scholarly journals Collaborative Postgraduate Programme in Applied Science in Earth Observation, Geographic Information Systems and Remote Sensing

2018 ◽  
Vol 6 ◽  
pp. 368-369
Author(s):  
Frikkie Louw ◽  
Lameck Mwewa ◽  
Joyce Maphanyane ◽  
Enock Sakala ◽  
Siddique Motola
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Geographic Information ◽  
Applied Science ◽  
Earth Observation ◽  
Postgraduate Programme

scholarly journals Application of geo-spatial technology in schistosomiasis modelling in Africa: a review

2015 ◽  
Vol 10 (2) ◽  
Author(s):  
Tawanda Manyangadze ◽  
Moses John Chimbari ◽  
Michael Gebreslasie ◽  
Samson Mukaratirwa
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Predictive Models ◽  
Geographic Information ◽  
Earth Observation ◽  
Sub Saharan Africa ◽  
Host Snail ◽  
Positioning Systems ◽  
Spatial Technology

Schistosomiasis continues to impact socio-economic development negatively in sub-Saharan Africa. The advent of spatial technologies, including geographic information systems (GIS), Earth observation (EO) and global positioning systems (GPS) assist modelling efforts. However, there is increasing concern regarding the accuracy and precision of the current spatial models. This paper reviews the literature regarding the progress and challenges in the development and utilization of spatial technology with special reference to predictive models for schistosomiasis in Africa. Peer-reviewed papers identified through a PubMed search using the following keywords: <em>geo-spatial analysis</em> OR <em>remote sensing</em> OR <em>modelling</em> OR <em>earth observation</em> OR <em>geographic information systems</em> OR <em>prediction</em> OR <em>mapping</em> AND <em>schistosomiasis</em> AND <em>Africa</em> were used. Statistical uncertainty, low spatial and temporal resolution satellite data and poor validation were identified as some of the factors that compromise the precision and accuracy of the existing predictive models. The need for high spatial resolution of remote sensing data in conjunction with ancillary data <em>viz.</em> ground-measured climatic and environmental information, local presence/absence intermediate host snail surveys as well as prevalence and intensity of human infection for model calibration and validation are discussed. The importance of a multidisciplinary approach in developing robust, spatial data capturing, modelling techniques and products applicable in epidemiology is highlighted.

Introducing Flood Susceptibility Index Using Remote-Sensing Data and Geographic Information Systems

2017 ◽  
pp. 381-400
Author(s):  
Nikolaos Stathopoulos ◽  
Kleomenis Kalogeropoulos ◽  
Christos Polykretis ◽  
Panagiotis Skrimizeas ◽  
Panagiota Louka ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Remote Sensing Data ◽  
Geographic Information ◽  
Sensing Data ◽  
Susceptibility Index ◽  
Flood Susceptibility

scholarly journals Spatial and temporal variation of vegetation cover in Al- Soudah area and its relationship to precipitation during the period (2014- 2018 AD) using remote sensing and geographic information systems: التباين المكاني والزمني للغطاء النباتي بمنطقة السودة وعلاقته بالتساقط خلال الفترة (2014- 2018م) باستخدام الاستشعار عن بعد ونظم المعلومات الجغرافية

2021 ◽  
Vol 5 (3) ◽  
pp. 107-87
Author(s):  
Fadi Abdullah alanazi, Yaser Rashed Alzannan, Faten Hamed Na Fadi Abdullah alanazi, Yaser Rashed Alzannan, Faten Hamed Na
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Geographic Information ◽  
Temporal Changes ◽  
Spatial And Temporal Variation ◽  
Spatial And Temporal Changes ◽  
Modified Soil

Souda is one of the important regions in Saudi Arabia in terms of spatial and temporal changes in vegetation cover; It includes the National Park, which is a leading tourist destination and one of the most beautiful parks in it. by tracking the spatial and temporal changes of vegetation cover by integrating remote sensing and geographic information systems, through the application of the modified soil vegetation index MSAVI during the period (2014- 2018), it became clear the decrease in the quantity and density of vegetation cover in the area. Thus, the study concluded that this indicator is one of the best indicators that can be used to extract vegetation cover from satellite images.

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