Skip to main content
SpringerLink
Log in

Detection of Land Use and Land Cover Change Using Remote Sensing and GIS in Ba Ria-Vung Tau Province, Vietnam

  • GEOGRAPHY ABROAD
  • Published:
Geography and Natural Resources Aims and scope Submit manuscript

Abstract

Changes in land use and land cover (LULC) are among the global changes resulting from human activity that have the biggest impacts on the ecosystem and the surrounding environment. Detecting and mapping changes in LULC in Ba Ria-Vung Tau province, Vietnam is critical for sustainable development, planning, and management. This study applies the supervised classifier maximum likelihood algorithm in ArcGIS 10.8 software to detect changes in LULC observed in the study area in the period 2000–2020 using multivariate satellite data. For each satellite scene, we applied supervised classification and spectral indices (NDVI-Normalized Difference Vegetation Index and NDWI-Normalized Difference Water Index) for the classification and assessment of LULC changes. Areas obtained from Landsat 5 TM for 2000 and 2010 and Landsat 8 OLI for 2020 were checked for accuracy using kappa coefficients of 0.882, 0.891, and 0.915, respectively. The area was classified into five main LULC classes including agriculture, water bodies, forest, settlement, and bare soil/rock. The LULC status and change maps created in ArcGIS 10.8 show a significant change in LULC. The settlement class has increased continuously for 20 years from 128.09 km2 (2000) to 300.30 km2 (2020); the agricultural land class has increased by 124.96 km2 in the period 2000–2020. The remaining three classes, forest, water bodies, and bare soil/rock, all decreased in area during this period. These LULC changes pose a serious threat, impacting and disturbing the environment. The results of this study can be used in management and planning of future land use in the area.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

REFERENCES

  1. Kumari, M., Das, A., Sharma, R., and Saikia, S., Change detection analysis using multi temporal satellite data of Poba reserve forest, Assam and Arunachal Pradesh, Int. J. Geomat. Geosci., 2014, vol. 4, no. 3, p. 517.

    Google Scholar 

  2. Han, H., Yang, C., and Song, J., Scenario simulation and the prediction of land use and land cover change in Beijing, China, Sustainability, 2015, vol. 7, no. 4, pp. 4260–4279. https://doi.org/10.3390/su7044260

    Article  Google Scholar 

  3. Liu, W., Zhan, J., Zhao, F., Yan, H., Zhang, F., and Wei, X., Impacts of urbanization-induced land-use changes on ecosystem services: A case study of the Pearl River Delta Metropolitan Region, China, Ecol. Indic., 2019, vol. 98, pp. 228–238. https://doi.org/10.1016/j.ecolind.2018.10.054

    Article  Google Scholar 

  4. Pielke Sr, R.A., Pitman, A., Niyogi, D., Mahmood, R., McAlpine, C., Hossain, F., Goldewijk, K.K., Nair, U., Betts, R., Fall, S., Reichstein, M., Kabat, P., and de Noblet, N., Land use/land cover changes and climate: Modeling analysis and observational evidence, Wiley Interdiscip. Rev.: Clim. Change, 2011, vol. 2, no. 6, pp. 828–850. https://doi.org/10.1002/wcc.144

    Article  Google Scholar 

  5. Hegazy, I.R. and Kaloop, M.R., Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt, Int. J. Sustain. Built Environ., 2015, vol. 4, no. 1, pp. 117–124. https://doi.org/10.1016/j.ijsbe.2015.02.005

    Article  Google Scholar 

  6. Larasati, D.A. and Hariyanto, B., The impact of land use changes in the Banjarsari village, Cerme district of Gresik Regency, East Java Province, J. Phys.: Conf. Ser., 2018, vol. 953, no. 1, p. 012178. https://doi.org/10.1088/1742-6596/953/1/012178

    Article  Google Scholar 

  7. Erb, K.H., Kastner, T., Plutzar, C., Bais, A.L.S., Carvalhais, N., Fetzel, T., Gingrich, S., Haberl, H., Lauk, C., Niedertscheider, M., Pongratz, J., Thurner, M. and Luyssaert, S., Unexpectedly large impact of forest management and grazing on global vegetation biomass, Nature, 2018, vol. 553, no. 7686, pp. 73–76. https://doi.org/10.1038/nature25138

    Article  CAS  Google Scholar 

  8. Hyandye, C.B., Worqul, A., Martz, L.W., and Muzuka, A.N., The impact of future climate and land use/cover change on water resources in the Ndembera watershed and their mitigation and adaptation strategies, Environ. Syst. Res., 2018, vol. 7, no. 1, pp. 1–24. https://doi.org/10.1186/s40068-018-0110-4

    Article  Google Scholar 

  9. Azimi Sardari, M.R., Bazrafshan, O., Panagopoulos, T., and Sardooi, E.R., Modeling the impact of climate change and land use change scenarios on soil erosion at the Minab Dam Watershed, Sustainability, 2019, vol. 11, no. 12, p. 3353. https://doi.org/10.3390/su11123353

    Article  Google Scholar 

  10. Thien, B.B., Sosamphanh, B., Yachongtou, B., and Phuong, V.T., Land use/land cover changes in the period of 2015–2020 in AngYai Village, Sikhottabong District, Vientiane Capital, Lao PDR, Geol. Geophys. Environ., 2022, vol. 48, no. 3, pp. 279–286. https://doi.org/10.7494/geol.2022.48.3.279

    Article  Google Scholar 

  11. Meshesha, T.W., Tripathi, S.K., and Khare, D., Analyses of land use and land cover change dynamics using GIS and remote sensing during 1984 and 2015 in the Beressa Watershed Northern Central Highland of Ethiopia, Model. Earth Syst. Environ., 2016, vol. 2, no. 4, pp. 1–12. https://doi.org/10.1007/s40808-016-0233-4

    Article  Google Scholar 

  12. Olorunfemi, I.E., Fasinmirin, J.T., Olufayo, A.A., and Komolafe, A.A., GIS and remote sensing-based analysis of the impacts of land use/land cover change (LULCC) on the environmental sustainability of Ekiti State, southwestern Nigeria, Environ., Dev. Sustain., 2020, vol. 22, no. 2, pp. 661–692. https://doi.org/10.1007/s10668-018-0214-z

    Article  Google Scholar 

  13. Banerjee, R. and Srivastava, P.K., Reconstruction of contested landscape: Detecting land cover transformation hosting cultural heritage sites from Central India using remote sensing, Land Use Policy, 2013, vol. 34, pp. 193–203. https://doi.org/10.1016/j.landusepol.2013.03.005

    Article  Google Scholar 

  14. Schaefer, M. and Thinh, N.X., Evaluation of land cover change and agricultural protection sites: A GIS and remote sensing approach for Ho Chi Minh city, Vietnam, Heliyon, 2019, vol. 5, no. 5, p. e01773. https://doi.org/10.1016/j.heliyon.2019.e01773

    Article  Google Scholar 

  15. Elagouz, M.H., Abou-Shleel, S.M., Belal, A.A., and El-Mohandes, M.A.O., Detection of land use/cover change in Egyptian Nile Delta using remote sensing, Egypt. J. Remote. Sens. Space Sci., 2020, vol. 23, no. 1, pp. 57–62. https://doi.org/10.1016/j.ejrs.2018.10.004

    Article  Google Scholar 

  16. Mishra, P.K., Rai, A., and Rai, S.C., Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India, Egypt. J. Remote. Sens. Space Sci., 2020, vol. 23, no. 2, pp. 133–143. https://doi.org/10.1016/j.ejrs.2019.02.001

    Article  Google Scholar 

  17. Singh, S.K., Laari, P.B., Mustak, S.K., Srivastava, P.K., and Szabó, S., Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India, Geocarto Int., 2018, vol. 33, no. 11, pp. 1202–1222. https://doi.org/10.1080/10106049.2017.1343390

    Article  Google Scholar 

  18. Hussain, S., Lu, L., Mubeen, M., Nasim, W., Karuppannan, S., Fahad, S., Tariq, A., Mousa, B.G., Mumtaz, F., and Aslam, M., Spatiotemporal variation in land use land cover in the response to local climate change using multispectral remote sensing data, Land, 2022, vol. 11, no. 5, p. 595. https://doi.org/10.3390/land11050595

    Article  Google Scholar 

  19. Huete, A.R., Vegetation indices, remote sensing and forest monitoring, Geogr. Compass, 2012, vol. 6, no. 9, pp. 513–532. https://doi.org/10.1111/j.1749-8198.2012.00507.x

    Article  Google Scholar 

  20. Cao, L., Liu, T. and Wei, L., A comparison of multi-resource remote sensing data for vegetation indices, IOP Conf. Ser.: Earth Environ. Sci., 2014, vol. 17, no. 1, p. 012067. https://doi.org/10.1088/1755-1315/17/1/012067

  21. Gao, B.C., NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sens. Environ., 1996, vol. 58, no. 3, pp. 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3

    Article  Google Scholar 

  22. Hazarika, N., Das, A.K., and Borah, S.B., Assessing land-use changes driven by river dynamics in chronically flood affected Upper Brahmaputra plains, India, using RS-GIS techniques, Egypt. J. Remote. Sens. Space Sci., 2015, vol. 18, no. 1, pp. 107–118. https://doi.org/10.1016/j.ejrs.2015.02.001

    Article  Google Scholar 

  23. Shawul, A.A. and Chakma, S., Spatiotemporal detection of land use/land cover change in the large basin using integrated approaches of remote sensing and GIS in the Upper Awash basin, Ethiopia, Environ. Earth Sci., 2019, vol. 78, no. 5, pp. 1–13. https://doi.org/10.1007/s12665-019-8154-y

    Article  Google Scholar 

  24. Rahman, M.N., Urban expansion analysis and land use changes in Rangpur city corporation area, Bangladesh, using remote sensing (RS) and geographic information system (GIS) techniques, Geos. Ind., 2019, vol. 4, no. 3, pp. 217–229. https://doi.org/10.19184/geosi.v4i3.13921

    Article  Google Scholar 

  25. Avand, M. and Moradi, H., Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability, J. Hydrol., 2021, vol. 595, p. 125663. https://doi.org/10.1016/j.jhydrol.2020.125663

    Article  Google Scholar 

  26. Lu, D., Mausel, P., Brondizio, E., and Moran, E., Change detection techniques, Int. J. Remote Sens., 2004, vol. 25, no. 12, pp. 2365–2401. https://doi.org/10.1080/0143116031000139863

    Article  Google Scholar 

  27. Alawamy, J.S., Balasundram, S.K., Mohd. Hanif, A.H., and Boon Sung, C.T., Detecting and analyzing land use and land cover changes in the region of Al-Jabal Al-Akhdar, Libya using time-series landsat data from 1985 to 2017, Sustainability, 2020, vol. 12, no. 11, p. 4490. https://doi.org/10.3390/su12114490

    Article  Google Scholar 

  28. Pande, C.B., Moharir, K.N., Khadri, S.F.R. and Patil, S., Study of land use classification in an arid region using multispectral satellite images, Appl. Water Sci., 2018, vol. 8, no. 5, pp. 1–11. https://doi.org/10.1007/s13201-018-0764-0

    Article  CAS  Google Scholar 

  29. Ba Ria-Vung Tau Statistics Office, Ba Ra-Vung Tau Statistical Yearbook 2017, Statistical Publishing House, 2018.

    Google Scholar 

  30. Ikiel, C., Ustaoglu, B., Dutucu, A.A., and Kilic, D.E., Remote sensing and GIS-based integrated analysis of land cover change in Duzce plain and its surroundings (north western Turkey), Environ. Monit. Assess., 2013, vol. 185, no. 2, pp. 1699–1709. https://doi.org/10.1007/s10661-012-2661-6

    Article  Google Scholar 

  31. Thien, B.B., Phuong, V.T., and Huong, D.T., Detection and assessment of the spatio-temporal land use/cover change in the Thai Binh province of Vietnam’s Red River delta using remote sensing and GIS, Model. Earth Syst. Environ., 2023, vol. 9, no. 2, pp. 2711–2722. https://doi.org/10.1007/s40808-022-01636-8

    Article  Google Scholar 

  32. Congalton, R.G. and Green, K., Assessing the accuracy of remotely sensed data: Principles and practices, CRC Press, 2019, p. 346.

    Book  Google Scholar 

  33. Alam, A., Bhat, M.S., and Maheen, M., Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley, GeoJournal, 2020, vol. 85, no. 6, pp. 1529–1543. https://doi.org/10.1007/s10708-019-10037-x

    Article  Google Scholar 

  34. Manonmani, R. and Suganya, G., Remote sensing and GIS application in change detection study in urban zone using multi temporal satellite, Int. J. Geomat. Geosci., 2010, vol. 1, no. 1, pp. 60–65.

    Google Scholar 

  35. Lea, C. and Curtis, A.C., Thematic Accuracy Assessment Procedures: National Park Service Vegetation Inventory, Version 2.0 (Natural Resource Report NPS/2010/NRR—2010/204), Fort Collins, CO: National Park Service, 2010.

  36. Labbé, D., Examining the governance of emerging urban regions in Vietnam: The case of the Red River Delta, Int. Plan. Stud., 2019, vol. 24, no. 1, pp. 40–52. https://doi.org/10.1080/13563475.2018.1517593

    Article  Google Scholar 

  37. Meyfroidt, P., Rudel, T.K., and Lambin, E.F., Forest transitions, trade, and the global displacement of land use, Proc. Natl. Acad. Sci. U.S.A., 2010, vol. 107, no. 49, pp. 20917–20922. https://doi.org/10.1073/pnas.1014773107

    Article  Google Scholar 

  38. Chakravarty, S., Ghosh, S.K., Suresh, C.P., Dey, A.N., and Shukla, G., Deforestation: Causes, effects and control strategies, in Global Perspectives on Sustainable Forest Management, London: Intech, 2012, vol. 1, pp. 1–26.

    Google Scholar 

  39. Thien, B.B. and Phuong, V.T., Using Landsat satellite imagery for assessment and monitoring of long-term forest cover changes in Dak Nong province, Vietnam, Geogr. Pannonica, 2023, vol. 27, no. 1, pp. 69–82. https://doi.org/10.5937/gp27-41813

    Article  Google Scholar 

  40. Salerno, F., Gaetano, V., and Gianni, T., Urbanization and climate change impacts on surface water quality: Enhancing the resilience by reducing impervious surfaces, Water Res., 2018, vol. 144, pp. 491–502. https://doi.org/10.1016/j.watres.2018.07.058

    Article  CAS  Google Scholar 

  41. Shashikant, V., Mohamed Shariff, A.R., Wayayok, A., Kamal, M.R., Lee, Y.P., and Takeuchi, W., Utilizing TVDI and NDWI to classify severity of agricultural drought in Chuping, Malaysia, Agron., 2021, vol. 11, no. 6, p. 1243. https://doi.org/10.3390/agronomy11061243

    Article  Google Scholar 

  42. Ashok, A., Rani, H.P., and Jayakumar, K.V., Monitoring of dynamic wetland changes using NDVI and NDWI based landsat imagery, Remote Sens. Appl.: Soc. Environ., 2021, vol. 23, p. 100547. https://doi.org/10.1016/j.rsase.2021.100547

    Article  Google Scholar 

  43. Yu, Z., Yao, Y., Yang, G., Wang, X., and Vejre, H., Strong contribution of rapid urbanization and urban agglomeration development to regional thermal environment dynamics and evolution, For. Ecol. Manag., 2019, vol. 446, pp. 214–225. https://doi.org/10.1016/j.foreco.2019.05.046

    Article  Google Scholar 

  44. Butt, A., Shabbir, R., Ahmad, S.S. and Aziz, N., Land use change mapping and analysis using Remote Sensing and GIS: A case study of Simly watershed, Islamabad, Pakistan, Egypt. J. Remote Sens. Space Sci., 2015, vol. 18, no. 2, pp. 251–259. https://doi.org/10.1016/j.ejrs.2015.07.003

    Article  Google Scholar 

Download references

Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

Author information

Authors and Affiliations

  1. Southern Federal University, 344006, Rostov-on-Don, Russia

    B. B. Thien

  2. Faculty of Social Sciences, National University of Laos, Vientiane, Laos

    B. B. Thien

  3. Innovation Startup Support Center, Hong Duc University, Thanh Hoa, Vietnam

    V. T. Phuong

  4. Faculty of Social Sciences, Hong Duc University, Thanh Hoa, Vietnam

    V. T. Phuong

Authors
  1. B. B. Thien
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. T. Phuong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B. B. Thien.

Ethics declarations

The authors of this work declare that they have no conflicts of interest.

Additional information

Publisher’s Note.

Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thien, B.B., Phuong, V.T. Detection of Land Use and Land Cover Change Using Remote Sensing and GIS in Ba Ria-Vung Tau Province, Vietnam. Geogr. Nat. Resour. 44, 383–393 (2023). https://doi.org/10.1134/S1875372823040133

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1875372823040133

Keywords:

Search

Navigation