Proceedings of the 18th Mediterranean Electrotechnical Conference: Intelligent and Efficient Technologies and Services for the Citizen, MELECON 2016 | Year: 2016
Snow Cover Area monitoring is an important factor in studies of global climate change, regional water balance and soil moisture. Recently, the usage of remote sensing techniques has flourished. In fact, remote sensing data provides timely adequate snow cover information for large areas. While the National Center for Remote Sensing in Lebanon (CNRS) has recently established an operational monitoring room for natural resources and natural disasters, this paper presents the implementation of a fully automated snow cover monitoring system based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. The system uses snow products from EOS Terra, and Aqua satellites to monitor the Snow Cover of Lebanon during the snow season (i.e. November-April). The importance of this project lies in its daily and fully automated process of acquiring, processing, storing and displaying statistics of the snow covered areas in Lebanon. Applying a custom algorithm based on combining Terra and Aqua snow products will reduce cloud contamination. © 2016 IEEE.
Awad M.,National Center for Remote Sensing
Ecological Informatics | Year: 2014
The use of satellite hyperspectral images has improved the extraction of information compared to multispectral images. Although designed as a technical demonstration for land applications, Hyperion satellite hyperspectral images are used to estimate sea water parameters in the coastal area. A combination of turbid river inputs, as well as the open sea flushing, determines the quality of the sea water in the coastal area and the status of its environment. In addition, the existence of different source of pollution adds to the complexity of the coastal sea water analysis. The field campaigns to retrieve sea water parameters provided by the past completed projects were coincident with acquisition of the Hyperion image covering the pilot area. A robust method based on a supervised Feed-Forward Back-Propagation Artificial Neural Network (ANN-BP) algorithm is applied to retrieve the concentration of chlorophyll-a from hyperspectral image. In addition, Hyperion images are used to show the variation of chlorophyll-a during two different periods of time. The variation is due to many manmade environmental disasters such as oil spill and continuous discharge of chemical and solid wastes. The research proves that the new method based on ANN has improved the mathematical regression methods to a coefficient of determination almost equal 1 compared to about 0.4 for the methods not based on ANN-BP. © 2014 Elsevier B.V.
Ogunmola J.K.,National Center for Remote Sensing |
Gajere E.N.,National Center for Remote Sensing |
Jeb D.N.,National Center for Remote Sensing |
Agene I.J.,National Center for Remote Sensing
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2014
This study is a research programme carried out to detect the change in land use/land cover of Yelwa-Heipang area of Plateau State, North Central Nigeria. It lies within the South-Eastern part of the Jos-Plateau. It is about 40km South of Jos city. It is located between latitude 9°35'16.65"N, longitude 8°52'29.91"E and Latitude 9°38'38.92"N, longitude 8°57'03.87"E (Naraguta topomap, sheet 168S.E). Two sets of Landsat images of 1975, 1986 and NigeriaSat-X image of 2012 were subjected to various image processing techniques and a supervised classification was carried out on the various images using ILWIS (Integrated land and water information system) software. The classification scheme used are bare-surface, built-up, farmland and vegetation. A follow up field work was carried out to confirm the results of the classification. The results were subjected to various statistical analyses and it shows natural vegetated area coverage increased from 5.80sqkm in 1975 to 18.47sqkm in 1986 and later reduced to 16.85sqkm in 2012. Non-vegetated area which comprised built-up area, farmlands and bare surface, decreased from 42.2sqkm in 1975 to 33.82sqkm in 1986, then to 35.86sqkm in 2012.The rate of change of natural vegetation between 1975 and 1986 was 1.152sqkm per annum, while that of 1986 and 2012 was 0.108sqkm per annum. Loss of naturally vegetated area in Yelwa-Heipang Barkin-Ladi is mainly as a result of urban growth and expansion, farming and gully erosion. Another important issue in the study area is the problem of soil erosion. In the past mining activity had led to accelerated gully erosion which has stripped substantial areas of lands of their vegetations. This has led to the formation of bare surface. Land cover of the study area during the period between 1975 and 2012 changed from a forested area to other land uses as a result of increase in population, demand for land for agricultural purposes and increase in the demand for firewood.