Identification of Urban Growth Patterns in Sri Lanka Using Viirs Night-Time Lights Data

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This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9250627/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 Today, urban growth is a multidimensional spatial and dynamic process that inclines towards increasing the significance of urban planning for developing countries with rapid growth of population and economy. Unplanned urban growth decreases the quality of urban environment. It is vital to study the urban change patterns to help for the decisionmaking process of urban planning. But, in developing countries like Sri Lanka, facing challenges in acquiring information to investigate the urban patterns. Therefore, the use of advanced technologies is indispensable for the identification of urban growth and for sustainable urban planning. Nighttime light data obtained from the Suomi National Polar Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor provides a new source of information that is advantageous for mapping and monitoring urban growth. Aiming at the knowledge gap in the use of VIIRS Nighttime Light data for urban studies in Sri Lanka, our study examined the capability of using VIIRS NTL data for the urban growth pattern identification. We used the nighttime data derived from the Suomi National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor for the years from 2013 to 2021 to extract the urban extent and Regional Light Index for the spatial-temporal analysis. The results revealed that VIIRS NTL data has high potential in identification of urban growth patterns. Furthermore, we employed the CA-Markov model to predict future urban growth for 2026. The findings of the research will be very useful for the urban planners and policy makers to formulate better policies and strategies for future urban development in Sri Lanka. VIIRS Nighttime Light Urban growth pattern Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. INTRODUCTION All over the world, urbanization has become a universal and significant phenomenon in society and economics. Urbanization is a complex socio-economic process that transforms the built environment, converting formerly rural into urban settlements, while also shifting the spatial distribution of a population from rural to urban areas (United Nations, 2018). In the past few decades, urban areas have been expanding drastically in many developing countries. According to the latest World Urbanization Prospects (United Nations, 2018), 55% of the world’s population lived in urban areas in 2018 and by 2050, 68% of the world’s population is projected to be urban, with almost 90% of the growth happen in Asia and Africa. As a developing country, Sri Lanka is experiencing rapid urbanization, driven by population growth, rural-to-urban migration, and economic development. As cities expand rapidly, understanding urban growth patterns becomes imperative for sustainable development and effective urban planning. Understanding how cities are expanding and evolving is crucial for policymakers and urban planners to make informed decisions and mitigate the negative impacts of rapid urbanization. By studying urban growth patterns in Sri Lanka, valuable insights can be gained to guide future development strategies and promote sustainable urbanization. However,identifying of the urban growth patterns in Sri Lanka is still a difficult task and on-going research. Up to present, various attempts have been employed to extract urban extents in the field of geographic information science including field surveys and classification of remote sensing images (Bhatta, 2009 ; Dadras, 2015; Weerakoon, 2017). However, field surveys are resource-intensive and impractical for large-scale urban studies due to their time and labour demands. Advancements in remote sensing have led to an increased reliance on satellite imagery for urban studies. Traditional methods typically involve classifying medium to high-resolution data such as Landsat satellite images (Subasinghe et al., 2016 ; Jayasinghe et al., 2021 ). But, they have several shortcomings such as a huge workload to process a large amount of cloud-free data and addressing issues of spectral and spatial inconsistency across different images. Moreover, their limited temporal resolution constrains their suitability for dynamic analyses at broader scales (Sexton et al., 2013 ). To overcome the limitations of traditional land-use and land-cover mapping techniques, night time lights data has emerged as a powerful tool for analysing urban growth patterns. Night time light images measure emitted radiation rather than reflected radiation, which offers certain advantages for distinguishing between developed and non-developed land cover (Sutton, 2003). The DMSP-OLS and SNPP VIIRS are two commonly used instruments for collecting night-time lights data. In other countries around the world, DMSP-OLS night time lights data has been used successfully for urban analysis (Zhou et. el., 2015; Jiang et. el., 2020). For example, Liu et al., ( 2012 ) highlights the significance of night time stable light data obtained from the Defense Meteorological Satellite Program's Operational Line-scan System (DMSP-OLS) Night time Lights Time Series dataset, for studying urban expansion in China. Also, DMSP/OLS NTL data have been used in other countries and proved the uniqueness and usefulness of night-time light data for analysing and monitoring urbanization, particularly in areas with limited statistical data (Jiang et. el., (2020). In Sri Lankan context, DMSP-OLS NTL data have been used successfully for urban growth pattern analysis by Yapa and Gunawardena ( 2022 ). However, these data have some shortcomings include limited data availability for certain years, blooming effect, pixel saturation, conflation of electricity production with gas flares, signal decay, and systematic shifts in the geospatial position of digital numbers. Acknowledging the limitations of DMSP-OLS NTL data, the researchers have turned to newer and more advanced sensors like Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) to overcome these limitations (Shi et. el., 2014; Yu et. el., 2018,). The VIIRS offers significant improvements over the OLS, including higher spatial resolution, wider dynamic range, better quantization, more accurate calibrations, and the availability of spectral bands suitable for discriminating thermal sources of light emissions (Elvidge et al., 2013 ). Few research works have demonstrated the applicability of VIIRS images for analysing urban growth patterns in other countries in the world. For instance, one study used the night time stable light data from VIIRS for studying the spatial structure of urban agglomerations in China (Zheng et. el., 2021). Unfortunately, no research has yet been developed related to analysing the relationship between VIIRS NTL data and urban growth patterns in Sri Lanka. So, the research problem is “is VIIRS nighttime light data suitable to analysis the urban growth patterns in Sri Lanka?”. Therefore, to fill the knowledge gap in Sri Lanka, the study aims to analysis the relationship between the VIIRS NTL image data (illumination patterns) and urban growth patterns in Sri Lanka. 2. METHODOLOGY 2.1 Study Area The study covers the Colombo district which is in the western part of the Sri Lanka (Fig. 1 ). It has 13 Divisional Secretariat Divisions. Colombo is located at 6.8602 o N latitude and 80.0535 o E Longitude geographic coordinates and has a total administrative area of approximately 699 km 2 . It is the commercial capital and most financial district in the Sri Lanka. Colombo District's population was 2,375,000 and urban area population was 2,219,782 in 2015. It has the highest population and population density in Sri Lanka. The population density is 11.4%. 2.2 Data Used The VIIRS Night time Day/Night Annual Band Composites Version 2.1 imagery was acquired from the NOAA website at http://payneinstitute.mines.edu/eog/ . Data was downloaded for the study period of 2013 to 2021. The spatial resolution of the products was 15 arc seconds. The version 4 DMSP-OLS stable nighttime lights annual image composites for the year 2013 was obtained from the NOAA website at http://payneinstitute.mines.edu/eog/ . The spatial resolution of the products was 30 arc seconds. The data was used to extract the urban area and compare the result with the VIIRS urban extraction information. Global annual Land cover map of 2013 produced by the European Space Agency (ESA) Climate Change Initiative (CCI) was obtained from ESA CCI land cover website https://www.esa-landcover-cci.org/ . The spatial resolution of the data is 300m. 2.3 Data Pre-Processing For the VIIRS images, the downloaded images were ready to be used in research as they had already been filtered to depict stable light. Therefore, the cloud cover, stray lights, lightning, and lunar illumination are filtered out of the images available for download. The DMSP/OLS and VIIRS NTL data series were accurately projected to the WGS 84 / UTM zone 44N coordinate system and images were resampled to a spatial resolution of 300 meters. 2.4 Extraction of Urban Information The Optimal Threshold Method (OTM) was employed to extract urban and non-urban areas from the study area. A DN value of 3 was selected as the optimal threshold value for VIIRS data. After that, a classification accuracy assessment was conducted to evaluate the accuracy of the results. 2.5 Spatial-Temporal Changes Analysis The Region Light Index (RLI) model offers an effective approach to assess nighttime light (NTL) intensity by considering both DN values and regional characteristics (Xu et al., 2016; Yapa & Gunawardena, 2022 ). RLI = \(\:\frac{{\varvec{S}}_{\varvec{u}}\times\:\:{\varvec{N}}_{\varvec{u}}\times\:\varvec{a}}{\varvec{S}}+\:\frac{{\varvec{S}}_{\varvec{n}}\times\:{\varvec{N}}_{\varvec{n}}\times\:\varvec{a}}{\varvec{S}}\) ………….. Eq. (1) Where, S u and S n represent the respective areas of urban and non-urban regions within the study area. N u and N n represent the number of pixels corresponding to each land use and a, b refer to the average DN values of each region. S represents the total area of the study region. 2.6 Prediction The study applied the CA-Markov model to simulate and predict future urban growth in Colombo. The change analysis was done using Land Change Modeller and the transition matrix was calculated using Markov chain model in IDRISI software concerning variations from 2014 to 2020. Applying the CA Markov chain technique, a projection for 2026 was done. 3. RESULTS ANALYSIS AND DISCUSSION 3.1 Accuracy assessment based on finer-resolution remote sensing data The accuracy of the extracted urban information was evaluated using finer-resolution remotely sensed data. The evaluation aimed to validate the accuracy and reliability of the extracted urban information by comparing it with higher-resolution datasets (Fig. 3 ). The use of finer-resolution remotely sensed data, such as high-resolution satellite imagery or aerial photographs, allowed for a detailed comparison with the extracted urban information. The ESA Land Cover data has a finer spatial resolution (300 m) compared to the NSL data (1 km or 500 m). So, evaluating the results using ESA Land Cover is feasible and widely accepted (Cao et al., 2009; Henderson et al., 2003; Small et al., 2005; Sutton et al., 2006). The accuracy assessment, conducted through the random selection of urban and non-urban points, demonstrates a high level of accuracy in the urban area extracted using VIIRS NTL data. The overall accuracy for urban area detection is 90.95%, with a kappa coefficient of 0.82 (Table 1 ). Table 1 Error matrices and accuracy assessment for urban and non-urban area extracted using VIIRS NTL data. Classified Data Reference Data User’s Accuracy Urban Non-urban Row total Urban 87 14 101 86.14% Non-urban 05 104 109 95.41% Column total 92 118 210 Producer’s Accuracy 94.57% 88.14% Overall Accuracy = 90.95% ; Kappa = 0.82 A comparative analysis was performed by evaluating the results obtained from the DMSP data (Table 2 ). The findings from the accuracy assessment indicate an overall accuracy of 87.38% for the DMSP data, with a corresponding kappa coefficient of 0.75. These metrics provide a measure of the agreement between the classified urban areas and the reference data. Comparing the accuracy assessment results between the VIIRS and DMSP data, it is evident that the VIIRS data outperforms the DMSP data in terms of detecting urban areas. Table 2 Error matrices and accuracy assessment for urban and non-urban area extracted using DMSP NTL data. Classified Data Reference Data User’s Accuracy Urban Non-urban Row total Urban 92 25 117 78.63% Non-urban 1 88 89 98.88% Column total 93 113 206 Producer’s Accuracy 98.92% 77.87% Overall Accuracy = 87.38%; Kappa = 0.75 3.2 Urban growth pattern identification The change trend of land use types in Colombo from 2013 to 2021 is the non-urban area is decreasing, and the urban land is increasing noticeably (Fig. 4 ). Over the study period from 2013 to 2021, the urban area experienced consistent expansion, increasing from 351.721 sq. km to 401.805 sq. km. This growth signifies the continuous development of urban areas within the district. Concurrently, the non-urban areas witnessed a decline, indicating the conversion of non-urban land into urban use. The non-urban area decreased from 333.230 sq. km to 283.176 sq. km, suggesting the encroachment of urbanization into previously non-urbanized regions (Table 3 ). Table 3 Area Statistics of urban and non-urban extents in Colombo from 2013–2021, Area units; km 2 Year Urban Non-urban RLI 2013 351.72 333.23 57888.14 2014 355.72 329.23 59083.75 2015 363.27 321.71 61406.25 2016 367.72 317.17 62793.70 2017 376.72 308.08 65656.35 2018 378.89 306.09 64196.93 2019 387.04 297.85 69037.61 2020 389.97 295.01 70018.44 2021 401.81 283.18 74050.26 Regional Light Index (RLI) displayed an upward trend (Fig. 5 ). This intensification of urban brightness and activity indicates the increasing development and population density in urban areas. According to the results, the expansion of urban extents during 2013–2021 periods shows Edge expansion pattern (Fig. 6 ). Edge expansion results with the increase in the size of urban areas, as well as the emergence of new patches near or within existing patches. Furthermore, results were compared with the past studies conducted in Colombo District. In Fig. 8 , (a) shows the spatial pattern variation derived from the DMSP/OLS Night time Light data (Yapa and Gunawardena,2022) and (b) represents the built-up areas and other land use types extracted from Landsat satellite images (Antalyn and Weerasinghe, 2020). Both studies also highlighted the edge expansion in urban growth pattern in Colombo District which is same as our analysis. 3.3 Stimulation and Prediction The dynamics of urban and non-urban areas from 2014 to 2020 shows that urban areas have witnessed a substantial increase and indicate the urbanization and development (Fig. 9 ). Notably, urban areas exhibited substantial growth with the significant net increase of 78.49 km 2 . Non-urban areas experienced a notable decrease, with the net loss of -78.49 km 2 . It shows that most of the non-urban areas transformed into urban areas (Fig. 10 ). A matrix of transition probability offers a quantitative understanding of how urban and non-urban extents change over a specific period using Markov chain analysis (Table 4 ). The probabilities indicate the specific information about the likelihood of class transitions. The Transition Probability Matrix indicates that there is a high probability (0.7861) of non-urban land transitioning into urban land. Conversely, the probability of urban land changing into non-urban land is relatively lower (0.0046). Table 4 Transition Probability Matrix Class Non-urban Urban Non-urban 0.7861 0.2139 Urban 0.0046 0.9954 Based on the Transition Probability Matrix, the projection for land use in 2026 was done(Fig. 11 ). It is expected that there will be a substantial increase in urban land, as non-urban areas are likely to transform into urban areas with a high probability. This suggests that urban expansion will continue to change the landscape in the study area 4. CONCLUSION The present study aimed to assess the suitability of VIIRS nighttime light (NTL) data for urban studies in Sri Lanka. Specifically, we conducted an analysis of the urban growth pattern in the Colombo District using VIIRS NTL data spanning the period from 2013 to 2021. The findings of our study provide compelling evidence that VIIRS NTL data can effectively be employed for urban extraction and offer an accurate and comprehensive approach to mapping urban growth. By leveraging the spatial distribution of nighttime lights, VIIRS data enables the identification and delineation of urban areas with a high degree of precision. The significant advantage of utilizing VIIRS NTL data is its ability to overcome the limitations associated with traditional methods of urban growth pattern analysis. Unlike conventional techniques, VIIRS NTL data provides a robust and objective source of information. This not only enhances the accuracy of urban mapping but also facilitates rigorous and consistent analysis of urban growth dynamics. The application of VIIRS NTL data in urban studies can greatly support urban planning, land use management, and policy formulation in Sri Lanka. The accurate and reliable mapping of urban growth patterns enables decision-makers to better understand the spatial extent and intensity of urbanization, facilitating the identification of areas that require targeted interventions for sustainable development. It is important to acknowledge the limitations of our study. Firstly, the use of threshold methods, although straightforward, is subject to several limitations. The process of dividing sub-regions and determining the optimal threshold is subjective and timeconsuming, potentially introducing bias and inconsistency into the results. Moreover, the challenge of overestimating urban areas persists, particularly in the case of large cities. While there is a strong correlation between nighttime light (NTL) and urban extent, relying solely on NTL data may not capture the complete urban landscape accurately, leading to potential overestimation. Furthermore, our analysis focused solely on the Colombo District, and further investigations are needed to other regions in Sri Lanka. By acknowledging the above limitations and addressing them through further research, we can enhance the reliability and applicability of VIIRS NTL data for urban studies in Sri Lanka. In conclusion, our study demonstrates that VIIRS nighttime light data holds immense potential for urban studies in Sri Lanka. The adoption of VIIRS NTL data as a tool for urban extraction offers an accurate and comprehensive method for mapping urban growth, overcoming the limitations of traditional approaches. The findings from the study provide valuable insights for policymakers, planners, and researchers, enabling evidence-based decision-making and supporting sustainable urban development efforts in Sri Lanka. Declarations Data Availability: The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. References Bhatta, B. (2009) ‘Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India’, International Journal of Remote Sensing, 30(18), p.4733-4746. Elvidge, C. D. et al. (2013) ‘Why VIIRS Data Are Superior to DMSP for Mapping Nighttime Lights’, Proceedings of the AsiaPacific Advanced Network, 35, 62. Jayasinghe, P., Raghavan, V. and Yonezawa, G., (2021) ‘Exploration of expansion patterns and prediction of urban growth for Colombo City, Sri Lanka’, Spatial Information Research, pp.1-14. Jiang, S.et al. (2020) ‘Detecting the Dynamics of Urban Growth in Africa Using DMSP/OLS Nighttime Light Data’, Land, 10(1), p.13. Liu, Z. et al. (2012) ‘Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008’, Landscape and Urban Planning, 106(1), pp.62-72. Sexton, J.O. et al. (2013). ‘Urban growth of the Washington, DC–Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover’, Remote Sensing of Environment, 129, pp.42-53. Shi, K. et al. (2014) ‘Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas’, Remote Sensing Letters, 5(4), pp.358-366. Subasinghe, S., Estoque, R.C. and Murayama, Y. (2016) ‘Spatiotemporal analysis of urban growth using GIS and remote sensing: A case study of the Colombo Metropolitan Area, Sri Lanka’, ISPRS international journal of geo-information, 5(11), p.197. United Nations, Department of Economic and Social Affairs, Population Division (2019). World Urbanization Prospects 2018: Highlights (ST/ESA/SER.A/421). Yapa, R.D.W.S. and Gunawardena, W. (2022) ‘Examination of the spatio-temporal urban growth patterns using DMSP-OLS night-time lights data: an experiment in urban area, Sri Lanka’. Yu, B. et al. (2018) ‘Urban built-up area extraction from log-transformed NPP-VIIRS nighttime light composite data’, IEEE Geoscience and Remote Sensing Letters, 15(8), pp.1279-1283. Zheng, W. et al. (2021) ‘Analysing the spatial structure of urban growth across the Yangtze River Middle reaches urban agglomeration in China using NPP-VIIRS night-time lights data’, GeoJournal, 46 pp.1-18. Zhou, N., Hubacek, K. and Roberts, M. (2015) ‘Analysis of spatial patterns of urban growth across South Asia using DMSP-OLS nighttime lights data’, Applied Geography, 63, pp.292-303. Additional Declarations The authors declare no competing interests. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9250627","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613656754,"identity":"5d48840c-edcc-4f79-a42f-6076b7763450","order_by":0,"name":"Kajarani 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2013-2021\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9250627/v1/1ce81f08964c585d79be8645.png"},{"id":105882649,"identity":"9bc51f14-a90b-4259-9645-5771772abc03","added_by":"auto","created_at":"2026-04-01 06:57:30","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":165546,"visible":true,"origin":"","legend":"\u003cp\u003eUrban patterns in Colombo District from the Past studies.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9250627/v1/6ffbeb347a7b31c44328cee1.jpeg"},{"id":105882678,"identity":"8f2042e0-529a-482a-928b-cb53f017d07a","added_by":"auto","created_at":"2026-04-01 06:57:44","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":54467,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in land use between 2014 and 2020.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9250627/v1/60ef98c621ff9401f92d742a.png"},{"id":105882689,"identity":"5282ec53-86d1-4664-94e7-20214a624d7c","added_by":"auto","created_at":"2026-04-01 06:57:46","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":14884,"visible":true,"origin":"","legend":"\u003cp\u003eGraph of Net change, gain and loss in km\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-9250627/v1/16c00f6a083aba7a6f395d00.png"},{"id":105882691,"identity":"64821bba-5717-4d38-8714-4e1ce5ab91bf","added_by":"auto","created_at":"2026-04-01 06:57:46","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":63933,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction Map - 2026\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-9250627/v1/375ce80290b66b997b9005ae.png"},{"id":105906678,"identity":"ac7db506-2b83-446f-ada3-18655f5edc91","added_by":"auto","created_at":"2026-04-01 10:24:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1696085,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9250627/v1/04745cfb-3dc0-4bd2-b798-afa81d493655.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIdentification of Urban Growth Patterns in Sri Lanka Using Viirs Night-Time Lights Data\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eAll over the world, urbanization has become a universal and significant phenomenon in society and economics. Urbanization is a complex socio-economic process that transforms the built environment, converting formerly rural into urban settlements, while also shifting the spatial distribution of a population from rural to urban areas (United Nations, 2018). In the past few decades, urban areas have been expanding drastically in many developing countries. According to the latest World Urbanization Prospects (United Nations, 2018), 55% of the world\u0026rsquo;s population lived in urban areas in 2018 and by 2050, 68% of the world\u0026rsquo;s population is projected to be urban, with almost 90% of the growth happen in Asia and Africa.\u003c/p\u003e \u003cp\u003eAs a developing country, Sri Lanka is experiencing rapid urbanization, driven by population growth, rural-to-urban migration, and economic development. As cities expand rapidly, understanding urban growth patterns becomes imperative for sustainable development and effective urban planning. Understanding how cities are expanding and evolving is crucial for policymakers and urban planners to make informed decisions and mitigate the negative impacts of rapid urbanization. By studying urban growth patterns in Sri Lanka, valuable insights can be gained to guide future development strategies and promote sustainable urbanization. However,identifying of the urban growth patterns in Sri Lanka is still a difficult task and on-going research.\u003c/p\u003e \u003cp\u003eUp to present, various attempts have been employed to extract urban extents in the field of geographic information science including field surveys and classification of remote sensing images (Bhatta, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dadras, 2015; Weerakoon, 2017). However, field surveys are resource-intensive and impractical for large-scale urban studies due to their time and labour demands. Advancements in remote sensing have led to an increased reliance on satellite imagery for urban studies. Traditional methods typically involve classifying medium to high-resolution data such as Landsat satellite images (Subasinghe et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jayasinghe et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). But, they have several shortcomings such as a huge workload to process a large amount of cloud-free data and addressing issues of spectral and spatial inconsistency across different images. Moreover, their limited temporal resolution constrains their suitability for dynamic analyses at broader scales (Sexton et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo overcome the limitations of traditional land-use and land-cover mapping techniques, night time lights data has emerged as a powerful tool for analysing urban growth patterns. Night time light images measure emitted radiation rather than reflected radiation, which offers certain advantages for distinguishing between developed and non-developed land cover (Sutton, 2003). The DMSP-OLS and SNPP VIIRS are two commonly used instruments for collecting night-time lights data. In other countries around the world, DMSP-OLS night time lights data has been used successfully for urban analysis (Zhou et. el., 2015; Jiang et. el., 2020). For example, Liu et al., (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) highlights the significance of night time stable light data obtained from the Defense Meteorological Satellite Program's Operational Line-scan System (DMSP-OLS) Night time Lights Time Series dataset, for studying urban expansion in China. Also, DMSP/OLS NTL data have been used in other countries and proved the uniqueness and usefulness of night-time light data for analysing and monitoring urbanization, particularly in areas with limited statistical data (Jiang et. el., (2020). In Sri Lankan context, DMSP-OLS NTL data have been used successfully for urban growth pattern analysis by Yapa and Gunawardena (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, these data have some shortcomings include limited data availability for certain years, blooming effect, pixel saturation, conflation of electricity production with gas flares, signal decay, and systematic shifts in the geospatial position of digital numbers.\u003c/p\u003e \u003cp\u003eAcknowledging the limitations of DMSP-OLS NTL data, the researchers have turned to newer and more advanced sensors like Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) to overcome these limitations (Shi et. el., 2014; Yu et. el., 2018,). The VIIRS offers significant improvements over the OLS, including higher spatial resolution, wider dynamic range, better quantization, more accurate calibrations, and the availability of spectral bands suitable for discriminating thermal sources of light emissions (Elvidge et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Few research works have demonstrated the applicability of VIIRS images for analysing urban growth patterns in other countries in the world. For instance, one study used the night time stable light data from VIIRS for studying the spatial structure of urban agglomerations in China (Zheng et. el., 2021). Unfortunately, no research has yet been developed related to analysing the relationship between VIIRS NTL data and urban growth patterns in Sri Lanka.\u003c/p\u003e \u003cp\u003eSo, the research problem is \u0026ldquo;is VIIRS nighttime light data suitable to analysis the urban growth patterns in Sri Lanka?\u0026rdquo;. Therefore, to fill the knowledge gap in Sri Lanka, the study aims to analysis the relationship between the VIIRS NTL image data (illumination patterns) and urban growth patterns in Sri Lanka.\u003c/p\u003e"},{"header":"2. METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eThe study covers the Colombo district which is in the western part of the Sri Lanka (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It has 13 Divisional Secretariat Divisions. Colombo is located at 6.8602\u003csup\u003eo\u003c/sup\u003eN latitude and 80.0535\u003csup\u003eo\u003c/sup\u003eE Longitude geographic coordinates and has a total administrative area of approximately 699 km\u003csup\u003e2\u003c/sup\u003e. It is the commercial capital and most financial district in the Sri Lanka. Colombo District's population was 2,375,000 and urban area population was 2,219,782 in 2015. It has the highest population and population density in Sri Lanka. The population density is 11.4%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Used\u003c/h2\u003e \u003cp\u003eThe VIIRS Night time Day/Night Annual Band Composites Version 2.1 imagery was acquired from the NOAA website at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://payneinstitute.mines.edu/eog/\u003c/span\u003e\u003cspan address=\"http://payneinstitute.mines.edu/eog/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Data was downloaded for the study period of 2013 to 2021. The spatial resolution of the products was 15 arc seconds. The version 4 DMSP-OLS stable nighttime lights annual image composites for the year 2013 was obtained from the NOAA website at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://payneinstitute.mines.edu/eog/\u003c/span\u003e\u003cspan address=\"http://payneinstitute.mines.edu/eog/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The spatial resolution of the products was 30 arc seconds. The data was used to extract the urban area and compare the result with the VIIRS urban extraction information. Global annual Land cover map of 2013 produced by the European Space Agency (ESA) Climate Change Initiative (CCI) was obtained from ESA CCI land cover website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.esa-landcover-cci.org/\u003c/span\u003e\u003cspan address=\"https://www.esa-landcover-cci.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The spatial resolution of the data is 300m.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Pre-Processing\u003c/h2\u003e \u003cp\u003eFor the VIIRS images, the downloaded images were ready to be used in research as they had already been filtered to depict stable light. Therefore, the cloud cover, stray lights, lightning, and lunar illumination are filtered out of the images available for download. The DMSP/OLS and VIIRS NTL data series were accurately projected to the WGS 84 / UTM zone 44N coordinate system and images were resampled to a spatial resolution of 300 meters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Extraction of Urban Information\u003c/h2\u003e \u003cp\u003eThe Optimal Threshold Method (OTM) was employed to extract urban and non-urban areas from the study area. A DN value of 3 was selected as the optimal threshold value for VIIRS data. After that, a classification accuracy assessment was conducted to evaluate the accuracy of the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Spatial-Temporal Changes Analysis\u003c/h2\u003e \u003cp\u003eThe Region Light Index (RLI) model offers an effective approach to assess nighttime light (NTL) intensity by considering both DN values and regional characteristics (Xu et al., 2016; Yapa \u0026amp; Gunawardena, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRLI = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\varvec{S}}_{\\varvec{u}}\\times\\:\\:{\\varvec{N}}_{\\varvec{u}}\\times\\:\\varvec{a}}{\\varvec{S}}+\\:\\frac{{\\varvec{S}}_{\\varvec{n}}\\times\\:{\\varvec{N}}_{\\varvec{n}}\\times\\:\\varvec{a}}{\\varvec{S}}\\)\u003c/span\u003e\u003c/span\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. Eq.\u0026nbsp;(1)\u003c/p\u003e \u003cp\u003eWhere, S\u003cem\u003eu\u003c/em\u003e and S\u003cem\u003en\u003c/em\u003e represent the respective areas of urban and non-urban regions within the study area. N\u003cem\u003eu\u003c/em\u003e and N\u003cem\u003en\u003c/em\u003e represent the number of pixels corresponding to each land use and a, b refer to the average DN values of each region. S represents the total area of the study region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Prediction\u003c/h2\u003e \u003cp\u003eThe study applied the CA-Markov model to simulate and predict future urban growth in Colombo. The change analysis was done using Land Change Modeller and the transition matrix was calculated using Markov chain model in IDRISI software concerning variations from 2014 to 2020. Applying the CA Markov chain technique, a projection for 2026 was done.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS ANALYSIS AND DISCUSSION","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Accuracy assessment based on finer-resolution remote sensing data\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe accuracy of the extracted urban information was evaluated using finer-resolution remotely sensed data. The evaluation aimed to validate the accuracy and reliability of the extracted urban information by comparing it with higher-resolution datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe use of finer-resolution remotely sensed data, such as high-resolution satellite imagery or aerial photographs, allowed for a detailed comparison with the extracted urban information. The ESA Land Cover data has a finer spatial resolution (300 m) compared to the NSL data (1 km or 500 m). So, evaluating the results using ESA Land Cover is feasible and widely accepted (Cao et al., 2009; Henderson et al., 2003; Small et al., 2005; Sutton et al., 2006).\u003c/p\u003e \u003cp\u003eThe accuracy assessment, conducted through the random selection of urban and non-urban points, demonstrates a high level of accuracy in the urban area extracted using VIIRS NTL data. The overall accuracy for urban area detection is 90.95%, with a kappa coefficient of 0.82 (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\u003e\u003cem\u003eError matrices and accuracy assessment for urban and non-urban area extracted using VIIRS NTL data.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClassified Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUser\u0026rsquo;s Accuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-urban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRow total\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.41%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColumn total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProducer\u0026rsquo;s Accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eOverall Accuracy\u0026thinsp;=\u0026thinsp;90.95% ; Kappa\u0026thinsp;=\u0026thinsp;0.82\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eA comparative analysis was performed by evaluating the results obtained from the DMSP data (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The findings from the accuracy assessment indicate an overall accuracy of 87.38% for the DMSP data, with a corresponding kappa coefficient of 0.75. These metrics provide a measure of the agreement between the classified urban areas and the reference data. Comparing the accuracy assessment results between the VIIRS and DMSP data, it is evident that the VIIRS data outperforms the DMSP data in terms of detecting urban areas.\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\u003eError matrices and accuracy assessment for urban and non-urban area extracted using DMSP NTL data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClassified Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUser\u0026rsquo;s Accuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-urban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRow total\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColumn total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProducer\u0026rsquo;s Accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eOverall Accuracy\u0026thinsp;=\u0026thinsp;87.38%; Kappa\u0026thinsp;=\u0026thinsp;0.75\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Urban growth pattern identification\u003c/h2\u003e \u003cp\u003eThe change trend of land use types in Colombo from 2013 to 2021 is the non-urban area is decreasing, and the urban land is increasing noticeably (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOver the study period from 2013 to 2021, the urban area experienced consistent expansion, increasing from 351.721 sq. km to 401.805 sq. km. This growth signifies the continuous development of urban areas within the district. Concurrently, the non-urban areas witnessed a decline, indicating the conversion of non-urban land into urban use. The non-urban area decreased from 333.230 sq. km to 283.176 sq. km, suggesting the encroachment of urbanization into previously non-urbanized regions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea Statistics of urban and non-urban extents in Colombo from 2013\u0026ndash;2021, Area units; km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-urban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRLI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57888.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e355.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e329.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59083.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e363.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e321.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61406.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e367.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e317.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62793.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e376.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e308.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65656.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e378.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e306.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64196.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e387.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69037.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e389.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e295.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70018.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e401.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74050.26\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\u003eRegional Light Index (RLI) displayed an upward trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This intensification of urban brightness and activity indicates the increasing development and population density in urban areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the results, the expansion of urban extents during 2013\u0026ndash;2021 periods shows Edge expansion pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Edge expansion results with the increase in the size of urban areas, as well as the emergence of new patches near or within existing patches.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, results were compared with the past studies conducted in Colombo District. In Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, (a) shows the spatial pattern variation derived from the DMSP/OLS Night time Light data (Yapa and Gunawardena,2022) and (b) represents the built-up areas and other land use types extracted from Landsat satellite images (Antalyn and Weerasinghe, 2020). Both studies also highlighted the edge expansion in urban growth pattern in Colombo District which is same as our analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Stimulation and Prediction\u003c/h2\u003e \u003cp\u003eThe dynamics of urban and non-urban areas from 2014 to 2020 shows that urban areas have witnessed a substantial increase and indicate the urbanization and development (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNotably, urban areas exhibited substantial growth with the significant net increase of 78.49 km\u003csup\u003e2\u003c/sup\u003e. Non-urban areas experienced a notable decrease, with the net loss of -78.49 km\u003csup\u003e2\u003c/sup\u003e. It shows that most of the non-urban areas transformed into urban areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA matrix of transition probability offers a quantitative understanding of how urban and non-urban extents change over a specific period using Markov chain analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The probabilities indicate the specific information about the likelihood of class transitions. The Transition Probability Matrix indicates that there is a high probability (0.7861) of non-urban land transitioning into urban land. Conversely, the probability of urban land changing into non-urban land is relatively lower (0.0046).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTransition Probability Matrix\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\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-urban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9954\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\u003eBased on the Transition Probability Matrix, the projection for land use in 2026 was done(Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). It is expected that there will be a substantial increase in urban land, as non-urban areas are likely to transform into urban areas with a high probability. This suggests that urban expansion will continue to change the landscape in the study area\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. CONCLUSION","content":"\u003cp\u003eThe present study aimed to assess the suitability of VIIRS nighttime light (NTL) data for urban studies in Sri Lanka. Specifically, we conducted an analysis of the urban growth pattern in the Colombo District using VIIRS NTL data spanning the period from 2013 to 2021. The findings of our study provide compelling evidence that VIIRS NTL data can effectively be employed for urban extraction and offer an accurate and comprehensive approach to mapping urban growth. By leveraging the spatial distribution of nighttime lights, VIIRS data enables the identification and delineation of urban areas with a high degree of precision. The significant advantage of utilizing VIIRS NTL data is its ability to overcome the limitations associated with traditional methods of urban growth pattern analysis. Unlike conventional techniques, VIIRS NTL data provides a robust and objective source of information. This not only enhances the accuracy of urban mapping but also facilitates rigorous and consistent analysis of urban growth dynamics.\u003c/p\u003e \u003cp\u003eThe application of VIIRS NTL data in urban studies can greatly support urban planning, land use management, and policy formulation in Sri Lanka. The accurate and reliable mapping of urban growth patterns enables decision-makers to better understand the spatial extent and intensity of urbanization, facilitating the identification of areas that require targeted interventions for sustainable development.\u003c/p\u003e \u003cp\u003eIt is important to acknowledge the limitations of our study. Firstly, the use of threshold methods, although straightforward, is subject to several limitations. The process of dividing sub-regions and determining the optimal threshold is subjective and timeconsuming, potentially introducing bias and inconsistency into the results. Moreover, the challenge of overestimating urban areas persists, particularly in the case of large cities. While there is a strong correlation between nighttime light (NTL) and urban extent, relying solely on NTL data may not capture the complete urban landscape accurately, leading to potential overestimation. Furthermore, our analysis focused solely on the Colombo District, and further investigations are needed to other regions in Sri Lanka. By acknowledging the above limitations and addressing them through further research, we can enhance the reliability and applicability of VIIRS NTL data for urban studies in Sri Lanka.\u003c/p\u003e \u003cp\u003eIn conclusion, our study demonstrates that VIIRS nighttime light data holds immense potential for urban studies in Sri Lanka. The adoption of VIIRS NTL data as a tool for urban extraction offers an accurate and comprehensive method for mapping urban growth, overcoming the limitations of traditional approaches. The findings from the study provide valuable insights for policymakers, planners, and researchers, enabling evidence-based decision-making and supporting sustainable urban development efforts in Sri Lanka.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData Availability:\u003c/h2\u003e \u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBhatta, B. (2009) \u0026lsquo;Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India\u0026rsquo;, International Journal of Remote Sensing, 30(18), p.4733-4746.\u003c/li\u003e\n\u003cli\u003eElvidge, C. D. et al. (2013) \u0026lsquo;Why VIIRS Data Are Superior to DMSP for Mapping Nighttime Lights\u0026rsquo;, Proceedings of the AsiaPacific Advanced Network, 35, 62.\u003c/li\u003e\n\u003cli\u003eJayasinghe, P., Raghavan, V. and Yonezawa, G., (2021) \u0026lsquo;Exploration of expansion patterns and prediction of urban growth for Colombo City, Sri Lanka\u0026rsquo;, Spatial Information Research, pp.1-14.\u003c/li\u003e\n\u003cli\u003eJiang, S.et al. (2020) \u0026lsquo;Detecting the Dynamics of Urban Growth in Africa Using DMSP/OLS Nighttime Light Data\u0026rsquo;, Land, 10(1), p.13.\u003c/li\u003e\n\u003cli\u003eLiu, Z. et al. (2012) \u0026lsquo;Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008\u0026rsquo;, Landscape and Urban Planning, 106(1), pp.62-72.\u003c/li\u003e\n\u003cli\u003eSexton, J.O. et al. (2013). \u0026lsquo;Urban growth of the Washington, DC\u0026ndash;Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover\u0026rsquo;, Remote Sensing of Environment, 129, pp.42-53.\u003c/li\u003e\n\u003cli\u003eShi, K. et al. (2014) \u0026lsquo;Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas\u0026rsquo;, Remote Sensing Letters, 5(4), pp.358-366.\u003c/li\u003e\n\u003cli\u003eSubasinghe, S., Estoque, R.C. and Murayama, Y. (2016) \u0026lsquo;Spatiotemporal analysis of urban growth using GIS and remote sensing: A case study of the Colombo Metropolitan Area, Sri Lanka\u0026rsquo;, ISPRS international journal of geo-information, 5(11), p.197.\u003c/li\u003e\n\u003cli\u003eUnited Nations, Department of Economic and Social Affairs, Population Division (2019). World Urbanization Prospects 2018: Highlights (ST/ESA/SER.A/421). \u003c/li\u003e\n\u003cli\u003eYapa, R.D.W.S. and Gunawardena, W. (2022) \u0026lsquo;Examination of the spatio-temporal urban growth patterns using DMSP-OLS night-time lights data: an experiment in urban area, Sri Lanka\u0026rsquo;.\u003c/li\u003e\n\u003cli\u003eYu, B. et al. (2018) \u0026lsquo;Urban built-up area extraction from log-transformed NPP-VIIRS nighttime light composite data\u0026rsquo;, IEEE Geoscience and Remote Sensing Letters, 15(8), pp.1279-1283.\u003c/li\u003e\n\u003cli\u003eZheng, W. et al. (2021) \u0026lsquo;Analysing the spatial structure of urban growth across the Yangtze River Middle reaches urban agglomeration in China using NPP-VIIRS night-time lights data\u0026rsquo;, GeoJournal, 46 pp.1-18.\u003c/li\u003e\n\u003cli\u003eZhou, N., Hubacek, K. and Roberts, M. (2015) \u0026lsquo;Analysis of spatial patterns of urban growth across South Asia using DMSP-OLS nighttime lights data\u0026rsquo;, Applied Geography, 63, pp.292-303.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Sabaragamuwa University of Sri Lanka","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":"VIIRS Nighttime Light, Urban growth pattern","lastPublishedDoi":"10.21203/rs.3.rs-9250627/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9250627/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eToday, urban growth is a multidimensional spatial and dynamic process that inclines towards increasing the significance of urban planning for developing countries with rapid growth of population and economy. Unplanned urban growth decreases the quality of urban environment. It is vital to study the urban change patterns to help for the decisionmaking process of urban planning. But, in developing countries like Sri Lanka, facing challenges in acquiring information to investigate the urban patterns. Therefore, the use of advanced technologies is indispensable for the identification of urban growth and for sustainable urban planning. Nighttime light data obtained from the Suomi National Polar Orbiting Partnership\u0026rsquo;s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor provides a new source of information that is advantageous for mapping and monitoring urban growth. Aiming at the knowledge gap in the use of VIIRS Nighttime Light data for urban studies in Sri Lanka, our study examined the capability of using VIIRS NTL data for the urban growth pattern identification. We used the nighttime data derived from the Suomi National Polar-Orbiting Partnership\u0026rsquo;s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor for the years from 2013 to 2021 to extract the urban extent and Regional Light Index for the spatial-temporal analysis. The results revealed that VIIRS NTL data has high potential in identification of urban growth patterns. Furthermore, we employed the CA-Markov model to predict future urban growth for 2026. The findings of the research will be very useful for the urban planners and policy makers to formulate better policies and strategies for future urban development in Sri Lanka.\u003c/p\u003e","manuscriptTitle":"Identification of Urban Growth Patterns in Sri Lanka Using Viirs Night-Time Lights Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 06:53:49","doi":"10.21203/rs.3.rs-9250627/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":"9824bdfb-8518-4ecd-9c3d-bb1ed5a11ed5","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T06:53:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 06:53:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9250627","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9250627","identity":"rs-9250627","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}