scholarly journals Investigation of Groundwater Potential Using Remote Sensing and Hydro-geophysical Techniques: A Case Study of the Telidjene Basin (Eastern Algeria)

2021 ◽  
Vol 77 (4) ◽  
pp. 99-121
Author(s):  
Amel Hibi ◽  
Layachi Gouaidia ◽  
Omar Guefaifia

The present study aims to assess groundwater potential in the Telidjene Basin located in the semi-arid part of eastern Algeria, applying an innovative approach combining both remote sensing and hydrogeophysics methods. A re-interpretation of geophysical data and vertical electrical sounding (VES) measurements were applied and calibrated with the borehole data to map the deep structures that may control the presence of groundwater and identify the geological and hydrogeological setting. Morphometric factors affecting recharge were mapped using several types of remote sensing data (SRTM DEM, Landsat-8). Thematic maps were overlaid using the multicriteria method and GIS to detect potential recharge areas. The results show that the main factors influencing recharge are fracturing and drainage density. Four potential recharge areas were identified over a 547 km2 area of the basin. 20% of the area falls in the weakest class, 32% in the weak class, 3% in the moderate, and 16% in the strongest. Furthermore, the study reveals that an alluvial aquifer with a thickness of up to 60m, spreading over the surface, along the Wadi Telijene and the alluvial soil, is deposited unconformably on Cretaceous terrain containing aquifer horizons of varying thickness and different electrical resistivities (10–150 Ωm), drawing an anticlinal structure with lithostratigraphy interrupted by a series of faults and spurs of Aptian and Triassic age. The south-western part of the basin has a high to moderate recharge and storage capacity. Its alluvial cover is directly fed by precipitation and fractured limestones deposited in a syncline outcropping on the edges forming an alluvial and carbonate bilayer aquifer. This study concluded that an integrated approach, involving recent, efficient, and inexpensive technology, such as remote sensing and conventional geophysical method, can be successfully used to identify groundwater potential in the study area.

2021 ◽  
Author(s):  
Olubukola Ogungbade ◽  
Stephen Oluwafemi Ariyo ◽  
Sodiq Abiodun Alimi ◽  
Vincent Cephas Alepa ◽  
Saheed Akinwumi Aromoye ◽  
...  

Abstract This research combined GIS, Remote sensing and geophysical methods for groundwater potential investigation. The study aimed at delineating groundwater potential zones within Ilora, Oyo central, Nigeria. Unavailability of water is a major menace in these area and agricultural activities are suffering as a result. Landsat 8 OLI, ASTER DEM, geological, geophysical, and soil data of the research area were acquired for this study. In order to map groundwater potential of the area, eight thematic layers that influence groundwater occurrences and movement controlling factors such as, geology, elevation, slope, land use land cover (LULC), fault proximity, soil, lineament density, and drainage density were mapped out from the acquired data of the area. The influence of each theme and sub unit/class to groundwater recharge based on previous studies was evaluated using Analytical Hierarchical Process (AHP). The groundwater potential of the area of study was qualitatively classified into five classes, namely; very high, high, moderate, low, and very low which account for 0.3%, 7.8%, 54.8%, 35.6%, and 1.5% of the total area respectively. The results were cross-validated using well data from the area and 89% correlation was achieved. The groundwater potential map generated in this research could be used as a preliminary reference in selecting suitable sites for groundwater resource exploitation in the area in order to ameliorate the current scarcity of water in this region.


2013 ◽  
Vol 45 (4-5) ◽  
pp. 631-644 ◽  
Author(s):  
Gouri Sankar Bhunia ◽  
Nandini Chatterjee ◽  
Dilip Kumar Pal

A study was conducted to delineate the groundwater potential zones of Nawada district of Southern Bihar using satellite-derived information layers, namely, elevation, slope, drainage density, land use, fractional impervious surface (FIS) and also using existing thematic maps (lithology, soil, pre- and post-monsoon water level) based on the weighted linear combination method. On the basis of the relative contribution of each datum towards groundwater potential, the weight of each thematic map has been computed. The normalized weights of the individual themes and their different features were obtained through Saaty's analytical hierarchy process. Results showed that zones of ‘very high’ and ‘high’ groundwater potential are present in the central and northeastern part of the study area. ‘Very low’ and ‘low’ groundwater potential zones are found in the southeastern and very small pockets of the eastern corner of the district. Thus surface investigation of groundwater has proved that an integrated approach involving remote sensing and geographic information system (GIS) technique can be successfully used in identifying potential groundwater zones in a short time and at low cost, while the yield could be ascertained with some sample ground truth ‘test drillings’.


2021 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Muhammad Fawad Akbar Khan ◽  
Khan Muhammad ◽  
Shahid Bashir ◽  
Shahab Ud Din ◽  
Muhammad Hanif

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.


2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Clement Kwang ◽  
Edward Matthew Osei Jnr ◽  
Adwoa Sarpong Amoah

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.


2016 ◽  
Vol 76 (s1) ◽  
Author(s):  
Mariano Bresciani ◽  
Claudia Giardino ◽  
Rosaria Lauceri ◽  
Erica Matta ◽  
Ilaria Cazzaniga ◽  
...  

Cyanobacterial blooms occur in many parts of the world as a result of entirely natural causes or human activity. Due to their negative effects on water resources, efforts are made to monitor cyanobacteria dynamics. This study discusses the contribution of remote sensing methods for mapping cyanobacterial blooms in lakes in northern Italy. Semi-empirical approaches were used to flag scum and cyanobacteria and spectral inversion of bio-optical models was adopted to retrieve chlorophyll-a (Chl-a) concentrations. Landsat-8 OLI data provided us both the spatial distribution of Chl-a concentrations in a small eutrophic lake and the patchy distribution of scum in Lake Como. ENVISAT MERIS time series collected from 2003 to 2011 enabled the identification of dates when cyanobacterial blooms affected water quality in three small meso-eutrophic lakes in the same region. On average, algal blooms occurred in the three lakes for about 5 days a year, typically in late summer and early autumn. A suite of hyperspectral sensors on air- and space-borne platforms was used to map Chl-a concentrations in the productive waters of the Mantua lakes, finding values in the range of 20 to 100 mgm-3. The present findings were obtained by applying state of the art of methods applied to remote sensing data. Further research will focus on improving the accuracy of cyanobacteria mapping and adapting the algorithms to the new-generation of satellite sensors.


Author(s):  
Ratih Dewanti Dimyati ◽  
Projo Danoedoro ◽  
Hartono Hartono ◽  
Kustiyo Kustiyo

<p>The need for remote sensing minimum cloud cover or cloud free mosaic images is now increasing in line with the increased of national development activities based on one map policy. However, the continuity and availability of cloud and haze free remote sensing data for the purpose of monitoring the natural resources are still low. This paper presents a model of medium resolution remote sensing data processing of Landsat-8 uses a new approach called mosaic tile based model (MTB), which is developed from the mosaic pixel based model (MPB) algorithm, to obtain an annual multitemporal mosaic image with minimum cloud cover mosaic imageries. The MTB model is an approach constructed from a set of pixels (called tiles) considering the image quality that is extracted from cloud and haze free areas, vegetation coverage, and open land coverage of multitemporal imageries. The data used in the model are from Landsat-8 Operational Land Imager (OLI) covering 10 scenes area, with 2.5 years recording period from June 2015 to June 2017; covered Riau, West Sumatra and North Sumatra Provinces. The MTB model is examined with tile size of 0.1 degrees (11x11 km2), 0.05 degrees (5.5x5.5 km2), and 0.02 degrees (2.2x2.2 km2). The result of the analysis shows that the smallest tile size 0.02 gives the best result in terms of minimum cloud cover and haze (or named clear area). The comparison of clear area values to cloud cover and haze for three years (2015, 2016 and 2017) for the three mosaic images of MTB are 68.2%, 78.8%, and 86.4%, respectively.</p>


2020 ◽  
Vol 42 ◽  
pp. e32
Author(s):  
George Colares Silva Filho ◽  
Juliana Martins dos Santos ◽  
Paulo Cesar Mendes Villis ◽  
Ingrid Santos Gonçalves ◽  
Isael Coelho Correia ◽  
...  

Natural or anthropogenic chemical compounds of different origins often accumulate in estuarine regions. These compounds may alter the water quality. Therefore, It is important to constantly monitor the quality of estuarine regions. A combination of remote sensing and traditional sampling can lead to a better monitoring program for water quality parameters. The objective of this work is to assess the spatiotemporal variability of the physicochemical properties of water in the lower region of the Mearim River and estimate water quality parameters via remote sensing. Samples were collected at 16 points, from Baixo Arari to the mouth of the watershed, using a multiparameter meter and Landsat 8 satellite images. The physicochemical parameters of the water had high salinity levels, between 2.30 and 20.10 parts per trillion; a high total dissolved solids content, between 2.77 and 19.70 g/L; and minimum dissolved oxygen values. Estimating the physicochemical properties of the water via remote sensing proved feasible, particularly in the dry season when there is less cloud cover.


Author(s):  
K Choudhary ◽  
M S Boori ◽  
A Kupriyanov

The main objective of this study was to detect groundwater availability for agriculture in the Orenburg, Russia. Remote sensing data (RS) and geographic information system (GIS) were used to locate potential zones for groundwater in Orenburg. Diverse maps such as a base map, geomorphological, geological structural, lithology, drainage, slope, land use/cover and groundwater potential zone were prepared using the satellite remote sensing data, ground truth data, and secondary data. ArcGIS software was utilized to manipulate these data sets. The groundwater availability of the study was classified into different classes such as very high, high, moderate, low and very low based on its hydro-geomorphological conditions. The land use/cover map was prepared using a digital classification technique with the limited ground truth for mapping irrigated areas in the Orenburg, Russia.


2020 ◽  
pp. 69-77
Author(s):  
Anju Jangra ◽  
Anurag Airon ◽  
Ram Niwas

Forest is an essential part or backbone of the earth ecological system. In a country like India, the people and the economy of nation is mainly relies on the diversity of natural resources. In today's world degradation of forest resources is a prime concern for many of the scientists and environmentalists because the canvas had been transformed from last few decades to cultivated and non-cultivated land. In India, Haryana state has lowest forest cover i.e. 3.59% followed by Punjab 3.65%. Over the several decades, the advancement of Remote Sensing and Geographical Information System (GIS) technique has emerged as an efficient tool to monitor and analyse deforestation rate in hilly areaor over a variety of location. Remote sensing based vegetation indices show better sensitivity than individual band reflectance and hence are more preferred for assessment and monitoring of tress. The aim of the present study was to analyse the deforestation in hilly areas in Haryana State (India) by remote sensing data with a special focus on Panchkula and Yamunanagar. The information was collected through the LANDSAT 8 satellite of NASA. The result revealed that the deforestation rate is high in Hilly areas of Haryana. The study shows that the forest cover in hilly areas of Haryana in 2013 was 50,879.07 hectares and in 2019 it was 44,445.51 hectares of land. Thereby decrease in forest cover of 6,433.56 hectares had been observed in the study period of 2013-2019 i.e. 6 years. Spatial variations in deforestation were also mapped in GIS for the hilly areas in Panchkula and Yamunanagar districts of Haryana.  


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