Mohamed Elhag

Evaluation of Different Soil Salinity Mapping Using Remote Sensing Techniques in Arid Ecosystems, Saudi Arabia

Land covers in Saudi Arabia are generally described as salty soils with sand dunes and sand sheets. Waterlogging and higher soil salinity are major challenges to sustaining agricultural practices in Saudi Arabia principally within closed drainage basins. Agricultural practices in Saudi Arabia were flourishing in the last two decades. The newly reclaimed lands were added annually and distributed all over the country. Irrigation techniques are mostly modernized to fulfill water saving strategies. Nevertheless, water resources in Saudi Arabia are under stress and groundwater levels are depleted rapidly due to heavy abstraction that may exceed crop water requirements in most of the cases due to high evaporation rates. The excess use of irrigational water leads to severe soil salinity problems. Applications of remote sensing technique in agricultural practices became widely distinctive and cover multidisciplinary principal interests on both local and regional levels. The most important remote sensing applications in agricultural practices are vegetation indices which are related to vegetation and water especially in an arid environment. Soil salinity mapping in an arid ecosystem using remote sensing data is a demanding task. Several soil salinity indices were implemented and evaluated to detect soil salinity effectively and quantitatively. Thematic maps of soil salinity were satisfactorily produced and assessed.

Land use changes and its impacts on water resources in Nile Delta region using remote sensing techniques

Sustainable water resources management plans depend on reliable monitoring of land use –land cover (LULC) changes. The use of the remote sensing techniques in LULC changes detection brings consistency and reliability to the decision maker at regional scale. Three temporal data sets of images were used to obtain the land cover changes in this study: Landsat-5 Thematic Mapper (TM) acquired in 1984, and Landsat-7 enhanced Thematic Mapper acquired in 2000 and 2005 consequently. Each temporal data set consists of four Landsat scenes, which were mosaicked to cover the whole Nile Delta. Two different supervised classification algorithms were implemented to produce classification maps in thematic form. Support vector machine showed higher classification accuracies in comparison with maximum likelihood classification. The results indicated that the rapid imbalance changes occurred among three land cover classes (urban, desert, and agricultural land). These changes powered the land degradation and land fragmentation processes over the agricultural land exclusively due to urban encroachment. Slight land cover changes were detected between fish farms and surface water land cover classes.

Soil Erosion Estimation Using Remote Sensing Techniques in Wadi Yalamlam Basin, Saudi Arabia

Soil erosion is one of the major environmental problems in terms of soil degradation in Saudi Arabia. Soil erosion leads to significant on- and off-site impacts such as significant decrease in the productive capacity of the land and sedimentation. The key aspects influencing the quantity of soil erosion mainly rely on the vegetation cover, topography, soil type, and climate. This research studies the quantification of soil erosion under different levels of data availability in Wadi Yalamlam. Remote Sensing (RS) and Geographic Information Systems (GIS) techniques have been implemented for the assessment of the data, applying the Revised Universal Soil Loss Equation (RUSLE) for the calculation of the risk of erosion. Thirty-four soil samples were randomly selected for the calculation of the erodibility factor, based on calculating the -factor values derived from soil property surfaces after interpolating soil sampling points. Soil erosion risk map was reclassified into five erosion risk classes and 19.3% of the Wadi Yalamlam is under very severe risk (37,740 ha). GIS and RS proved to be powerful instruments for mapping soil erosion risk, providing sufficient tools for the analytical part of this research. The mapping results certified the role of RUSLE as a decision support tool.

Inconsistencies of SEBS Model Output Based on the Model Inputs: Global Sensitivity Contemplations

Assessment of evapotranspiration is always a foremost element in water resources management. The consistent assessment of daily evapotranspiration provisions help decision makers to review the existing land use practices in terms of water management, while empowering them to recommend accurate land use changes. Earth observation satellite sensors are used in conjunction with Surface Energy Balance (SEB) models to overcome difficulties in obtaining daily evapotranspiration quantities on a regional scale. SEB System (SEBS) is used to estimate daily evapotranspiration and evaporative fraction over the Nile Delta along with Remote Sensing data acquired by different sensors and data from 15 in-situ meteorological stations. The consequential maps and the following correlation analysis show agreement, signifying SEBS’ applicability and accurateness in the estimation of daily evapotranspiration over agricultural areas. Sensitivity analysis evaluates the influences of the inputs to the total uncertainty in the analysis outcomes. SEBS inputs parameters are interconnected. Interconnections between different biophysical parameters are anticipated, but the magnitude of the features sensitivity is uncertain. Four different biophysical parameters are involved to provide a comparative analysis of Gaussian process emulators for performing a global sensitivity analysis (GSA). Conclusions conducted from the current work are anticipated to contribute decisively towards an inclusive SEBS inputs parameter assessment of its overall verification.

Sensitivity analysis assessment of remotely based vegetation indices to improve water resources management

Abstract Normalized Difference Vegetation Index (NDVI) is estimated from Landsat 8 sensor acquired in June 2013 to drive four different water-related indices calculated as NDVI derivatives. Different vegetation indices (VIs) have been extracted exclusively in estimation of different VIs: Leaf Area Index, Water Supply Vegetation Index, Crop Water Shortage Index, and Drought Severity Index in addition to estimation of daily evapotranspiration (ET). Sensitivity analysis assesses the contributions of the inputs to the total uncertainty in the analysis outcomes. Vegetation indices are complex and intercepted, therefore the interceptions of the five different vegetation indices are considered in the current study. A comparative analysis of Gaussian process emulators for performing global sensitivity analysis was used to conduct a variance-based sensitivity analysis to identify which uncertain inputs are driving the output uncertainty. The results showed that the interconnections between different VIs vary, but the extent of the features sensitivity is uncertain. Findings from the current work conducted are anticipated to contribute decisively toward an inclusive VIs assessment of its overall verification. Daily ET is the less sensitive and more certain index followed by Drought Vegetation Index.

Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data

Landscape fragmentation is noticeably practiced in Mediterranean regions and imposes substantial complications in several satellite image classification methods. To some extent, high spatial resolution data were able to overcome such complications. For better classification performances in Land Use Land Cover (LULC) mapping, the current research adopts different classification methods comparison for LULC mapping using Sentinel-2 satellite as a source of high spatial resolution. Both of pixel-based and an object-based classification algorithms were assessed; the pixel-based approach employs Maximum Likelihood (ML), Artificial Neural Network (ANN) algorithms, Support Vector Machine (SVM), and, the object-based classification uses the Nearest Neighbour (NN) classifier. Stratified Masking Process (SMP) that integrates a ranking process within the classes based on spectral fluctuation of the sum of the training and testing sites was implemented. An analysis of the overall and individual accuracy of the classification results of all four methods reveals that the SVM classifier was the most efficient overall by distinguishing most of the classes with the highest accuracy. NN succeeded to deal with artificial surface classes in general while agriculture area classes, and forest and semi-natural area classes were segregated successfully with SVM. Furthermore, a comparative analysis indicates that the conventional classification method yielded better accuracy results than the SMP method overall with both classifiers used, ML and SVM.

Stream network pollution by olive oil wastewater risk assessment in Crete, Greece

Abstract Discharge of olive mill wastewater (OMWW) into rivers system in Crete had led to heavy organic pollution and several drastic environmental impacts. The current research study aims to map and evaluate the environmental hazards initiated by olive mill wastewater pollution discharged into surface stream network of Kolymvari agricultural area located in western Crete, Greece. Implemented methodology is based on locating source points of pollution and determining pollutant surface flow paths under GIS environment. Hydrological features of the area were delineated in the GIS environment using basically elevation data provided by the Ministry of Agriculture. On a microscale, it was proved that the implementation of MCA can quantify the environmental risk to surface water resources caused by OMWW. On a macroscale, risk mapping was implemented by establishing a spatial connection between the source points of pollution and the possible sedimentation areas. Furthermore, mapping of olive mill waste tanks will positively improve the exercised methodology in term of assessing the potential risks of soil and groundwater pollution.

Potential rainwater harvesting improvement using advanced remote sensing applications.

The amount of water on earth is the same and only the distribution and the reallocation of water forms are altered in both time and space. To improve the rainwater harvesting a better understanding of the hydrological cycle is mandatory. Clouds are major component of the hydrological cycle; therefore, clouds distribution is the keystone of better rainwater harvesting. Remote sensing technology has shown robust capabilities in resolving challenges of water resource management in arid environments. Soil moisture content and cloud average distribution are essential remote sensing applications in extracting information of geophysical, geomorphological, and meteorological interest from satellite images. Current research study aimed to map the soil moisture content using recent Landsat 8 images and to map cloud average distribution of the corresponding area using 59 MERIS satellite imageries collected from January 2006 to October 2011. Cloud average distribution map shows specific location in the study area where it is always cloudy all the year and the site corresponding soil moisture content map came in agreement with cloud distribution. The overlay of the two previously mentioned maps over the geological map of the study area shows potential locations for better rainwater harvesting.

Non-woven Textile Materials from Waste Fibers for Cleanup of Waters Polluted with Petroleum and Oil Products

The aim of this work was to investigate the possibility of using non-woven materials (NWM) from waste fibers for oil spill cleanup and their subsequent recovery. Manufacture of textile and readymade products generates a significant amount of solid waste. A major part of it is deposited in landfills or disposed of uncontrollably. This slowly degradable waste causes environmental problems. In the present study are used two types of NWM obtained by methods where waste fibers are utilized. Thus, real textile products are produced (blankets) with which spills are covered and removed by adsorption. These products are produced by two methods: the strengthening of the covering from recovered fibers is made by entanglement when needles of special design pass through layers (needle-punching) or by stitching with thread (technology Maliwatt). Regardless of the random nature of the fiber mixture, the investigated products are good adsorbents of petroleum products. The nature of their structure (a significant void volume and developed surface) leads to a rapid recovery of the spilled petroleum products without sinking of the fiber layer for the sampled times. The used NWM can be burned under special conditions.

Understanding of the Geomorphological Elements in Discrimination of Typical Mediterranean Land Cover Types

Quantification of geomorphometric features is the keystone concern of the current study. The quantification was based on the statistical approach in term of multivariate analysis of local topographic features. The implemented algorithm utilizes the Digital Elevation Model (DEM) to categorize and extract the geomorphometric features embedded in the topographic dataset. The morphological settings were exercised on the central pixel of 3x3 per-defined convolution kernel to evaluate the surrounding pixels under the right directional pour point model (D8) of the azimuth viewpoints. Realization of unsupervised classification algorithm in term of Iterative Self-Organizing Data Analysis Technique (ISODATA) was carried out on ASTER GDEM within the boundary of the designated study area to distinguish 10 morphometric classes. The morphometric classes expressed spatial distribution variation in the study area. The adopted methodology is successful to appreciate the spatial distribution of the geomorphometric features under investigation. The conducted results verified the superimposition of the delineated geomorphometric elements over a given remote sensing imagery to be further analyzed. Robust relationship between different Land Cover types and the geomorphological elements was established in the context of the study area. The domination and the relative association of different Land Cover types in corresponding to its geomorphological elements were demonstrated.