Exploration of Geothermal Potential in Mbeya Region by Using Remotely Sensed Data

Exploration of Geothermal Potential in Mbeya Region by Using Remotely Sensed Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploration of Geothermal Potential in Mbeya Region by Using Remotely Sensed Data Mercy Masanga, Julian Ijumulana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5648410/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Amid a global push for sustainable energy solutions, Tanzania is emerging as a frontrunner in exploring renewable energy resources. The nation's strong commitment to combating climate change has driven extensive research into renewable energy alternatives sources such as geothermal, solar, and wind energy. Traditionally dependent on hydropower, Tanzania's energy sector has faced disruptions due to decreasing water levels and technical difficulties with hydropower plants. Geothermal energy, however, has emerged as a promising alternative. While conventional geological methods for detecting geothermal zones are known for their accuracy, they come with substantial costs. Integrating these methods with remotely sensed data has led to significant improvements in efficiency and precision. In response to the challenges the government of Tanzania has launched ambitious plans to advance geothermal exploration and extraction by establishing Tanzania Geothermal Development Company (TGDC) in 2014. This study employs advanced Geographical Information System (GIS) tools and remotely sensed data to identify geothermal potential zones in Mbeya, Rungwe, and Kyela districts. The methodology includes spatial analysis, by generating flow direction maps with major and minor faults, then overlaying hot springs to create a geological suitability factor. The Normalized Difference Vegetation Index (NDVI) threshold method was used to generate thermal elements. Additionally, the Random Forest method was then applied to create a land cover suitability map. Thereafter classifying regions into three primary categories: most suitable, moderately suitable, and least suitable. The study’s results were compared with existing field survey data to validate the effectiveness of the GIS based approach. To ensure high reliability, this research proposes validating remotely detected potential zones using various models, aiming for a confidence level of at least 95%. These efforts lay the foundation for unlocking Tanzania's geothermal potential, paving the way for a transformative shift towards sustainable energy leadership both within Africa and globally. Geothermal Energy Geographical Information System Tanzania Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 INTRODUCTION 1.1 Background Geothermal energy (GE) is a renewable resource derived from the heat naturally generated from beneath the Earth’s surface (Glassley, 2010 ). It is found in regions with anomalously high crustal heat flow, mostly associated with young igneous bodies or hot rocks deep within the earth’s crust(DiPippo, 2015 ). Currently, geothermal energy account for around 1.5% of the global energy supply. The observations of subsoil temperatures to assess geothermal parameters such as temperature, porosity, and permeability bolster the confidence in the presence of a potential geothermal energy resource (Heasler et al., 2019 ). Also, geological methods predict the geometry of existing fracture systems, estimate fluid flow, and assess volcanic activity and fault structures to enhance the likelihood of locating a Geothermal Potential Zones (GPZ) (Fanning et al., 2017 ). With advancements in technology, remote sensing which involves gathering information about objects without direct contact, by techniques such as multi-spectral imaging can help identify the most promising geothermal zones (González & Rodríguez-Gonzálvez, 2019 ). In this study remotely sensed data has been analysed by GIS techniques to improve the process of exploration of geothermal potential zones. Findings of the research connotes integration of this modern approach will increase efficiency and reduce cost of the exploration. 1.2 Problem statement The need for sustainable energy in the world has led to prospection of renewable energy sources such as, geothermal energy. Due to climatic change in Tanzania, there is a great need for environmentally friendly sustainable energy solutions. Before, the country depended on hydropower as the main source of energy. However, the effect of climate change has led to a decrease in water level in hydropower sources, compromising the sustainability of electrical energy production. Research has shown that geothermal energy is both sustainable and environmentally friendly. Traditional investigation of potential geothermal zones using geological methods is costly and requires considerable time, yet accurate. Hence, methods of detection of the geothermal energy require further studies to improve accuracy by integration of GIS techniques and the use of remotely sensed data. 1.3 Aims and objectives. The main objective of this project was to investigate the potentiality of remotely sensed data to identify potential zones for geothermal energy exploitation in Tanzania. The specific objectives of this study were: To estimation the magnitude of geological structures using the Shutter Radar Topography Mission Global Digital Elevation Model SRTM GDEM and existing geological data. To Identify and analyse the suitability of geothermal indicators through mapping using multi-spectral images. 2 STUDY AREA The study area extends from latitude 8°30ʹ00ʺ S to 9°00ʹ00ʺ N and longitude 33°00ʹ00ʺ to 34°00ʹ00ʺ E in the southern part of Tanzania. For this case study (Fig. 1 ), the focus was on three districts within Mbeya region: Mbeya, Rungwe, and Kyela. The Mbeya region, is in the southern parts of Tanzania, as shown in Fig. 1 below. It is within the East Africa Rift system and features volcanic elements such as Rungwe Mountain and the Ngozi caldera, showing its volcanic nature. 2.1. Topography. The topography of the Mbeya region features low elevation zones in the western part, encompassing Songwe Region and the southern lake Nyasa, as well as the eastern Rift Valley. The Usangu plains are dominant, and the areas between this zone are the Rungwe Uporoto highlands district. The elevations in the regions vary widely, ranging between 473 m at Lake Nyasa to over 2000 m at the Rungwe peak (2981 m). The Mbeya region is divided into three drainage basins: Rufiji, Rukwa, and Lake Nyasa. Mbarali and a small part of Mbeya districts fall within the Rufiji Basin. Rungwe and Kyela districts are in the Lake Nyasa basin. Chunya and a substantial portion of Mbeya districts are in the Lake Rukwa Basin. This study will address both basins, as they include the districts of Mbeya, Kyela, and Rungwe. 2.2. Geology and Climate. The geology of the study area includes rock types such as volcanic lava, metasediments, sandstone, clastic, and fine clastic: The Rift Valley floor in the Mbeya Region predominantly consists of volcanic rocks, including pyroclastic deposits, rhyolite, basalt, and climate of Mbeya is subtropical, varying in altitude. The region experiences heavy rainfall from December to April with total annual rainfall around 2068 mm, and a dry, cool season from May to August. The average annual temperature is 17.5 ᵒC. 3 MATERIALS AND METHODS 3.1 Materials This study used two categories of spatial data: primary and secondary data sets. The primary datasets include Landsat image and SRTMGDEM data, both obtained from the United States Geological Survey (USGS) and SRTMGDEM using Earth Explorer ( https://search.earthdata.nasa.gov/search ). For this study, only bands 4, 5, and 10 were used corresponding to the red, near infrared, and thermal infrared bands, respectively. These bands were selected due to their ability to show the anomalous thermal properties of the study area. Digital Elevation Model (DEM) https://dwtkns.com/srtm30m/ with a resolution of 30m was used for slope and other geological structural analyses. The high resolution of the DEM enhances the accuracy in better determination of geological structures. Also, ground-truthing points (GTP), which include reference coordinates of the hot springs with their respective temperature, were used for georeferencing the geological Map. Geological Map of Ngozi area, Geological Map of Tanzania, and the Geothermal manifestation map were used. Secondary data, including existing geological shapefiles, were collected from the Tanzania Geothermal Development Company and the School of Mines and Geosciences at the University of Dar es Salaam. These data were assessed to extract reference points, identify signatures for classification, and the validation of the result of this project by mapping GPZ. A summary of materials used in this study is indicated in Table 1 . Table 1 Types of Materials and Software used. S/N TYPE DESCRIPTION SOURCE 1. Map ● Tanzania Geological shapefiles ● Tanzania Geothermal manifestation map ● Ngozi Geological Map ● TGDC ● SOMG/UDSM 2. Images ● LANDSAT 8 ● SRTM GDEM ● USGS https://search.earthdata.nasa.gov/search https://dwtkns.com/srtm30m/ 3. Software ● ArcGIS 10.7.1 ● R studio ● QGIS 3.16 ● ESRI ● www.rstudio.org ● www.qgis.org (Source: Authors) 3.2 Methods The project was conducted in three steps as shown in Fig. 2 . The first step involved estimating geological structures from SRTM GDEM (subdivided based on the geological zones), using a spatial analyst tool such as hill shade and flow direction. The second step involved mapping geothermal indicators on the multi-spectral Image (Landsat 8). This involved calculating the land surface temperatures by the Normalized Difference Vegetation Index (NDVI) threshold method, and generating land cover data with the Random Forest algorithm. The last step was delineating GPZ using GIS tools through a suitability analysis. The following sections provide brief descriptions of each of these procedures. 3.2.1 Estimation of geological structures. The targeted geological structures included lineaments, major and minor faults, and hot springs. Elevation data from the DEM model, were pre-processed by clipping to focus on the study area. The data were then categorized based on geological shapefiles. A hillshade was created using the spatial analyst tool with azimuth and altitude settings adjusted based on known structures in the study area. Default settings (azimuth 45, altitude 315) were used, resulting in values ranging from 0 to 255 after processing the DEM. Moderate illumination values effectively highlighted the area's topography. Post-processing confirmed illumination values (0-180) within the expected range (0-255) for hillshade. The flow direction was created in order to determine the steepest slope (the drop as in Eq. 1) in each pixel in data (DEM). The algorithm calculates the drop by considering the elevation (Z-value) and slope differences between the target pixel and its 8 neighbouring pixels. the overall flow direction was determined using the following equation: \(\:Drop=\frac{\left(Z\:Value\:Difference\right)}{Distance*100}\) …………………………………………………. Eq. 1 The flow of direction was used to estimate the water flow on the surface by using elevation data. This was used to determine the potential geothermal zones by estimating the direction in which thermal water will flow Heated thermal water tends to flow from its source towards areas of lower pressure. By analysing the flow direction, we could easily estimate and track the movement of this water based on the geological zone. 3.2.2 Mapping Geothermal Indicators on multi-spectral image Calculation of Land surface temperature from the Landsat 8 image was done in the following steps: First, Landsat 8 image bands 4 and 5 were pre-processed to correct atmospheric and radiometric effects. Next, we used band 10 in calculating LST. To reduce the influence of atmosphere gases, molecules, and particles, we applied the Normalized Difference Vegetation Index (NDVI) threshold method. This method estimated land surface emissivity (LSE) and extract the LST. Preprocessing included atmospheric correction and radiometric calibration of Landsat data, near infrared, and thermal infrared bands, using the Dark Object Subtraction (DOS) algorithm. This data was sourced from https://search.earthdata.nasa.gov/search . The resulting land surface temperatures (LST) ranged from 47.71 ᵒF to 81.68 ᵒF. The highest LST values were observed centrally around Ngozi and Kyela, with the mid-eastern volcanic rock and water areas showing consistently elevated temperatures. In contrast the elevated regions with dense vegetation, such as the Rungwe mountains, showed lower LST values. 3.2.3 Classification of multi-spectral Images The third step of the study involved the classifying multi-spectral Images. We applied supervised classification, specifically random forest classification that assumes statistics for every class in each band and calculates the probability of each pixel. This method is more effective since each pixel would be assigned to the class with a higher probability. Additionally, since the assumption that all class covariance is equal, the processing time is fast(Cheng et al., 2020 ). The image classification process involved training samples collected from Google Earth and QGIS. We had five classes: water, sparse vegetation, dense vegetation, built-up area, and bare land. The training input 149 features with 6 fields, which were collected in polygon format. The samples were converted to points which were then projected to the same coordinate system as the image. Moreover, random forest algorithm was utilized in RStudio to assign pixels to their respective classes. Despite the magnitude of the dataset, RStudio runs efficiently and maintains accuracy within a short time (5 minutes for this project). Afterwards, validation of the classification results was done by cross-validation of the samples collected with the help of QGIS 3.16 and Google Earth. 3.2.4 Delineation of Geothermal potential zones by using GIS tools Suitability analysis approach was used to delineate geothermal potential zones. This involved describing a GIS-based multi-criterion decision support system using geoscientific data. The criterion has been across three disciplines; - geology, thermal and land cover data sets in a GIS environment (ArcGIS). This aids the decision-makers in targeting the detection of the potential geothermal zone. Additionally, reclassification of values into intervals by area divides the input data range into an equal number. When values are reclassified into intervals or by area, all values and their distributions in a raster are considered at once, and the values are reclassified into a predetermined number of groups. Reclassification was required to give the applied criteria for each of the relevant layer’s priority values. Subsequently, we weighed all factors in consideration of exploration. The higher the numeric values of a given data set, the greater the weight for that feature. When some features are more important, the weights can be used to reflect those feature differences. For the three data sets considered in this study, weights were awarded to each layer depending on the scientific importance of the field in detecting GPZ. Table 2 General criteria for detection of geothermal potential zones Factor % Influence Score Geological 50 3- Most suitable Thermal 30 2-moderate Land Cover 20 1-Least suitable Table 3 Criteria applied for geological factor (Flow Direction) Rank Weight Geological factor First priority 3 All (volcanic lava, faults, hot springs) Second priority 2 Volcanic lava and faults Third priority 1 faults Table 4 Criteria applied for thermal factor. Rank Weight Thermal factor First priority 3 All (high NDVI, LST) Second priority 2 High LST Third priority 1 High NDVI Table 5 Criteria applied for Land cover. Rank Weight Land cover factor First priority 3 All (dense vegetation, water) Second priority 2 Sparse vegetation and water Third priority 1 Sparse vegetation Table 2 to 5 (Source: Modified from (Omwenga, 2020 )) Iterations for sensitivity analysis of geoscientific data sets First iteration: consider Geological and Thermal (60/40) Second iteration: consider Geological and Land cover (60/40) Third iteration: consider Thermal and Land cover (60/40) Fourth iteration: consider All factors (G40- T-30 L-30) 4 RESULT AND DISCUSSION 4.1 Results SRTM GDEM. 4.1.1 Geothermal potential detection from hillshade The hillshade results were overlain with known geological data, revealing that both major and minor faults align with the illuminated lines. However, depicting more faults and lineaments was challenging, as the hillshade method is more accurate for areas of at least one square kilometre. Therefore, it was visualized based on smaller geological zones, such as the volcanic lava zone. The results indicate significant potential for lineaments in the volcanic lava zone, with multiple strips suggesting faults. Overlaying this with a structural map confirmed that major faults align with areas showing potential lineaments. 4.1.2 Geothermal potential detection from Flow Direction Image (FDI) The flow direction image results revealed various patterns of water movement, with major and minor faults aligning with fractures. By using the flow direction method, additional lineaments were identified, which correlated with existing faults(Fig. 3 ). This method proved effective; however, it is recommended to apply it to smaller areas for more accuracy. Ground-truthing points, such as hot springs, were used to validate this approach. Potential geothermal zones typically exhibit faults with flow directions primarily NW-SE, NE-SW, N-S, and E-W. Interpretation of the results was based on both hillshade and flow direction analyses. One geological zone, characterized by volcanic lava rock, contains numerous scoria cones and craters formed from basaltic magma eruptions. These features indicate the movement of hot magma that heats underground water, creating geothermal energy. Hillshade illumination revealed numerous lineaments, showing a correlation between faults, and illuminated features. Similarly, the flow direction analysis showed alignment with geological structures, such as the East African Rift Valley, a major fault overlying volcanic lava. These results suggest significant geothermal potential in the area. However, further suitability analysis using other geothermal factors is required for verification. 4.2 Results of multi-spectral image. 4.2.1 Results of Land Surface Temperature (LST). The results show a direct relationship between hot spring temperature and NDVI. Areas with high NDVI are likely to have geothermal potential, as confirmed by ground-truthing points (hot springs). It has also been shown that most hot springs with high temperatures are in areas with volcanic lava rock. Additionally, these areas have a high land surface temperature. 4.2.1.1 Validation of Land surface temperature anomalies It may not always be possible to assess the reliability of LST results derived from TIR sensor with ground surface temperature due to the non-availability of field data concomitantly with satellite overpass. nevertheless, LST for a given area can be retrieved from other TIR sensors and compared on spatial scales if their overpass times are close to that of the primary satellite being used. In this study, field data from hot springs, NDVI, and the characteristics of the geological zone were used to show the correlation between these factors and the obtained LST, as shown in the table below. Table 6 showing relationship between LST and other geothermal factors. S/N E(m) N(m) Hot spring Temp. NDVI GEOLOGICAL ZONE ID 1 33.25462 -8.86362 42°C 0.14 volcanic lava MSHEWE 2 33.79481 -9.54761 67°C 0.25 metasediments MAMPULO1 3 33.79722 -9.55049 55°C 0.26 metasediments MAMPULO2 4 33.79558 -9.55048 55°C 0.25 metasediments MAMPULO3 5 33.7618 -9.58129 36°C 0.24 metasediments KASUMULU 6 33.55379 -9.00922 89°C 0.42 volcanic lava NGOZI 7 33.55387 -9.009 80°C 0.36 volcanic lava NGOZI2 8 33.55557 -9.00297 77°C 0.34 volcanic lava NGOZI3 Source: Tanzania Geothermal Development Company 4.2.2 Results of Land cover. During the classification process, random forest combined multiple trees to predict the class of the dataset. Every decision tree predicted correct output, while those that did not belong to the class were subdivided again to another tree branch until reaching their respective final dataset. The decision trees which were generated fit on respective subsets of the given dataset and their outputs were averaged, which improved the model’s predictive accuracy and help control over-fitting. This is represented as a percentage in the figure below. Table 7 accuracy assessment of supervised classified image class Water Sparse Vegetation Dense Vegetation Built Areas Bare land Total Water 1811 1 9 10 30 1861 Sparse Vegetation 0 319 29 0 19 367 Dense Vegetation 0 0 2959 0 1 2960 Built Areas 0 0 0 1042 5 1047 Bare land 26 2 0 4 4257 4289 Total 1837 322 2997 1056 4312 10524 Image accuracy and kappa statistics Overall classification accuracy = 98.71% Overall kappa statistics = 0.9818 Source: Author’s Analysis 4.2.2 Land Cover Image The land cover represents the physical state of a geographic feature and plays a crucial role in identifying potential geothermal zone. Distinct types of land cover influence variations in soil conditions, which in turn affect the occurrence and flow of geothermal energy. Figure 5 illustrates five land cover classes. Water bodies and sparse vegetation are particularly important for delineating potential geothermal zones. Vegetation cover influences soil moisture levels, with different grassland species affecting moisture at different soil depths. Sparse vegetation is typically found in potential geothermal zones, as geothermal grasses often grow around hot springs. This output was used in a suitability analysis to assess the sensitivity of each land cover class in determining geothermal potential. 4.3 Results for suitability analysis 4.3.1 Individual Suitability Maps. After overlaying relevant sub-factors for three geoscientific disciplines, individual maps were created showing three distinct suitability classes per factor. Each class was color-coded consistently, enabling rapid comparison of suitability levels across factors. The study area was classified into these classes using specific colours for clarity, as detailed in the accompanying table. Table 8 Showing the Colour Code Identification Criteria Color code Description Red Most suitable Yellow Moderate Green Least Suitable Source: Author’s Analysis When integrated into a GIS environment, the three data layers enabled the program to calculate and identify potential zones based on the number of layers used. Areas common to all three layers were given the highest priority, followed by those described by geological data, then thermal data, and finally, land cover data. 4.3.2 Multi Criterion Suitability Analysis and Mapping Results The final geological-thermal suitability map prioritised geological data over thermal data, given the seasonal limitation of the latter's LST measurements. Ground-truthing of geological features, such as hot springs, validated the dataset, supplemented by resampled components and SRTM GDEM to detect hidden geological structures such as faults. In contrast, the geological-land cover suitability map emphasised geological interpretations over land cover assessments across a large study area with varying geothermal potential. Despite relying on thermal data for a single season, the thermal-land cover suitability map gave more importance to factors like NDVI and LST which were verified through ground-truthing. However, land cover data was not considered as significant due to its reliance on single-season measurements. The final geological-thermal-land cover suitability map was created using ArcMap's spatial analyst tools, employing a multi-criteria approach to enhance accuracy and efficiency. This comprehensive method resulted in a fourth iteration of the suitability map which has been used for validation in Fig. 7 at combined all datasets for detailed analysis. 4.3.3 Validation of Geothermal potential zone suitability map. By using the existing locations within the study area (as shown in Fig. 7 ), a sample of three hot springs was selected, all located within the Volcanic lava geological zone, especially around Ngozi and the major fault line passing through the area. Moreover, thermal analysis indicated high NDVI values, which shows the relationship between the three data sets used to create the final geothermal potential suitability map. Table 9 shows actors used for the evaluation of the Geothermal potential zone suitability map. GEOLOGICAL THERMAL LAND COVER All All All Hot springs High NDVI and high LST Dense vegetation Major Faults High NDVI Sparse vegetation Minor Faults Low LST Water Volcanic Lava (Source: Author’s Analysis) 5 CONCLUSION This study used remotely sensed data and GIS in a multi-criteria approach to create a suitability map for identifying potential geothermal zones. By applying various spatial analysis tools to geological data and multi-spectral images from Landsat 8, we produced digital data layers and factor shapefiles. These were integrated using statistical tools to develop a comprehensive suitability map. The resulting geothermal potential zones were assessed through weighted overlays and reclassification, revealing that geological factors are more dependable than thermal and land cover factors. Areas common to all three suitability maps were classified as the most promising geothermal potential zones. This multi-criteria approach proved to be more accurate than single-method approaches. The methodology was developed in collaboration with a geologist and GIS specialist at TGDC, highlighting the importance of expert knowledge in defining suitability classes for each factor. 6 RECOMMENDATION This study highlights the importance of integrating several approaches to delineate potential geothermal zones, which allows mapping of geothermal manifestations and their correlation with remotely sensed data and geological structures, leading to a better understanding of the criteria that define potential geothermal zone. Based on the findings of this research, the following recommendations were proposed: It is recommended that the TGDC adopts the multi-criterion approach in delineating geothermal potential zones and generating suitability maps. Adopting this method will lead to accuracy in delineating GPZ and minimize the costs of performing field surveys to estimate the areas that might have potential geothermal energy. Additionally, it is advised that any adopted approach include model variation. For example, integrating the geophysical data into the multi-criteria approach would enhance the project’s accuracy and reliability. Incorporating factors such as soil temperature, which can be correlated with LST and validated through ground-truthing, would further improve the results. Validating each method will increase confidence in decision making. Declarations Author Contribution All authors reviewed the manuscript References Cheng, G., Xie, X., Han, J., Guo, L., & Xia, G. S. (2020). Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 13 . https://doi.org/10.1109/JSTARS.2020.3005403 DiPippo, R. (2015). Geothermal Power Plants: Principles, Applications, Case Studies and Environmental Impact: Fourth Edition. In Geothermal Power Plants: Principles, Applications, Case Studies and Environmental Impact: Fourth Edition . https://doi.org/10.1016/C2014-0-02885-7 Fanning, K. A., Pilson, M., Kato, K., Ueda, A., Mogi, K., Nakazawa, H., Shimizu, K., Musić, S., Filipović-Vinceković, N., Sekovanić, L., Zhu, C., Lu, P., Zheng, Z., Ganor, J., Alekseyev, V. A., Medvedeva, L. S., Prisyagina, N. I., Meshalkin, S. S., Balabin, A. I., … Moore, J. (2017). SUB-SURFACE GEOLOGY AND HYDROTHERMAL ALTERATION OF WELLS LA-9D AND LA-10D OF ALUTO LANGANO GEOTHERMAL Fig. 1: Location map of the Main Ethiopian Rift (MER). Geochimica et Cosmochimica Acta, 17 (1). Glassley, W. E. (2010). Geothermal energy: Renewable energy and the environment. In Geothermal Energy: Renewable Energy and the Environment . https://doi.org/10.1201/EBK1420075700 González, D. L., & Rodríguez-Gonzálvez, P. (2019). Detection of geothermal potential zones using remote sensing techniques. Remote Sensing, 11 (20). https://doi.org/10.3390/rs11202403 Heasler, H. P., Jaworowski, C., & Foley, D. (2019). Geothermal systems and monitoring hydrothermal features. In Geological Monitoring . https://doi.org/10.1130/2009.monitoring(05 ) Omwenga, B. M. (2020). Geothermal Well Site Suitability Selection Using Geographic Information Systems (GIS) and Remote Sensing: Case Study of the Eburru Geothermal Field. 45th Workshop on Geothermal Reservoir Engineering , 1 , 1–6. Websites: United States Geological Survey (USGS) and SRTMGDEM using Earth Explorer ( https://search.earthdata.nasa.gov/search ) Digital Elevation Model (DEM) https://dwtkns.com/srtm30m/ Applications: www.rstudio.org www.qgis.org Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5648410","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":403755949,"identity":"1d448765-368d-48f2-ba08-c7eca3e362fc","order_by":0,"name":"Mercy Masanga","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYPACZgYG9gYgbWBBihaeAyAtEqRokUgAMYjQotve+/AzT4V14oabz69u+FEgwcDf3p2AV4vZmePG0jxn0hM33M4pu9kDdJjEmbMb8Gu5kcbGzNt2GKQl7QYPUIuBRC4BLfefAbX8A2q5eSbt5h+itNxgA2ppAGq5wX7sNnG2nEljlpxzLN145pkcttsyBhI8hP1y/Bjjhzc11rJ9x48/u/nmj40cf3svfi0gwMTDwOC44ACPAYjDQ1A5CDD+YGCwl29gf0CU6lEwCkbBKBh5AABNXExfnGabrQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Oxford","correspondingAuthor":true,"prefix":"","firstName":"Mercy","middleName":"","lastName":"Masanga","suffix":""},{"id":403755950,"identity":"3c24267c-ccd6-45dd-b5fd-d4d8e57984b9","order_by":1,"name":"Julian Ijumulana","email":"","orcid":"","institution":"University of Dar es salaam","correspondingAuthor":false,"prefix":"","firstName":"Julian","middleName":"","lastName":"Ijumulana","suffix":""}],"badges":[],"createdAt":"2024-12-15 16:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5648410/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5648410/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74512185,"identity":"1bc0b9cf-c3a4-4a3c-85cc-0f34216b2de0","added_by":"auto","created_at":"2025-01-23 04:02:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":165594,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area in Mbeya Region (source: Author)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5648410/v1/50c0c4deb68d3fbe5c2aabe4.jpg"},{"id":74512184,"identity":"da1d3d1a-3746-4bd4-8122-b26571cca587","added_by":"auto","created_at":"2025-01-23 04:02:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85614,"visible":true,"origin":"","legend":"\u003cp\u003eA flowchart showing sequence of procedures. (Source: Author’s Analysis)\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5648410/v1/61bc4df8ec0a04c78fde0ea3.jpg"},{"id":74513240,"identity":"3565edaa-35a4-4efc-b241-3cd589304d96","added_by":"auto","created_at":"2025-01-23 04:10:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":481918,"visible":true,"origin":"","legend":"\u003cp\u003eCase study volcanic lava flow direction map (Source: Author’s Analysis)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5648410/v1/38002d4dd7985bcf4a6021d8.jpg"},{"id":74512190,"identity":"f58b9bfc-a642-442a-8638-eec0fb8a6957","added_by":"auto","created_at":"2025-01-23 04:02:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":149108,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized Difference Vegetation Index Map subdivided based on the geological zones (Source: Author’s Analysis)\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5648410/v1/10ecdacdedfd5ba233ebb318.jpg"},{"id":74512188,"identity":"83a799e4-99b6-4570-9cd4-38dceadf842d","added_by":"auto","created_at":"2025-01-23 04:02:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":72704,"visible":true,"origin":"","legend":"\u003cp\u003esupervised classification imagery of the study area (Source: Author’s Analysis)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5648410/v1/265dc4bc11ea9ce78bb22fe1.png"},{"id":74512189,"identity":"d25d4238-7e64-4e2a-955d-8697e19697a5","added_by":"auto","created_at":"2025-01-23 04:02:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eThis image is not available with this version.\u003c/p\u003e","description":"","filename":"placeholderimage.png","url":"https://assets-eu.researchsquare.com/files/rs-5648410/v1/8f2441f8c553e70e00a5b062.png"},{"id":74513244,"identity":"2e54e634-47b4-4bd4-9221-e9f0becf83cf","added_by":"auto","created_at":"2025-01-23 04:10:22","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":710196,"visible":true,"origin":"","legend":"\u003cp\u003eGeothermal validation suitability map (Source: Author’s Analysis)\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5648410/v1/b192f644d2151a3f95e5c7b1.jpg"},{"id":74514589,"identity":"37232f1d-7f07-4a0a-9591-170d7149377e","added_by":"auto","created_at":"2025-01-23 04:26:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2760092,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5648410/v1/4bfe1e15-7b32-409a-93a2-44a046706c7e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eExploration of Geothermal Potential in Mbeya Region by Using Remotely Sensed Data\u003c/p\u003e","fulltext":[{"header":"1 INTRODUCTION","content":"\n\u003ch3\u003e1.1 Background\u003c/h3\u003e\n\u003cp\u003eGeothermal energy (GE) is a renewable resource derived from the heat naturally generated from beneath the Earth\u0026rsquo;s surface (Glassley, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It is found in regions with anomalously high crustal heat flow, mostly associated with young igneous bodies or hot rocks deep within the earth\u0026rsquo;s crust(DiPippo, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Currently, geothermal energy account for around 1.5% of the global energy supply. The observations of subsoil temperatures to assess geothermal parameters such as temperature, porosity, and permeability bolster the confidence in the presence of a potential geothermal energy resource (Heasler et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Also, geological methods predict the geometry of existing fracture systems, estimate fluid flow, and assess volcanic activity and fault structures to enhance the likelihood of locating a Geothermal Potential Zones (GPZ) (Fanning et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). With advancements in technology, remote sensing which involves gathering information about objects without direct contact, by techniques such as multi-spectral imaging can help identify the most promising geothermal zones (Gonz\u0026aacute;lez \u0026amp; Rodr\u0026iacute;guez-Gonz\u0026aacute;lvez, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this study remotely sensed data has been analysed by GIS techniques to improve the process of exploration of geothermal potential zones. Findings of the research connotes integration of this modern approach will increase efficiency and reduce cost of the exploration.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Problem statement\u003c/h2\u003e \u003cp\u003eThe need for sustainable energy in the world has led to prospection of renewable energy sources such as, geothermal energy. Due to climatic change in Tanzania, there is a great need for environmentally friendly sustainable energy solutions. Before, the country depended on hydropower as the main source of energy. However, the effect of climate change has led to a decrease in water level in hydropower sources, compromising the sustainability of electrical energy production. Research has shown that geothermal energy is both sustainable and environmentally friendly. Traditional investigation of potential geothermal zones using geological methods is costly and requires considerable time, yet accurate. Hence, methods of detection of the geothermal energy require further studies to improve accuracy by integration of GIS techniques and the use of remotely sensed data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Aims and objectives.\u003c/h2\u003e \u003cp\u003eThe main objective of this project was to investigate the potentiality of remotely sensed data to identify potential zones for geothermal energy exploitation in Tanzania.\u003c/p\u003e \u003cp\u003eThe specific objectives of this study were:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo estimation the magnitude of geological structures using the Shutter Radar Topography Mission Global Digital Elevation Model SRTM GDEM and existing geological data.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo Identify and analyse the suitability of geothermal indicators through mapping using multi-spectral images.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2 STUDY AREA","content":"\u003cp\u003eThe study area extends from latitude 8\u0026deg;30ʹ00ʺ S to 9\u0026deg;00ʹ00ʺ N and longitude 33\u0026deg;00ʹ00ʺ to 34\u0026deg;00ʹ00ʺ E in the southern part of Tanzania. For this case study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the focus was on three districts within Mbeya region: Mbeya, Rungwe, and Kyela. The Mbeya region, is in the southern parts of Tanzania, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below. It is within the East Africa Rift system and features volcanic elements such as Rungwe Mountain and the Ngozi caldera, showing its volcanic nature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Topography.\u003c/h2\u003e \u003cp\u003eThe topography of the Mbeya region features low elevation zones in the western part, encompassing Songwe Region and the southern lake Nyasa, as well as the eastern Rift Valley. The Usangu plains are dominant, and the areas between this zone are the Rungwe Uporoto highlands district. The elevations in the regions vary widely, ranging between 473 m at Lake Nyasa to over 2000 m at the Rungwe peak (2981 m).\u003c/p\u003e \u003cp\u003eThe Mbeya region is divided into three drainage basins: Rufiji, Rukwa, and Lake Nyasa. Mbarali and a small part of Mbeya districts fall within the Rufiji Basin. Rungwe and Kyela districts are in the Lake Nyasa basin. Chunya and a substantial portion of Mbeya districts are in the Lake Rukwa Basin. This study will address both basins, as they include the districts of Mbeya, Kyela, and Rungwe.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Geology and Climate.\u003c/h2\u003e \u003cp\u003eThe geology of the study area includes rock types such as volcanic lava, metasediments, sandstone, clastic, and fine clastic: The Rift Valley floor in the Mbeya Region predominantly consists of volcanic rocks, including pyroclastic deposits, rhyolite, basalt, and climate of Mbeya is subtropical, varying in altitude. The region experiences heavy rainfall from December to April with total annual rainfall around 2068 mm, and a dry, cool season from May to August. The average annual temperature is 17.5 ᵒC.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Materials\u003c/h2\u003e \u003cp\u003eThis study used two categories of spatial data: primary and secondary data sets. The primary datasets include Landsat image and SRTMGDEM data, both obtained from the United States Geological Survey (USGS) and SRTMGDEM using Earth Explorer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://search.earthdata.nasa.gov/search\u003c/span\u003e\u003cspan address=\"https://search.earthdata.nasa.gov/search\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For this study, only bands 4, 5, and 10 were used corresponding to the red, near infrared, and thermal infrared bands, respectively. These bands were selected due to their ability to show the anomalous thermal properties of the study area.\u003c/p\u003e \u003cp\u003eDigital Elevation Model (DEM) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dwtkns.com/srtm30m/\u003c/span\u003e\u003cspan address=\"https://dwtkns.com/srtm30m/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e with a resolution of 30m was used for slope and other geological structural analyses. The high resolution of the DEM enhances the accuracy in better determination of geological structures. Also, ground-truthing points (GTP), which include reference coordinates of the hot springs with their respective temperature, were used for georeferencing the geological Map.\u003c/p\u003e \u003cp\u003eGeological Map of Ngozi area, Geological Map of Tanzania, and the Geothermal manifestation map were used. Secondary data, including existing geological shapefiles, were collected from the Tanzania Geothermal Development Company and the School of Mines and Geosciences at the University of Dar es Salaam. These data were assessed to extract reference points, identify signatures for classification, and the validation of the result of this project by mapping GPZ. A summary of materials used in this study is indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTypes of Materials and Software used.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTYPE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDESCRIPTION\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSOURCE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e● Tanzania Geological shapefiles\u003c/p\u003e \u003cp\u003e● Tanzania Geothermal manifestation map\u003c/p\u003e \u003cp\u003e● Ngozi Geological Map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● TGDC\u003c/p\u003e \u003cp\u003e● SOMG/UDSM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e● LANDSAT 8\u003c/p\u003e \u003cp\u003e● SRTM GDEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● USGS\u003c/p\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://search.earthdata.nasa.gov/search\u003c/span\u003e\u003cspan address=\"https://search.earthdata.nasa.gov/search\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dwtkns.com/srtm30m/\u003c/span\u003e\u003cspan address=\"https://dwtkns.com/srtm30m/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoftware\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e● ArcGIS 10.7.1\u003c/p\u003e \u003cp\u003e● R studio\u003c/p\u003e \u003cp\u003e● QGIS 3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● ESRI\u003c/p\u003e \u003cp\u003e● \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.rstudio.org\u003c/span\u003e\u003cspan address=\"http://www.rstudio.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e● \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.qgis.org\u003c/span\u003e\u003cspan address=\"http://www.qgis.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Source: Authors)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Methods\u003c/h2\u003e \u003cp\u003eThe project was conducted in three steps as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The first step involved estimating geological structures from SRTM GDEM (subdivided based on the geological zones), using a spatial analyst tool such as hill shade and flow direction. The second step involved mapping geothermal indicators on the multi-spectral Image (Landsat 8). This involved calculating the land surface temperatures by the Normalized Difference Vegetation Index (NDVI) threshold method, and generating land cover data with the Random Forest algorithm. The last step was delineating GPZ using GIS tools through a suitability analysis. The following sections provide brief descriptions of each of these procedures.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Estimation of geological structures.\u003c/h2\u003e \u003cp\u003eThe targeted geological structures included lineaments, major and minor faults, and hot springs. Elevation data from the DEM model, were pre-processed by clipping to focus on the study area. The data were then categorized based on geological shapefiles.\u003c/p\u003e \u003cp\u003eA hillshade was created using the spatial analyst tool with azimuth and altitude settings adjusted based on known structures in the study area. Default settings (azimuth 45, altitude 315) were used, resulting in values ranging from 0 to 255 after processing the DEM. Moderate illumination values effectively highlighted the area's topography. Post-processing confirmed illumination values (0-180) within the expected range (0-255) for hillshade.\u003c/p\u003e \u003cp\u003eThe flow direction was created in order to determine the steepest slope (the drop as in Eq.\u0026nbsp;1) in each pixel in data (DEM). The algorithm calculates the drop by considering the elevation (Z-value) and slope differences between the target pixel and its 8 neighbouring pixels. the overall flow direction was determined using the following equation:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Drop=\\frac{\\left(Z\\:Value\\:Difference\\right)}{Distance*100}\\)\u003c/span\u003e \u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. Eq.\u0026nbsp;1\u003c/p\u003e \u003cp\u003eThe flow of direction was used to estimate the water flow on the surface by using elevation data. This was used to determine the potential geothermal zones by estimating the direction in which thermal water will flow Heated thermal water tends to flow from its source towards areas of lower pressure. By analysing the flow direction, we could easily estimate and track the movement of this water based on the geological zone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Mapping Geothermal Indicators on multi-spectral image\u003c/h2\u003e \u003cp\u003eCalculation of Land surface temperature from the Landsat 8 image was done in the following steps: First, Landsat 8 image bands 4 and 5 were pre-processed to correct atmospheric and radiometric effects. Next, we used band 10 in calculating LST. To reduce the influence of atmosphere gases, molecules, and particles, we applied the Normalized Difference Vegetation Index (NDVI) threshold method. This method estimated land surface emissivity (LSE) and extract the LST.\u003c/p\u003e \u003cp\u003ePreprocessing included atmospheric correction and radiometric calibration of Landsat data, near infrared, and thermal infrared bands, using the Dark Object Subtraction (DOS) algorithm. This data was sourced from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://search.earthdata.nasa.gov/search\u003c/span\u003e\u003cspan address=\"https://search.earthdata.nasa.gov/search\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The resulting land surface temperatures (LST) ranged from 47.71 ᵒF to 81.68 ᵒF. The highest LST values were observed centrally around Ngozi and Kyela, with the mid-eastern volcanic rock and water areas showing consistently elevated temperatures. In contrast the elevated regions with dense vegetation, such as the Rungwe mountains, showed lower LST values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Classification of multi-spectral Images\u003c/h2\u003e \u003cp\u003eThe third step of the study involved the classifying multi-spectral Images. We applied supervised classification, specifically random forest classification that assumes statistics for every class in each band and calculates the probability of each pixel. This method is more effective since each pixel would be assigned to the class with a higher probability. Additionally, since the assumption that all class covariance is equal, the processing time is fast(Cheng et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe image classification process involved training samples collected from Google Earth and QGIS. We had five classes: water, sparse vegetation, dense vegetation, built-up area, and bare land. The training input 149 features with 6 fields, which were collected in polygon format. The samples were converted to points which were then projected to the same coordinate system as the image. Moreover, random forest algorithm was utilized in RStudio to assign pixels to their respective classes. Despite the magnitude of the dataset, RStudio runs efficiently and maintains accuracy within a short time (5 minutes for this project). Afterwards, validation of the classification results was done by cross-validation of the samples collected with the help of QGIS 3.16 and Google Earth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Delineation of Geothermal potential zones by using GIS tools\u003c/h2\u003e \u003cp\u003eSuitability analysis approach was used to delineate geothermal potential zones. This involved describing a GIS-based multi-criterion decision support system using geoscientific data. The criterion has been across three disciplines; - geology, thermal and land cover data sets in a GIS environment (ArcGIS). This aids the decision-makers in targeting the detection of the potential geothermal zone.\u003c/p\u003e \u003cp\u003eAdditionally, reclassification of values into intervals by area divides the input data range into an equal number. When values are reclassified into intervals or by area, all values and their distributions in a raster are considered at once, and the values are reclassified into a predetermined number of groups. Reclassification was required to give the applied criteria for each of the relevant layer\u0026rsquo;s priority values.\u003c/p\u003e \u003cp\u003eSubsequently, we weighed all factors in consideration of exploration. The higher the numeric values of a given data set, the greater the weight for that feature. When some features are more important, the weights can be used to reflect those feature differences. For the three data sets considered in this study, weights were awarded to each layer depending on the scientific importance of the field in detecting GPZ.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral criteria for detection of geothermal potential zones\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Influence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3- Most suitable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThermal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2-moderate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-Least suitable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCriteria applied for geological factor (Flow Direction)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeological factor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll (volcanic lava, faults, hot springs)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVolcanic lava and faults\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efaults\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCriteria applied for thermal factor.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThermal factor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll (high NDVI, LST)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh LST\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh NDVI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCriteria applied for Land cover.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLand cover factor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll (dense vegetation, water)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSparse vegetation and water\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSparse vegetation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e to \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (Source: Modified from (Omwenga, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e))\u003c/p\u003e \u003cp\u003e \u003cb\u003eIterations for sensitivity analysis of geoscientific data sets\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFirst iteration: consider Geological and Thermal (60/40)\u003c/p\u003e \u003cp\u003eSecond iteration: consider Geological and Land cover (60/40)\u003c/p\u003e \u003cp\u003eThird iteration: consider Thermal and Land cover (60/40)\u003c/p\u003e \u003cp\u003eFourth iteration: consider All factors (G40- T-30 L-30)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 RESULT AND DISCUSSION","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Results SRTM GDEM.\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Geothermal potential detection from hillshade\u003c/h2\u003e \u003cp\u003eThe hillshade results were overlain with known geological data, revealing that both major and minor faults align with the illuminated lines. However, depicting more faults and lineaments was challenging, as the hillshade method is more accurate for areas of at least one square kilometre. Therefore, it was visualized based on smaller geological zones, such as the volcanic lava zone. The results indicate significant potential for lineaments in the volcanic lava zone, with multiple strips suggesting faults. Overlaying this with a structural map confirmed that major faults align with areas showing potential lineaments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Geothermal potential detection from Flow Direction Image (FDI)\u003c/h2\u003e \u003cp\u003eThe flow direction image results revealed various patterns of water movement, with major and minor faults aligning with fractures. By using the flow direction method, additional lineaments were identified, which correlated with existing faults(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This method proved effective; however, it is recommended to apply it to smaller areas for more accuracy. Ground-truthing points, such as hot springs, were used to validate this approach. Potential geothermal zones typically exhibit faults with flow directions primarily NW-SE, NE-SW, N-S, and E-W.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterpretation of the results was based on both hillshade and flow direction analyses. One geological zone, characterized by volcanic lava rock, contains numerous scoria cones and craters formed from basaltic magma eruptions. These features indicate the movement of hot magma that heats underground water, creating geothermal energy. Hillshade illumination revealed numerous lineaments, showing a correlation between faults, and illuminated features. Similarly, the flow direction analysis showed alignment with geological structures, such as the East African Rift Valley, a major fault overlying volcanic lava. These results suggest significant geothermal potential in the area. However, further suitability analysis using other geothermal factors is required for verification.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Results of multi-spectral image.\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Results of Land Surface Temperature (LST).\u003c/h2\u003e \u003cp\u003eThe results show a direct relationship between hot spring temperature and NDVI. Areas with high NDVI are likely to have geothermal potential, as confirmed by ground-truthing points (hot springs). It has also been shown that most hot springs with high temperatures are in areas with volcanic lava rock. Additionally, these areas have a high land surface temperature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section4\"\u003e \u003ch2\u003e4.2.1.1 Validation of Land surface temperature anomalies\u003c/h2\u003e \u003cp\u003eIt may not always be possible to assess the reliability of LST results derived from TIR sensor with ground surface temperature due to the non-availability of field data concomitantly with satellite overpass. nevertheless, LST for a given area can be retrieved from other TIR sensors and compared on spatial scales if their overpass times are close to that of the primary satellite being used. In this study, field data from hot springs, NDVI, and the characteristics of the geological zone were used to show the correlation between these factors and the obtained LST, as shown in the table below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eshowing relationship between LST and other geothermal factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE(m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN(m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHot spring Temp.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGEOLOGICAL ZONE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.25462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-8.86362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003evolcanic lava\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMSHEWE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.79481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.54761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003emetasediments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMAMPULO1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.79722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.55049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003emetasediments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMAMPULO2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.79558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.55048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003emetasediments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMAMPULO3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.7618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.58129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003emetasediments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKASUMULU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.55379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.00922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003evolcanic lava\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNGOZI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.55387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003evolcanic lava\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNGOZI2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.55557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.00297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003evolcanic lava\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNGOZI3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Tanzania Geothermal Development Company\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Results of Land cover.\u003c/h2\u003e \u003cp\u003eDuring the classification process, random forest combined multiple trees to predict the class of the dataset. Every decision tree predicted correct output, while those that did not belong to the class were subdivided again to another tree branch until reaching their respective final dataset. The decision trees which were generated fit on respective subsets of the given dataset and their outputs were averaged, which improved the model\u0026rsquo;s predictive accuracy and help control over-fitting. This is represented as a percentage in the figure below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eaccuracy assessment of supervised classified image\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eclass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSparse Vegetation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDense Vegetation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBuilt Areas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBare land\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWater\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSparse Vegetation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDense Vegetation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBuilt Areas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBare land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eImage accuracy and kappa statistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOverall classification accuracy\u0026thinsp;=\u0026thinsp;98.71%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOverall kappa statistics\u0026thinsp;=\u0026thinsp;0.9818\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Author\u0026rsquo;s Analysis\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Land Cover Image\u003c/h2\u003e \u003cp\u003eThe land cover represents the physical state of a geographic feature and plays a crucial role in identifying potential geothermal zone. Distinct types of land cover influence variations in soil conditions, which in turn affect the occurrence and flow of geothermal energy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates five land cover classes. Water bodies and sparse vegetation are particularly important for delineating potential geothermal zones. Vegetation cover influences soil moisture levels, with different grassland species affecting moisture at different soil depths. Sparse vegetation is typically found in potential geothermal zones, as geothermal grasses often grow around hot springs. This output was used in a suitability analysis to assess the sensitivity of each land cover class in determining geothermal potential.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Results for suitability analysis\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Individual Suitability Maps.\u003c/h2\u003e \u003cp\u003eAfter overlaying relevant sub-factors for three geoscientific disciplines, individual maps were created showing three distinct suitability classes per factor. Each class was color-coded consistently, enabling rapid comparison of suitability levels across factors. The study area was classified into these classes using specific colours for clarity, as detailed in the accompanying table.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eShowing the Colour Code Identification Criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColor code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMost suitable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeast Suitable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eSource: Author\u0026rsquo;s Analysis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen integrated into a GIS environment, the three data layers enabled the program to calculate and identify potential zones based on the number of layers used. Areas common to all three layers were given the highest priority, followed by those described by geological data, then thermal data, and finally, land cover data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Multi Criterion Suitability Analysis and Mapping Results\u003c/h2\u003e \u003cp\u003eThe final geological-thermal suitability map prioritised geological data over thermal data, given the seasonal limitation of the latter's LST measurements. Ground-truthing of geological features, such as hot springs, validated the dataset, supplemented by resampled components and SRTM GDEM to detect hidden geological structures such as faults. In contrast, the geological-land cover suitability map emphasised geological interpretations over land cover assessments across a large study area with varying geothermal potential. Despite relying on thermal data for a single season, the thermal-land cover suitability map gave more importance to factors like NDVI and LST which were verified through ground-truthing. However, land cover data was not considered as significant due to its reliance on single-season measurements. The final geological-thermal-land cover suitability map was created using ArcMap's spatial analyst tools, employing a multi-criteria approach to enhance accuracy and efficiency. This comprehensive method resulted in a fourth iteration of the suitability map which has been used for validation in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e at combined all datasets for detailed analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Validation of Geothermal potential zone suitability map.\u003c/h2\u003e \u003cp\u003eBy using the existing locations within the study area (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e), a sample of three hot springs was selected, all located within the Volcanic lava geological zone, especially around Ngozi and the major fault line passing through the area. Moreover, thermal analysis indicated high NDVI values, which shows the relationship between the three data sets used to create the final geothermal potential suitability map.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eshows actors used for the evaluation of the Geothermal potential zone suitability map.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGEOLOGICAL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHERMAL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLAND COVER\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot springs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh NDVI and high LST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDense vegetation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor Faults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh NDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSparse vegetation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinor Faults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow LST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolcanic Lava\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e(Source: Author\u0026rsquo;s Analysis)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5 CONCLUSION","content":"\u003cp\u003eThis study used remotely sensed data and GIS in a multi-criteria approach to create a suitability map for identifying potential geothermal zones. By applying various spatial analysis tools to geological data and multi-spectral images from Landsat 8, we produced digital data layers and factor shapefiles. These were integrated using statistical tools to develop a comprehensive suitability map.\u003c/p\u003e \u003cp\u003eThe resulting geothermal potential zones were assessed through weighted overlays and reclassification, revealing that geological factors are more dependable than thermal and land cover factors. Areas common to all three suitability maps were classified as the most promising geothermal potential zones. This multi-criteria approach proved to be more accurate than single-method approaches.\u003c/p\u003e \u003cp\u003eThe methodology was developed in collaboration with a geologist and GIS specialist at TGDC, highlighting the importance of expert knowledge in defining suitability classes for each factor.\u003c/p\u003e"},{"header":"6 RECOMMENDATION","content":"\u003cp\u003eThis study highlights the importance of integrating several approaches to delineate potential geothermal zones, which allows mapping of geothermal manifestations and their correlation with remotely sensed data and geological structures, leading to a better understanding of the criteria that define potential geothermal zone. Based on the findings of this research, the following recommendations were proposed:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIt is recommended that the TGDC adopts the multi-criterion approach in delineating geothermal potential zones and generating suitability maps. Adopting this method will lead to accuracy in delineating GPZ and minimize the costs of performing field surveys to estimate the areas that might have potential geothermal energy.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAdditionally, it is advised that any adopted approach include model variation. For example, integrating the geophysical data into the multi-criteria approach would enhance the project\u0026rsquo;s accuracy and reliability. Incorporating factors such as soil temperature, which can be correlated with LST and validated through ground-truthing, would further improve the results. Validating each method will increase confidence in decision making.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCheng, G., Xie, X., Han, J., Guo, L., \u0026amp; Xia, G. S. (2020). Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities. \u003cem\u003eIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/JSTARS.2020.3005403\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eDiPippo, R. (2015). Geothermal Power Plants: Principles, Applications, Case Studies and Environmental Impact: Fourth Edition. In \u003cem\u003eGeothermal Power Plants: Principles, Applications, Case Studies and Environmental Impact: Fourth Edition\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/C2014-0-02885-7\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eFanning, K. A., Pilson, M., Kato, K., Ueda, A., Mogi, K., Nakazawa, H., Shimizu, K., Musić, S., Filipović-Vinceković, N., Sekovanić, L., Zhu, C., Lu, P., Zheng, Z., Ganor, J., Alekseyev, V. A., Medvedeva, L. S., Prisyagina, N. I., Meshalkin, S. S., Balabin, A. I., \u0026hellip; Moore, J. (2017). SUB-SURFACE GEOLOGY AND HYDROTHERMAL ALTERATION OF WELLS LA-9D AND LA-10D OF ALUTO LANGANO GEOTHERMAL Fig. 1: Location map of the Main Ethiopian Rift (MER). Geochimica et Cosmochimica Acta, \u003cem\u003e17\u003c/em\u003e(1).\u003c/li\u003e\n\u003cli\u003eGlassley, W. E. (2010). Geothermal energy: Renewable energy and the environment. In \u003cem\u003eGeothermal Energy: Renewable Energy and the Environment\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1201/EBK1420075700\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez, D. L., \u0026amp; Rodr\u0026iacute;guez-Gonz\u0026aacute;lvez, P. (2019). Detection of geothermal potential zones using remote sensing techniques. Remote Sensing, \u003cem\u003e11\u003c/em\u003e(20). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs11202403\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eHeasler, H. P., Jaworowski, C., \u0026amp; Foley, D. (2019). Geothermal systems and monitoring hydrothermal features. In \u003cem\u003eGeological Monitoring\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1130/2009.monitoring(05\u003c/span\u003e\u003c/span\u003e)\u003c/li\u003e\n\u003cli\u003eOmwenga, B. M. (2020). Geothermal Well Site Suitability Selection Using Geographic Information Systems (GIS) and Remote Sensing: Case Study of the Eburru Geothermal Field. \u003cem\u003e45th Workshop on Geothermal Reservoir Engineering\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e, 1\u0026ndash;6.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eWebsites:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnited States Geological Survey (USGS) and SRTMGDEM using Earth Explorer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://search.earthdata.nasa.gov/search\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eDigital Elevation Model (DEM) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dwtkns.com/srtm30m/\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApplications:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ewww.rstudio.org\u003c/p\u003e\n\u003cp\u003ewww.qgis.org\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Geothermal Energy, Geographical Information System, Tanzania","lastPublishedDoi":"10.21203/rs.3.rs-5648410/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5648410/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAmid a global push for sustainable energy solutions, Tanzania is emerging as a frontrunner in exploring renewable energy resources. The nation's strong commitment to combating climate change has driven extensive research into renewable energy alternatives sources such as geothermal, solar, and wind energy. Traditionally dependent on hydropower, Tanzania's energy sector has faced disruptions due to decreasing water levels and technical difficulties with hydropower plants. Geothermal energy, however, has emerged as a promising alternative. While conventional geological methods for detecting geothermal zones are known for their accuracy, they come with substantial costs. Integrating these methods with remotely sensed data has led to significant improvements in efficiency and precision. In response to the challenges the government of Tanzania has launched ambitious plans to advance geothermal exploration and extraction by establishing Tanzania Geothermal Development Company (TGDC) in 2014. This study employs advanced Geographical Information System (GIS) tools and remotely sensed data to identify geothermal potential zones in Mbeya, Rungwe, and Kyela districts. The methodology includes spatial analysis, by generating flow direction maps with major and minor faults, then overlaying hot springs to create a geological suitability factor. The Normalized Difference Vegetation Index (NDVI) threshold method was used to generate thermal elements. Additionally, the Random Forest method was then applied to create a land cover suitability map. Thereafter classifying regions into three primary categories: most suitable, moderately suitable, and least suitable. The study\u0026rsquo;s results were compared with existing field survey data to validate the effectiveness of the GIS based approach. To ensure high reliability, this research proposes validating remotely detected potential zones using various models, aiming for a confidence level of at least 95%. These efforts lay the foundation for unlocking Tanzania's geothermal potential, paving the way for a transformative shift towards sustainable energy leadership both within Africa and globally.\u003c/p\u003e","manuscriptTitle":"Exploration of Geothermal Potential in Mbeya Region by Using Remotely Sensed Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-23 04:02:17","doi":"10.21203/rs.3.rs-5648410/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7cbc94c0-2828-4e62-b44a-e1195b2ec639","owner":[],"postedDate":"January 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-23T04:02:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-23 04:02:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5648410","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5648410","identity":"rs-5648410","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}