Multi-temporal analysis of land use and land cover change detection in Binh Duong province, Vietnam using geospatial techniques

• Autorzy: Thien Bui Bao , Phuong VU Thi

Identyfikatory

DOI

10.24425/agg.2024.150683

• Języki publikacji: EN

Abstrakty

EN

Understanding changes in land use and land cover (LULC) is crucial for effective land management, environmental planning, and decision-making. It helps identify areas of environmental concern, assess the impacts of human activities on ecosystems, and develop strategies for conservation efforts and sustainable land use. In this study, remote sensing and geographic information systems (GIS) were used to monitor LULC changes in Binh Duong province, Vietnam from 1988 to 2023. The supervised classification method in ArcGIS 10.8 software was applied to Landsat satellite data (Landsat 5-TM for 1988 and 2004, and Landsat 9-OLI/TIRS for 2023) to detect and classify five main LULC types: arable land, barren land, built-up areas, forests and waterbodies. The classification accuracy was evaluated using kappa coefficients, which were 0.877, 0.894 and 0.908 for 1988, 2004, and 2023, respectively. During the period of 1988–2023, the forest, barren land, and waterbodies class areas decreased by 560.55 km2, 200.04 km2, and 19.68 km2, respectively. Meanwhile, the arable land and built-up areas classes increased by 343.80 km2 and 436.47 km2, respectively. Furthermore, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) were used to quickly assess changes in LULC, and their trends were found to be consistent with the supervised classification results. These changes in LULC pose significant threats to the environment and the findings of this study can serve as valuable resources for future land management and planning in the region.

Słowa kluczowe

EN

Landsat; land management; vegetation index; remote sensing; maximum likelihood classification

PL

zarządzanie gruntami; teledetekcja; roślinność

• Wydawca: Komitet Geodezji PAN
• Czasopismo: Advances in Geodesy and Geoinformation
• Rocznik: 2024
• Tom: Vol. 73, no. 2
• Strony: art. no. e54, 2024
• Opis fizyczny: Bibliogr. 60 poz., rys., tab., wykr.

Twórcy

autor

Thien Bui Bao

• Southern Federal University, Rostov-on-Don, Russia

• https://orcid.org/0000-0003-2964-0012

autor

Phuong VU Thi

• Hong Duc University, Thanh Hoa, Vietnam

• https://orcid.org/0000-0001-9277-2013

Bibliografia

• 1. Aboelnour, M., and Engel, B.A. (2018). Application of remote sensing techniques and geographic information systems to analyze land surface temperature in response to land use/land cover change in Greater Cairo Region. Egypt. J. Geogr. Inf. Syst., 10(1), 57–88. DOI: 10.4236/jgis.2018.101003.
• 2. Adegboyega, S.A.A. (2021). Multi-temporal land use/land cover change detection and urban watershed degradation in Olorunda Local Government Area, Osun State, Nigeria. Appl. Geomat., 13(4), 659–676. DOI: 10.1007/s12518-021-00382-3.
• 3. Anderson, J.R., Hardy, E.E., Roach, J.T. et al. (1976). A land use and land cover classification system for use with remote sensor data. Geological survey professional paper, U.S. government printing office. Washington DC, 964, 1–28.
• 4. Angessa, A.T., Lemma, B., and Yeshitela, K. (2021). Land-use and land-cover dynamics and their drivers in the central highlands of Ethiopia with special reference to the Lake Wanchi watershed. GeoJournal, 86(3), 1225–1243. DOI: 10.1007/s10708-019-10130-1.
• 5. Awotwi, A., Anornu, G.K., Quaye-Ballard, J.A. et al. (2018). Monitoring land use and land cover changes due to extensive gold mining, urban expansion, and agriculture in the Pra River Basin of Ghana, 1986–2025. Land Degrad. Dev., 29(10), 3331-3343. DOI: 10.1002/ldr.3093.
• 6. Balawejder, M., Warchol, A., and Konttinen, K. (2023). Energy Efficiency in Agricultural Production – Experience from Land Consolidation in Poland and Finland. Energies, 16(22), 7598. DOI:10.3390/en16227598.
• 7. Binh Duong Statistics Office (2022). Binh Duong Statistical Yearbook 2021. Statistical Publishing House. Retrieved May, 2023 from https://thongke.binhduong.gov.vn/Lists/AnPhamThongKe/DispForm.aspx?ID=23&CategoryId=Ni%C3%AAn%20gi%C3%A1m%20th%E1%BB%91ng%20k%C3%AA%20c%E1%BA%A5p%20t%E1%BB%89nh&InitialTabId=Ribbon.Read.
• 8. Bo, N.V., Hoanh, C.T., Du, P.V. et al. (2019). Adaptation options for agricultural cultivation systems in the South Central Coast under the context of climate change: Assessment Report. CCAFS Working Paper. Retrieved 2023, May from https://hdl.handle.net/10568/106575.
• 9. Chatterjee, U., and Majumdar, S. (2022). Impact of land use change and rapid urbanization on urban heat island in Kolkata city: A remote sensing based perspective. J. Urban Manag., 11(1), 59–71. DOI:10.1016/j.jum.2021.09.002.
• 10. Chughtai, A.H., Abbasi, H., and Karas, I.R. (2021). A review on change detection method and accuracy assessment for land use land cover. Remote Sens. Appl.: Soc. Environ., 22, 100482. DOI:10.1016/j.rsase.2021.100482.
• 11. Dash, P., Sanders, S.L., Parajuli, P. et al. (2023). Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data in an Agricultural Watershed. Remote Sens., 15(16), 4020. DOI:10.3390/rs15164020.
• 12. Dutta, D., Rahman, A., Paul, S.K. et al. (2021). Impervious surface growth and its inter-relationship with vegetation cover and land surface temperature in peri-urban areas of Delhi. Urban Clim., 37, 100799. DOI: 10.1016/j.uclim.2021.100799.
• 13. Fisher, R.A., and Koven, C.D. (2020). Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems. J. Adv. Model. Earth Syst., 12(4), e2018MS001453. DOI:10.1029/2018MS001453.
• 14. Florim, I., Albert, B., and Shpejtim, B. (2021). Measuring UHI using Landsat 8 OLI and TIRS data with NDVI and NDBI in Municipality of Prishtina. Disaster Adv., 14, 25–36.
• 15. Foody, G.M. (2020). Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sens. Environ., 239, 111630. DOI: 10.1016/j.rse.2019.111630.
• 16. Fox, J., and Vogler, J.B. (2005). Land-use and land-cover change in montane mainland southeast Asia. Environ. Manag., 36, 394–403. DOI: 10.1007/s00267-003-0288-7.
• 17. Gadal, S., and Gloaguen, T. (2023). Monitoring Land Cover Change in the Southeastern Baltic Sea Since the 1980s by Remote Sensing. In. S. Niculescu (Ed.) European Spatial Data for Coastal and Marine Remote Sensing. Springer, Cham, 59–79. DOI: 10.1007/978-3-031-16213-8_4.
• 18. Gozdowski, D., Žukovskis, J., Razinkovas-Baziukas, A. et al. (2022). Land cover changes in selected areas next to lagoons located on the southern coast of the Baltic Sea, 1984–2021. Sustainability, 14(4), 2006. DOI: 10.3390/su14042006.
• 19. Hanh, H.Q., Azadi, H., Dogot, T. et al. (2017). Dynamics of agrarian systems and land use change in North Vietnam. Land Degrad. Dev., 28(3), 799–810. DOI: 10.1002/ldr.2609.
• 20. Heredia-R, M., Torres, B., Cabrera-Torres, F. et al. (2021). Land Use and Land Cover Changes in the Diversity and Life Zone for Uncontacted Indigenous People: Deforestation Hotspots in the Yasuní Biosphere Reserve, Ecuadorian Amazon. Forests, 12(11), 1539. DOI: 10.3390/f12111539.
• 21. Islami, F.A., Tarigan, S.D., Wahjunie, E.D. et al. (2022). Accuracy assessment of land use change analysis using Google Earth in Sadar Watershed Mojokerto Regency. IOP Conf. Ser.: Earth Environ. Sci., 950(1), 012091. DOI: 10.1088/1755-1315/950/1/012091.
• 22. Kovyazin, V.F., Lepikhina, O.Y., Demidova, P.M. et al. (2023). Problems of Forest Resource Management in the Arctic Zone of the Russian Federation. Lesnoy Zhurnal, 3, 185–194. DOI: 10.37482/0536-1036-2023-3-185-194.
• 23. Kumari, M., Sarma, K., and Sharma, R. (2019). Using Moran’s I and GIS to study the spatial pattern of land surface temperature in relation to land use/cover around a thermal power plant in Singrauli district, Madhya Pradesh, India. Remote Sens. Appl.: Soc. Environ., 15, 100239. DOI:10.1016/j.rsase.2019.100239.
• 24. Lemenkova, P., and Debeir, O. (2023). Multispectral Satellite Image Analysis for Computing Vegetation Indices by R in the Khartoum Region of Sudan, Northeast Africa. J. Imaging, 9(5), 98. DOI:10.3390/jimaging9050098.
• 25. Lenzen, M., Wier, M., Cohen, C. et al. (2006). A comparative multivariate analysis of household energy requirements in Australia, Brazil, Denmark, India and Japan. Energy, 31(2–3), 181–207. DOI:10.1016/j.energy.2005.01.009.
• 26. Majeed, M., Tariq, A., Anwar, M.M. et al. (2021). Monitoring of land use–Land cover change and potential causal factors of climate change in Jhelum district, Punjab, Pakistan, through GIS and multi-temporal satellite data. Land, 10(10), 1026. DOI: 10.3390/land10101026.
• 27. Malinowski, R., Lewinski, S., Rybicki, M. et al. (2020). Automated production of a land cover/use map of Europe based on Sentinel-2 imagery. Remote Sen., 12(21), 3523. DOI: 10.3390/rs12213523.
• 28. Mehdi, S.M., Pant, N.C., Saini, H.S. et al. (2016). Identification of palaeochannel configuration in the Saraswati River basin in parts of Haryana and Rajasthan, India, through digital remote sensing and GIS. Episodes J. Int. Geosci., 39(1), 29–38. DOI: 10.18814/epiiugs/2016/v39i1/89234.
• 29.Mingarro, M., and Lobo, J.M. (2023). EuropeanNational Parks protect their surroundings but not everywhere: A study using land use/land cover dynamics derived from CORINE Land Cover data. Land Use Policy, 124, 106434. DOI: 10.1016/j.landusepol.2022.106434.
• 30. Moharram, M.A., and Sundaram, D.M. (2023). Land Use and Land Cover Classification with Hyperspectral Data: A comprehensive review of methods, challenges and future directions. Neurocomp., 536, 90–113. DOI: 10.1016/j.neucom.2023.03.025.
• 31. Motlagh, Z.K., Lotfi, A., Pourmanafi, S. et al. (2020). Spatial modeling of land-use change in a rapidly urbanizing landscape in central Iran: Integration of remote sensing, CA-Markov, and landscape metrics. Environ. Monit. Assess., 192, 1–19. DOI: 10.1007/s10661-020-08647-x.
• 32. Msofe, N.K., Sheng, L., and Lyimo, J., (2019). Land use change trends and their driving forces in the Kilombero Valley Floodplain, Southeastern Tanzania. Sustainability, 11(2), 505. DOI: 10.3390/su11020505.
• 33. Niu, X., Hu, Y., Lei, Z. et al. (2022). Temporal and Spatial Evolution Characteristics and Its Driving Mechanism of Land Use/Cover inVietnam from 2000 to 2020. Land, 11(6), 920. DOI: 10.3390/land11060920.
• 34. Pal, S., and Ziaul, S.K. (2017). Detection of land use and land cover change and land surface temperaturę in English Bazar urban centre. Egypt. J. Remote Sens. Space Sci., 20(1), 125–145. DOI:10.1016/j.ejrs.2016.11.003.
• 35. Pravalie, R. (2018). Major perturbations in the Earth’s forest ecosystems. Possible implications for global warming. Earth-Sci. Rev., 185, 544–571. DOI: 10.1016/j.earscirev.2018.06.010.
• 36. Rajkhowa, S., Khanom, N.A., and Sarma, J. (2021). Environmental issues and priority areas for ecological engineering initiatives. In Handbook of Ecological and Ecosystem Engineering, 47–66. DOI:10.1002/9781119678595.ch3.
• 37. Regasa, M.S., Nones, M., and Adeba, D. (2021). A review on land use and land cover change in Ethiopian basins. Land, 10(6), 585. DOI: 10.3390/land10060585.
• 38. Rimal, B., Zhang, L., Stork, N. et al. (2018). Urban expansion occurred at the expense of agricultural lands in the Tarai region of Nepal from 1989 to 2016. Sustainability, 10(5), 1341. DOI: 10.3390/su10051341.
• 39. Rivière, F., Widad, F.Z., Speyer, E. et al. (2018). Reliability and validity of the French version of the global physical activity questionnaire. J. Sport Health Sci., 7(3), 339–345. DOI: 10.1016/j.jshs.2016.08.004.
• 40. Rouse, J.W., Haas, R.H., Schell, J.A. et al. (1973). Monitoring vegetation systems in the great plains with ERTS. In. C. Freden and M.A. Becker (Eds.) Third Earth Resources Technology Satellite–1 Symposium. Washington DC: National Aeronautics and Space Administration, 1, 309–317.
• 41. Rousta, I., Olafsson, H., Moniruzzaman, M. et al. (2020). Impacts of drought on vegetation assessed by vegetation indices and meteorological factors in Afghanistan. Remote Sens., 12(15), 2433. DOI:10.3390/rs12152433.
• 42. Rustiadi, E., Pravitasari, A.E., Setiawan, Y. et al. (2021). Impact of continuous Jakarta megacity urban expansion on the formation of the Jakarta-Bandung conurbation over the rice farm regions. Cities, 111, 103000. DOI: 10.1016/j.cities.2020.103000.
• 43. Singh, S.K., Srivastava, P.K., Gupta, M. et al. (2014). Appraisal of land use/land cover of mangrowe forest ecosystem using support vector machine. Environ. Earth Sci., 71(5), 2245–2255. DOI:10.1007/s12665-013-2628-0.
• 44. Singh, P., Sarkar Chaudhuri, A., Verma, P. et al. (2022). Earth observation data sets in monitoring of urbanization and urban heat island of Delhi, India. Geomat. Nat. Hazards Risk, 13(1), 1762–1779. DOI: 10.1080/19475705.2022.2097452.
• 45. Slamet, B., Syahputra, O.K.H., Kurniawan, H. et al. (2021). Analysis of vegetation cover and built-up areas in the Percut watershed landscape, North Sumatra Province using sentinel-2 imagery. IOP Conf. Ser.: Earth Environ. Sci., 912(1), 012089. DOI: 10.1088/1755-1315/912/1/012089.
• 46. Sokolov, A., Veselitskaya, N., Carabias, V. et al. (2019). Scenario-based identification of key factors for smart cities development policies. Technol. Forecast. Soc. Change, 148, 119729. DOI:10.1016/j.techfore.2019.119729.
• 47. Surya, B., Salim, A., Hernita, H. et al. (2021). Land use change, urban agglomeration, and urban sprawl: A sustainable development perspective of Makassar City, Indonesia. Land, 10(6), 556. DOI:10.3390/land10060556.
• 48. Theres, B.L., and Selvakumar, R., (2022). Comparison of landuse/landcover classifier for monitoring urban dynamics using spatially enhanced landsat dataset. Environ. Earth Sci., 81(5), 142. DOI:10.1007/s12665-022-10242-x.
• 49. Thien, B.B., Sosamphanh, B., Yachongtou, B. et al. (2022). Land use/land cover changes in the period of 2015–2020 in AngYai Village, Sikhottabong District, Vientiane Capital, Lao PDR. Geol. Geophys. Environ., 48(3), 279–286. DOI: 10.7494/geol.2022.48.3.279.
• 50. Thien, B.B., and Phuong, V.T. (2023). Using Landsat satellite imagery for assessment and monitoring of long-term forest cover changes in Dak Nong province, Vietnam. Geogr. Pannonica, 27(1), 69–82. DOI: 10.5937/gp27-41813.
• 51. Thien, B.B., Phuong, V.T., and Komolafe, A.A. (2023a). Assessment of forest cover and forest loss using satellite images in Thua Thien Hue province, Vietnam. AUC Geogr., 58(2), 172–186. DOI:10.14712/23361980.2023.13.
• 52. Thien, B.B., Yachongtou, B., and Phuong, V.T., (2023b). Long-term monitoring of forest cover change resulting in forest loss in the capital of Luang Prabang province, Lao PDR. Environ. Monit. Assess., 195(8), 1–17. DOI: 10.1007/s10661-023-11548-4.
• 53. Vadrevu, K., Heinimann, A., Gutman, G. et al. (2019). Remote sensing of land use/cover changes in South and Southeast Asian Countries. Int. J. Digit. Earth, 12(10), 1099–1102. DOI:10.1080/17538947.2019.1654274.
• 54. Verma, P., Raghubanshi, A., Srivastava, P.K. et al. (2020). Appraisal of kappa-based metrics and disagreement indices of accuracy assessment for parametric and nonparametric techniques used in LULC classification and change detection. Model. Earth Syst. Environ., 6, 1045–1059. DOI: 10.1007/s40808-020-00740-x.
• 55. Wahla, S.S., Kazmi, J.H., and Tariq, A. (2023). Mapping and monitoring of spatio-temporal land use and land cover changes and relationship with normalized satellite indices and driving factors. Geol. Ecol. Landsc., 1–17. DOI: 10.1080/24749508.2023.2187567.
• 56. Waiyasusri, K. (2021). Monitoring the land cover changes in mangrove areas and urbanization using normalized difference vegetation index and normalized difference built-up index in Krabi Estuary Wetland, Krabi province, Thailand. Appl. Environ. Res., 43(3), 1–16. DOI: 10.35762/AER.2021.43.3.1.
• 57. Wiatkowska, B., Slodczyk, J., and Stokowska, A., (2021). Spatial-temporal land use and land cover changes in urban areas using remote sensing images and GIS analysis: The case study of Opole, Poland. Geosci., 11(8), 312. DOI: 10.3390/geosciences11080312.
• 58. Wu, Z., Xiong, K., Zhu, D. et al. (2022). Revelation of coupled ecosystem quality and landscape patterns for agroforestry ecosystem services sustainability improvement in the karst desertification control. Agric., 13(1), 43. DOI: 10.3390/agriculture13010043.
• 59. Zadbagher, E., Becek, K., and Berberoglu, S. (2018). Modeling land use/land cover change using remote sensing and geographic information systems: case study of the Seyhan Basin, Turkey. Environ. Monit. Assess., 190, 1–15. DOI: 10.1007/s10661-018-6877-y.
• 60. Zheng, Y., Tang, L., and Wang, H. (2021). An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI. J. Clean. Prod., 328, 129488. DOI:10.1016/j.jclepro.2021.129488.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025)