Land Cover Analysis in the Yangtze River Basin for Detection of Wetland Agriculture and Urban Dynamics in Wuhan Area (China)

Lemenkova, Polina

doi:10.2478/trser-2025-0001

Abstract

This study presents environmental analysis of the Yangtze River Basin, Wuhan region of central China, performed using machine learning (ML) methods of Remote Sensing (RS) data classification. The workflow is performed using Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) scripting software for processing Landsat images by two approaches: unsupervised clustering and supervised ML algorithms. Six Landsat images were taken biennially in autumn from 2013 to 2023 to detect wetland changes in the Wuhan area. This article demonstrates the application of ML in GIS for analysis of landscape dynamics to detect changes in riverine and lacustrine areas around the Yangtze River.

References

Amankulova K., Farmonov N., Akramova P., Tursunov I. and Mucsi L., 2023 – Comparison of PlanetScope, Sentinel-2, and landsat 8 data in soybean yield estimation within-field variability with random forest regression, Heliyon, 9, 6, e17432.
Search in Google Scholar Back to article
Brown De Colstoun E. C., Story M. H., Thompson C., Commisso K., Smith T. G. and Irons J. R., 2003 – National Park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier, Remote Sensing of Environment, 85, 3, 316-327.
Search in Google Scholar Back to article
Cao Q., Huang H., Hong Y., Huang X., Wang S., Wang L. and Wang L., 2022 – Modeling intra-urban differences in thermal environments and heat stress based on local climate zones in central Wuhan, Building and Environment, 225, 109625.
Search in Google Scholar Back to article
Dai X., Wang L., Li X., Gong J. and Cao Q., 2023 – Characteristics of the extreme precipitation and its impacts on ecosystem services in the Wuhan Urban Agglomeration, Science of The Total Environment, 864, 161045.
Search in Google Scholar Back to article
Deng Y., Shao Z., Dang C., Huang X., Wu W., Zhuang Q. and Ding Q., 2023 – Assessing urban wetlands dynamics in Wuhan and Nanchang, China, Science of The Total Environment, 901, 165777.
Search in Google Scholar Back to article
Ding W. and Chen H., 2022 – Urban-rural fringe identification and spatial form transformation during rapid urbanization: A case study in Wuhan, China, Building and Environment, 226, 109697.
Search in Google Scholar Back to article
Dou Y., Yu X., Liu L., Ning Y., Bi X. and Liu J., 2022 – Effects of hydrological connectivity project on heavy metals in Wuhan urban lakes on the time scale, Science of The Total Environment, 853, 158654.
Search in Google Scholar Back to article
Fan F., Wen X., Feng Z., Gao Y. and Li W., 2022 – Optimizing urban ecological space based on the scenario of ecological security patterns: The case of central Wuhan, China, Applied Geography, 138, 102619.
Search in Google Scholar Back to article
Fu, C., Jiang, Z., Guan, Z., He, J. and Xu, Z., 2008 – 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.
Search in Google Scholar Back to article
Geng L., Zhao X., An Y., Peng L. and Ye D., 2022 – 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, 16, 10022.
Search in Google Scholar Back to article
Godinho S., Guiomar N. and Gil A., 2016 – 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, 151-162.
Search in Google Scholar Back to article
Guan D., He X., He C., Cheng L. and Qu S., 2020 – 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, 102611.
Search in Google Scholar Back to article
He Q., Tan R., Gao Y., Zhang M., Xie P. and Liu Y., 2018 – Modeling urban growth boundary based on the evaluation of the extension potential: A case study of Wuhan city in China, Habitat International, 72, 57-65.
Search in Google Scholar Back to article
Hu S., Tong L., Frazier A. E. and Liu Y., 2015 – Urban boundary extraction and sprawl analysis using Landsat images: A case study in Wuhan, China, Habitat International, 47, 183-195.
Search in Google Scholar Back to article
Hu Y., Li L., Li B., Peng L., Xu Y., Zhou X., Li R. and Song K., 2023 – 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, 828-837.
Search in Google Scholar Back to article
Huang X., Wang H. and Xiao F., 2022 –Simulating urban growth affected by national and regional land use policies: Case study from Wuhan, China, Land Use Policy, 112, 105850.
Search in Google Scholar Back to article
Joshi P. P., Wynne R. H. and Thomas A., 2019 – Cloud detection algorithm using SVM with SWIR2 and tasseled cap applied to Landsat 8, International Journal of Applied Earth Observation and Geoinformation, 82, 101898.
Search in Google Scholar Back to article
Kana C. E. and Etouna J. E., 2006 – 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, 357. (in French)
Search in Google Scholar Back to article
Lan H., Zheng P. and Li Z., 2021 – 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, 120569.
Search in Google Scholar Back to article
Lebaut S. and Manceau, L., 2015 – 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, 1, 125-140. (in French)
Search in Google Scholar Back to article
Lemenkova P., 2022a – Handling Dataset with Geophysical and Geological Variables on the Bolivian Andes by the GMT Scripts, Data 7, 6, 74.
Search in Google Scholar Back to article
Lemenkova P., 2022b – Mapping Climate Parameters over the Territory of Botswana Using GMT and Gridded Surface Data from TerraClimate, ISPRS International Journal of Geo-Information 11, 9, 473.
Search in Google Scholar Back to article
Lemenkova P., 2023a – Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts, Analytics, 2, 3, 745-780.
Search in Google Scholar Back to article
Lemenkova P., 2023b – A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria, Applied System Innovation 6, 4, 61.
Search in Google Scholar Back to article
Lemenkova P., 2023c – Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS, Land, 12, 11, 1995.
Search in Google Scholar Back to article
Lemenkova P., 2024 ‒ Exploitation d'images satellitaires Landsat de la région du Cap (Afrique du Sud) pour le calcul et la cartographie d'indices de végétation à l'aide du logiciel GRASS GIS, Physio-Géo, 20, 113-129.
Search in Google Scholar Back to article
Li G. and Li F., 2019 – Urban sprawl in China: Differences and socioeconomic drivers, Science of The Total Environment, 673, 367-377.
Search in Google Scholar Back to article
Liu D., Clarke K. C. and Chen N., 2020 – Integrating spatial nonstationarity into SLEUTH for urban growth modeling: A case study in the Wuhan metropolitan area, Computers, Environment and Urban Systems, 84, 101545.
Search in Google Scholar Back to article
Liu D., Chen N., Zhang X., Wang C. and 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, 337-351.
Search in Google Scholar Back to article
Long D., Du J. and Xin Y., 2023 – Assessing the nexus between natural resource consumption and urban sprawl: Empirical evidence from 288 cities in China, Resources Policy, 85, 103915.
Search in Google Scholar Back to article
Mahmoud M. S. A., 2021 – 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, 1-4.
Search in Google Scholar Back to article
Mountrakis G. and Heydari S. S., 2023 – 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, 106-119.
Search in Google Scholar Back to article
Nagaraj R. and Kumar L. S., 2023 – 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, 1-6.
Search in Google Scholar Back to article
Shirazi R. A., Shahbazi F., Rezaei H. and Biswas A., 2024 – 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, 101123.
Search in Google Scholar Back to article
Selvaraju S., Jancy P. L., Vinod Kumar D., Prabha R., Karthikeyan C. and Babu D., 2021 – Support Vector Machine based Remote Sensing using Satellite Data Image, 2^nd International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 871-874.
Search in Google Scholar Back to article
Shendryk Y., Rossiter-Rachor N. A., Setterfield S. A. and Levick S. R., 2020 – 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
Search in Google Scholar Back to article
Tan R., Liu Y., Zhou K., Jiao L. and Tang W., 2015 – 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, 15-29.
Search in Google Scholar Back to article
Teng, M., Zhou, Z., Wang, P., Xiao, W., Wu, C. and Lord E., 2016 – Geotechnology-Based Modeling to Optimize Conservation of Forest Network in Urban Area, Environmental Management, 57, 601–619.
Search in Google Scholar Back to article
Tong L., Hu S. and Frazier A. E., 2019 – Hierarchically measuring urban expansion in fast urbanizing regions using multi-dimensional metrics: A case of Wuhan metropolis, China, Habitat International, 94, 102070
Search in Google Scholar Back to article
Wang Q. and 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, 106716.
Search in Google Scholar Back to article
Wu D., Zheng L., Wang Y., Gong J., Li J. and Chen Q., 2024 – 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, 111322.
Search in Google Scholar Back to article
Xing S., Yang S., Sun H. and Wang Y., 2023 – Spatiotemporal Changes of Terrestrial Carbon Storage in Rapidly Urbanizing Areas and Their Influencing Factors: A Case Study of Wuhan, China, Land, 12, 12, 2134.
Search in Google Scholar Back to article
Yuan Q. and Zhu J., 2019 – Logistics sprawl in Chinese metropolises: Evidence from Wuhan, Journal of Transport Geography, 74, 242-252.
Search in Google Scholar Back to article
Zhang L., Zhang M. and Wang Q., 2023 – Monitoring of subpixel impervious surface dynamics using seasonal time series Landsat 8 OLI imagery, Ecological Indicators, 154, 110772.
Search in Google Scholar Back to article
Zheng Z., Yang B., Liu S., Xia J. and Zhang X., 2023 – Extraction of impervious surface with Landsat based on machine learning in Chengdu urban, China, Remote Sensing Applications: Society and Environment, 30, 100974
Search in Google Scholar Back to article
Zeng C., Liu Y., Stein A. and 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, 10-24
Search in Google Scholar Back to article
Zhou X., Wu B., Liu Y., Zhou . and Cheng W., 2023 – Synergistic effects of heat and carbon on sustainable urban development: Case study of the Wuhan Urban Agglomeration, Journal of Cleaner Production, 425, 138971.
Search in Google Scholar Back to article

Land Cover Analysis in the Yangtze River Basin for Detection of Wetland Agriculture and Urban Dynamics in Wuhan Area (China)

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