National Center for Remote Sensing

Jos, Nigeria

National Center for Remote Sensing

Jos, Nigeria
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Mhawej M.,National Center for Remote Sensing | Mhawej M.,Damascus University | Faour G.,National Center for Remote Sensing | Adjizian-Gerard J.,Damascus University
Climate Risk Management | Year: 2017

Natural phenomena, such as wildfires, usually require the coincidence of several related factors in both time and space. In wildfire studies, literature-based factors were collected and listed in Mhawej et al. (2015). The question remains: which combination of factors leads to wildfires? In this context, a novel combination of wildfire likelihood factors was proposed in three different Lebanese forest covers (i.e., pine, oak, and mixed) and related literature-based factors to historical wildfire occurrences. The threshold values of each factor were deduced from the relationship between the element and number of fire occurrences. Each combination of factors was given a unique number. These mixtures corresponded to two, three, four or five factor groupings. The result was the association of each likelihood probability (i.e., low, medium, high, and very high) with different combinations of factors. Ultimately, using these combinations, the wildfire likelihood in Lebanese forests was efficiently and instantaneously generated. This approach could be portable to other Mediterranean regions and applied to several natural hazards. © 2017 The Authors.


Najem S.,National Center for Remote Sensing
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2017

We explore the scaling of cities' solar potentials with their number of buildings and reveal a latent dependence between the solar potential and the length of the corresponding city's road network. This scaling is shown to be valid at the grid and block levels and is attributed to a common street length distribution. Additionally, we compute the buildings' solar potential correlation function and length in order to determine the set of critical exponents typifying the urban solar potential universality class. © 2017 American Physical Society.


Mhawej M.,National Center for Remote Sensing | Mhawej M.,Damascus University | Faour G.,National Center for Remote Sensing | Adjizian-Gerard J.,Damascus University
Urban Forestry and Urban Greening | Year: 2017

Dwellers experience a constant threat of wildfires when constructing their residences in woodland settings in or near forests. In these regions, also recognized as Wildland-Urban Interface (WUI), consequences of wildfires can be fatal to humans, animals and vegetation. The establishment of wildfire risk indexes is useful when produced on a community scale; these indexes proved their effectiveness worldwide to identify vulnerabilities to ignition and fire spread, to understand the underlying science and to simulate exposure. In this paper, four main inputs (i.e. wildfire likelihood, defensible space, building envelope, and community infrastructure) are used to produce the WUI Building Risk Index (WUIBRI), representing the wildfire likelihood at the property-level, in a typical WUI area, namely, Beit-Meri – Lebanon. Results show that one fourth of the total buildings have an undesirable WUIBRI value – greater than eight, demanding fast response and effective mitigation techniques. This index illustrates a weak positive spatial autocorrelation in the study area, which measures dependency among sub-regions, and no relationship with the buildings’ prices. A Cost-Benefit Analysis (CBA) is conducted and projected over the next five years from the date of the study. Under the conservative scenario, output reveals that implementing simple measures (i.e. pruning and trimming vegetation around the home, creating two-ways roads with two exits, establishing fire sprinklers system) reduces WUIBRI values; accordingly, decreasing the wildfire threat would require only 20% on the first year and 6.15% on the long run (for the next five years) of the total costs when only one wildfire occurs per year. It is recommended that homeowners, municipalities, and decision-makers, join forces to create a fire-adapted community. The approach outlined in this study is achievable in other Mediterranean regions to reduce wildfire suppression costs. © 2017 Elsevier GmbH


Fadel A.,National Center for Remote Sensing | Lemaire B.J.,ParisTech National School of Bridges and Roads | Vincon-Leite B.,ParisTech National School of Bridges and Roads | Atoui A.,Lebanese Atomic Energy Commission CNRS | And 2 more authors.
Environmental Science and Pollution Research | Year: 2017

Many freshwater bodies worldwide that suffer from harmful algal blooms would benefit for their management from a simple ecological model that requires few field data, e.g. for early warning systems. Beyond a certain degree, adding processes to ecological models can reduce model predictive capabilities. In this work, we assess whether a simple ecological model without nutrients is able to describe the succession of cyanobacterial blooms of different species in a hypereutrophic reservoir and help understand the factors that determine these blooms. In our study site, Karaoun Reservoir, Lebanon, cyanobacteria Aphanizomenon ovalisporum and Microcystis aeruginosa alternatively bloom. A simple configuration of the model DYRESM-CAEDYM was used; both cyanobacteria were simulated, with constant vertical migration velocity for A. ovalisporum, with vertical migration velocity dependent on light for M. aeruginosa and with growth limited by light and temperature and not by nutrients for both species. The model was calibrated on two successive years with contrasted bloom patterns and high variations in water level. It was able to reproduce the measurements; it showed a good performance for the water level (root-mean-square error (RMSE) lower than 1 m, annual variation of 25 m), water temperature profiles (RMSE of 0.22–1.41 °C, range 13–28 °C) and cyanobacteria biomass (RMSE of 1–57 μg Chl a L−1, range 0–206 μg Chl a L−1). The model also helped understand the succession of blooms in both years. The model results suggest that the higher growth rate of M. aeruginosa during favourable temperature and light conditions allowed it to outgrow A. ovalisporum. Our results show that simple model configurations can be sufficient not only for theoretical works when few major processes can be identified but also for operational applications. This approach could be transposed on other hypereutrophic lakes and reservoirs to describe the competition between dominant phytoplankton species, contribute to early warning systems or be used for management scenarios. © 2017 Springer-Verlag GmbH Germany


Urbic T.,University of Ljubljana | Najem S.,National Center for Remote Sensing | Dias C.L.,New Jersey Institute of Technology
Biophysical Chemistry | Year: 2016

In this manuscript we use a two-dimensional coarse-grained model to study how amyloid fibrils grow towards an equilibrium state where they coexist with proteins dissolved in a solution. Free-energies to dissociate proteins from fibrils are estimated from the residual concentration of dissolved proteins. Consistent with experiments, the concentration of proteins in solution affects the growth rate of fibrils but not their equilibrium state. Also, studies of the temperature dependence of the equilibrium state can be used to estimate thermodynamic quantities, e.g., heat capacity and entropy. © 2017 Elsevier B.V.


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.


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.


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.

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