Artificial intelligence for visualizing land cover changes in central China

Authors

DOI:

https://doi.org/10.47456/geo.v5i41.50748

Keywords:

remote sensing, machine learning, image processing

Abstract

Remote sensing (RS) data are essential sources of information for mapping landscape dynamics in urban areas. Algorithms of artificial intelligence (AI), including machine learning (ML), provide robust methods for processing RS data. This study used ML methods of GRASS GIS software for processing satellite images to analyse landscape changes in central China. The aim is to analyse landscape dynamics through detected land cover changes during 10 years with 2-year time gap. The workflow included Random Forest ML algorithm of image classification. Data included six Landsat 8-9 OLI/TIRS images taken in autumn in 2013, 2015, 2017, 2019, 2021 and 2023. The results indicated the expansion of the area of Wuhan city, which indicates the processes of urbanization and intensive land development. This article demonstrated the application of AI-enhanced approach in cartography to image analysis for analysis of landscape dynamics in central China.

Downloads

Download data is not yet available.

Author Biography

  • Polina Lemenkova, Università di Bologna

    Polina Lemenkova is a cartographer with strong experience in data analysis in natural, engineering and technical sciences: https://orcid.org/0000-0002-5759-1089 .

    Her research areas include geoinformatics, machine learning and artificial intelligence, remote sensing data processing, programming algorithms (Python, R, GMT, GRASS GIS) and geophysics. She is an active scientific writer who authored articles on satellite image analysis, environmental monitoring and mapping, applied geology and geography. Examples of her recent published works include the following articles:

    1. Lemenkova, P. (2025). Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python. Examples and Counterexamples, 7(10):100180. https://doi.org/10.1016/j.exco.2025.100180
    2. Lemenkova, P. (2024). Artificial Intelligence for Computational Remote Sensing: Quantifying Patterns of Land Cover Types around Cheetham Wetlands, Port Phillip Bay, Australia. Journal of Marine Science and Engineering, 12(8), 1279. https://doi.org/10.3390/jmse12081279
    3. Lemenkova P. (2024). Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data. Journal of Marine Science and Engineering (12)5, 1-29. https://doi.org/10.3390/jmse12050709
    4. Lemenkova, P. (2024). Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh. Water, 16(8), 1141. https://doi.org/10.3390/w16081141
    5. Lemenkova, P. (2025). Reclassification Scheme for Image Analysis in GRASS GIS Using Gradient Boosting Algorithm: A Case of Djibouti, East Africa. Journal of Imaging, 11(8), 249. https://doi.org/10.3390/jimaging11080249

References

AMANKULOVA, K.; FARMONOV, N.; AKRAMOVA, P.; TURSUNOV; I. ET MUCSI, L. Comparison of PlanetScope, Sentinel-2, and landsat 8 data in soybean yield estimation within-field variability with random forest regression. Heliyon, vol. 9, no 6, p. e17432, 2023. https://doi.org/10.1016/j.heliyon.2023.e17432

BROWN DE COLSTOUN, E. C.; STORY, M. H., THOMPSON, C., COMMISSO, K., SMITH, T. G., IRONS, J. R. National Park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier. Remote Sensing of Environment, 85, no 3, p. 316-327, 2003. https://doi.org/10.1016/S0034-4257(03)00010-5

CAO, Q.; HUANG, H.; HONG, Y.; HUANG, X.; WANG, S.; WANG, L.; WANG, L. Modeling intra-urban differences in thermal environments and heat stress based on local climate zones in central Wuhan. Building and Environment, 225, no 109625, 2022. https://doi.org/10.1016/j.buildenv.2022.109625

DAI, X.; WANG, L.; LI, X.; GONG, J.; CAO, Q. Characteristics of the extreme precipitation and its impacts on ecosystem services in the Wuhan Urban Agglomeration. Science of The Total Environment, 864, no 161045, 2023. https://doi.org/10.1016/j.scitotenv.2022.161045

DENG, Y.; SHAO, Z.; DANG, C.; HUANG, X.; WU, W.; ZHUANG, Q.; DING, Q. Assessing urban wetlands dynamics in Wuhan and Nanchang, China. Science of The Total Environment, 901, no 165777, 2023. https://doi.org/10.1016/j.scitotenv.2023.165777

DING, W.; CHEN, H. (2022) - Urban-rural fringe identification and spatial form transformation during rapid urbanization: A case study in Wuhan, China. Building and Environment, 226, no 109697. https://doi.org/10.1016/j.buildenv.2022.109697

DOU, Y.; YU, X.; LIU, L.; NING, Y.; BI, X.; LIU, J. Effects of hydrological connectivity project on heavy metals in Wuhan urban lakes on the time scale. Science of The Total Environment, 853, no 158654, 2022. https://doi.org/10.1016/j.scitotenv.2022.158654

FAN, F.; WEN, X.; FENG, Z.; GAO, Y.; LI, W. Optimizing urban ecological space based on the scenario of ecological security patterns: The case of central Wuhan, China. Applied Geography, 138, no 102619, 2022. https://doi.org/10.1016/j.apgeog.2021.102619

FU, C.; JIANG, Z.; GUAN, Z.; HE, J.; XU, Z. Impacts of Climate Change on Water Resources and Agriculture in China”, In: Fu, C., Jiang, Z., Guan, Z., He, J., Xu, Z. (eds) Regional Climate Studies of China. Regional Climate Studies. Springer, Berlin, Heidelberg, 2008. https://doi.org/10.1007/978-3-540-79242-0_11

GENG, L.; ZHAO, X.; AN, Y.; PENG, L.; YE, D. Study on the Spatial Interaction between Urban Economic and Ecological Environment—A Case Study of Wuhan City. International Journal of Environmental Research and Public Health, 19, no 16, p. 10022, 2022. https://doi.org/10.3390/ijerph191610022

GODINHO, S.; GUIOMAR, N.; GIL, A. Using a stochastic gradient boosting algorithm to analyse the effectiveness of Landsat 8 data for montado land cover mapping: Application in southern Portugal. International Journal of Applied Earth Observation and Geoinformation, 49, p. 151-162, 2016. https://doi.org/10.1016/j.jag.2016.02.008

GUAN, D.; HE, X.; HE, C.; CHENG, L.; QU, S. Does the urban sprawl matter in Yangtze River Economic Belt, China? An integrated analysis with urban sprawl index and one scenario analysis model. Cities, 99, no 102611, 2020. https://doi.org/10.1016/j.cities.2020.102611

HE, Q.; TAN, R.; GAO, Y.; ZHANG, M.; XIE, P.; LIU, Y. Modeling urban growth boundary based on the evaluation of the extension potential: A case study of Wuhan city in China. Habitat International, 72, p. 57-65, 2018. https://doi.org/10.1016/j.habitatint.2016.11.006

HU, S.; TONG, L.; FRAZIER, A. E.; LIU, Y. Urban boundary extraction and sprawl analysis using Landsat images: A case study in Wuhan, China. Habitat International, 47, p. 183-195, 2015. https://doi.org/10.1016/j.habitatint.2015.01.017

HU, Y.; LI, L.; LI, B.; PENG, L.; XU, Y.; ZHOU, X.; LI, R.; SONG, K. Spatial variations and ecological risks assessment of pharmaceuticals and personal care products (PPCPs) in typical lakes of Wuhan, China. Process Safety and Environmental Protection, 174, p. 828-837, 2023. https://doi.org/10.1016/j.psep.2023.05.006

HUANG, X.; WANG, H.; XIAO, F. Simulating urban growth affected by national and regional land use policies: Case study from Wuhan, China. Land Use Policy, 112, no 105850, 2022. https://doi.org/10.1016/j.landusepol.2021.105850

JOSHI, P. P.; WYNNE, R. H.; THOMAS, V. A. Cloud detection algorithm using SVM with SWIR2 and tasseled cap applied to Landsat 8. International Journal of Applied Earth Observation and Geoinformation, 82, no 101898, 2019. https://doi.org/10.1016/j.jag.2019.101898

KANA, C. E.; ETOUNA, J. E. Apport de trois méthodes de détection des surfaces brûlées par imagerie Landsat ETM+ : application au contact forêt-savane du Cameroun. Cybergeo: European Journal of Geography, Environnement, Nature, Paysage, no 357, 2006. https://doi.org/10.4000/cybergeo.2711

LAN, H.; ZHENG, P.; LI, Z. Constructing urban sprawl measurement system of the Yangtze River economic belt zone for healthier lives and social changes in sustainable cities. Technological Forecasting and Social Change, 165, no 120569, 2021. https://doi.org/10.1016/j.techfore.2021.120569

LEBAUT, S.; MANCEAU, L. Potentialités des images Landsat pour l'identification et la délimitation de zones humides à l'échelle régionale : l'exemple de l'Est de la France. Physio-Géo, 9, no 1, p. 125-140, 2015. https://doi.org/10.4000/physio-geo.4563

ANONYM

LI, G.; LI, F. Urban sprawl in China: Differences and socioeconomic drivers. Science of The Total Environment, v. 673, p. 367-377, 2019. https://doi.org/10.1016/j.scitotenv.2019.04.080

LIU, D.; CLARKE, K. C.; CHEN, N. Integrating spatial nonstationarity into SLEUTH for urban growth modeling: A case study in the Wuhan metropolitan area. Computers, Environment and Urban Systems, 84, no 101545, 2020. https://doi.org/10.1016/j.compenvurbsys.2020.101545

LIU, D.; CHEN, N.; ZHANG, X.; WANG, C.; DU, W. (2020) - Annual large-scale urban land mapping based on Landsat time series in Google Earth Engine and OpenStreetMap data: A case study in the middle Yangtze River basin. ISPRS Journal of Photogrammetry and Remote Sensing, 159, p. 337-351. https://doi.org/10.1016/j.isprsjprs.2019.11.021

LONG, D.; DU, J.; XIN, Y. Assessing the nexus between natural resource consumption and urban sprawl: Empirical evidence from 288 cities in China. Resources Policy, 85, no 103915, 2023. https://doi.org/10.1016/j.resourpol.2023.103915

MAHMOUD, M. S. A. Classification of high-resolution satellite images from urban areas based hybrid supporting vector machines and Multi-instance learning. International Telecommunications Conference (ITC-Egypt), Alexandria, Egypt, 2021, p. 1-4, 2021. https://doi.org/10.1109/ITC-Egypt52936.2021.9513882

MOUNTRAKIS, G.; HEYDARI, S. S. Harvesting the Landsat archive for land cover land use classification using deep neural networks: Comparison with traditional classifiers and multi-sensor benefits. ISPRS Journal of Photogrammetry and Remote Sensing, 200, p. 106-119, 2023. https://doi.org/10.1016/j.isprsjprs.2023.05.005

NAGARAJ, R.; KUMAR, L. S. Surface water body extraction and Change Detection Analysis using Machine Learning Algorithms: A Case study of Vaigai Dam, India. International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), Karaikal, India, p. 1-6, 2023. https://doi.org/10.1109/IConSCEPT57958.2023.10170342

RAHBAR ALAM SHIRAZI, F.; SHAHBAZI, F.; REZAEI, H.; BISWAS, A. Multi-property digital soil mapping at 30-m spatial resolution down to 1 m using extreme gradient boosting tree model and environmental covariates. Remote Sensing Applications: Society and Environment, 33, no 101123, 2024. https://doi.org/10.1016/j.rsase.2023.101123

SELVARAJU, S.; JANCY, P. L.; VINOD KUMAR, D.; PRABHA, R.; KARTHIKEYAN, C.; BABU D. V. Support Vector Machine based Remote Sensing using Satellite Data Image. 2nd International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, p. 871-874, 2021. https://doi.org/10.1109/ICOSEC51865.2021.9591631

SHENDRYK, Y.; ROSSITER-RACHOR, N. A.; SETTERFIELD, S. A.; LEVICK, S. R. Leveraging High-Resolution Satellite Imagery and Gradient Boosting for Invasive Weed Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4443-4450, 2020. https://doi.org/10.1109/JSTARS.2020.3013663

TAN, R.; LIU, Y.; ZHOU, K.; JIAO, L.; TANG, W. A game-theory based agent-cellular model for use in urban growth simulation: A case study of the rapidly urbanizing Wuhan area of central China. Computers, Environment and Urban Systems, 49, p. 15-29, 2015. https://doi.org/10.1016/j.compenvurbsys.2014.09.001

TENG, M.; ZHOU, Z.; WANG, P.; XIAO, W.; WU, C.; LORD E. Geotechnology-Based Modeling to Optimize Conservation of Forest Network in Urban Area. Environmental Management, 57, p. 601–619, 2016. https://doi.org/10.1007/s00267-015-0642-6

TONG, L.; HU, S.; FRAZIER, A. E.Hierarchically measuring urban expansion in fast urbanizing regions using multi-dimensional metrics: A case of Wuhan metropolis, China. Habitat International, v. 94, no 102070, 2019. https://doi.org/10.1016/j.habitatint.2019.102070

WANG, Q.; WANG, H. (2022) - Spatiotemporal dynamics and evolution relationships between land-use/land cover change and landscape pattern in response to rapid urban sprawl process: A case study in Wuhan, China. Ecological Engineering, 182, no 106716. https://doi.org/10.1016/j.ecoleng.2022.106716

WU, D.; ZHENG, L.; WANG, Y.; GONG, J.; LI, J.; CHEN, Q. Characteristics of urban expansion in megacities and its impact on water-related ecosystem services: A comparative study of Chengdu and Wuhan, China. Ecological Indicators, 158, no 111322, 2024. https://doi.org/10.1016/j.ecolind.2023.111322

XING, S.; YANG, S.; SUN, H.; WANG, Y. Spatiotemporal Changes of Terrestrial Carbon Storage in Rapidly Urbanizing Areas and Their Influencing Factors: A Case Study of Wuhan, China. Land 12, no 12, p. 2134, 2023. https://doi.org/10.3390/land12122134

YUAN, Q.; ZHU, J. (2019) - Logistics sprawl in Chinese metropolises: Evidence from Wuhan. Journal of Transport Geography, 74, p. 242-252. https://doi.org/10.1016/j.jtrangeo.2018.11.019

ZHANG L.; ZHANG M.; WANG Q. Monitoring of subpixel impervious surface dynamics using seasonal time series Landsat 8 OLI imagery. Ecological Indicators, 154, no 110772, 2023. https://doi.org/10.1016/j.ecolind.2023.110772

ZHENG, Z.; YANG, B.; LIU, S.; XIA, J.; ZHANG, X. Extraction of impervious surface with Landsat based on machine learning in Chengdu urban, China. Remote Sensing Applications: Society and Environment, 30, no 100974, 2023. https://doi.org/10.1016/j.rsase.2023.100974

ZENG, C.; LIU, Y.; STEIN, A.; JIAO, L. Characterization and spatial modeling of urban sprawl in the Wuhan Metropolitan Area, China. International Journal of Applied Earth Observation and Geoinformation, 34, p. 10-24, 2015. https://doi.org/10.1016/j.jag.2014.06.012

ZHOU, X.; WU, B.; LIU, Y.; ZHOU, Q.; CHENG, W. Synergistic effects of heat and carbon on sustainable urban development: Case study of the Wuhan Urban Agglomeration. Journal of Cleaner Production, 425, no 138971, 2023. https://doi.org/10.1016/j.jclepro.2023.138971

Relevo topográfico da China

Downloads

Published

15-12-2025

Issue

Vol. 5 No. 41 (2025): Revista Geografares

Section

Papers

License

Copyright (c) 2025 Geografares

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyrights Declaration

Authors who publish in the journal agree with the following terms:

  1. Authors will keep their copyrights and grant the journal the right to their first publishing, simultaneously licenced under Creative Commons Attribution License which allows sharing their work with authorship recognition and initial release through this journal. 
  2. Authors may sign additional contracts separately diffusing a non-exclusively version of the paper published in this journal (i.g. publishing in institutional repository or as a book chapter), once citing the authorship and initial release through this journal.  
  3. Authors are encouraged to publicize and diffuse their paper online, for example onto institutional repositories or on their personal websites.

How to Cite

Artificial intelligence for visualizing land cover changes in central China. Geografares, Vitória, Brasil, v. 5, n. 41, p. e-50748, 2025. DOI: 10.47456/geo.v5i41.50748. Disponível em: https://periodicos.ufes.br/geografares/article/view/50748. Acesso em: 7 jan. 2026.