Water Quality Indices and Impact of Land Use/Land Cover Changes on Freshwater System in Ibadan North-West, Nigeria Using Remote Sensing Techniques

Water Quality Indices and Impact of Land Use/Land Cover Changes on Freshwater System in Ibadan North-West, Nigeria Using Remote Sensing Techniques | 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 Water Quality Indices and Impact of Land Use/Land Cover Changes on Freshwater System in Ibadan North-West, Nigeria Using Remote Sensing Techniques Kawiyu Omomayowa Rafiu, Akpofure Miller Fakpor This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7840911/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 The degradation of freshwater systems due to rapid urbanization has become a critical environmental concern in many developing urban centers. Assessing the spatial and temporal dynamics of land use/land cover (LULC) changes is essential for understanding their implications on surface water quality. This study assessed water quality indices to examine the impact of land use/land cover (LULC) change on the freshwaters in Ibadan North-West, Nigeria, using remote sensing techniques. Landsat-7 ETM+ (2014) and Landsat-8/9 OLI/TIRS (2023) imagery were analyzed to assess spatio-temporal LULC changes and extract water indices. Water quality was assessed using the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI). Over a period of 9 years, water bodies declined from 89 to 9 hectares, and vegetation cover reduced from 1,315 to 359 hectares. In contrast, built-up areas expanded from 1,450 to 2,486 hectares, reflecting intensified urbanization. The MNDWI values in 2014 ranged from − 0.3687 to 0.0744, while in 2023 they declined to -0.2282 to -0.0129. NDWI values, already negative in 2014 (ranging from − 0.3430 to -0.2812), declined further in 2023 (ranging from − 0.0101 to -0.0612). These shifts reflect a diminishing presence of strong water signals across both years. Change detection further reveals that NDWI recorded a total water loss of 368.38 ha, while MNDWI showed a smaller loss of 77.21 ha. The contrast highlights that while NDWI detected broader signals, including turbid and mixed-pixel zones, MNDWI more accurately delineated freshwater systems by suppressing noise from surrounding land covers. The findings reveal a substantial reduction in both the extent and spectral quality of freshwater systems over the 9-year period, driven by land conversion and urban encroachment. land use land cover change freshwater systems NDWI MNDWI Landsat imagery urbanization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1.0 INTRODUCTION Surface freshwater refers to inland water bodies such as rivers, lakes, reservoirs, and streams that exist above ground and are directly influenced by climatic and anthropogenic factors (Syeed et al., 2023 ). Unlike groundwater, which is stored beneath Earth's surface, surface freshwater is more directly exposed to environmental influences like precipitation, evaporation, and human activities. It is critical in ecosystems, agriculture, the drinking water supply, and industry. Among the freshwater resources, rivers are critical to the survival of communities worldwide. Rivers play an essential role in sustaining local ecosystems and human communities by providing freshwater, regulating floods, supporting nutrient cycling, enabling transportation, and offering recreational opportunities (Heusinkveld et al., 2024 ). The availability of safe and dependable water sources remains fundamental to development, particularly in regions undergoing rapid urban transformation where land use changes threaten ecosystem stability (Lynch, 2023). Globally, freshwater systems are increasingly impacted by anthropogenic pressures such as urban expansion, pollution, climate variability, and land conversion. These stressors disrupt hydrological processes, reduce water quality, and impair the ecological functioning of river systems (Heusinkveld et al., 2024 ; Lynch, 2023). Recent findings highlight those multiple stressors, including altered flow regimes, nutrient loading, and habitat degradation, often interact to intensify risks to freshwater biodiversity in urban areas (Heusinkveld et al., 2024 ). In Ibadan North-West, Nigeria, rapid urbanization, population and industrial growth, and other anthropogenic activities drive changes in land use/land cover (Tella & Balogun, 2020 ). The region experiences mass generation of domestic, municipal, and industrial wastes, which are often discharged into surrounding water bodies, making them unfit for human use and threatening aquatic life and diversity. Also, converting natural land covers to built-up areas raises serious environmental concerns. Urbanization has increased impervious surfaces, altering water runoff patterns and contributing to environmental problems. Studies have shown that unregulated urban expansion often leads to deforestation, loss of wetlands, increased surface runoff, and urban heat island effects, worsening environmental degradation (Adepoju et al., 2019 ). Using remote sensing techniques, Oyinloye & Oloukoi ( 2018 ) conducted a spatio-temporal analysis of urban growth and its environmental impacts in Ibadan North-West, Nigeria. This research observed the conversion of vegetative land to built-up areas and raised concerns about flood vulnerability, water pollution, and declining biodiversity. Studies such as Zhang et al. ( 2021 ), Said and Khan ( 2021 ), Sultana and Dewan (2021), Ahmed et al. ( 2023 ) have shown the effectiveness of using water quality indices such as Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) in remote sensing to assess surface water quality influenced by LULC changes. However, few studies have fully explored the use of water quality indices to provide an up-to-date assessment of the freshwater systems in the study area. This work is meant to provide further insight by employing a spatio-temporal approach that not only maps surface water features with NDWI and MNDWI but also links observed patterns to recent land transitions and their implications for water sustainability. By addressing this gap with 2014 and 2023 datasets, the study introduces a more contemporary perspective that can inform context-specific urban and environmental policies in Ibadan North-West. A data-driven inquiry utilizing remote sensing and GIS is essential to precisely map and quantify LULC changes over time and also assess its impact on the quantity and quality of freshwater bodies in the study area. These are vital to understanding the environmental and natural resource management issues affecting the region and to developing sustainable land use policies and strategies. Remote sensing and Geographic Information System (GIS) techniques offer effective means to assess LULC changes and their environmental impacts over time (Yuan et al., 2021 ). This study builds on this framework to examine the spatio-temporal dynamics of land cover transformation and its consequences for surface water systems in Ibadan North-West. Specifically, it aims to classify and analyze LULC changes across a decade, extract and monitor surface water variations using NDWI and MNDWI, and assess how shifts in land use influence water resources. The findings are expected to provide evidence for planning interventions, contribute to environmental monitoring, and support the development of sustainable land and water resource policies in rapidly urbanizing areas. 2.0 MATERIALS AND METHODS 2.1 Study Area Ibadan North-West Local Government Area (LGA) is situated in the southwestern part of Nigeria, specifically within the city of Ibadan, one of the largest cities in the country. The geographical coordinates for the study area lies approximately between latitudes 7° 22ꞌ to 7° 26ꞌ N and longitudes 3° 50ꞌ to 3° 56ꞌ E (Fig. 1 ). The area exhibits diverse land use patterns, ranging from residential and commercial areas to agricultural and natural spaces. The study area encompasses a mix of urban and peri-urban landscapes, reflecting the dynamic nature of development in the region. The spatial extent covers several square kilometers, allowing for a comprehensive analysis of land use changes over time. The significance of Ibadan North-West as the focus of the research lies in its role as a representative of urbanization challenges faced by many rapidly growing cities in developing countries. The area's susceptibility to land use changes, coupled with its environmental changes, makes it an ideal case study for understanding broader urban development trends. The freshwater river receives effluents discharged from the cassava processing site, waste water from domestic activities from the neighboring homes. Ibadan North-West is blessed with nature with vast areas for tourist attraction. The Water Works is surrounded by small scales industries such as metals scrap dealers and mechanic workshops. The river is also dam for treatment to supply potable water to the people in Ibadan. Eleyele reservoir is an essential resource for fishery, domestic water supply and flood control; the reservoir is fast being degraded due to various anthropogenic activities around its catchments (Bolaji, 2010 ; Olanrewaju et al., 2017 ). 2.2 Data Collection This study primarily utilized spatial datasets sourced from the United States Geological Survey (USGS) Earth Explorer platform to support its research objectives. To enhance analytical precision and ensure consistency, the data were subjected to systematic pre-processing. All spatial layers were subsequently projected to the Universal Transverse Mercator (UTM) coordinate system to facilitate accurate geospatial analysis. The core datasets employed include Landsat-7 ETM + imagery for 2014 and Landsat-8/9 OLI/TIRS imagery for 2023 (see Table 1 ). Table 1 Datasets utilized S/N Satellite Date Acquired Resolution Path/Row Source 1. Landsat-7 ETM+ 11/01/2014 30m 191/055 USGS 2. Landsat-8/9 OLI/TIRS 21/02/2023 30m 191/055 USGS 2.3 Pre-Processing Stage In remote sensing, pre-processing is a crucial step used to correct sensor- and platform-specific distortions, ensuring the data is accurate, consistent, and suitable for analysis over space and time. These operations, often referred to as image restoration and rectification, are necessary to remove geometric and radiometric distortions caused by variations in scene illumination, atmospheric conditions, sensor noise, or acquisition geometry. Radiometric correction was performed to convert the raw digital numbers (DNs) recorded by the sensor into meaningful physical quantities such as top-of-atmosphere (TOA) reflectance. This conversion accounts for solar elevation angle, Earth–sun distance, and sensor-specific calibration parameters (gain, bias) extracted from the Landsat metadata file. This step was implemented in ENVI 5.3, which allows direct conversion of Landsat images to TOA reflectance. Geometric correction involved projecting all imagery to UTM Zone 31N (WGS 1984 datum) and clipping to the study boundary using ArcGIS Pro. To ensure consistency across datasets, cloud masking was conducted in Google Earth Engine (GEE) using the QA_PIXEL band with a cloud probability threshold of 5%, creating cloud-free composites for each target year. A false colour composite (bands 5-4-3 for Landsat 8/OLI and 4-3-2 for Landsat 7/ETM+) was also generated to aid visual interpretation and training data collection. The study boundary was delineated using Google Earth Pro, and the pre-processed images were cross-referenced with Sentinel-2 imagery and field GPS points to validate visible features and improve classification reliability. This comprehensive pre-processing ensured that the satellite datasets used were geometrically aligned and radiometrically consistent, enabling robust temporal comparison and analysis. 2.4 Image Classification (Supervised) Using supervised classification, Landsat ETM + and OLI/TIRS images were classified to identify the land uses and the marshy area (including water). Supervised classification uses the spectral signature defined in the training set. For example, it determines what each class resembles most in the training set. The common supervised classification algorithms are maximum likelihood and minimum-distance classification. The bands utilized for analysis are Landsat 7 ETM+ (5, 4, 3) and Landsat 8/9 OLI/TIRS (5, 4, 3). The study area was extracted from the false color composite. The images retained their original pixel size despite the chances that there might be a difference in classification accuracies. The process of image classification assigns the pixels of a raster image to predefined land cover classes. The basic way of image classification is by visual interpretation, where tone, texture, size, shape, and association are considered (Qasim et al., 2011 ). ArcGIS Pro software performs supervised classification using Maximum Likelihood Classifier (MLC). The maximum likelihood classification algorithm was selected because it can incorporate the training samples' statistics before assigning the land covers to each pixel. The maximum likelihood classification is a parametric classifier that assumes that individual class data are distributed normally. It evaluates both the variance and covariance of the category spectral response patterns when classifying an unknown pixel; it requires more computation per pixel. The maximum likelihood classification requires sufficient spectral training sample data for each class to accurately estimate the statistics the classification algorithm needs. Using the signature files of each of the classes, supervised classification was carried out using maximum likelihood classification which was ground-truthed using GPS coordinates obtained from the field and a high-resolution satellite image (Sentinel-2). The following LULC classes were used; Water body, Built-up and Vegetation (Table 2 ). The distinct separability of these classes is illustrated in the spectral signatures derived from Landsat 7 ETM + and Landsat 8/9 OLI/TIRS (Fig. 2 ), which provided the foundation for the classification process. Table 2 Land Use/ Land Cover classifications in the study area S/N Features Description 1. Water body Represents areas covered by water, freshwater river system and freshwater bodies/lakes 2. Built up Encompasses regions with man-made structures, including residential, commercial, and industrial developments. 3. Vegetation Includes areas covered by natural plant life, such as forests, grasslands, or agricultural fields. 2.5 Water Quality Assessment Using Remote Sensing Indices Water quality was assessed indirectly through spectral indices, specifically the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI). These indices provide proxy information about the clarity and presence of water, with values ranging from − 1 to + 1. Positive values generally denote open water surfaces, while negative values indicate non-water features such as soil, built-up land, or vegetation. Water quality assessment is necessary for understanding and managing aquatic ecosystems. Spectral indices, especially the Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Water Index (NDWI) are useful instruments that provide insights into water quality indicators by utilizing data from remote sensing. According Li et al. ( 2023 ), the Modified Normalized Difference Water Index (MNDWI) precisely defines water bodies and detects alterations in surface water dynamics. It is computed as per Eq. ( 1 ): $$\:MNDWI=\frac{(Green\:-\:SWIR)}{(Green\:+\:SWIR)}$$ 1 Green represents the pixel values from the green band, while SWIR represents the pixel values from the short-wave infrared band. Similarly, Normalized Difference Water Index (NDWI) widely applies to water quality assessment, excelling in discriminating water from other land cover types. Zhang et al. ( 2021 ) underscore its utility as a potential proxy for remote sensing applications in mapping water quality indexes, formulated in Eq. ( 2 ): $$\:NDWI=\frac{(Green\:-\:NIR)}{(Green\:+\:NIR)}$$ 2 Green represents the pixel values from the green band, while NIR represents the pixel values from the near infrared band. These spectral indices extend to evaluating river water quality, as demonstrated by Najafzadeh and Basirian ( 2023 ), who showcase the efficacy of remote sensing technologies with MNDWI and Normalized Difference Water Index (NDWI). Traditional, often costly methods are complemented by the efficiency of these indices, providing timely and accurate information. In surface water quality assessment, Sultana and Dewan (2021) introduce a reflectance-based water quality index, incorporating spectral indices like Normalized Difference Water Index (NDWI). This semi-quantitative measure enables relative evaluation of overall water quality changes, enhancing assessment accuracy. In their study on the Kali River, data-driven approaches using spectral indices, exemplified by Said and Khan ( 2021 ) demonstrate the potential for accurate water quality index estimation through Normalized Difference Water Index and machine learning techniques. Ahmed et al. ( 2023 ) research on Tigris River quality monitoring combines field-based sampling with remote sensing, leveraging MNDWI and Normalized Difference Water Index for enhanced precision in diverse regions. The remote sensing data analysis yields indices enabling efficient monitoring of changes in water bodies. Versatile applications include change detection and river water quality assessment. Integrating spectral indices with advanced techniques represents a contemporary approach to understanding and managing water resources. 2.6 Change Detection Analysis Change detection assesses land feature variations over time, utilizing classified images to determine area extents and observing temporal progressions. This involves a comparative analysis through independently produced classifications, employing a pixel-by-pixel mathematical combination of different data. It identifies differences in an object or phenomenon's state by observing it at different times. Timely and accurate change detection of Earth's surface features establish a foundation for understanding relationships and interactions between human and natural phenomena. Widely applied in remote sensing, it serves as an effective means to monitor environmental changes. 2.7 Reclassification Reclassifying attributes in GIS and database software involves creating a new categorical attribute by classifying features based on existing attributes or criteria like location. Reclassification serves purposes such as swiftly updating cells with new information, compiling data for suitability analyses, and eliminating unnecessary details by reclassifying cells as No Data. The primary aim is often to simplify output data for easier interpretation. Reclassification tools alter cell values to alternative values using various methods. You can reclassify individual values or groups simultaneously through alternative fields, based on criteria like specified intervals (e.g., grouping values into 10 intervals) or by area (e.g., grouping values into 10 groups with the same cell count). These tools are designed for efficiently changing numerous values in an input raster to desired, specified, or alternative values. The steps followed in this study, from data collection to water quality analysis and change detection, are shown in Fig. 3 . 3.0 RESULTS AND DISCUSSION 3.1 Land Use/Land Cover (LULC) Dynamics (2014–2023) In Fig. 4 , the study area spans 2854ha. During the investigated periods, water body covered 89ha, representing 3.12% in 2014, and 9ha, representing the proportion of 0.32% in 2023. Similarly, built up area covered 1450ha (50.81%), and 2486ha (87.11%) in 2014 and 2023 respectively. The analysis of Landsat-7 ETM+ (2014) and Landsat-8/9 OLI/TIRS (2023) imagery provided a comprehensive view of land use and land cover changes in Ibadan North-West over nine years. The classified LULC categories—water bodies, vegetation, and built-up areas—exhibited notable transitions (Figs. 4 and 5 ). Water bodies significantly reduced from 89 hectares in 2014 to 9 hectares in 2023, suggesting extensive surface water loss. This loss corresponds to visible drying up or conversion of water surfaces, likely due to infrastructural expansion or climate variability. Vegetation cover also experienced a significant decline from 1,315 to 359 hectares. This pattern signals increased anthropogenic pressure on vegetated landscapes, likely through deforestation or conversion for urban development. In contrast, built-up areas surged from 1,450 hectares to 2,486 hectares, underscoring the district's rapid urbanization and associated land conversion. This trend is consistent with broader urban expansion patterns observed across major Nigerian cities (Adepoju et al., 2019 ). The LULC changes from 2014 to 2023 emphasize the dynamic nature of the Ibadan North-West region, which is susceptible to anthropogenic influences. A significant drop in water body coverage and rapid built-up area growth signifies an urbanization shift, likely fueled by increased human activities and population growth. The dominance of built-up areas in 2023 raises environmental sustainability concerns. Urban expansion often results in habitat loss, increased pollution, and altered hydrological patterns, threatening the overall ecological balance. The declining vegetation cover worsens environmental concerns, impacting aesthetic value and contributing to air and water pollution. Additionally, vegetation loss may disrupt the natural ecosystem and local fauna. 3.2 Water Quality Indices In 2014, MNDWI values ranged from − 0.3687 to 0.0744, reflecting the presence of some clearer water bodies amid mixed or turbid areas (Fig. 6 ). By 2023, these values dropped to between − 0.2282 and − 0.0129, indicating a reduced water signal and potential decline in water clarity or extent. Similarly, as shown in Fig. 7, NDWI values which were already negative in 2014 (ranging from − 0.3430 to -0.2812), declined further in 2023 (from − 0.0101 to -0.0612). These negative values point to increasingly weaker water reflectance, likely due to land conversion, pollution, sedimentation, or reduced surface water coverage. 3.3 Change Detection Analysis: NDWI vs. MNDWI The change detection analysis (Figs. 8 and 9 ) highlights the spatial dynamics of water-related changes. NDWI recorded a water loss of approximately 368.38 hectares, while MNDWI captured a lower figure of 77.21 hectares. This discrepancy reflects the indices' sensitivity and interpretation differences. NDWI tends to register broader changes, including turbid or mixed pixels that may not represent pure water bodies. Therefore, its higher water loss figure includes marginal or degraded water zones. In contrast, MNDWI works better in urban areas because it reduces confusion with built-up surfaces. While NDWI often overestimates water by including mixed or turbid pixels, MNDWI gives a smaller but clearer estimate of actual surface water. 3.4 Spatial and Temporal Interpretation Spatially, the change maps show a concentration of water loss in the southern and western parts of the district (Fig. 9 ). Gains in water presence were minimal and sporadically distributed. Areas classified as stable dominated the landscape but with minor reductions between the two periods. Temporally, these shifts correspond with heightened urban expansion, particularly between 2014 and 2023, when population growth and infrastructure development in Ibadan intensified. NDWI captured a broad water loss, especially around areas where LULC is changing fast - likely due to urban development. MNDWI is better at picking out clearer water in built-up areas and shows fewer losses but still points to shrinking water surfaces. Both indices tell the same story: surface water is disappearing, which is likely tied to how land is being used and changed across the study area. showing surface freshwater disappearing. 3.5 Implications for Water Resource Management The results indicate a strong link between LULC changes and surface freshwater quality. The decline in both the quantity and spectral quality of surface water suggests increased pressure on hydrological systems, with implications for water availability, urban drainage, and ecosystem health. As urban land encroaches on vegetated and water-covered zones, runoff, sediment load, and potential pollution rise, exacerbating the degradation of surface freshwater resources. The spatio-temporal analysis demonstrates a clear trajectory of urban-driven land transformation at the expense of natural water and vegetation systems. NDWI and MNDWI indices, when used in tandem, reveal complementary perspectives on water presence and clarity. The integration of these findings provides empirical evidence for policy formulation on sustainable urban development and freshwater conservation in Ibadan North-West. 3.6 Discussion The results of this study confirm that land use and land cover change in Ibadan North-West is strongly shaped by rapid urbanization. Water bodies declined sharply, while built-up areas expanded between 2014 and 2023, showing a direct conversion of natural landscapes into residential and commercial land. Similar observations have been reported in other Nigerian cities, where population growth and infrastructure development put continuous pressure on surface waters and vegetation (Adepoju et al., 2019 ). The NDWI and MNDWI analyses both highlight the declining condition of surface water, though with differences in sensitivity. NDWI captured larger areas of water loss, including turbid or mixed pixels, while MNDWI presented a more conservative estimate by filtering out built-up background noise. Together, these indices give a clearer picture of water degradation, showing not just the loss of extent but also weakening water signals that may point to sedimentation, pollution, or shrinking channels. The link between built-up expansion and water loss is particularly important. Urban encroachment reduces infiltration, increases runoff, and channels pollutants into rivers and streams. The decline in vegetation further weakens natural buffers, leaving freshwater more exposed to stress. If unchecked, these changes could affect water availability, increase flood risks, and damage the ecological integrity of urban freshwater systems. Placing these findings within the wider regional context, the patterns in Ibadan North-West align with broader urban trends in West Africa, where rapid growth often outpaces land-use planning. Without stronger planning frameworks, surface water will continue to decline, threatening both human and ecological wellbeing. 4.0: CONCLUSION AND RECOMMENDATIONS This study examined land use and land cover changes in Ibadan North-West between 2014 and 2023. The results show that water bodies and vegetation have declined, while built-up areas have increased. These changes reflect growing urban pressure on the environment, which could affect water resources and ecological balance in the area. To address these issues, urban development should be better planned to reduce environmental stress. Protecting green spaces and improving water management are important steps. Regular monitoring using indices such as NDWI and MNDWI can help track changes in water resources and guide decision-making. Future studies could also link satellite observations with socio-economic data to better explain the drivers of land cover change and support stronger policy actions. Declarations Funding: The authors declared that this study has received no financial support. Author Contribution K.O. planned the study, collected and analyzed the data, prepared the figures, and wrote the main draft. A.M. helped with literature review, and editing. K.O. is the corresponding author and took the lead in the final write-up. Both authors read and approved the final manuscript. Data Availability The satellite images used in this study were downloaded from the USGS Earth Explorer website ( [https://earthexplorer.usgs.gov/](https:/earthexplorer.usgs.gov) ). 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Water 13(22):3250 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7840911","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":548011625,"identity":"d1781b8e-d5a4-4158-b4f2-638446dabc4c","order_by":0,"name":"Kawiyu Omomayowa Rafiu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYLCCCgMGORB94AHRWs4YMBiDtSQQr4WBIbEBxCBKC9/x3ocfDhQcTp8fdvgh0BY7Od0GAlokzxw3ljhgcDh34+00A6CWZGOzAwS0GNxIY5D+ANIyOwGk5UDiNiK0MP8A2pJuODv9A9Fa2EAOS5CXziHSFskzx9gsDhikG26Qzik4kGBAhF/4jrcx3zjwx1pefnb65g8fKuzkCGphgCkwADMMCClH1iLfQIzqUTAKRsEoGJEAAEvvSungWFw1AAAAAElFTkSuQmCC","orcid":"","institution":"Federal University of Technology, Akure","correspondingAuthor":true,"prefix":"","firstName":"Kawiyu","middleName":"Omomayowa","lastName":"Rafiu","suffix":""},{"id":548011626,"identity":"982ef10a-2993-4fc1-90dd-8049ff248bde","order_by":1,"name":"Akpofure Miller Fakpor","email":"","orcid":"","institution":"Federal University of Technology, Akure","correspondingAuthor":false,"prefix":"","firstName":"Akpofure","middleName":"Miller","lastName":"Fakpor","suffix":""}],"badges":[],"createdAt":"2025-10-12 13:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7840911/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7840911/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96431090,"identity":"455e04a0-80b2-40d0-aebb-72c83575f353","added_by":"auto","created_at":"2025-11-21 04:01:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86663,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIbadan North-West L.G.A. Nigeria (Highlighted in Red)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7840911/v1/946b934fdfc9b8f4ef06fc18.jpg"},{"id":96431091,"identity":"69aee0a0-f74a-4a45-a193-9836d5b04808","added_by":"auto","created_at":"2025-11-21 04:01:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77840,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpectral Signatures of Land Use/Land Cover Classes for Landsat 7 and Landsat 8\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7840911/v1/1b501dab293768ed866b3799.png"},{"id":96431094,"identity":"1998d728-6d4f-4878-8a4c-176c80360b43","added_by":"auto","created_at":"2025-11-21 04:01:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25586,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMethodology Flowchart\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7840911/v1/5c919d0414caf70107f7cfca.png"},{"id":96454637,"identity":"28440dcf-981b-449d-90aa-f57cbc5b822a","added_by":"auto","created_at":"2025-11-21 10:02:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14963,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of Land Use/Land Cover for 2014 and 2023\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7840911/v1/2ce04a12976368caa93fcd8e.png"},{"id":96454034,"identity":"43f2ad08-6138-42e9-b576-434d7fca1328","added_by":"auto","created_at":"2025-11-21 10:02:15","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45913,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMap of Land Use/Land Cover changes in Ibadan North-West LGA for 2014 and 2023\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7840911/v1/6de902450b2909351d3f63fa.jpg"},{"id":96455323,"identity":"0043ded8-067c-4fc9-89e6-631e76954000","added_by":"auto","created_at":"2025-11-21 10:03:58","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":58793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModified Normalized Difference Water Index for 2014 and 2023\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7840911/v1/c371ebcb8d39707e1471b72a.jpg"},{"id":96455049,"identity":"57b7d1b8-b9ee-4131-beda-75af86e9a0ad","added_by":"auto","created_at":"2025-11-21 10:03:27","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":52358,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNormalized Difference Water Index for 2014 and 2023\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7840911/v1/a42f0c831a4f08e319820826.jpg"},{"id":96431098,"identity":"ddfb327a-a234-4766-b435-a39a7a62d6ca","added_by":"auto","created_at":"2025-11-21 04:01:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":17597,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eComparison of NDWI and MNDWI Water Change Classification (2014–2023)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7840911/v1/5eb4caf87a2b8f1c8afbbef9.png"},{"id":96431097,"identity":"54e752b0-79fc-4966-b377-0c29a3c13c07","added_by":"auto","created_at":"2025-11-21 04:01:11","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":81988,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNDWI and MNDWI-Based Classification of Water Body Changes (2014–2023)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7840911/v1/2983d59b487c2cd3551e0cfd.png"},{"id":96456855,"identity":"41648812-2698-406d-be2e-be8be9d9938e","added_by":"auto","created_at":"2025-11-21 10:07:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1097629,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7840911/v1/cd29346c-e813-43ed-a82f-8ab0237bddb3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Water Quality Indices and Impact of Land Use/Land Cover Changes on Freshwater System in Ibadan North-West, Nigeria Using Remote Sensing Techniques","fulltext":[{"header":"1.0 INTRODUCTION","content":"\u003cp\u003eSurface freshwater refers to inland water bodies such as rivers, lakes, reservoirs, and streams that exist above ground and are directly influenced by climatic and anthropogenic factors (Syeed et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Unlike groundwater, which is stored beneath Earth's surface, surface freshwater is more directly exposed to environmental influences like precipitation, evaporation, and human activities. It is critical in ecosystems, agriculture, the drinking water supply, and industry. Among the freshwater resources, rivers are critical to the survival of communities worldwide. Rivers play an essential role in sustaining local ecosystems and human communities by providing freshwater, regulating floods, supporting nutrient cycling, enabling transportation, and offering recreational opportunities (Heusinkveld et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The availability of safe and dependable water sources remains fundamental to development, particularly in regions undergoing rapid urban transformation where land use changes threaten ecosystem stability (Lynch, 2023).\u003c/p\u003e\u003cp\u003eGlobally, freshwater systems are increasingly impacted by anthropogenic pressures such as urban expansion, pollution, climate variability, and land conversion. These stressors disrupt hydrological processes, reduce water quality, and impair the ecological functioning of river systems (Heusinkveld et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lynch, 2023). Recent findings highlight those multiple stressors, including altered flow regimes, nutrient loading, and habitat degradation, often interact to intensify risks to freshwater biodiversity in urban areas (Heusinkveld et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Ibadan North-West, Nigeria, rapid urbanization, population and industrial growth, and other anthropogenic activities drive changes in land use/land cover (Tella \u0026amp; Balogun, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The region experiences mass generation of domestic, municipal, and industrial wastes, which are often discharged into surrounding water bodies, making them unfit for human use and threatening aquatic life and diversity. Also, converting natural land covers to built-up areas raises serious environmental concerns. Urbanization has increased impervious surfaces, altering water runoff patterns and contributing to environmental problems.\u003c/p\u003e\u003cp\u003eStudies have shown that unregulated urban expansion often leads to deforestation, loss of wetlands, increased surface runoff, and urban heat island effects, worsening environmental degradation (Adepoju et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Using remote sensing techniques, Oyinloye \u0026amp; Oloukoi (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) conducted a spatio-temporal analysis of urban growth and its environmental impacts in Ibadan North-West, Nigeria. This research observed the conversion of vegetative land to built-up areas and raised concerns about flood vulnerability, water pollution, and declining biodiversity. Studies such as Zhang et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Said and Khan (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Sultana and Dewan (2021), Ahmed et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have shown the effectiveness of using water quality indices such as Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) in remote sensing to assess surface water quality influenced by LULC changes. However, few studies have fully explored the use of water quality indices to provide an up-to-date assessment of the freshwater systems in the study area. This work is meant to provide further insight by employing a spatio-temporal approach that not only maps surface water features with NDWI and MNDWI but also links observed patterns to recent land transitions and their implications for water sustainability. By addressing this gap with 2014 and 2023 datasets, the study introduces a more contemporary perspective that can inform context-specific urban and environmental policies in Ibadan North-West.\u003c/p\u003e\u003cp\u003eA data-driven inquiry utilizing remote sensing and GIS is essential to precisely map and quantify LULC changes over time and also assess its impact on the quantity and quality of freshwater bodies in the study area. These are vital to understanding the environmental and natural resource management issues affecting the region and to developing sustainable land use policies and strategies. Remote sensing and Geographic Information System (GIS) techniques offer effective means to assess LULC changes and their environmental impacts over time (Yuan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study builds on this framework to examine the spatio-temporal dynamics of land cover transformation and its consequences for surface water systems in Ibadan North-West. Specifically, it aims to classify and analyze LULC changes across a decade, extract and monitor surface water variations using NDWI and MNDWI, and assess how shifts in land use influence water resources. The findings are expected to provide evidence for planning interventions, contribute to environmental monitoring, and support the development of sustainable land and water resource policies in rapidly urbanizing areas.\u003c/p\u003e"},{"header":"2.0 MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area\u003c/h2\u003e\u003cp\u003eIbadan North-West Local Government Area (LGA) is situated in the southwestern part of Nigeria, specifically within the city of Ibadan, one of the largest cities in the country. The geographical coordinates for the study area lies approximately between latitudes 7\u0026deg; 22ꞌ to 7\u0026deg; 26ꞌ N and longitudes 3\u0026deg; 50ꞌ to 3\u0026deg; 56ꞌ E (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The area exhibits diverse land use patterns, ranging from residential and commercial areas to agricultural and natural spaces. The study area encompasses a mix of urban and peri-urban landscapes, reflecting the dynamic nature of development in the region. The spatial extent covers several square kilometers, allowing for a comprehensive analysis of land use changes over time.\u003c/p\u003e\u003cp\u003eThe significance of Ibadan North-West as the focus of the research lies in its role as a representative of urbanization challenges faced by many rapidly growing cities in developing countries. The area's susceptibility to land use changes, coupled with its environmental changes, makes it an ideal case study for understanding broader urban development trends. The freshwater river receives effluents discharged from the cassava processing site, waste water from domestic activities from the neighboring homes. Ibadan North-West is blessed with nature with vast areas for tourist attraction. The Water Works is surrounded by small scales industries such as metals scrap dealers and mechanic workshops. The river is also dam for treatment to supply potable water to the people in Ibadan. Eleyele reservoir is an essential resource for fishery, domestic water supply and flood control; the reservoir is fast being degraded due to various anthropogenic activities around its catchments (Bolaji, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Olanrewaju et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Collection\u003c/h2\u003e\u003cp\u003eThis study primarily utilized spatial datasets sourced from the United States Geological Survey (USGS) Earth Explorer platform to support its research objectives. To enhance analytical precision and ensure consistency, the data were subjected to systematic pre-processing. All spatial layers were subsequently projected to the Universal Transverse Mercator (UTM) coordinate system to facilitate accurate geospatial analysis. The core datasets employed include Landsat-7 ETM\u0026thinsp;+\u0026thinsp;imagery for 2014 and Landsat-8/9 OLI/TIRS imagery for 2023 (see 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\u003eDatasets utilized\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\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\u003eSatellite\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDate Acquired\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResolution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePath/Row\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\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\u003eLandsat-7 ETM+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11/01/2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e191/055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUSGS\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\u003eLandsat-8/9 OLI/TIRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21/02/2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e191/055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUSGS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Pre-Processing Stage\u003c/h2\u003e\u003cp\u003eIn remote sensing, pre-processing is a crucial step used to correct sensor- and platform-specific distortions, ensuring the data is accurate, consistent, and suitable for analysis over space and time. These operations, often referred to as image restoration and rectification, are necessary to remove geometric and radiometric distortions caused by variations in scene illumination, atmospheric conditions, sensor noise, or acquisition geometry.\u003c/p\u003e\u003cp\u003eRadiometric correction was performed to convert the raw digital numbers (DNs) recorded by the sensor into meaningful physical quantities such as top-of-atmosphere (TOA) reflectance. This conversion accounts for solar elevation angle, Earth\u0026ndash;sun distance, and sensor-specific calibration parameters (gain, bias) extracted from the Landsat metadata file. This step was implemented in ENVI 5.3, which allows direct conversion of Landsat images to TOA reflectance.\u003c/p\u003e\u003cp\u003eGeometric correction involved projecting all imagery to UTM Zone 31N (WGS 1984 datum) and clipping to the study boundary using ArcGIS Pro. To ensure consistency across datasets, cloud masking was conducted in Google Earth Engine (GEE) using the QA_PIXEL band with a cloud probability threshold of 5%, creating cloud-free composites for each target year. A false colour composite (bands 5-4-3 for Landsat 8/OLI and 4-3-2 for Landsat 7/ETM+) was also generated to aid visual interpretation and training data collection.\u003c/p\u003e\u003cp\u003eThe study boundary was delineated using Google Earth Pro, and the pre-processed images were cross-referenced with Sentinel-2 imagery and field GPS points to validate visible features and improve classification reliability. This comprehensive pre-processing ensured that the satellite datasets used were geometrically aligned and radiometrically consistent, enabling robust temporal comparison and analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Image Classification (Supervised)\u003c/h2\u003e\u003cp\u003eUsing supervised classification, Landsat ETM\u0026thinsp;+\u0026thinsp;and OLI/TIRS images were classified to identify the land uses and the marshy area (including water). Supervised classification uses the spectral signature defined in the training set. For example, it determines what each class resembles most in the training set. The common supervised classification algorithms are maximum likelihood and minimum-distance classification.\u003c/p\u003e\u003cp\u003eThe bands utilized for analysis are Landsat 7 ETM+ (5, 4, 3) and Landsat 8/9 OLI/TIRS (5, 4, 3). The study area was extracted from the false color composite. The images retained their original pixel size despite the chances that there might be a difference in classification accuracies. The process of image classification assigns the pixels of a raster image to predefined land cover classes. The basic way of image classification is by visual interpretation, where tone, texture, size, shape, and association are considered (Qasim et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eArcGIS Pro software performs supervised classification using Maximum Likelihood Classifier (MLC). The maximum likelihood classification algorithm was selected because it can incorporate the training samples' statistics before assigning the land covers to each pixel. The maximum likelihood classification is a parametric classifier that assumes that individual class data are distributed normally. It evaluates both the variance and covariance of the category spectral response patterns when classifying an unknown pixel; it requires more computation per pixel. The maximum likelihood classification requires sufficient spectral training sample data for each class to accurately estimate the statistics the classification algorithm needs. Using the signature files of each of the classes, supervised classification was carried out using maximum likelihood classification which was ground-truthed using GPS coordinates obtained from the field and a high-resolution satellite image (Sentinel-2). The following LULC classes were used; Water body, Built-up and Vegetation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The distinct separability of these classes is illustrated in the spectral signatures derived from Landsat 7 ETM\u0026thinsp;+\u0026thinsp;and Landsat 8/9 OLI/TIRS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which provided the foundation for the classification process.\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\u003eLand Use/ Land Cover classifications in the study area\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\u003eS/N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeatures\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\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\u003e1.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRepresents areas covered by water, freshwater river system and freshwater bodies/lakes\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\u003eBuilt up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEncompasses regions with man-made structures, including residential, commercial, and industrial developments.\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\u003eVegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncludes areas covered by natural plant life, such as forests, grasslands, or agricultural fields.\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\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Water Quality Assessment Using Remote Sensing Indices\u003c/h2\u003e\u003cp\u003eWater quality was assessed indirectly through spectral indices, specifically the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI). These indices provide proxy information about the clarity and presence of water, with values ranging from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1. Positive values generally denote open water surfaces, while negative values indicate non-water features such as soil, built-up land, or vegetation.\u003c/p\u003e\u003cp\u003eWater quality assessment is necessary for understanding and managing aquatic ecosystems. Spectral indices, especially the Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Water Index (NDWI) are useful instruments that provide insights into water quality indicators by utilizing data from remote sensing.\u003c/p\u003e\u003cp\u003eAccording Li et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the Modified Normalized Difference Water Index (MNDWI) precisely defines water bodies and detects alterations in surface water dynamics. It is computed as per Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:MNDWI=\\frac{(Green\\:-\\:SWIR)}{(Green\\:+\\:SWIR)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGreen represents the pixel values from the green band, while SWIR represents the pixel values from the short-wave infrared band.\u003c/p\u003e\u003cp\u003eSimilarly, Normalized Difference Water Index (NDWI) widely applies to water quality assessment, excelling in discriminating water from other land cover types. Zhang et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) underscore its utility as a potential proxy for remote sensing applications in mapping water quality indexes, formulated in Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:NDWI=\\frac{(Green\\:-\\:NIR)}{(Green\\:+\\:NIR)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGreen represents the pixel values from the green band, while NIR represents the pixel values from the near infrared band.\u003c/p\u003e\u003cp\u003eThese spectral indices extend to evaluating river water quality, as demonstrated by Najafzadeh and Basirian (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who showcase the efficacy of remote sensing technologies with MNDWI and Normalized Difference Water Index (NDWI). Traditional, often costly methods are complemented by the efficiency of these indices, providing timely and accurate information.\u003c/p\u003e\u003cp\u003eIn surface water quality assessment, Sultana and Dewan (2021) introduce a reflectance-based water quality index, incorporating spectral indices like Normalized Difference Water Index (NDWI). This semi-quantitative measure enables relative evaluation of overall water quality changes, enhancing assessment accuracy.\u003c/p\u003e\u003cp\u003eIn their study on the Kali River, data-driven approaches using spectral indices, exemplified by Said and Khan (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrate the potential for accurate water quality index estimation through Normalized Difference Water Index and machine learning techniques. Ahmed et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) research on Tigris River quality monitoring combines field-based sampling with remote sensing, leveraging MNDWI and Normalized Difference Water Index for enhanced precision in diverse regions.\u003c/p\u003e\u003cp\u003eThe remote sensing data analysis yields indices enabling efficient monitoring of changes in water bodies. Versatile applications include change detection and river water quality assessment. Integrating spectral indices with advanced techniques represents a contemporary approach to understanding and managing water resources.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Change Detection Analysis\u003c/h2\u003e\u003cp\u003eChange detection assesses land feature variations over time, utilizing classified images to determine area extents and observing temporal progressions. This involves a comparative analysis through independently produced classifications, employing a pixel-by-pixel mathematical combination of different data. It identifies differences in an object or phenomenon's state by observing it at different times. Timely and accurate change detection of Earth's surface features establish a foundation for understanding relationships and interactions between human and natural phenomena. Widely applied in remote sensing, it serves as an effective means to monitor environmental changes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Reclassification\u003c/h2\u003e\u003cp\u003eReclassifying attributes in GIS and database software involves creating a new categorical attribute by classifying features based on existing attributes or criteria like location. Reclassification serves purposes such as swiftly updating cells with new information, compiling data for suitability analyses, and eliminating unnecessary details by reclassifying cells as No Data. The primary aim is often to simplify output data for easier interpretation.\u003c/p\u003e\u003cp\u003eReclassification tools alter cell values to alternative values using various methods. You can reclassify individual values or groups simultaneously through alternative fields, based on criteria like specified intervals (e.g., grouping values into 10 intervals) or by area (e.g., grouping values into 10 groups with the same cell count). These tools are designed for efficiently changing numerous values in an input raster to desired, specified, or alternative values.\u003c/p\u003e\u003cp\u003eThe steps followed in this study, from data collection to water quality analysis and change detection, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.0 RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Land Use/Land Cover (LULC) Dynamics (2014\u0026ndash;2023)\u003c/h2\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the study area spans 2854ha. During the investigated periods, water body covered 89ha, representing 3.12% in 2014, and 9ha, representing the proportion of 0.32% in 2023. Similarly, built up area covered 1450ha (50.81%), and 2486ha (87.11%) in 2014 and 2023 respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe analysis of Landsat-7 ETM+ (2014) and Landsat-8/9 OLI/TIRS (2023) imagery provided a comprehensive view of land use and land cover changes in Ibadan North-West over nine years. The classified LULC categories\u0026mdash;water bodies, vegetation, and built-up areas\u0026mdash;exhibited notable transitions (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Water bodies significantly reduced from 89 hectares in 2014 to 9 hectares in 2023, suggesting extensive surface water loss. This loss corresponds to visible drying up or conversion of water surfaces, likely due to infrastructural expansion or climate variability.\u003c/p\u003e\u003cp\u003eVegetation cover also experienced a significant decline from 1,315 to 359 hectares. This pattern signals increased anthropogenic pressure on vegetated landscapes, likely through deforestation or conversion for urban development. In contrast, built-up areas surged from 1,450 hectares to 2,486 hectares, underscoring the district's rapid urbanization and associated land conversion. This trend is consistent with broader urban expansion patterns observed across major Nigerian cities (Adepoju et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe LULC changes from 2014 to 2023 emphasize the dynamic nature of the Ibadan North-West region, which is susceptible to anthropogenic influences. A significant drop in water body coverage and rapid built-up area growth signifies an urbanization shift, likely fueled by increased human activities and population growth.\u003c/p\u003e\u003cp\u003eThe dominance of built-up areas in 2023 raises environmental sustainability concerns. Urban expansion often results in habitat loss, increased pollution, and altered hydrological patterns, threatening the overall ecological balance. The declining vegetation cover worsens environmental concerns, impacting aesthetic value and contributing to air and water pollution. Additionally, vegetation loss may disrupt the natural ecosystem and local fauna.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Water Quality Indices\u003c/h2\u003e\u003cp\u003eIn 2014, MNDWI values ranged from \u0026minus;\u0026thinsp;0.3687 to 0.0744, reflecting the presence of some clearer water bodies amid mixed or turbid areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). By 2023, these values dropped to between \u0026minus;\u0026thinsp;0.2282 and \u0026minus;\u0026thinsp;0.0129, indicating a reduced water signal and potential decline in water clarity or extent. Similarly, as shown in Fig.\u0026nbsp;7, NDWI values which were already negative in 2014 (ranging from \u0026minus;\u0026thinsp;0.3430 to -0.2812), declined further in 2023 (from \u0026minus;\u0026thinsp;0.0101 to -0.0612). These negative values point to increasingly weaker water reflectance, likely due to land conversion, pollution, sedimentation, or reduced surface water coverage.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Change Detection Analysis: NDWI vs. MNDWI\u003c/h2\u003e\u003cp\u003eThe change detection analysis (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e) highlights the spatial dynamics of water-related changes. NDWI recorded a water loss of approximately 368.38 hectares, while MNDWI captured a lower figure of 77.21 hectares. This discrepancy reflects the indices' sensitivity and interpretation differences. NDWI tends to register broader changes, including turbid or mixed pixels that may not represent pure water bodies. Therefore, its higher water loss figure includes marginal or degraded water zones.\u003c/p\u003e\u003cp\u003eIn contrast, MNDWI works better in urban areas because it reduces confusion with built-up surfaces. While NDWI often overestimates water by including mixed or turbid pixels, MNDWI gives a smaller but clearer estimate of actual surface water.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Spatial and Temporal Interpretation\u003c/h2\u003e\u003cp\u003eSpatially, the change maps show a concentration of water loss in the southern and western parts of the district (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Gains in water presence were minimal and sporadically distributed. Areas classified as stable dominated the landscape but with minor reductions between the two periods.\u003c/p\u003e\u003cp\u003eTemporally, these shifts correspond with heightened urban expansion, particularly between 2014 and 2023, when population growth and infrastructure development in Ibadan intensified. NDWI captured a broad water loss, especially around areas where LULC is changing fast - likely due to urban development. MNDWI is better at picking out clearer water in built-up areas and shows fewer losses but still points to shrinking water surfaces. Both indices tell the same story: surface water is disappearing, which is likely tied to how land is being used and changed across the study area.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eshowing surface freshwater disappearing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Implications for Water Resource Management\u003c/h2\u003e\u003cp\u003eThe results indicate a strong link between LULC changes and surface freshwater quality. The decline in both the quantity and spectral quality of surface water suggests increased pressure on hydrological systems, with implications for water availability, urban drainage, and ecosystem health. As urban land encroaches on vegetated and water-covered zones, runoff, sediment load, and potential pollution rise, exacerbating the degradation of surface freshwater resources.\u003c/p\u003e\u003cp\u003eThe spatio-temporal analysis demonstrates a clear trajectory of urban-driven land transformation at the expense of natural water and vegetation systems. NDWI and MNDWI indices, when used in tandem, reveal complementary perspectives on water presence and clarity. The integration of these findings provides empirical evidence for policy formulation on sustainable urban development and freshwater conservation in Ibadan North-West.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Discussion\u003c/h2\u003e\u003cp\u003eThe results of this study confirm that land use and land cover change in Ibadan North-West is strongly shaped by rapid urbanization. Water bodies declined sharply, while built-up areas expanded between 2014 and 2023, showing a direct conversion of natural landscapes into residential and commercial land. Similar observations have been reported in other Nigerian cities, where population growth and infrastructure development put continuous pressure on surface waters and vegetation (Adepoju et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe NDWI and MNDWI analyses both highlight the declining condition of surface water, though with differences in sensitivity. NDWI captured larger areas of water loss, including turbid or mixed pixels, while MNDWI presented a more conservative estimate by filtering out built-up background noise. Together, these indices give a clearer picture of water degradation, showing not just the loss of extent but also weakening water signals that may point to sedimentation, pollution, or shrinking channels.\u003c/p\u003e\u003cp\u003eThe link between built-up expansion and water loss is particularly important. Urban encroachment reduces infiltration, increases runoff, and channels pollutants into rivers and streams. The decline in vegetation further weakens natural buffers, leaving freshwater more exposed to stress. If unchecked, these changes could affect water availability, increase flood risks, and damage the ecological integrity of urban freshwater systems.\u003c/p\u003e\u003cp\u003ePlacing these findings within the wider regional context, the patterns in Ibadan North-West align with broader urban trends in West Africa, where rapid growth often outpaces land-use planning. Without stronger planning frameworks, surface water will continue to decline, threatening both human and ecological wellbeing.\u003c/p\u003e\u003c/div\u003e"},{"header":"4.0: CONCLUSION AND RECOMMENDATIONS","content":"\u003cp\u003eThis study examined land use and land cover changes in Ibadan North-West between 2014 and 2023. The results show that water bodies and vegetation have declined, while built-up areas have increased. These changes reflect growing urban pressure on the environment, which could affect water resources and ecological balance in the area.\u003c/p\u003e\u003cp\u003eTo address these issues, urban development should be better planned to reduce environmental stress. Protecting green spaces and improving water management are important steps. Regular monitoring using indices such as NDWI and MNDWI can help track changes in water resources and guide decision-making. Future studies could also link satellite observations with socio-economic data to better explain the drivers of land cover change and support stronger policy actions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThe authors declared that this study has received no financial support.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.O. planned the study, collected and analyzed the data, prepared the figures, and wrote the main draft. A.M. helped with literature review, and editing. K.O. is the corresponding author and took the lead in the final write-up. Both authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe satellite images used in this study were downloaded from the USGS Earth Explorer website ( [https://earthexplorer.usgs.gov/](https:/earthexplorer.usgs.gov) ). Other data and analysis results can be obtained from the corresponding author on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdepoju AO, Salami AT, Adepoju KA (2019) Urban expansion and environmental sustainability in Ibadan, Nigeria. J Environ Planning Manage 62(4):621\u0026ndash;641. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09640568.2018.1479623\u003c/span\u003e\u003cspan address=\"10.1080/09640568.2018.1479623\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmed W, Mohammed S, El-Shazly A, Morsy S (2023) Tigris River water surface quality monitoring using remote sensing data and GIS techniques. 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Water 13(22):3250\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"land use land cover change, freshwater systems, NDWI, MNDWI, Landsat imagery, urbanization","lastPublishedDoi":"10.21203/rs.3.rs-7840911/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7840911/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe degradation of freshwater systems due to rapid urbanization has become a critical environmental concern in many developing urban centers. Assessing the spatial and temporal dynamics of land use/land cover (LULC) changes is essential for understanding their implications on surface water quality. This study assessed water quality indices to examine the impact of land use/land cover (LULC) change on the freshwaters in Ibadan North-West, Nigeria, using remote sensing techniques. Landsat-7 ETM+ (2014) and Landsat-8/9 OLI/TIRS (2023) imagery were analyzed to assess spatio-temporal LULC changes and extract water indices. Water quality was assessed using the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI). Over a period of 9 years, water bodies declined from 89 to 9 hectares, and vegetation cover reduced from 1,315 to 359 hectares. In contrast, built-up areas expanded from 1,450 to 2,486 hectares, reflecting intensified urbanization. The MNDWI values in 2014 ranged from \u0026minus;\u0026thinsp;0.3687 to 0.0744, while in 2023 they declined to -0.2282 to -0.0129. NDWI values, already negative in 2014 (ranging from \u0026minus;\u0026thinsp;0.3430 to -0.2812), declined further in 2023 (ranging from \u0026minus;\u0026thinsp;0.0101 to -0.0612). These shifts reflect a diminishing presence of strong water signals across both years. Change detection further reveals that NDWI recorded a total water loss of 368.38 ha, while MNDWI showed a smaller loss of 77.21 ha. The contrast highlights that while NDWI detected broader signals, including turbid and mixed-pixel zones, MNDWI more accurately delineated freshwater systems by suppressing noise from surrounding land covers. The findings reveal a substantial reduction in both the extent and spectral quality of freshwater systems over the 9-year period, driven by land conversion and urban encroachment.\u003c/p\u003e","manuscriptTitle":"Water Quality Indices and Impact of Land Use/Land Cover Changes on Freshwater System in Ibadan North-West, Nigeria Using Remote Sensing Techniques","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 04:01:07","doi":"10.21203/rs.3.rs-7840911/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":"87657a9d-acc3-4c7d-94e7-87d80632177f","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-21T04:01:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-21 04:01:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7840911","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7840911","identity":"rs-7840911","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}