Remotely Sensed Soil Data Analysis Using Artificial Neural Networks: A Case Study of El-Fayoum Depression, Egypt

Abstract

Earth observation and monitoring of soil quality, long term changes of soil characteristics and deterioration processes such as degradation or desertification are among the most important objectives of remote sensing. The georeferenciation of such information contributes to the development and progress of the Digital Earth project in the framework of the information globalization process. Earth observation and soil quality monitoring via remote sensing are mostly based on the use of satellite spectral data. Advanced techniques are available to predict the soil or land use/cover categories from satellite imagery data. Artificial Neural Networks (ANNs) are among the most widely used tools for modeling and prediction purposes in various fields of science. The assessment of satellite image quality and suitability for analysing the soil conditions (e.g., soil classification, land use/cover estimation, etc.) is fundamental. In this paper, methodology for data screening and subsequent application of ANNs in remote sensing is presented. The first stage is achieved

On the Use of Multivariate Analysis and Land Evaluation for Potential Agricultural Development of the Northwestern Coast of Egypt

“…Moreover, the northwest coast is one of the desert areas in Egypt suffering from land degradation which leads to decreased agricultural production. The assessment of soil capability, crop suitability, remote sensing analysis and GIS helps the decision making for sustainable agriculture development [85][86][87][88][89][90].…”

Monitoring the Extent of Reclamation of Small Scale Mining Areas Using Artificial Neural Networks

“…Existing research on small-scale mining, reclamation and artificial neural networks have placed emphasis on environmental and livelihood effects of small – scale mining (Ontoyin and Agyemang, 2014), the effect of small-scale mining on soil physical properties (Mensah et al., 2014), using tasseled cap transformation, at-brightness temperature and K-means algorithm to monitor coal surface mining and reclamation (Alden, 2009), artificial neural networks for soil analysis (Amato et al., 2015) and wildfire detection using artificial neural networks (Miller et al., 2003). However, not much research has been done using artificial neural networks to monitor reclamation activities in small-scale mining areas which is why this research focused on it.…”

“…Reclamation is needed therefore, to restore mined areas to an acceptable condition. Existing research on small-scale mining, reclamation and artificial neural networks have placed emphasis on environmental and livelihood effects of small – scale mining (Ontoyin and Agyemang, 2014), the effect of small-scale mining on soil physical properties (Mensah et al., 2014), using tasseled cap transformation, at-brightness temperature and K-means algorithm to monitor coal surface mining and reclamation (Alden, 2009), artificial neural networks for soil analysis (Amato et al., 2015). Artificial neural networks (ANN) are also being used recently for soil analysis, land use/land cover analysis, wildfire detection (Miller et al., 2003) etc.…”

Environmental sensitivity to mosquito transmitted diseases in El-Fayoum using spatial analyses

“…In Egypt, there are different environmental applications of remotely sensed data and GIS which are extensively utilized for assessing, modeling and monitoring different environmental variables and problems [e.g. [47][48][49][50][51][52][53][54][55][56][57][58][59]. Adding the application of mosquito studies will reinforce sustainability and vulnerability studies which will be adopted in the upcoming studies for supporting the Egyptian developmental plans.…”