A Multidisciplinary Approach for Groundwater Potential Mapping in a Fractured Semi-Arid Terrain (Kerdous Inlier, Western Anti-Atlas, Morocco)

This study is focused on developing an approach for spatial mapping of groundwater by considering four types of factors (geological, topographical, hydrological, and climatic factors), and by using different bivariate statistical models, such as frequency ratio (FR) and Shannon’s entropy (SE). The developed approach was applied in a fractured aquifer basin (Ameln Basin, Western Anti-Atlas, Morocco), to map the spatial variation of groundwater potential. Fifteen factors (15) influencing groundwater were considered in this study, including slope degree, slope aspect, elevation, topographic wetness index (TWI), slope length (LS), topographic position index (TPI), plane curvature, profile curvature, drainage density, lineament density, distance to rivers and fault network, normalized difference vegetation index (NDVI), lithology, and land surface temperature (LST). The potential maps produced were then classified into five classes to illustrate the spatial view of each potential class obtained. The predictive capacity of the frequency ratio and Shannon’s entropy models was determined using two different methods, the first one based on the use of flow data from 49 boreholes drilled in the study area, to test and statistically calibrate the predictive capacity of each model. The results show that the percentage of positive water points corresponds to the most productive areas (high water flow) (42.86% and 30.61% for the FR and SE models, respectively). On the other hand, the low water flows are consistent with the predicted unfavorable areas for hydrogeological prospecting (4.08% for the FR model and 6.12% for the SE model). Additionally, the second validation method involves the integration of 7200 Hz apparent resistivity data to identify conductive zones that are groundwater circulation zones. The interpretation of the geophysical results shows that the high-potential zones match with low apparent resistivity zones, and therefore promising targets for hydrogeological investigation. The FR and SE models have proved very efficient for hydrogeological mapping at a fractured basement area and suggest that the northern and southern part of the study area, specifically the two major fault zones (Ameln Valley in the north, and the Tighmi-Tifermit Valley in the south) has an adequate availability of groundwater, whereas the central part, covering the localities of Tarçouat, Boutabi, Tililan, and Ighalen, presents a scarcity of groundwater. The trend histogram of the evolution of positive water points according to each potentiality class obtained suggests that the FR model was more accurate than the SE model in predicting the potential groundwater areas. The results suggest that the proposed approach is very important for hydrogeological mapping of fractured aquifers, and the resulting maps can be helpful to managers and planners to generate groundwater development plans and attenuate the consequences of future drought

Developments in the remote sensing of soil erosion in the perspective of sub-Saharan Africa. Implications on future food security and biodiversity

The remote sensing of soil erosion has gained substantial consideration, with considerable scientific research work having been conducted in the past, due to technological improvements that have resulted in the release of robust, cheap and high resolution datasets with a global foot-print. This paper reviews developments in the application of remote sensing technologies in sub-Saharan Africa with a explicit emphasis on soil erosion monitoring. Soil loss due to soil erosion by water has been identified by African geomorphologists, environmentalists and governments, as the primary threat to agriculture, biodiversity and food security across the continent. The article offers a detailed review of the progress in the remote sensing as it summarises research work that have been conducted, using various remote sensing sensors and platforms and further evaluates the significance of variations in sensor resolutions and data availability for sub-Saharan Africa. Explicit application examples are used to highlight and outline this progress. Although some progress has been made, this review has revealed the necessity for further remote sensing work to provide time-series soil erosion modelling and its implications on future food security and biodiversity in the face of changing climate and food insecurity. Overall, this review have shown the immediate need for a drastical move towards the use of new generation sensors with a plausible spatial, temporal characteristics and more importantly a global foot-print

A qualitative assessment of desertification change in the Tarfaya basin (Morocco) using panchromatic data of Landsat ETM

The purpose of the present work is to assess desertification change in the Tarfaya basin (Morocco) based on quantifying sand dunes mass change at the corridor scale using two Panchromatic bands of Landsat ETM+ and OLI with 15 m of resolution covering the study area for ten years (2005–2016). In this work, the sand dunes quantification is qualitative and is based on automatic extraction and classification of sand dunes shape using co-occurence texture filters and Support Vector Machine (SVM) classifier. The statistical results show that the area covered by sand was increased during the last ten years, which reveal that desertification becomes more intense

An easy method for barchan dunes automatic extraction from multispectral satellite data

AbstractThis work presents an easy method for barchan dunes automatic extraction from multispectral satellite data. The proposed method based on unsupervised classifications of commonly used bands for sand dunes mapping in literature. First, the collected data were atmospherically and spatially enhanced. Moreover, each selected band (band ratio or redness index or crust index) were filtered using low-pass (3x3) filter and transformed with original image (non-filtered) by using principal component analysis (PCA). Additionally, the classifications were achieved for each selected band by using three different algorithms (K-means, Expectation Maximization (EM), and IsoData) after data transformation. Eventually, the obtained maps were segmented and compared with natural colour image. The results indicate that unsupervised classification of crust index selected band, which achieved by IsoData algorithm, presents high performance for barchan dunes detection.</jats:p

Classification of air pollutants API Inter-Correlation using decision tree algorithms

Abstract The automated classification of ambient air pollutants is an important task in air pollution hazards assessment and life quality research. Faced with various classification algorithms, environmental scientists should select the most appropriate method according to their requirements and data availability. This study describes several types of Decision Tree algorithms for finding the inter-correlation between dominant air pollution index (API) for PM10 percentile values and four other air pollutants such as Sulphur Dioxide (SO2), Ozone (O3), Nitrogen Dioxide (NO2) and Carbon monoxide (CO), in addition to two other meteorological parameters: ambient temperature and humidity, using 22 months records of active air monitoring station in Penang island (northern Malaysia). Classification analysis for the PM10 API was then performed using non-linear Decision Trees within the R programming environment including: Boosted C5.0, Random Forest, PART, and Naive Bayes tree (NBtree). This is in addition to rpart and tree algorithms, which were used to plot the classification trees. The classification performance of the methods is presented and the best classifier in terms of accuracy and processing time was recommended. In R statistical environment, the process of classification by decision tree methods and the classification rules were easy to obtain, while geographic information systems (GIS) software’ was used for mapping the study area. Furthermore, the results are clear and easy to understand for environmental and geospatial scientists and relevant agencies, which will facilitate the mitigation of air pollution related disasters in the affected communities.</jats:p