A Preliminary Geothermal Prospectivity Mapping Based on Integrated GIS, Remote-Sensing, and Geophysical Techniques around Northeastern Nigeria

Spatial mapping of potential geothermal areas is an effective tool for a preliminary investigation and the development of a clean and renewable energy source around the globe. Specific locations within the Earth’s crust display some manifestations of sub-surface geothermal occurrences, such as hot springs, a volcanic plug, mud volcanoes, and hydrothermal alterations, that need to be investigated further. The present area of investigations also reveals some of these manifestations. However, no attempt was made to examine the prospectivity of this terrain using the efficient GIS-based multicriteria evaluation (MCE) within the scope of the Analytic hierarchy process (AHP). The integration of remote sensing, Geographic information system (GIS), and other geophysical methods (Magnetic and gravity) was performed to map the promising geothermal areas. Multiple input data sets such as aero-magnetic, aero-gravity, aero-radiometric, digital elevation model (DEM), geological map, and Landsat-8 Operational Land Imager (OLI) data were selected, processed, and use to generate five thematic layers, which include heat flow, temperature gradients, integrated lineaments, residual gravity, and lithology maps. The five thematic layers were standardized and synthesized into a geothermal prospectivity map. The respective ranks and weight of the thematic layers and their classes were assigned based on expert opinion and knowledge of the local geology. This research aims to apply an efficient method to evaluate the factors influencing the geothermal energy prospects, identify and map prospective geothermal regions, and, finally, create a geothermal prospectivity model.

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Determination of Hydrothermal Prospects in Paipa Geothermal Region (Boyacá, Colombia) Using Remote Sensing and Field Data

Earth Sciences Research Journal ◽

10.15446/esrj.v23n4.77810 ◽

2019 ◽

Vol 23 (4) ◽

pp. 265-282

Author(s):

Rafael Andrés Calderón-Chaparro ◽

German Vargas-Cuervo

Keyword(s):

Remote Sensing ◽

Soil Temperature ◽

Field Data ◽

Hot Springs ◽

Landsat 8 ◽

Geothermal Resources ◽

Temperature Index ◽

Landsat 7 ◽

Geothermal Region

Geothermal resources (e.g. hot springs) are found with the help of field techniques, such as geological, geochemistry and geophysical. These techniques in some occasions are difficult to apply because of the limit access to the research area, rising operational costs and constrained spatially the exploration areas. The thermal infrared (TIR) remote sensing is an important tool for the exploration of geothermal resources, due to the low cost and high efficiency in the study of large geographic areas. The aim of this study is to use thermal imagery of satellite remote sensing and combined with geological-geophysical data, for spatial determination of exploratory prospects of hot springs in the geothermal region of Paipa, Boyacá. The images used in this study are from satellites Landsat-7 ETM+, Landsat-8 OLI/TIRS, MODIS, ALOS-PALSAR and Pléiades. Also, field data is used, such as soil temperature, surface temperature, air temperature, relative humidity, atmospheric pressure and thermal imagery of surface geothermal manifestations. The Landsat thermal bands were radiometrically calibrated, then atmospherically and surface emissivity corrected, applying single channel and split window algorithms, for Landsat-7 ETM+ and Landsat-8 TIRS, respectively. The field data helped to correct the thermal bands. And the soil temperature data are used to create a subsurface temperature map at 1-meter depth. Once primary and secondary data is had, in a geographic information system (GIS) is implemented an unweighted spatial model, which use four input indicators (satellite temperature index, soil temperature index, structural lineaments index and iso-resistivity index) to determine the areas with higher probability to find geothermal fluids. Six prospects are highlighted for hydrothermal fluid extraction, in which two of them are already known. Results allow to concluded that thermal remote sensing are useful to map geothermal anomalies in the Paipa region, and by using these anomalies plus geological-geophysical information is possible to determine exact exploration areas.

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Impact of sea level rise and shoreline changes in the tropical island ecosystem of Andaman and Nicobar region, India

10.21203/rs.3.rs-245124/v1 ◽

2021 ◽

Author(s):

mageswaran thangaraj ◽

Sachithanandam V ◽

Sridhar R ◽

Manik Mahapatra ◽

R Purvaja ◽

...

Keyword(s):

Remote Sensing ◽

Sea Level Rise ◽

Sea Level ◽

Remote Sensing Data ◽

Data Sets ◽

Andaman Island ◽

Shoreline Changes ◽

Digital Elevation ◽

Analysis System ◽

Elevation Model

Abstract We report here a four decades of shoreline changes and possible sea level rise (SLR) impact on landuse/landcover (LULC) in Little Andaman Island by using remote sensing (RS) and GIS techniques. A total of six remote sensing data sets covering years between 1976 and 2018 were used to understand the shoreline changes. Moreover, a Digital Shoreline Analysis System (DSAS) was used to estimate short- and long- term shoreline changes from ArcGIS environment. Besides, the Island vulnerability due to SLR was studied through using digital elevation model (DEM). As a result of Sumatra earthquake (2004), the results were showed a significant variation in shorline upliftment and subsidence. The land subsidence was noticed in the range of 1042-3077 ha with sea level rise between 1 and 5 m. Hence, we conclude that Little Andaman Island is vulnerable to SLR and overwhelm low elvation coastal zone.

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A Combined GIS, Remote Sensing And Geophysical Methods For Groundwater Potential Assessment Of Ilora, Oyo Central, Nigeria

10.21203/rs.3.rs-267236/v1 ◽

2021 ◽

Author(s):

Olubukola Ogungbade ◽

Stephen Oluwafemi Ariyo ◽

Sodiq Abiodun Alimi ◽

Vincent Cephas Alepa ◽

Saheed Akinwumi Aromoye ◽

...

Keyword(s):

Remote Sensing ◽

Geophysical Methods ◽

Research Area ◽

Drainage Density ◽

Groundwater Potential ◽

Landsat 8 ◽

Resource Exploitation ◽

Unit Class ◽

Groundwater Potential Map ◽

Well Data

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.

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Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

Remote Sensing ◽

10.3390/rs10091340 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1340 ◽

Cited By ~ 24

Author(s):

Dennis Helder ◽

Brian Markham ◽

Ron Morfitt ◽

Jim Storey ◽

Julia Barsi ◽

...

Keyword(s):

Remote Sensing ◽

Sensor Calibration ◽

Landsat 8 ◽

Data Sets ◽

Optical Remote Sensing ◽

Data Interoperability ◽

Satellite Systems ◽

Combining Data ◽

The Impact ◽

Sentinel 2

Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable.

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IDENTIFIKASI STRUKTUR GEOLOGI DAN PENGARUHNYA TERHADAP SUHU PERMUKAAN TANAH BERDASARKAN DATA LANDSAT 8 DI LAPANGAN PANASBUMI BLAWAN

Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital ◽

10.30536/j.pjpdcd.2016.v13.a2557 ◽

2016 ◽

Vol 13 (1) ◽

Author(s):

Anjar Pranggawan Azhari ◽

Sukir Maryanto ◽

Arief Rachmansyah

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Hot Springs ◽

Remote Sensing Data ◽

Geological Structure ◽

Geothermal Field ◽

Geothermal System ◽

Landsat 8

This paper presented used remote sensing method for identification geological structure on Blawan-Ijengeothermal field and its system. Remote sensing data, specifically Landsat 8 and DEM SRTM, provide lineaments from the 753 multispectral band and the land surface temperature (LST) from single thermal infra red band using a retrieval method. Surface emissivity was determined based on Normalized Difference Vegetation Index (NDVI) of study area. Remote sensing analysis is good approach to identification of geological structure from surface that control thermal manifestation in Blawan geothermal field. It shows Blawan fault is the main structure in geothermal field which associated with high LST and hot springs. Interpretation indicated reservoir of Blawan-Ijen geothermal system spread from Plalangan to southwest area. Abstrak Penelitian ini bertujuan untuk mengidentifikasi struktur geologi dan gambaran sistem panasbumi Blawan-Ijen dengan aplikasi penginderaan jauh. Data penginderaan jauh khususnya citra multispektral komposit 753 Landsat 8 dan DEM SRTM digunakan sebagai data untuk mendelineasi struktur patahan di permukaan. Suhu permukaan tanah diperoleh dari pengolahan citra thermal inframerah Landsat 8 dengan bantuan metode semi empiris. Emisivitas permukaan diperoleh berdasarkan klasifikasi indeks vegetasi NDVI daerah penelitian. Analisis data penginderaan jauh merupakan pendekatan yang cukup baik dalam mengidentifikasi struktur geologi yang mengontrol manifestasi panasbumi Blawan. Hasil interpretasi menunjukkan patahan Blawan adalah struktur utama di daerah geothermal Blawan yang berasosiasi dengan suhu permukaan tanah yang tinggi dan deretan mata air panas. Interpretasi mengindikasikan reservoir sistem panasbumi Blawan berada di bawah permukaan Plalangan dan menerus dari Plalangan menuju arah barat daya daerah penelitian.

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МАСШТАБНО-ЗАВИСИМЫЕ ЭФФЕКТЫ В СТРУКТУРЕ ЭКОЛОГИЧЕСКОЙ НИШИ ЛЕБЕДЯ-ШИПУНА CYGNUSOLOR (GMELIN, 1803) В ПЕРИОД ЗИМОВКИ В ПРЕДЕЛАХ ЗАЛИВА СИВАШ

Biological Bulletin of Bogdan Chmelnitskiy Melitopol State Pedagogical University ◽

10.15421/201691 ◽

2016 ◽

Vol 6 (3) ◽

pp. 234-247

Author(s):

A. Yu. Andrushenko ◽

A. V. Zhukov

Keyword(s):

Remote Sensing ◽

Ecological Niche ◽

Random Distribution ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Information Value ◽

Landsat 8 ◽

Digital Elevation ◽

Elevation Model

<p>The assessment of the information value of ecogeographical predictors based on remote sensing data from satellites to reflect features of the ecological niche of the Swan-mute <em>Cygnus up</em> (Gmelina, 1803) in wintering within the Gulf Sivash have been presented. Two groups predictors of ecogeographical landscape data have been considered. The first group is assigned digital elevation model and its derivatives. The second set of classified vegetation indices obtained from Landsat 8 image. Ecological niche has been described using ENFA-procedure. The procedure of random distribution of the pseudo-absent points which range from the presence points restricted by some distance has been applied to assess the role of scale in ecological niche. Ecological niche of Swan mute has been shown to be described in terms of landscape ecogeographical variables. The properties of the ecological niche of the Swan-mute have been found to be depends upon the scale of its consideration. Under various boundary ranges we can get an entirely different, but statistically valid, assess the structure of the ecological niche of the Swan-mute based landscape ecogeographical predictors. The role of the various ecogeographical predictors depending on the scale can vary greatly.</p>

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Application of Landsat-8, Sentinel-2, ASTER and WorldView-3 Spectral Imagery for Exploration of Carbonate-Hosted Pb-Zn Deposits in the Central Iranian Terrane (CIT)

Remote Sensing ◽

10.3390/rs12081239 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1239 ◽

Cited By ~ 6

Author(s):

Milad Sekandari ◽

Iman Masoumi ◽

Amin Beiranvand Pour ◽

Aidy M Muslim ◽

Omeid Rahmani ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Remote Sensing ◽

Landsat 8 ◽

Logic Modeling ◽

Thematic Layers ◽

Fuzzy Logic Modeling ◽

Mineral Prospectivity Maps ◽

Band Ratios ◽

Alteration Minerals ◽

Sentinel 2

The exploration of carbonate-hosted Pb-Zn mineralization is challenging due to the complex structural-geological settings and costly using geophysical and geochemical techniques. Hydrothermal alteration minerals and structural features are typically associated with this type of mineralization. Application of multi-sensor remote sensing satellite imagery as a fast and inexpensive tool for mapping alteration zones and lithological units associated with carbonate-hosted Pb-Zn deposits is worthwhile. Multiple sources of spectral data derived from different remote sensing sensors can be utilized for detailed mapping a variety of hydrothermal alteration minerals in the visible near infrared (VNIR) and the shortwave infrared (SWIR) regions. In this research, Landsat-8, Sentinel-2, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing sensors were used for prospecting Zn-Pb mineralization in the central part of the Kashmar–Kerman Tectonic Zone (KKTZ), the Central Iranian Terrane (CIT). The KKTZ has high potential for hosting Pb-Zn mineralization due to its specific geodynamic conditions (folded and thrust belt) and the occurrence of large carbonate platforms. For the processing of the satellite remote sensing datasets, band ratios and principal component analysis (PCA) techniques were adopted and implemented. Fuzzy logic modeling was applied to integrate the thematic layers produced by image processing techniques for generating mineral prospectivity maps of the study area. The spatial distribution of iron oxide/hydroxides, hydroxyl-bearing and carbonate minerals and dolomite were mapped using specialized band ratios and analyzing eigenvector loadings of the PC images. Subsequently, mineral prospectivity maps of the study area were generated by fusing the selected PC thematic layers using fuzzy logic modeling. The most favorable/prospective zones for hydrothermal ore mineralizations and carbonate-hosted Pb-Zn mineralization in the study region were particularly mapped and indicated. Confusion matrix, field reconnaissance and laboratory analysis were carried out to verify the occurrence of alteration zones and highly prospective locations of carbonate-hosted Pb-Zn mineralization in the study area. Results indicate that the spectral data derived from multi-sensor remote sensing satellite datasets can be broadly used for generating remote sensing-based prospectivity maps for exploration of carbonate-hosted Pb-Zn mineralization in many metallogenic provinces around the world.

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The Sector Development at Tanjung Sakti, Mount Dempo, Bengkulu by Using Integration of Remote Sensing Data and TOPEX Gravity Satellite

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/873/1/012015 ◽

2021 ◽

Vol 873 (1) ◽

pp. 012015

Author(s):

Zahrah Athirah ◽

Muhammad Dhery Mahendra

Keyword(s):

Remote Sensing ◽

Satellite Data ◽

Land Surface ◽

Vegetation Index ◽

Hot Springs ◽

Gravity Data ◽

Remote Sensing Data ◽

Landsat 8 ◽

Volcanic Complex ◽

Sensing Data

Abstract Mount Dempo is the highest volcano in South Sumatra, which lies between the Bukit Barisan mountains and Gumai. The mountain located in Dempo Makmur Village, Sub-district of Pagar Alam, Lahat Regency, South Sumatra is located at an altitude of 3173 meters above sea level with coordinates of 4.03 ° S 103.13 °E. Mount Dempo’s morphology is formed by pyroclastic deposits consisting of Tuff and Sand rocks. Mount Dempo’s vegetation is dominated by Cassia sp. and Camellia sinensis for upper vegetation, while Strobilanthes hamiltoniana and Strophanthus membranifolium dominate the undergrowth. The purpose of this study is to identify geological structures to predict geothermal prospect areas by integrating remote sensing data and TOPEX Gravity Satellite Data. The remote sensing data used in this study is Landsat 8. This data is used to analyze Land Surface Temperature (LST) from a single thermal infrared band, surface emissivity based on Normalization Difference Vegetation Index (NDVI) from the study area and determine structure delineation. Gravity Satellite Data is used to map gravity anomalies in the volcanic complex of Mount Dempo. Gravity data processing produces a high anomaly zone in the northern part of the study area and is predicted as a prospect area because it is assumed to be related to the plutonic body. High density contrast indicates that there is an error in that area. In line with the error, there are several hot springs because the error serves as a pathway for geothermal fluid to rise to the surface. The study believes that with all the facts stated above, the spots which are located in Tanjung Sakti, Mount Dempo district are very prospective to be developed as a geotourism complex, in which could also increase the welfare of the local citizens.

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Identify the Archaeological Research of Thatta District Through Geo-Spatial Technologies: A Case Study of Makli Graveyard and Banbhore Fort

Pakistan Journal of Scientific & Industrial Research Series A: Physical Sciences ◽

10.52763/pjsir.phys.sci.63.2.2020.94.100 ◽

2020 ◽

Vol 63 (2) ◽

pp. 94-100

Author(s):

Zia Ur Rehman ◽

Asif Gul ◽

Syed Jamil Hasan Kazmi ◽

Danish Ahmed

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Human Life ◽

Regional Analysis ◽

Chemical Properties ◽

Archaeological Site ◽

Landsat 8 ◽

Digital Elevation ◽

Sindh Province ◽

Elevation Model

Archaeological studies with the help of geographic information systems and remote sensing have been used in temporal, spatial, regional analysis and to investigate traditional and historical ways of human life. Remote sensing alludes to a wide variety of high-technology methods for collecting data pertaining to the physical or chemical properties of an archaeological site survey. The aim of this study is to identify the archaeological site of Makli graveyard and Banbhore fort through satellite images and explore the major land cover patterns on the southern part of Sindh province using geospatial technologies. Additional goals are to evaluate and visualize the Digital Elevation Model (DEM) for the southern part of Sindh province. A landsat-8 OLI / TIRS of 20th December 2014 and a DEM image were used to classify land cover and artifacts at the site. The result indicates that historical monuments at Makli, and Banbhore fort, Thatta testify in an outstanding manner, to the civilization of the Sindh region. geographically, its location is vulnerable around the river. Banbhore has survived such threats and continued to flourish as the only and most important port of Sindh.

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Semantic Labeling in Remote Sensing Corpora Using Feature Fusion-Based Enhanced Global Convolutional Network with High-Resolution Representations and Depthwise Atrous Convolution

Remote Sensing ◽

10.3390/rs12081233 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1233 ◽

Cited By ~ 2

Author(s):

Teerapong Panboonyuen ◽

Kulsawasd Jitkajornwanich ◽

Siam Lawawirojwong ◽

Panu Srestasathiern ◽

Peerapon Vateekul

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Feature Fusion ◽

Semantic Segmentation ◽

Route Optimization ◽

Landsat 8 ◽

Data Sets ◽

Convolutional Network ◽

Low Level ◽

Backbone Network

One of the fundamental tasks in remote sensing is the semantic segmentation on the aerial and satellite images. It plays a vital role in applications, such as agriculture planning, map updates, route optimization, and navigation. The state-of-the-art model is the Enhanced Global Convolutional Network (GCN152-TL-A) from our previous work. It composes two main components: (i) the backbone network to extract features and ( i i ) the segmentation network to annotate labels. However, the accuracy can be further improved, since the deep learning network is not designed for recovering low-level features (e.g., river, low vegetation). In this paper, we aim to improve the semantic segmentation network in three aspects, designed explicitly for the remotely sensed domain. First, we propose to employ a modern backbone network called “High-Resolution Representation (HR)” to extract features with higher quality. It repeatedly fuses the representations generated by the high-to-low subnetworks with the restoration of the low-resolution representations to the same depth and level. Second, “Feature Fusion (FF)” is added to our network to capture low-level features (e.g., lines, dots, or gradient orientation). It fuses between the features from the backbone and the segmentation models, which helps to prevent the loss of these low-level features. Finally, “Depthwise Atrous Convolution (DA)” is introduced to refine the extracted features by using four multi-resolution layers in collaboration with a dilated convolution strategy. The experiment was conducted on three data sets: two private corpora from Landsat-8 satellite and one public benchmark from the “ISPRS Vaihingen” challenge. There are two baseline models: the Deep Encoder-Decoder Network (DCED) and our previous model. The results show that the proposed model significantly outperforms all baselines. It is the winner in all data sets and exceeds more than 90% of F 1 : 0.9114, 0.9362, and 0.9111 in two Landsat-8 and ISPRS Vaihingen data sets, respectively. Furthermore, it achieves an accuracy beyond 90% on almost all classes.

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