Vol. 15 No. 3 (2024): (Issue in progress)
Review Article

Tracking Urban Sprawl: A Systematic Review and Bibliometric Analysis of Spatio-Temporal Patterns Using Remote Sensing and GIS

Mohammad Raditia Pradana
Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia
Muhammad Dimyati
Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia
Bibliometric result for country scientific production and collaboration

Published 2024-09-07

Keywords

  • bibliometric,
  • geographic information system,
  • landsat,
  • remote sensing,
  • urban sprawl,
  • systematic literature review
  • ...More
    Less

How to Cite

Pradana, Mohammad Raditia, and Muhammad Dimyati. 2024. “Tracking Urban Sprawl: A Systematic Review and Bibliometric Analysis of Spatio-Temporal Patterns Using Remote Sensing and GIS”. European Journal of Geography 15 (3):190-203. https://doi.org/10.48088/ejg.m.pra.15.3.190.203.
Received 2024-06-13
Accepted 2024-09-06
Published 2024-09-07

Abstract

The urban sprawl phenomenon refers to the expansion of urban areas driven by high population growth and migration. A spatio-temporal approach is indispensable in urban sprawl research. Monitoring and evaluating urban sprawl in a region is crucial for controlling drastic environmental changes. Integrated Remote Sensing (RS) and Geographic Information System (GIS) technologies can serve as essential tools for this purpose. The aim of this systematic literature review paper is to gather information on the latest data, methods, and findings to be considered in future urban sprawl research. The PRISMA method was employed, involving filtering from the Scopus database, resulting in 30 papers selected for an in-depth review to address the objectives of this paper. Landsat data remains the preferred choice for monitoring changes due to its extensive historical archive compared to other data sources. Landscape metrics represent a more advanced method com-pared to conventional change detection in quantifying urban sprawl. Other indices and quantifiers are also used to support the quantification of urban sprawl. Two perspectives exist in selecting the study's temporal intervals: consistent and inconsistent, which are adjusted based on the natural characteristics of "change," namely "abrupt" and "gradual." Suggestions for future research include using data with detailed spatial resolution and narrow study intervals while considering the patterns of urban sprawl formation.

Highlights:

  • Spatio-temporal approach: Vital for understanding urban sprawl dynamics.
  • Remote Sensing and GIS integration: Key for monitoring and controlling sprawl.
  • Indices and landscape metrics: Crucial tools for quantifying sprawl change.

Downloads

Download data is not yet available.

References

  1. Ahmed, S. (2018). Assessment of urban heat islands and impact of climate change on socioeconomic over Suez Governorate using remote sens-ing and GIS techniques. Egyptian Journal of Remote Sensing and Space Science, 21(1), 15–25. https://doi.org/10.1016/j.ejrs.2017.08.001
  2. Akubia, J. E. K., & Bruns, A. (2019). Unravelling the frontiers of urban growth: Spatio-Temporal dynamics of land-use change and urban expan-sion in greater Accra metropolitan area, Ghana. Land, 8(9), 1–23. https://doi.org/10.3390/land8090131
  3. Al-Dousari, A. E., Mishra, A., & Singh, S. (2023). Land use land cover change detection and urban sprawl prediction for Kuwait metropolitan region, using multi-layer perceptron neural networks (MLPNN). Egyptian Journal of Remote Sensing and Space Science, 26(2), 381–392. https://doi.org/10.1016/j.ejrs.2023.05.003
  4. Al-shalabi, M., Billa, L., Pradhan, B., Mansor, S., & Al-Sharif, A. A. A. (2013). Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana’a metropolitan city, Yemen. Environmental Earth Sciences, 70(1), 425–437. https://doi.org/10.1007/s12665-012-2137-6
  5. Alsharif, A. A. A., & Pradhan, B. (2014). Urban Sprawl Analysis of Tripoli Metropolitan City (Libya) Using Remote Sensing Data and Multivariate Logistic Regression Model. Journal of the Indian Society of Remote Sensing, 42(1), 149–163. https://doi.org/10.1007/s12524-013-0299-7
  6. Alzahrani, A., Aldossary, N., & Alghamdi, J. (2024). Observing the dynamics of urban growth of Al-Baha City using GIS (2006–2021). Alexandria Engineering Journal, 95, 114–131. https://doi.org/10.1016/j.aej.2024.03.096
  7. Anand, A., & Deb, C. (2024). The potential of remote sensing and GIS in urban building energy modelling. Energy and Built Environment, 5(6), 957–969. https://doi.org/10.1016/j.enbenv.2023.07.008
  8. Aslam, R. W., Shu, H., & Yaseen, A. (2023). Monitoring the population change and urban growth of four major Pakistan cities through spatial analysis of open source data. Annals of GIS, 29(3), 355–367. https://doi.org/10.1080/19475683.2023.2166989
  9. Aurora, R. M., & Furuya, K. (2023). Spatiotemporal Analysis of Urban Sprawl and Ecological Quality Study Case: Chiba Prefecture, Japan. Land, 12(11). https://doi.org/10.3390/land12112013
  10. Balandi, J. B., To Hulu, J. P. P. M., Sambieni, K. R., Sikuzani, Y. U., Bastin, J. F., Musavandalo, C. M., Nguba, T. B., Molo, J. E. L., Selemani, T. M., Mweru, J. P. M., & Bogaert, J. (2023). Urban Sprawl and Changes in Landscape Patterns: The Case of Kisangani City and Its Periphery (DR Congo). Land, 12(11), 1–14. https://doi.org/10.3390/land12112066
  11. Baqa, M. F., Chen, F., Lu, L., Qureshi, S., Tariq, A., Wang, S., Jing, L., Hamza, S., & Li, Q. (2021). Monitoring and modeling the patterns and trends of urban growth using urban sprawl matrix and CA-Markov model: A case study of Karachi, Pakistan. Land, 10(7). https://doi.org/10.3390/land10070700
  12. Behnisch, M., Krüger, T., & Jaeger, J. A. G. (2022). Rapid rise in urban sprawl: Global hotspots and trends since 1990. PLOS Sustainability and Transformation, 1(11), e0000034. https://doi.org/10.1371/journal.pstr.0000034
  13. Berila, A., & Isufi, F. (2021). Two Decades (2000–2020) Measuring Urban Sprawl Using GIS, RS and Landscape Metrics: a Case Study of Municipali-ty of Prishtina (Kosovo). Journal of Ecological Engineering, 22(6), 114–125. https://doi.org/10.12911/22998993/137078
  14. Bhatta, B. (2010). Mapping and Monitoring Urban Growth BT - Analysis of Urban Growth and Sprawl from Remote Sensing Data (B. Bhatta, Ed.; pp. 65–83). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-05299-6_5
  15. Boori, M. S., Netzband, M., Choudhary, K., & Voženílek, V. (2015). Monitoring and modeling of urban sprawl through remote sensing and GIS in Kuala Lumpur, Malaysia. Ecological Processes, 4(1), 1–10. https://doi.org/10.1186/s13717-015-0040-2
  16. Bozkurt, S. G., & Basaraner, M. (2024). Spatio-temporal investigation of urbanization and its impact on habitat fragmentation in natural ecosys-tems of Istanbul using Shannon’s entropy and landscape metrics in GIS. Environment, Development and Sustainability, 0123456789. https://doi.org/10.1007/s10668-023-04410-7
  17. Brueckner, J. K. (2000). Urban Sprawl: Diagnosis and Remedies. International Regional Science Review, 23(2), 160–171. https://doi.org/10.1177/016001700761012710
  18. Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., Lu, M., Zhang, W., Tong, X., & Mills, J. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 7–27. https://doi.org/10.1016/j.isprsjprs.2014.09.002
  19. Copernicus. (2020). CORINE Land Cover. https://land.copernicus.eu/en/products/corine-land-cover?tab=main
  20. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E. (2004). Digital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing, 25(9), 1565–1596. https://doi.org/10.1080/0143116031000101675
  21. Cramer-Greenbaum, S. (2023). Quantifying displacement: Using turnover data to measure physical and psychological neighborhood change. European Journal of Geography, 14(1), 35–46. https://doi.org/10.48088/ejg.s.cra.14.1.35.46
  22. D’Agata, A., Nosova, B., Vardopoulos, I., Rontos, K., Clemente, M., Colombo, M. C., Sateriano, A., & Salvati, L. (2024). Urban decline, economic crisis and fringe landscapes: The mediterranean experience. In Urban Crisis: Social and Economic Implications for Southern Europe.
  23. Dai, E., Wu, Z., & Du, X. (2018). A gradient analysis on urban sprawl and urban landscape pattern between 1985 and 2000 in the Pearl River Delta, China. Frontiers of Earth Science, 12(4), 791–807. https://doi.org/10.1007/s11707-017-0637-0
  24. Dai, X., Jin, J., Chen, Q., & Fang, X. (2022). On Physical Urban Boundaries, Urban Sprawl, and Compactness Measurement: A Case Study of the Wen-Tai Region, China. Land, 11(10). https://doi.org/10.3390/land11101637
  25. Deng, J. S., Qiu, L. F., Wang, K., Yang, H., & Shi, Y. Y. (2011). An integrated analysis of urbanization-triggered cropland loss trajectory and implica-tions for sustainable land management. Cities, 28(2), 127–137. https://doi.org/10.1016/j.cities.2010.09.005
  26. Deng, J. S., Wang, K., Hong, Y., & Qi, J. G. (2009). Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. Landscape and Urban Planning, 92(3–4), 187–198. https://doi.org/10.1016/j.landurbplan.2009.05.001
  27. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
  28. Downs, A. (1999). Some realities about sprawl and urban decline. Housing Policy Debate, 10(4), 955–974. https://doi.org/10.1080/10511482.1999.9521356
  29. Dutta, I., & Das, A. (2019). Application of geo-spatial indices for detection of growth dynamics and forms of expansion in English Bazar Urban Agglomeration, West Bengal. Journal of Urban Management, 8(2), 288–302. https://doi.org/10.1016/j.jum.2019.03.007
  30. El Garouani, A., Mulla, D. J., El Garouani, S., & Knight, J. (2017). Analysis of urban growth and sprawl from remote sensing data: Case of Fez, Mo-rocco. International Journal of Sustainable Built Environment, 6(1), 160–169. https://doi.org/10.1016/j.ijsbe.2017.02.003
  31. European Environment Agency. (2008). Urban Sprawl in Europe, the Ignored Challenge. In Urban Sprawl in Europe: Landscapes, Land-Use Change & Policy (Issue 10). https://doi.org/10.1002/9780470692066
  32. Frenkel, A., & Ashkenazi, M. (2008). Measuring urban sprawl : how can we deal with it ? 35, 56–80. https://doi.org/10.1068/b32155
  33. Fuladlu, K., Riza, M., & Ilkan, M. (2021). Monitoring Urban Sprawl Using Time-Series Data: Famagusta Region of Northern Cyprus. SAGE Open, 11(2). https://doi.org/10.1177/21582440211007465
  34. Fulton, W., Pendall, R., Nguyen, M., & Harrison, A. (2001). Who Sprawls Most? How Growth Patterns Differ Across the U.S. July, 1–24.
  35. Galster, G., Hanson, R., Ratcliffe, M. R., Wolman, H., Coleman, S., & Freihage, J. (2001). Wrestling sprawl to the ground: Defining and measuring an elusive concept. Housing Policy Debate, 12(4), 681–717. https://doi.org/10.1080/10511482.2001.9521426
  36. Gao, B.-C. (1996). NDWI?A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sensing of Environment, 58, 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
  37. Gilbert, K. M., & Shi, Y. (2023). Nighttime Lights and Urban Expansion: Illuminating the Correlation between Built-Up Areas of Lagos City and Changes in Climate Parameters. Buildings, 13(12). https://doi.org/10.3390/buildings13122999
  38. Glaeser, E. L., & Kahn, M. E. (2003). Sprawl and Urban Growth, Discussion paper no 2004. Handbook of Urban and Regional Economics, IV. http://www.econ.brown.edu/Faculty/henderson/sprawl.pdf
  39. Gogoi, D., Bhaskaran, G., & Gogoi, A. (2023). An analysis of land dynamics in relation to urban sprawl in the Guwahati city of Assam, India. Ecocy-cles, 9(1), 49–60. https://doi.org/10.19040/ecocycles.v9i1.258
  40. Gordon, P., & Richardson, H. W. (2000). Critiquing Sprawl’ s Critics. Policy Analysis, 368(365), 1–18.
  41. Habibi, S., & Asadi, N. (2011). Causes, results and methods of controlling urban sprawl. Procedia Engineering, 21, 133–141. https://doi.org/10.1016/j.proeng.2011.11.1996
  42. Hall, R. E., & Jones, C. I. (1999). Why Do Some Countries Produce So Much More Output Per Worker Than Others? The Quarterly Journal of Eco-nomics, 114(1), 83–116. http://www.jstor.org/stable/2586948
  43. Hamidi, S., Ewing, R., Preuss, I., & Dodds, A. (2015). Measuring Sprawl and Its Impacts: An Update. https://doi.org/10.1177/0739456X14565247
  44. He, T., Zhou, R., Ma, Q., Li, C., Liu, D., Fang, X., Hu, Y., & Gao, J. (2023). Quantifying the effects of urban development intensity on the surface urban heat island across building climate zones. Applied Geography, 158(June), 103052. https://doi.org/10.1016/j.apgeog.2023.103052
  45. Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X
  46. Hysa, A., Löwe, R., & Geist, J. (2024). Ecosystem services potential is declining across European capital metropolitan areas. Scientific Reports, 14(1), 1–19. https://doi.org/10.1038/s41598-024-59333-8
  47. Irwin, E. G., & Bockstael, N. E. (2007). The evolution of urban sprawl: Evidence of spatial heterogeneity and increasing land fragmentation. Pro-ceedings of the National Academy of Sciences of the United States of America, 104(52), 20672–20677. https://doi.org/10.1073/pnas.0705527105
  48. Jain, M., Dimri, A. P., & Niyogi, D. (2016). Urban sprawl patterns and processes in delhi from 1977 to 2014 based on remote sensing and spatial metrics approaches. Earth Interactions, 20(14), 1–29. https://doi.org/10.1175/EI-D-15-0040.1
  49. Jiao, L., Liu, J., Xu, G., Dong, T., Gu, Y., Zhang, B., Liu, Y., & Liu, X. (2018). Proximity Expansion Index: An improved approach to characterize evolu-tion process of urban expansion. Computers, Environment and Urban Systems, 70, 102–112. https://doi.org/10.1016/j.compenvurbsys.2018.02.005
  50. Jiao, L., Mao, L., & Liu, Y. (2015). Multi-order Landscape Expansion Index: Characterizing urban expansion dynamics. Landscape and Urban Plan-ning, 137, 30–39. https://doi.org/10.1016/j.landurbplan.2014.10.023
  51. Kadhim, N., Mourshed, M., & Bray, M. (2016). Advances in remote sensing applications for urban sustainability. Euro-Mediterranean Journal for Environmental Integration, 1(1), 1–22. https://doi.org/10.1007/s41207-016-0007-4
  52. Kar, R., Obi Reddy, G. P., Kumar, N., & Singh, S. K. (2018). Monitoring spatio-temporal dynamics of urban and peri-urban landscape using remote sensing and GIS – A case study from Central India. Egyptian Journal of Remote Sensing and Space Science, 21(3), 401–411. https://doi.org/10.1016/j.ejrs.2017.12.006
  53. Kawamura, M., Jayamanna, S., & Tsujiko, Y. (1997). Quantitative Evaluation of Urbanization in Developing Countries Using Satellite Data. Doboku Gakkai Ronbunshu, 1997(580), 45–54. https://doi.org/10.2208/jscej.1997.580_45
  54. Kulwant, M., & Patel, D. (2024). Application of remote sensing, GIS, and AI techniques in the agricultural sector. In Agri-Tech Approaches for Nutrients and Irrigation Water Management. https://doi.org/10.1201/9781003441175-15
  55. Kumar, J. A. V, Pathan, S. K., & Bhanderi, R. J. (2007). Spatio-temporal analysis for monitoring urban growth - a case study of Indore City. Journal of the Indian Society of Remote Sensing, 35(1), 11–20. https://doi.org/10.1007/BF02991829
  56. Liu, L., Peng, Z., Wu, H., Jiao, H., Yu, Y., & Zhao, J. (2018). Fast identification of urban sprawl based on K-means clustering with population density and local spatial entropy. Sustainability (Switzerland), 10(8). https://doi.org/10.3390/su10082683
  57. Liu, L., & Zhang, Y. (2011). Urban heat island analysis using the landsat TM data and ASTER Data: A case study in Hong Kong. Remote Sensing, 3(7), 1535–1552. https://doi.org/10.3390/rs3071535
  58. Liu, X., Li, X., Chen, Y., Tan, Z., Li, S., & Ai, B. (2010). A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data. Landscape Ecology, 25(5), 671–682. https://doi.org/10.1007/s10980-010-9454-5
  59. Mdari, Y. E., Daoud, M. A., Namir, A., & Hakdaoui, M. (2022). Casablanca Smart City Project: Urbanization, Urban Growth, and Sprawl Challenges Using Remote Sensing and Spatial Analysis. Lecture Notes in Networks and Systems, 216, 209–217. https://doi.org/10.1007/978-981-16-1781-2_20
  60. Medayese, S., Magidimisha-Chipungu, H. H., & Chipungu, L. (2023). Spatial Matrices of Urban Expansion in Lafia, North-Central Nigeria. Forum Geografi, 37(1), 66–79. https://doi.org/10.23917/forgeo.v37i1.18068
  61. Miller, L., Pelletier, C., & Webb, G. I. (2024). Deep Learning for Satellite Image Time-Series Analysis: A review. IEEE Geoscience and Remote Sensing Magazine, 2–45. https://doi.org/10.1109/MGRS.2024.3393010
  62. Mun, J., Lee, J. S., & Kim, S. (2024). Effects of urban sprawl on regional disparity and quality of life: A case of South Korea. Cities, 151, 105125. https://doi.org/10.1016/j.cities.2024.105125
  63. Nolè, G., Lasaponara, R., Lanorte, A., & Murgante, B. (2014). Quantifying urban sprawl with spatial autocorrelation techniques using multi-temporal satellite data. International Journal of Agricultural and Environmental Information Systems, 5(2), 20–38. https://doi.org/10.4018/IJAEIS.2014040102
  64. Page, M. J., McKenzie, J. E., Bossuyt, P., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The pris-ma 2020 statement: An updated guideline for reporting systematic reviews. Medicina Fluminensis, 57(4), 444–465. https://doi.org/10.21860/medflum2021_264903
  65. Patra, S., Sahoo, S., Mishra, P., & Mahapatra, S. C. (2018). Impacts of urbanization on land use /cover changes and its probable implications on local climate and groundwater level. Journal of Urban Management, 7(2), 70–84. https://doi.org/10.1016/j.jum.2018.04.006
  66. Pokhariya, H. S., Singh, D. P., & Prakash, R. (2021). Investigating the impacts of urbanization on different land cover classes and land surface temperature using GIS and RS techniques. International Journal of Systems Assurance Engineering and Management. https://doi.org/10.1007/s13198-021-01512-1
  67. Pranckutė, R. (2021). Web of Science (WoS) and Scopus: the titans of bibliographic information in today’s academic world. Publications, 9(1). https://doi.org/10.3390/publications9010012
  68. Rahman, M. N., Akter, K. S., & Faridatul, M. I. (2024). Assessing the impact of urban expansion on carbon emission. Environmental and Sustaina-bility Indicators, 23, 100416. https://doi.org/10.1016/j.indic.2024.100416
  69. Rana, B., Bandyopadhyay, J., & Halder, B. (2024). Investigating the relationship between urban sprawl and urban heat island using remote sens-ing and machine learning approaches. Theoretical and Applied Climatology, 0123456789. https://doi.org/10.1007/s00704-024-04874-1
  70. Rasul, A., Balzter, H., Ibrahim, G. R. F., Hameed, H. M., Wheeler, J., Adamu, B., Ibrahim, S., & Najmaddin, P. M. (2018). Applying built-up and bare-soil indices from Landsat 8 to cities in dry climates. Land, 7(3). https://doi.org/10.3390/land7030081
  71. Raza, A., Syed, N. R., Fahmeed, R., Acharki, S., Aljohani, T. H., Hussain, S., Zubair, M., Zahra, S. M., Islam, A. R. M. T., Almohamad, H., & Abdo, H. G. (2024). Investigation of changes in land use/land cover using principal component analysis and supervised classification from operational land imager satellite data: a case study of under developed regions, Pakistan. Discover Sustainability, 5(1). https://doi.org/10.1007/s43621-024-00263-w
  72. Roelfsema, C. (2010). Integrating field data with high spatial resolution multispectral satellite imagery for calibration and validation of coral reef benthic community maps. Journal of Applied Remote Sensing, 4(1), 043527. https://doi.org/10.1117/1.3430107
  73. Rouse, J. W., Jr., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA. Goddard Space Flight Center 3d ERTS-1 Symp., 1. https://doi.org/10.1021/jf60203a024
  74. Salvati, L., Munafo, M., Morelli, V. G., & Sabbi, A. (2012). Low-density settlements and land use changes in a Mediterranean urban region. Land-scape and Urban Planning, 105(1–2), 43–52. https://doi.org/10.1016/j.landurbplan.2011.11.020
  75. Selmy, S. A. H., Kucher, D. E., Mozgeris, G., Moursy, A. R. A., Jimenez-Ballesta, R., Kucher, O. D., Fadl, M. E., & Mustafa, A. rahman A. (2023). Detecting, Analyzing, and Predicting Land Use/Land Cover (LULC) Changes in Arid Regions Using Landsat Images, CA-Markov Hybrid Model, and GIS Techniques. Remote Sensing, 15(23). https://doi.org/10.3390/rs15235522
  76. Shaw, R., & Das, A. (2018). Identifying peri-urban growth in small and medium towns using GIS and remote sensing technique: A case study of English Bazar Urban Agglomeration, West Bengal, India. Egyptian Journal of Remote Sensing and Space Science, 21(2), 159–172. https://doi.org/10.1016/j.ejrs.2017.01.002
  77. Smith, D. (2020). Population and Urbanization. The State of the Middle East, 1(1), 120–121. https://doi.org/10.4324/9781315065977-35
  78. Sohn, J., Choi, S., Lewis, R., & Knaap, G. (2012). Characterising urban sprawl on a local scale with accessibility measures. Geographical Journal, 178(3), 230–241. https://doi.org/10.1111/j.1475-4959.2012.00468.x
  79. Sudhira, H. S., Ramachandra, T. V., & Jagadish, K. S. (2004). Urban sprawl: Metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformation, 5(1), 29–39. https://doi.org/10.1016/j.jag.2003.08.002
  80. Sultana, S., & Weber, J. (2014). The Nature of Urban Growth and the Commuting Transition: Endless Sprawl or a Growth Wave? Urban Studies, 51(3), 544–576. https://doi.org/10.1177/0042098013498284
  81. Sun, C., Wu, Z. F., Lv, Z. Q., Yao, N., & Wei, J. B. (2013). Quantifying different types of urban growth and the change dynamic in Guangzhou using multi-temporal remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 21(1), 409–417. https://doi.org/10.1016/j.jag.2011.12.012
  82. Taubenböck, H., Wegmann, M., Roth, A., Mehl, H., & Dech, S. (2009). Urbanization in India - Spatiotemporal analysis using remote sensing data. Computers, Environment and Urban Systems, 33(3), 179–188. https://doi.org/10.1016/j.compenvurbsys.2008.09.003
  83. Torrens, P. M. (2008). A Toolkit for Measuring Sprawl. May 2007, 5–36. https://doi.org/10.1007/s12061-008-9000-x
  84. Triantakonstantis, D., & Stathakis, D. (2015). Examining urban sprawl in Europe using spatial metrics. Geocarto International, 30(10), 1092–1112. https://doi.org/10.1080/10106049.2015.1027289
  85. UN Department of Economic and Social Affairs. (2018). World Urbanization Prospects. In Demographic Research (Vol. 12). https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf
  86. van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
  87. Vogelmann, J. E., Gallant, A. L., Shi, H., & Zhu, Z. (2016). Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data. Remote Sensing of Environment, 185, 258–270. https://doi.org/10.1016/j.rse.2016.02.060
  88. Waleed, M., Sajjad, M., & Shazil, M. S. (2024). Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020). Environmental Impact Assessment Review, 105(July 2023), 107396. https://doi.org/10.1016/j.eiar.2023.107396
  89. Wang, G., Peng, W., Zhang, L., Xiang, J., Shi, J., & Wang, L. (2023). Quantifying urban expansion and its driving forces in Chengdu, western China. Egyptian Journal of Remote Sensing and Space Science, 26(4), 1057–1070. https://doi.org/10.1016/j.ejrs.2023.11.010
  90. Woodcock, C. E., Loveland, T. R., Herold, M., & Bauer, M. E. (2020). Transitioning from change detection to monitoring with remote sensing: A paradigm shift. Remote Sensing of Environment, 238, 111558. https://doi.org/10.1016/j.rse.2019.111558
  91. World Bank. (2024). Urban population growth (annual %). https://data.worldbank.org/indicator/SP.URB.GROW?end=2023&locations=CN-IN&most_recent_value_desc=false&start=2023&view=bar
  92. Wu, Y., Li, S., & Yu, S. (2016). Monitoring urban expansion and its effects on land use and land cover changes in Guangzhou city, China. Environ-mental Monitoring and Assessment, 188(1), 1–15. https://doi.org/10.1007/s10661-015-5069-2
  93. Xu, C., Liu, M., Zhang, C., An, S., Yu, W., & Chen, J. M. (2007). The spatiotemporal dynamics of rapid urban growth in the Nanjing metropolitan region of China. Landscape Ecology, 22(6), 925–937. https://doi.org/10.1007/s10980-007-9079-5
  94. Xu, G., Zhou, Z., Jiao, L., & Zhao, R. (2020). Compact Urban Form and Expansion Pattern Slow Down the Decline in Urban Densities: A Global Perspective. Land Use Policy, 94, 104563. https://doi.org/10.1016/j.landusepol.2020.104563
  95. Xu, H. (2005). Study on extracting water body information using improved normalized difference water index (MNDWI). National Remote Sensing Bulletin, 5, 589–595. https://doi.org/10.11834/jrs.20050586
  96. Y. Zha, J. G., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. https://doi.org/10.1080/01431160304987
  97. Yan, Y., Yang, Y., & Yang, M. (2024). Unravelling the non-linear response of ecosystem services to urban-rural transformation in the Beijing-Tianjin-Hebei region, China. Ecological Informatics, 81, 102633. https://doi.org/10.1016/j.ecoinf.2024.102633
  98. Yulianto, F., Fitriana, H. L., & Sukowati, K. A. D. (2020). Integration of remote sensing, GIS, and Shannon’s entropy approach to conduct trend analysis of the dynamics change in urban/built-up areas in the Upper Citarum River Basin, West Java, Indonesia. Modeling Earth Systems and Environment, 6(1), 383–395. https://doi.org/10.1007/s40808-019-00686-9
  99. Zeng, C., Liub, Y., Steind, A., & Jiao, L. (2015). Characterization and spatial modeling of urban sprawl in the Wuhan Metropolitan Area, China. International Journal of Applied Earth Observation and Geoinformation, 34(1), 10–24. https://doi.org/10.1016/j.jag.2014.06.012
  100. Zhang, L., Zhang, J., Li, X., Zhou, K., & Ye, J. (2023). The Impact of Urban Sprawl on Carbon Emissions from the Perspective of Nighttime Light Remote Sensing: A Case Study in Eastern China. Sustainability (Switzerland), 15(15). https://doi.org/10.3390/su151511940
  101. Zhang, N., Hong, Y., Qin, Q., & Zhu, L. (2013). Evaluation of the visible and shortwave infrared drought index in China. International Journal of Disaster Risk Science, 4(2), 68–76. https://doi.org/10.1007/s13753-013-0008-8