Remote Sensing-Based Assessment of Environmental Determinants Influencing Malaria Distribution in Kano Metropolis

Remote Sensing-Based Assessment of Environmental Determinants Influencing Malaria Distribution in Kano Metropolis | 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 Remote Sensing-Based Assessment of Environmental Determinants Influencing Malaria Distribution in Kano Metropolis David Mkpanam Nyong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7881980/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 Malaria transmission is heavily conditioned by environmental determinants, while intra-urban spatial variability of these determinants is underinvestigated in most African cities. This research uses remote sensing and Geographic Information Systems (GIS) to evaluate the spatial distribution of important environmental determinants like Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Land Use/Land Cover (LULC) and the combined influence on malaria risk in Kano Metropolis, Nigeria. Satellite imagery from the Landsat 8 was edited to extract NDVI, LST, and LULC maps. A multi-criteria assessment approach was then undertaken to combine these parameters in a composite malaria risk map. Findings indicate a clear spatial gradient: urban core Local Government Areas (LGAs) such as Kano Municipal have lower NDVI, higher LST, and majority built-up cover, reflecting an urban heat island. By contrast, peripheral LGAs such as Kumbotso and Ungogo have higher vegetation cover and lower temperature. The analysis illustrates that high LST areas and moderate-to-high rainfall areas, especially in the southern and peri-urban LGAs, overlap with previously identified malaria high-risk areas. These conditions quicken mosquito and parasite development life cycles. In any case, results highlight that malaria risk in Kano Metropolis is non-uniform and conditioned by a complex set of urbanization-induced environment changes. The resultant risk map significantly identifies hotspots and serves as an important spatial tool, which public health authorities could utilize to guide and target intervention measures, hence towards a more efficient and data-informed malaria control program in the metropolis. Remote Sensing GIS Malaria NDVI Land Surface Temperature Urban Health Kano Spatial Epidemiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background and Review Malaria also remains a big public health problem, primarily in sub-Saharan Africa, where climatic conditions are conducive to the breeding of the Anopheles mosquito vector (World Health Organization [WHO], 2021). Malaria transmission is not random; it's very much related to the interaction of a set of environmental, climatic, and socio-economic factors. Traditional malaria surveillance methods, while helpful, remain localized and very much prone to missing the spatio-temporal changes of these factors (Howes et al., 2013). Advancements in remote sensing (RS) and Geographic Information Systems (GIS) have remarkably revolutionized the spatial epidemiology of malaria. Such advanced technology enables thorough and repeated examination of environmental parameters that influence vector habitat and parasite development (Beck et al., 2000). Largely important among these parameters are Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Land Use/Land Cover (LULC). LST is an important indicator of surrounding temperature, which on a direct scale influences mosquito developmental rates, biting rates, and the extrinsic incubation period of the parasite in its genus Plasmodium (Parham & Michael, 2010). A rise in temperature within a given interval has the capability to quicken the parasite's lifecycle and thereby increase the risk of transmission (Mordecai et al., 2019). Extracted from satellite imagery, the NDVI measures live green vegetation. It reveals possible breeding grounds (e.g., irrigated farming, dense vegetations with shade and moisture) and has been correlated with the number of mosquitoes (Hay et al., 1998). Furthermore, LULC changes, particularly rapid urbanization, drastically alters local ecological systems. Urbanization leads to reduced natural cover, increased impervious cover, and the development of new breeding grounds such as water in building construction and clogged ditches (Keiser et al., 2004). This produces a complex urban malaria ecology that varies from that in rural settings (De Silva & Marshall, 2012). Although several studies have used remote sensing in malaria risk mapping in rural Africa (Kibret et al., 2017), increased understanding of urban malaria's unique and developing character has led to this urban setting being considered a testing ground. Kano Metropolis, being among Africa's most concentrated and fast urbanizing cities, constitutes an important case study. Knowledge of the space-based composition of the environment's determinants in this complex urban environment is important in the creation of targeted and effective intervention. Novelty and Objectives Much of the earlier Nigerian research has involved broad regional or state-based assessments of malaria risk, to the relative omission of the significant intra-urban equity that informs urban public health planning. This project attempts to fill this void by carrying out a high-resolution, detailed estimation of Kano Metropolis's primary environment determinants of malaria. The innovation lies in the simultaneous application of multi-sensor remote sensing data to accurately model and characterize the space between NDVI, LST, and LULC and the resultant collective impact on malaria risk potential at the Local Government Area (LGA) scale. The primary objectives of this research are: To map the spatial distribution of NDVI, LST, and LULC across Kano Metropolis using satellite remote sensing. To analyze the relationship between these environmental variables and known malaria risk factors. To develop an integrated malaria risk map that identifies hotspots for targeted intervention. Materials and Methods 2.1 Study Area Explanation Kano Metropolis is the capital city of Kano State in Nigeria and also serves as a large commercial and industrial city in sub-Saharan Africa. Geographically, it falls between latitudinal coordinates of 11° 50' N to 12° 10' N and longitudinal coordinates of 8° 25'E to 8° 40'E (Gbiri et al., 2019). Kano Metropolis encompasses a number of densely settled Local Government Areas (LGAs), which include Dala, Fagge, Gwale, Kano Municipal, Kumbotso, Nassarawa, Tarauni, and Ungogo. A high and characteristic wet season runs between the months of May and September, while a long dry season runs between October and April, with annual average rainfall of approximately 900mm (Adeofun et al 2011 ). Kano Metropolis experiences a tropical savanna climatic characteristic with high temperature all year round, with conducive conditions to allow malaria transmission. The Study area map is shown in Fig. 1 of the study. 2.2. Sources and Preparation of Data In this project, a multi-source data methodology was adopted. Satellite data were primarily acquired from the USGS EarthExplorer website. Landsat 8 OLI/TIRS data was acquired during the high transmission season of malaria (e.g., October) under clear weather to allow calculation of NDVI and LST. 30-meter spatial resolution is suitable to perform intra-urban analysis. Land Use/Land Cover (LULC): LULC was classified using Landsat 8 imagery. A supervised classification technique, that is, the Maximum Likelihood algorithm was used to classify the land cover into classes such as Built-up areas, Vegetation, Bare Soil, and Water Bodies (Congedo, 2021 ). Ancillary Data: Administrative border shapefiles of Kano Metropolis and its LGAs were downloaded from the Nigerian National Bureau of Statistics. Malaria occurrence data or facility coordinates, where available to verify, will be sourced from Kano State Ministry of Health. All the spatial datasets were converted to a single coordinate system (WGS 84 UTM Zone 32N) and re-sampled to ensure a single spatial resolution for overall analysis in a Geographic Information System (GIS) environment. 2.3. Methodology Methodology consisted of a systematic progression of image processing, computation of indexes, and Geographic Information Analysis. Calculation of NDVI: NDVI was calculated with the conventional formula: (NIR - Red) / (NIR + Red) of the bands 5 and 4 of the Landsat 8, respectively (Tucker, 1979 ). Index value varies between − 1 and + 1, with increased value corresponding to increased density of the vegetation. Retrieving LST: LST was retrieved from the Thermal Infrared (TIR) bands (Bands 10 and 11) of the Landsat 8 satellite imagery. It was accomplished via the conversion of digital number to top-of-atmosphere spectral radiance and then to brightness temperature and also via incorporation of a land surface emissivity correction with the NDVI-based methodology (Avdan & Jovanovska, 2016 ). Classification of LULC: Supervised classification was performed and its precision was calculated from a confusion matrix with reference data gathered from high-resolution Google Earth imagery (Foody, 2002 ). Modeling Malaria Risk: A composite malaria risk map was created based on a multi-criteria evaluation (MCE). Derived NDVI, LST, and LULC layers and other related parameters like water body distances were also assigned relative weights that have been determined in the literature (e.g., Patz et al., 2008; Kibret et al., 2019). Weighted in this manner, these related layers were superimposed on each other by the weighted overlay analysis in the GIS to provide a final malaria risk map that was categorized into Low, Medium, and High risk. Result The analysis established distinctive space distributions of each of the environment variable across Kano Metropolis. Relatively lower vegetation indices in the central LGAs (e.g., Kano Municipal and Fagge) than in peripheral areas (e.g., Kumbotso and Ungogo) are shown on the NDVI map (Fig. 2 ). This gradient also suggests high urbanization in the central areas, which minimizes the cover of vegetation and changes local communities. It can be seen from the land use map that urban areas have associated reduced vegetation. Also, the map of the land surface temperature (LST) reveals elevated temperatures in urban areas (like Kano Municipal) and lower temperatures in the neighboring areas. An examination of land surface temperature (LST) in Kano Metropolis reveals its considerable impact on malaria susceptibility. Geographic distribution of LST data (Fig. 3 ) reveals that warm areas converge in the center and the south, while cooler areas are in the north and the peri-urban regions. Sites with elevated LST coincide with elevated malaria risk zones on the malaria risk map. Rainfall map (Fig. 4 ) presents a geographical gradient where high amounts of rainfall concentrated in the south part of the study area, particularly Kumbotso LGA, and then decreasing amounts of rainfall in the northern part including Ungogo. Risk Maps to Inform Focused Intervention Strategies The risk map of malaria (Fig. 5 ) defines areas categorized into high-risk, medium-risk, and low-risk areas based on a mixture of environmental and socio-economic factors. Areas that have been categorized as high-risk, highlighted in red, are common in areas around Ungogo, Kumbotso, and neighboring Tarauni and Gwale LGAs. Areas that have been categorized as high-risk have elevated population settlements, poor access to healthcare, and favorable environmental conditions, such as medium to high rainfall amounts and high land surface temperature. Areas that have been categorized as medium-risk, which mainly exist in Dala and Kano Municipal LGAs, have good healthcare access; however, they still contain high potential weaknesses due to population density and local environmental conditions. Low-risk areas exist in small number and mainly in central Kano Municipal and likely have access to good healthcare provisions and urban development measures. Discussion This study successfully presents the applicability of remote sensing methods in tracing the complex spatial variability of the environment's parameters driving malaria in an urban African setting. This outcome fits well with established ecological theories on malaria dissemination. This negative linkage between urbanized areas (defined by poor NDVI) and adjacent green cover, in conjunction with the urban heat island process (marked by elevated LST), characterizes a unique microclimate. Moffett et al.'s ( 2007 ) research indicates that reduced green cover inhibits the existence of natural predators that prey on mosquitoes, hence the rise in vector breeding in urban areas. Conversely, the peri-urban and rural fringes with high NDVI levels often have high mosquito populations that serve to act as malaria reservoirs and cause transmission in neighboring urban areas (Kibret et al., 2019). The observed rise in land surface temperature (LST) around urban areas is an important finding. Justified by Lindsay et al. (2000), high temperatures hasten the development processes in the Plasmodium parasite in mosquitoes and hence increase the risk of malaria transmission. High LST concentration in urban areas such as Kano Municipal bears witness to urbanization and its linkage with environmental factors in the epidemiology of malaria, also noted in several other cities in sub-Saharan Africa (Tatem et al., 2013 ). Malaria hotspots identified in the southern part of Kano confirm these findings to depict that increased temperatures lead to increased risk of malaria transmission. Rainfall's influence is confirmed where its pattern creates breeding grounds, primarily in the south LGAs. This aligns with McCann et al.'s ( 2017 ) research, which depict a very positive correlation between rainfall and malaria transmission in sub-Saharan Africa. Similarly, Alemu et al. (2020) outlined that areas with elevated rainfall have usually shown a high prevalence of malaria due to high opportunities in vector breeding. The ultimate risk map synthesizes these complex interactions. The high-risk localities in Local Government Areas (LGAs) such as Kumbotso and Ungogo arise due to an intersection of suitable environmental conditions (medium vegetation, high temperature, and adequate rainfall) with socio-economic vulnerabilties, especially constrained healthcare access. This statement holds true according to the research carried out by Tatem et al. ( 2013 ), which reveals that constrained healthcare access delays treatment and increases transmission rates due to unattended cases. A similar outcome supports the research of Ayele et al. (2022), confirming that land surface temperature and vegetation indexes are fundamental to malaria risk mapping. 5. Conclus This study yields an in-depth, spatially descriptive assessment of the ecological determinants that impact malaria risk in Kano Metropolis. Integration of the data of NDVI, LST, and LULC through GIS has considerably defined and mapped areas of high risk of malaria with high specificity. A main result states that malaria risk in Kano varies and is determined by a distinct spatial gradient that results from urbanization. The city center, characterized by the heat island and low cover of plants, and the peri-urban areas, with the conducive ecological conditions and high population densities, form areas of high concern. These findings have immediate utility to public health policy and planning of intervention. Rather than a single, generic solution, control measures should have top priority and be specially directed to individual LGAs based on risk profile. In high-temperature, urban central LGAs, measures might include urban drainage upgrades and house screening, while in high-NDVI peri-urban areas, larval source control has top priority. Future research should similarly include socio-economic and demographic data at a similarly fine scale and permit temporal analysis in order to permit assessment of seasonal and inter-annual variation in risk to significantly improve the evidence base to inform proactive and targeted malaria control. Declarations Competing Interests “The authors have no relevant financial or non-financial interests to disclose”. Ethics Approval “Not applicable ”. Consent to Participate “Not applicable”. Consent for Publication “Not applicable”. Clinical Trial Registration “Not applicable”. Funding “The authors declare that no funds, grants, or other support were received during the preparation of this manuscript”. Author Contribution “All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by David Mkpanam Nyong. The first draft of the manuscript was written by David Mkpanam Nyong and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript”. Data Availability “The datasets generated and analyzed during the current study are derived from publicly available remote sensing sources, primarily Landsat 8 satellite imagery, which can be accessed through the USGS EarthExplorer portal (https://earthexplorer.usgs.gov/). The derived data (e.g., processed NDVI, LST, and LULC maps) are available from the corresponding author upon reasonable request”. References Adeofun CO, Achi HA, Ufoegbune G, Gbadebo AM, Oyedepo J (2011) Application of remote sensing and geographic information system for selecting dumpsites and transport routes in Abeokuta, Nigeria. J Environ Chem Ecotoxicol 3(8):186–194 Alemu A, Abebe G, Tsegaye W, Golassa L (2011) Climatic variables and malaria transmission dynamics in Jimma town, South West Ethiopia. Parasites Vectors 4(30). https://doi.org/10.1186/1756-3305-4-30 Avdan U, Jovanovska G (2016) Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of Sensors , *2016*, 1–8. https://doi.org/10.1155/2016/1480307 Ayele DG, Zewotir T, Mwambi H (2013) The risk factor indicators of malaria in Ethiopia. Int J Med Med Sci 5(8):335–347 Beck LR, Lobitz BM, Wood BL (2000) Remote sensing and human health: New sensors and new opportunities. Emerg Infect Dis 6(3):217–227. https://doi.org/10.3201/eid0603.000301 Cohen JM, Smith DL, Cotter C, Ward A, Yamey G, Sabot OJ, Moonen B (2012) Malaria resurgence: A systematic review and assessment of its causes. Malar J. *11*(122 https://doi.org/10.1186/1475-2875-11-122 Congedo L (2021) Semi-automatic classification plugin: A Python tool for the download and processing of remote sensing images in QGIS. J Open Source Softw 6(64):1–3. https://doi.org/10.21105/joss.03172 De Silva PM, Marshall JM (2012) Factors contributing to urban malaria transmission in sub-Saharan Africa: A systematic review. Journal of Tropical Medicine , *2012*, 1–10. https://doi.org/10.1155/2012/819563 Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sensing of Environment , *80*(1), 185–201. https://doi.org/10.1016/S0034-4257(01)00295-4 Gbiri IA, Adeoye NO (2019) Analysis of pattern and extent of deforestation in Akure Forest Reserve, Ondo State, Nigeria. J Environ Geogr 12(1–2):1–11. https://doi.org/10.2478/jengeo-2019-0001 Hay SI, Snow RW, Rogers DJ (1998) Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data. Trans R Soc Trop Med Hyg 92(1):12–20. https://doi.org/10.1016/S0035-9203(98)90936-1 Howes RE, Patil AP, Piel FB, Nyangiri OA, Kabaria CW, Gething PW, Zimmerman PA, Barnadas C, Beall CM, Gebremedhin A, Ménard D, Williams TN, Weatherall DJ, Hay SI (2011) The global distribution of the Duffy blood group. Nat Commun. *2*(266 https://doi.org/10.1038/ncomms1265 Keiser J, De Castro MC, Maltese MF, Bos R, Tanner M, Singer BH, Utzinger J (2005) Effect of irrigation and large dams on the burden of malaria on a global and regional scale. The American Journal of Tropical Medicine and Hygiene , *72*(4), 392–406. https://doi.org/10.4269/ajtmh.2005.72.392 Kibret S, Wilson GG, Ryder D, Tekie H, Petros B (2017) The influence of dams on malaria transmission in sub-Saharan Africa. EcoHealth 14(2):408–419. https://doi.org/10.1007/s10393-015-1029-0 Lindsay SW, Martens WJM (2000) Malaria in the African highlands: Past, present and future. Bull World Health Organ 76(1):33–45 McCann RS, Messina JP, MacFarlane DW, Bayoh NM, Vulule JM, Gimnig JE, Walker ED (2017) Explaining variation in adult Anopheles indoor resting abundance: The relative effects of larval habitat proximity and insecticide-treated bed net use. Malar J. *16*(288 https://doi.org/10.1186/s12936-017-1938-1 Moffett A, Shackelford N, Sarkar S (2007) Malaria in Africa: Vector species' niche models and relative risk maps. PLoS ONE e824 *2*(9. https://doi.org/10.1371/journal.pone.0000824 Mordecai EA, Caldwell JM, Grossman MK, Lippi CA, Johnson LR, Neira M, Rohr JR, Ryan SJ, Savage V, Shocket MS, Sippy R, Ibarra S, Thomas AM, M. B., Villena O (2019) Thermal biology of mosquito-borne disease. Ecology Letters , *22*(10), 1690–1708. https://doi.org/10.1111/ele.13335 Parham PE, Michael E (2010) Modeling the effects of weather and climate change on malaria transmission. Environ Health Perspect 118(5):620–626. https://doi.org/10.1289/ehp.0901256 Tatem AJ, Gething PW, Smith DL, Hay SI (2013) Urbanization and the global malaria recession. Malar J. *12*(133 https://doi.org/10.1186/1475-2875-12-133 Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 2127–150. https://doi.org/10.1016/0034-4257(79)90013-0 World Health Organization (2021) World malaria report 2021 . World Health Organization. Retrieved from https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2021 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-7881980","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532540016,"identity":"4f5b4c83-0ef7-497a-b15c-ea28d46a68d1","order_by":0,"name":"David Mkpanam Nyong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDCCAwzMEAZ7A5AwsCBFC88BkBYJUrRIJIBJwjr4jp9ONi6ouSOvO/P51Q0/CiQY+Nu7E/BqkTyTuzl5xrFnhttu55Td7AE6TOLM2Q14tRgcyN18mIftMCNQS9oNHqAWA4lcAlrOvwVq+XfYftvNM2k3/xCl5QbQYbxthxO33WA/dpsoWyRvvN1sPLPvcPK2Mzlst2UMJHgI+oXvfO5m6YJvh223HT/+7OabPzZy/O29+LWAACwuDcAkQeVIWtgfEKV6FIyCUTAKRh4AAE9IUIZjzLBUAAAAAElFTkSuQmCC","orcid":"","institution":"Bayero University","correspondingAuthor":true,"prefix":"","firstName":"David","middleName":"Mkpanam","lastName":"Nyong","suffix":""}],"badges":[],"createdAt":"2025-10-17 02:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7881980/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7881980/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94050628,"identity":"d31a51ea-fb29-4c1f-a342-f2e95816e4fb","added_by":"auto","created_at":"2025-10-21 23:48:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":902943,"visible":true,"origin":"","legend":"","description":"","filename":"RemoteSensing1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/69a190a1314ca657e802c071.docx"},{"id":94050626,"identity":"e68f1aa9-fe8f-449a-b1c0-ad5651732381","added_by":"auto","created_at":"2025-10-21 23:48:03","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4199,"visible":true,"origin":"","legend":"","description":"","filename":"9d28c91e339841969e80d70b00f07104.json","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/e5de7b7118b05b6b1c092502.json"},{"id":94050627,"identity":"91ef3bb8-6ad5-4160-b5ea-15a37071e996","added_by":"auto","created_at":"2025-10-21 23:48:03","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":57461,"visible":true,"origin":"","legend":"","description":"","filename":"9d28c91e339841969e80d70b00f071041enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/7958b1291a8d33c215f76566.xml"},{"id":94050637,"identity":"a0079402-21e7-4348-8a69-2d3ca840f0ad","added_by":"auto","created_at":"2025-10-21 23:48:03","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49195,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/f01ae7eb8f1c20bed272415d.png"},{"id":94050638,"identity":"50f4c0a4-3eb9-44d4-9d81-6b34ea9a3fea","added_by":"auto","created_at":"2025-10-21 23:48:03","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29202,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/d9e1bc7c621c044c18c04bad.png"},{"id":94050640,"identity":"7818bb1e-cdcd-4da1-b391-f906c24a901b","added_by":"auto","created_at":"2025-10-21 23:48:03","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":94655,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/954101d50e20bd750913d3f0.png"},{"id":94051053,"identity":"2363ff5d-83e1-4edd-bcb5-4fc45c14e1c5","added_by":"auto","created_at":"2025-10-21 23:56:03","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":61835,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/e2679addb9d114ded755d22b.png"},{"id":94050636,"identity":"0f7a86ed-5a0b-4207-9aae-ed61cd816f93","added_by":"auto","created_at":"2025-10-21 23:48:03","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":70805,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/046e4320952d4c5155b5d266.png"},{"id":94050633,"identity":"eed3c26b-f070-48b0-8f39-b3e61488cc58","added_by":"auto","created_at":"2025-10-21 23:48:03","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":54651,"visible":true,"origin":"","legend":"","description":"","filename":"9d28c91e339841969e80d70b00f071041structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/6c753fa6bc635565a5dab738.xml"},{"id":94050639,"identity":"407ad775-c4e7-4419-9d86-9bc247d28bd2","added_by":"auto","created_at":"2025-10-21 23:48:03","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62679,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/6216d702076e813fcbb61230.html"},{"id":94051052,"identity":"c063d153-529a-4996-a564-4a0c4a547446","added_by":"auto","created_at":"2025-10-21 23:56:03","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182313,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Kano Metropolis\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/de137c1f830cdf06b61c1ec7.jpeg"},{"id":94050630,"identity":"87638f11-5120-4b6b-9ffb-9230f12bbffb","added_by":"auto","created_at":"2025-10-21 23:48:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54982,"visible":true,"origin":"","legend":"\u003cp\u003eMean NDVI Map of Kano Metropolis\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/acfb848dd46aca82ffa5259c.png"},{"id":94050634,"identity":"70dc232d-3c9f-4811-9fc2-62db93647fbd","added_by":"auto","created_at":"2025-10-21 23:48:03","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":257445,"visible":true,"origin":"","legend":"\u003cp\u003eMean Land Surface Temperature of Kano Metropolis\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/7fb285cb5822ff31a42ff29c.jpeg"},{"id":94050631,"identity":"7349ae27-7b4d-461d-90b1-aea5cfab17b2","added_by":"auto","created_at":"2025-10-21 23:48:03","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":155241,"visible":true,"origin":"","legend":"\u003cp\u003eRainfall distribution in Kano Metropolis\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/3948cde92fe9e67bead28b7d.jpeg"},{"id":94051054,"identity":"a0828e40-c93f-43c8-a6c4-ebbc85d0e282","added_by":"auto","created_at":"2025-10-21 23:56:03","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":219934,"visible":true,"origin":"","legend":"\u003cp\u003eMalaria Risk Map of Kano Metropolis\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/1125fde8a09f69fb4c5feba9.jpeg"},{"id":94051389,"identity":"bbef08c6-2d77-4231-a9f8-11e19f04b81f","added_by":"auto","created_at":"2025-10-22 00:04:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1290405,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7881980/v1/a2ed3736-5f59-4e51-8a12-1b2230ccd96f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Remote Sensing-Based Assessment of Environmental Determinants Influencing Malaria Distribution in Kano Metropolis","fulltext":[{"header":"Background and Review","content":"\u003cp\u003eMalaria also remains a big public health problem, primarily in sub-Saharan Africa, where climatic conditions are conducive to the breeding of the Anopheles mosquito vector (World Health Organization [WHO], 2021). Malaria transmission is not random; it's very much related to the interaction of a set of environmental, climatic, and socio-economic factors. Traditional malaria surveillance methods, while helpful, remain localized and very much prone to missing the spatio-temporal changes of these factors (Howes et al., 2013).\u003c/p\u003e\n\u003cp\u003eAdvancements in remote sensing (RS) and Geographic Information Systems (GIS) have remarkably revolutionized the spatial epidemiology of malaria. Such advanced technology enables thorough and repeated examination of environmental parameters that influence vector habitat and parasite development (Beck et al., 2000). Largely important among these parameters are Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Land Use/Land Cover (LULC). LST is an important indicator of surrounding temperature, which on a direct scale influences mosquito developmental rates, biting rates, and the extrinsic incubation period of the parasite in its genus Plasmodium (Parham \u0026amp; Michael, 2010). A rise in temperature within a given interval has the capability to quicken the parasite's lifecycle and thereby increase the risk of transmission (Mordecai et al., 2019).\u003c/p\u003e\n\u003cp\u003eExtracted from satellite imagery, the NDVI measures live green vegetation. It reveals possible breeding grounds (e.g., irrigated farming, dense vegetations with shade and moisture) and has been correlated with the number of mosquitoes (Hay et al., 1998). Furthermore, LULC changes, particularly rapid urbanization, drastically alters local ecological systems. Urbanization leads to reduced natural cover, increased impervious cover, and the development of new breeding grounds such as water in building construction and clogged ditches (Keiser et al., 2004). This produces a complex urban malaria ecology that varies from that in rural settings (De Silva \u0026amp; Marshall, 2012).\u003c/p\u003e\n\u003cp\u003eAlthough several studies have used remote sensing in malaria risk mapping in rural Africa (Kibret et al., 2017), increased understanding of urban malaria's unique and developing character has led to this urban setting being considered a testing ground. Kano Metropolis, being among Africa's most concentrated and fast urbanizing cities, constitutes an important case study. Knowledge of the space-based composition of the environment's determinants in this complex urban environment is important in the creation of targeted and effective intervention.\u003c/p\u003e"},{"header":"Novelty and Objectives","content":"\u003cp\u003eMuch of the earlier Nigerian research has involved broad regional or state-based assessments of malaria risk, to the relative omission of the significant intra-urban equity that informs urban public health planning. This project attempts to fill this void by carrying out a high-resolution, detailed estimation of Kano Metropolis's primary environment determinants of malaria. The innovation lies in the simultaneous application of multi-sensor remote sensing data to accurately model and characterize the space between NDVI, LST, and LULC and the resultant collective impact on malaria risk potential at the Local Government Area (LGA) scale. The primary objectives of this research are:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eTo map the spatial distribution of NDVI, LST, and LULC across Kano Metropolis using satellite remote sensing.\u003c/li\u003e\n \u003cli\u003eTo analyze the relationship between these environmental variables and known malaria risk factors.\u003c/li\u003e\n \u003cli\u003eTo develop an integrated malaria risk map that identifies hotspots for targeted intervention.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area Explanation\u003c/h2\u003e\u003cp\u003eKano Metropolis is the capital city of Kano State in Nigeria and also serves as a large commercial and industrial city in sub-Saharan Africa. Geographically, it falls between latitudinal coordinates of 11° 50' N to 12° 10' N and longitudinal coordinates of 8° 25'E to 8° 40'E (Gbiri et al., 2019). Kano Metropolis encompasses a number of densely settled Local Government Areas (LGAs), which include Dala, Fagge, Gwale, Kano Municipal, Kumbotso, Nassarawa, Tarauni, and Ungogo. A high and characteristic wet season runs between the months of May and September, while a long dry season runs between October and April, with annual average rainfall of approximately 900mm (Adeofun et al \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Kano Metropolis experiences a tropical savanna climatic characteristic with high temperature all year round, with conducive conditions to allow malaria transmission. The Study area map is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e of the study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.2. Sources and Preparation of Data\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIn this project, a multi-source data methodology was adopted. Satellite data were primarily acquired from the USGS EarthExplorer website.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eLandsat 8 OLI/TIRS data was acquired during the high transmission season of malaria (e.g., October) under clear weather to allow calculation of NDVI and LST. 30-meter spatial resolution is suitable to perform intra-urban analysis.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eLand Use/Land Cover (LULC): LULC was classified using Landsat 8 imagery. A supervised classification technique, that is, the Maximum Likelihood algorithm was used to classify the land cover into classes such as Built-up areas, Vegetation, Bare Soil, and Water Bodies (Congedo, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAncillary Data: Administrative border shapefiles of Kano Metropolis and its LGAs were downloaded from the Nigerian National Bureau of Statistics. Malaria occurrence data or facility coordinates, where available to verify, will be sourced from Kano State Ministry of Health.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll the spatial datasets were converted to a single coordinate system (WGS 84 UTM Zone 32N) and re-sampled to ensure a single spatial resolution for overall analysis in a Geographic Information System (GIS) environment.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3. Methodology\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMethodology consisted of a systematic progression of image processing, computation of indexes, and Geographic Information Analysis.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCalculation of NDVI: NDVI was calculated with the conventional formula: (NIR - Red) / (NIR + Red) of the bands 5 and 4 of the Landsat 8, respectively (Tucker, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Index value varies between − 1 and + 1, with increased value corresponding to increased density of the vegetation.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRetrieving LST: LST was retrieved from the Thermal Infrared (TIR) bands (Bands 10 and 11) of the Landsat 8 satellite imagery. It was accomplished via the conversion of digital number to top-of-atmosphere spectral radiance and then to brightness temperature and also via incorporation of a land surface emissivity correction with the NDVI-based methodology (Avdan \u0026amp; Jovanovska, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eClassification of LULC: Supervised classification was performed and its precision was calculated from a confusion matrix with reference data gathered from high-resolution Google Earth imagery (Foody, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Modeling Malaria Risk: A composite malaria risk map was created based on a multi-criteria evaluation (MCE). Derived NDVI, LST, and LULC layers and other related parameters like water body distances were also assigned relative weights that have been determined in the literature (e.g., Patz et al., 2008; Kibret et al., 2019). Weighted in this manner, these related layers were superimposed on each other by the weighted overlay analysis in the GIS to provide a final malaria risk map that was categorized into Low, Medium, and High risk.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003eThe analysis established distinctive space distributions of each of the environment variable across Kano Metropolis.\u003c/p\u003e\u003cp\u003eRelatively lower vegetation indices in the central LGAs (e.g., Kano Municipal and Fagge) than in peripheral areas (e.g., Kumbotso and Ungogo) are shown on the NDVI map (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This gradient also suggests high urbanization in the central areas, which minimizes the cover of vegetation and changes local communities.\u003c/p\u003e\u003cp\u003eIt can be seen from the land use map that urban areas have associated reduced vegetation. Also, the map of the land surface temperature (LST) reveals elevated temperatures in urban areas (like Kano Municipal) and lower temperatures in the neighboring areas.\u003c/p\u003e\u003cp\u003eAn examination of land surface temperature (LST) in Kano Metropolis reveals its considerable impact on malaria susceptibility. Geographic distribution of LST data (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) reveals that warm areas converge in the center and the south, while cooler areas are in the north and the peri-urban regions. Sites with elevated LST coincide with elevated malaria risk zones on the malaria risk map.\u003c/p\u003e\u003cp\u003eRainfall map (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) presents a geographical gradient where high amounts of rainfall concentrated in the south part of the study area, particularly Kumbotso LGA, and then decreasing amounts of rainfall in the northern part including Ungogo.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRisk Maps to Inform Focused Intervention Strategies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe risk map of malaria (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) defines areas categorized into high-risk, medium-risk, and low-risk areas based on a mixture of environmental and socio-economic factors. Areas that have been categorized as high-risk, highlighted in red, are common in areas around Ungogo, Kumbotso, and neighboring Tarauni and Gwale LGAs. Areas that have been categorized as high-risk have elevated population settlements, poor access to healthcare, and favorable environmental conditions, such as medium to high rainfall amounts and high land surface temperature. Areas that have been categorized as medium-risk, which mainly exist in Dala and Kano Municipal LGAs, have good healthcare access; however, they still contain high potential weaknesses due to population density and local environmental conditions. Low-risk areas exist in small number and mainly in central Kano Municipal and likely have access to good healthcare provisions and urban development measures.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study successfully presents the applicability of remote sensing methods in tracing the complex spatial variability of the environment's parameters driving malaria in an urban African setting. This outcome fits well with established ecological theories on malaria dissemination. This negative linkage between urbanized areas (defined by poor NDVI) and adjacent green cover, in conjunction with the urban heat island process (marked by elevated LST), characterizes a unique microclimate. Moffett et al.'s (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) research indicates that reduced green cover inhibits the existence of natural predators that prey on mosquitoes, hence the rise in vector breeding in urban areas. Conversely, the peri-urban and rural fringes with high NDVI levels often have high mosquito populations that serve to act as malaria reservoirs and cause transmission in neighboring urban areas (Kibret et al., 2019).\u003c/p\u003e\u003cp\u003eThe observed rise in land surface temperature (LST) around urban areas is an important finding. Justified by Lindsay et al. (2000), high temperatures hasten the development processes in the Plasmodium parasite in mosquitoes and hence increase the risk of malaria transmission. High LST concentration in urban areas such as Kano Municipal bears witness to urbanization and its linkage with environmental factors in the epidemiology of malaria, also noted in several other cities in sub-Saharan Africa (Tatem et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Malaria hotspots identified in the southern part of Kano confirm these findings to depict that increased temperatures lead to increased risk of malaria transmission.\u003c/p\u003e\u003cp\u003eRainfall's influence is confirmed where its pattern creates breeding grounds, primarily in the south LGAs. This aligns with McCann et al.'s (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) research, which depict a very positive correlation between rainfall and malaria transmission in sub-Saharan Africa. Similarly, Alemu et al. (2020) outlined that areas with elevated rainfall have usually shown a high prevalence of malaria due to high opportunities in vector breeding.\u003c/p\u003e\u003cp\u003eThe ultimate risk map synthesizes these complex interactions. The high-risk localities in Local Government Areas (LGAs) such as Kumbotso and Ungogo arise due to an intersection of suitable environmental conditions (medium vegetation, high temperature, and adequate rainfall) with socio-economic vulnerabilties, especially constrained healthcare access. This statement holds true according to the research carried out by Tatem et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which reveals that constrained healthcare access delays treatment and increases transmission rates due to unattended cases. A similar outcome supports the research of Ayele et al. (2022), confirming that land surface temperature and vegetation indexes are fundamental to malaria risk mapping. 5. Conclus This study yields an in-depth, spatially descriptive assessment of the ecological determinants that impact malaria risk in Kano Metropolis. Integration of the data of NDVI, LST, and LULC through GIS has considerably defined and mapped areas of high risk of malaria with high specificity. A main result states that malaria risk in Kano varies and is determined by a distinct spatial gradient that results from urbanization. The city center, characterized by the heat island and low cover of plants, and the peri-urban areas, with the conducive ecological conditions and high population densities, form areas of high concern. These findings have immediate utility to public health policy and planning of intervention. Rather than a single, generic solution, control measures should have top priority and be specially directed to individual LGAs based on risk profile. In high-temperature, urban central LGAs, measures might include urban drainage upgrades and house screening, while in high-NDVI peri-urban areas, larval source control has top priority. Future research should similarly include socio-economic and demographic data at a similarly fine scale and permit temporal analysis in order to permit assessment of seasonal and inter-annual variation in risk to significantly improve the evidence base to inform proactive and targeted malaria control.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;The authors have no relevant financial or non-financial interests to disclose\u0026rdquo;.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Not applicable\u003c/em\u003e\u0026rdquo;.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Not applicable\u0026rdquo;.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Not applicable\u0026rdquo;.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eClinical Trial Registration\u003c/h2\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Not applicable\u0026rdquo;.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003e\u0026ldquo;The authors declare that no funds, grants, or other support were received during the preparation of this manuscript\u0026rdquo;.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\u0026ldquo;All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by David Mkpanam Nyong. The first draft of the manuscript was written by David Mkpanam Nyong and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript\u0026rdquo;.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e\u0026ldquo;The datasets generated and analyzed during the current study are derived from publicly available remote sensing sources, primarily Landsat 8 satellite imagery, which can be accessed through the USGS EarthExplorer portal (https://earthexplorer.usgs.gov/). The derived data (e.g., processed NDVI, LST, and LULC maps) are available from the corresponding author upon reasonable request\u0026rdquo;.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdeofun CO, Achi HA, Ufoegbune G, Gbadebo AM, Oyedepo J (2011) Application of remote sensing and geographic information system for selecting dumpsites and transport routes in Abeokuta, Nigeria. J Environ Chem Ecotoxicol 3(8):186\u0026ndash;194\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlemu A, Abebe G, Tsegaye W, Golassa L (2011) Climatic variables and malaria transmission dynamics in Jimma town, South West Ethiopia. Parasites Vectors 4(30). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1756-3305-4-30\u003c/span\u003e\u003cspan address=\"10.1186/1756-3305-4-30\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAvdan U, Jovanovska G (2016) Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. \u003cem\u003eJournal of Sensors\u003c/em\u003e, *2016*, 1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2016/1480307\u003c/span\u003e\u003cspan address=\"10.1155/2016/1480307\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAyele DG, Zewotir T, Mwambi H (2013) The risk factor indicators of malaria in Ethiopia. Int J Med Med Sci 5(8):335\u0026ndash;347\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeck LR, Lobitz BM, Wood BL (2000) Remote sensing and human health: New sensors and new opportunities. Emerg Infect Dis 6(3):217\u0026ndash;227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3201/eid0603.000301\u003c/span\u003e\u003cspan address=\"10.3201/eid0603.000301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCohen JM, Smith DL, Cotter C, Ward A, Yamey G, Sabot OJ, Moonen B (2012) Malaria resurgence: A systematic review and assessment of its causes. Malar J. *11*(122 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1475-2875-11-122\u003c/span\u003e\u003cspan address=\"10.1186/1475-2875-11-122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCongedo L (2021) Semi-automatic classification plugin: A Python tool for the download and processing of remote sensing images in QGIS. J Open Source Softw 6(64):1\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21105/joss.03172\u003c/span\u003e\u003cspan address=\"10.21105/joss.03172\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Silva PM, Marshall JM (2012) Factors contributing to urban malaria transmission in sub-Saharan Africa: A systematic review. \u003cem\u003eJournal of Tropical Medicine\u003c/em\u003e, *2012*, 1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2012/819563\u003c/span\u003e\u003cspan address=\"10.1155/2012/819563\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFoody GM (2002) Status of land cover classification accuracy assessment. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, *80*(1), 185\u0026ndash;201. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0034-4257(01)00295-4\u003c/span\u003e\u003cspan address=\"10.1016/S0034-4257(01)00295-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGbiri IA, Adeoye NO (2019) Analysis of pattern and extent of deforestation in Akure Forest Reserve, Ondo State, Nigeria. J Environ Geogr 12(1\u0026ndash;2):1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2478/jengeo-2019-0001\u003c/span\u003e\u003cspan address=\"10.2478/jengeo-2019-0001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHay SI, Snow RW, Rogers DJ (1998) Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data. Trans R Soc Trop Med Hyg 92(1):12\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0035-9203(98)90936-1\u003c/span\u003e\u003cspan address=\"10.1016/S0035-9203(98)90936-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHowes RE, Patil AP, Piel FB, Nyangiri OA, Kabaria CW, Gething PW, Zimmerman PA, Barnadas C, Beall CM, Gebremedhin A, M\u0026eacute;nard D, Williams TN, Weatherall DJ, Hay SI (2011) The global distribution of the Duffy blood group. Nat Commun. *2*(266 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ncomms1265\u003c/span\u003e\u003cspan address=\"10.1038/ncomms1265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeiser J, De Castro MC, Maltese MF, Bos R, Tanner M, Singer BH, Utzinger J (2005) Effect of irrigation and large dams on the burden of malaria on a global and regional scale. \u003cem\u003eThe American Journal of Tropical Medicine and Hygiene\u003c/em\u003e, *72*(4), 392\u0026ndash;406. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4269/ajtmh.2005.72.392\u003c/span\u003e\u003cspan address=\"10.4269/ajtmh.2005.72.392\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKibret S, Wilson GG, Ryder D, Tekie H, Petros B (2017) The influence of dams on malaria transmission in sub-Saharan Africa. EcoHealth 14(2):408\u0026ndash;419. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10393-015-1029-0\u003c/span\u003e\u003cspan address=\"10.1007/s10393-015-1029-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLindsay SW, Martens WJM (2000) Malaria in the African highlands: Past, present and future. Bull World Health Organ 76(1):33\u0026ndash;45\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCann RS, Messina JP, MacFarlane DW, Bayoh NM, Vulule JM, Gimnig JE, Walker ED (2017) Explaining variation in adult Anopheles indoor resting abundance: The relative effects of larval habitat proximity and insecticide-treated bed net use. Malar J. *16*(288 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-017-1938-1\u003c/span\u003e\u003cspan address=\"10.1186/s12936-017-1938-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoffett A, Shackelford N, Sarkar S (2007) Malaria in Africa: Vector species' niche models and relative risk maps. PLoS ONE e824 *2*(9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0000824\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0000824\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMordecai EA, Caldwell JM, Grossman MK, Lippi CA, Johnson LR, Neira M, Rohr JR, Ryan SJ, Savage V, Shocket MS, Sippy R, Ibarra S, Thomas AM, M. B., Villena O (2019) Thermal biology of mosquito-borne disease. \u003cem\u003eEcology Letters\u003c/em\u003e, *22*(10), 1690\u0026ndash;1708. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ele.13335\u003c/span\u003e\u003cspan address=\"10.1111/ele.13335\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParham PE, Michael E (2010) Modeling the effects of weather and climate change on malaria transmission. Environ Health Perspect 118(5):620\u0026ndash;626. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1289/ehp.0901256\u003c/span\u003e\u003cspan address=\"10.1289/ehp.0901256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTatem AJ, Gething PW, Smith DL, Hay SI (2013) Urbanization and the global malaria recession. Malar J. *12*(133 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1475-2875-12-133\u003c/span\u003e\u003cspan address=\"10.1186/1475-2875-12-133\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 2127\u0026ndash;150. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0034-4257(79)90013-0\u003c/span\u003e\u003cspan address=\"10.1016/0034-4257(79)90013-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization (2021) \u003cem\u003eWorld malaria report 2021\u003c/em\u003e. World Health Organization. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2021\u003c/span\u003e\u003cspan address=\"https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2021\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"Remote Sensing, GIS, Malaria, NDVI, Land Surface Temperature, Urban Health, Kano, Spatial Epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-7881980/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7881980/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMalaria transmission is heavily conditioned by environmental determinants, while intra-urban spatial variability of these determinants is underinvestigated in most African cities. This research uses remote sensing and Geographic Information Systems (GIS) to evaluate the spatial distribution of important environmental determinants like Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Land Use/Land Cover (LULC) and the combined influence on malaria risk in Kano Metropolis, Nigeria. Satellite imagery from the Landsat 8 was edited to extract NDVI, LST, and LULC maps. A multi-criteria assessment approach was then undertaken to combine these parameters in a composite malaria risk map. Findings indicate a clear spatial gradient: urban core Local Government Areas (LGAs) such as Kano Municipal have lower NDVI, higher LST, and majority built-up cover, reflecting an urban heat island. By contrast, peripheral LGAs such as Kumbotso and Ungogo have higher vegetation cover and lower temperature. The analysis illustrates that high LST areas and moderate-to-high rainfall areas, especially in the southern and peri-urban LGAs, overlap with previously identified malaria high-risk areas. These conditions quicken mosquito and parasite development life cycles. In any case, results highlight that malaria risk in Kano Metropolis is non-uniform and conditioned by a complex set of urbanization-induced environment changes. The resultant risk map significantly identifies hotspots and serves as an important spatial tool, which public health authorities could utilize to guide and target intervention measures, hence towards a more efficient and data-informed malaria control program in the metropolis.\u003c/p\u003e","manuscriptTitle":"Remote Sensing-Based Assessment of Environmental Determinants Influencing Malaria Distribution in Kano Metropolis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 23:47:58","doi":"10.21203/rs.3.rs-7881980/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":"54cb9c5c-9dc9-4c9c-9068-760dcd4b6a49","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-30T10:53:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-21 23:47:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7881980","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7881980","identity":"rs-7881980","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}