Using GIS Tools to Detect the Land Use/Land Cover Changes in Ha Nam province, Vietnam
DOI:
https://doi.org/10.52575/2712-7443-2023-47-4-593-606Keywords:
Landsat, GIS, land use/land cover change, supervised classification, Ha Nam provinceAbstract
Land is a crucial natural resource for any country. The study of land use and land cover (LULC) change has been instrumental in various areas such as natural resource management, monitoring, land planning, landslides, erosion, and addressing global change issues. In this study, geographic information systems (GIS) and remote sensing (RS) techniques were used to monitor LULC changes in Ha Nam province, Vietnam from 1992 to 2022. The supervised classification method in ArcGIS 10.8 software was applied to Landsat satellite data (Landsat 5-TM for 1992 and 2003, and Landsat 8-OLI/TIRS for 2022) to detect and classify five main LULC types: agricultural land, barren land, built-up, forest, and waterbodies. The classification accuracy was evaluated using kappa coefficients, which were 0.886, 0.905, and 0.933 for 1992, 2003, and 2022, respectively. During the period of 1992–2022, the agricultural land, forest, and waterbodies classes areas decreased by 102.85 km2, 48.57 km2, and 5.25 km2, respectively. Meanwhile, the built-up and barren land classes areas increased by 150.08 km2 and 6.59 km2, respectively. Population growth, urbanization, urban planning policies, and the transition from an agricultural to an industrial economy have contributed to the expansion of built-up areas and the reduction of agricultural land, forests, and waterbodies in Ha Nam province. Moreover, we utilized the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) to rapidly evaluate LULC changes, and we observed that their trends aligned with the results obtained from supervised classification. The environment faces substantial risks due to these LULC changes, and the outcomes of this study can provide valuable insights for upcoming land management and planning initiatives in the area.
Downloads
References
Aghsaei H., Dinan N.M., Moridi A., Asadolahi Z., Delavar M., Fohrer N., Wagner P.D. 2020. Effects of Dynamic Land Use/Land Cover Change on Water Resources and Sediment Yield in the Anzali Wetland Catchment, Gilan, Iran. Science of the Total Environment, 712: 136449. https://doi.org/10.1016/j.scitotenv.2019.136449.
Anderson J.R., Hardy E.E., Roach J.T., Witmer R.E. 1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. In: Geological Survey Professional Paper, U.S. Government Printing Office. Washington DC: 1–28.
Dash P., Sanders S.L., Parajuli P., Ouyang Y. 2023. Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data in an Agricultural Watershed. Remote Sensing, 15(16): 4020. https://doi.org/10.3390/rs15164020.
Degerli B., Çetin M. 2022. Evaluation from Rural to Urban Scale for the Effect of NDVI-NDBI Indices on Land Surface Temperature, in Samsun, Türkiye. Turkish Journal of Agriculture-Food Science and Technology, 10(12): 2446–2452. https://doi.org/10.24925/turjaf.v10i12.2446-2452.5535.
Florim I., Albert B., 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.
Getu Engida T., Nigussie T.A., Aneseyee A.B., Barnabas J. 2021. Land Use/Land Cover Change Impact on Hydrological Process in the Upper Baro Basin, Ethiopia. Applied and Environmental Soil Science, 2021: 1–15. https://doi.org/10.1155/2021/6617541.
Herrera Arango J., Senent-De Frutos J.A., Molina E.H. 2022. Murky Waters: The Impact of Privatizing Water Use on Environmental Degradation and the Exclusion of Local Communities in the Caribbean. International Journal of Water Resources Development, 38(1): 152–172. https://doi.org/10.1080/07900627.2021.1931052.
Islami F.A., Tarigan S.D., Wahjunie E.D., Dasanto B.D. 2022. Accuracy Assessment of Land Use Change Analysis Using Google Earth in Sadar Watershed Mojokerto Regency. In IOP Conference Series: Earth and Environmental Science, 950(1): 012091. https://doi.org/10.1088/1755-1315/950/1/012091.
Isma'il M., Zubairu S.M., Aliyu A., Ahmed M.H., Ibrahim S., Magaji A., Hassan I.M. 2023. Evaluating the Performance of Machine Learning Algorithms and Maximum Likelihood Classifier for Land-Use and Land-Cover Change Detection in Yola, Nigeria. SLU Journal of Science and Technology, 7(1): 97–115. https://doi.org/10.56471/slujst.v7i.380.
Kumari M., Sarma K., 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 Sensing Applications: Society and Environment, 15: 100239. https://doi.org/10.1016/j.rsase.2019.100239.
Majeed M., Tariq A., Anwar M.M., Khan A.M., Arshad F., Mumtaz F., Farhan M., Zhang L., Zafar A., Aziz M., Abbasi S., Rahman G., Hussain S., Waheed M., Fatima K., Shaukat S. 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. https://doi.org/10.3390/land10101026.
Mehdi S.M., Pant N.C., Saini H.S., Mujtaba S.A.I., Pande P. 2016. Identification of Palaeochannel Configuration in the Saraswati River Basin in Parts of Haryana and Rajasthan, India, Through Digital Remote Sensing and GIS. Episodes Journal of International Geoscience, 39(1): 29–38. https://doi.org/10.18814/epiiugs/2016/v39i1/89234.
Msofe N.K., Sheng L., Lyimo J. 2019. Land Use Change Trends and Their Driving Forces in the Kilombero Valley Floodplain, Southeastern Tanzania. Sustainability, 11(2): 505. https://doi.org/10.3390/su11020505.
Niu X., Hu Y., Lei Z., Yan H., Ye J., Wang H. 2022. Temporal and Spatial Evolution Characteristics and Its Driving Mechanism of Land Use/Cover in Vietnam from 2000 to 2020. Land, 11(6): 920. https://doi.org/10.3390/land11060920.
Pal S., Ziaul S.K. 2017. Detection of Land Use and Land Cover Change and Land Surface Temperature in English Bazar Urban Centre. The Egyptian Journal of Remote Sensing and Space Science, 20(1): 125–145. https://doi.org/10.1016/j.ejrs.2016.11.003.
Phuong V.T., Thien B.B. 2023a. A Multi-Temporal Landsat Data Analysis for Land-Use/Land-Cover Change in the Northwest Mountains Region of Vietnam Using Remote Sensing Techniques. Forum Geografic, 22 (1): 54–66. https://doi.org/10.5775/fg.2023.030.i.
Phuong V.T., Thien B.B. 2023b. Using Landsat Satellite Images to Detect Forest Cover Changes in the Northeast Region of Vietnam. Bulletin of the Transilvania University of Brasov. Series II: Forestry Wood Industry Agricultural Food Engineering, 16(1): 19–36. https://doi.org/10.31926/but.fwiafe.2023.16.65.1.2.
Regasa M.S., Nones M., Adeba D. 2021. A Review on Land Use and Land Cover Change in Ethiopian Basins. Land, 10(6): 585. https://doi.org/10.3390/land10060585.
Singh P., Sarkar Chaudhuri A., Verma P., Singh V.K., Meena S.R. 2022. Earth Observation Data Sets in Monitoring of Urbanization and Urban Heat Island of Delhi, India. Geomatics, Natural Hazards and Risk, 13(1): 1762–1779. https://doi.org/10.1080/19475705.2022.2097452.
Tariq A., Yan J., Mumtaz F., 2022. Land Change Modeler and CA-Markov Chain Analysis for Land Use Land Cover Change Using Satellite Data of Peshawar, Pakistan. Physics and Chemistry of the Earth, Parts A/B/C, 128: 103286. https://doi.org/10.1016/j.pce.2022.103286.
Thekkeyil A., George A., Abdurazak F., Kuriakose G., Nameer P.O., Abhilash P.C., Joseph S. 2023. Land Use Change in Rapidly Developing Economies – a Case Study on Land Use Intensification and Land Fallowing in Kochi, Kerala, India. Environmental Monitoring and Assessment, 195(9): 1089. https://doi.org/10.1007/s10661-023-11731-7.
Thien B.B., Huong D.T.V., Liem N.D. 2022. Assessment of Mangroves Forest Change from Satellite Images in Can Gio District, Ho Chi Minh City in Period of 1990-2020. In: Proceeding of the 13th National Conference on Geography Science, Hanoi, 133–140.
Thien B.B., Phuong V.T. 2023. Using Landsat Satellite Imagery for Assessment and Monitoring of Long-Term Forest Cover Changes in Dak Nong Province, Vietnam. Geographica Pannonica, 27(1): 69–82. https://doi.org/10.5937/gp27-41813
Thien B.B., Yachongtou B., Phuong V.T. 2023. Long-Term Monitoring of Forest Cover Change Resulting in Forest Loss in the Capital of Luang Prabang Province, Lao PDR. Environmental Monitoring and Assessment, 195(8): 1–17. https://doi.org/10.1007/s10661-023-11548-4.
Vadrevu K., Heinimann A., Gutman G., Justice C. 2019. Remote Sensing of Land Use/Cover Changes in South and Southeast Asian Countries. International Journal of Digital Earth, 12(10): 1099–1102. https://doi.org/10.1080/17538947.2019.1654274
Verma P., Raghubanshi A., Srivastava P.K., Raghubanshi A.S. 2020. Appraisal of Kappa-Based Metrics and Disagreement Indices of Accuracy Assessment for Parametric and Nonparametric Techniques Used in LULC Classification and Change Detection. Modeling Earth Systems and Environment, 6: 1045–1059. https://doi.org/10.1007/s40808-020-00740-x
Wahla S.S., Kazmi J.H., Tariq A. 2023. Mapping and Monitoring of Spatio-Temporal Land Use and Land Cover Changes and Relationship with Normalized Satellite Indices and Driving Factors. Geology, Ecology, and Landscapes: 1–17. https://doi.org/10.1080/24749508.2023.2187567
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. Applied Environmental Research, 43(3): 1–16. https://doi.org/10.35762/AER.2021.43.3.1
Wang S., Bai X., Zhang X., Reis S., Chen D., Xu J., Gu B. 2021. Urbanization can Benefit Agricultural Production with Large-Scale Farming in China. Nature Food, 2(3): 183–191. https://doi.org/10.1038/s43016-021-00228-6
Zadbagher E., Becek K., Berberoglu S. 2018. Modeling Land Use/Land Cover Change Using Remote Sensing and Geographic Information Systems: Case Study of the Seyhan Basin, Turkey. Environmental Monitoring and Assessment, 190: 1–15. https://doi.org/10.1007/s10661-018-6877-y
Zheng Y., Tang L., Wang H. 2021. An Improved Approach for Monitoring Urban Built-Up Areas by Combining NPP-VIIRS Nighttime Light, NDVI, NDWI, and NDBI. Journal of Cleaner Production, 328: 129488. https://doi.org/10.1016/j.jclepro.2021.129488
Abstract views: 170
Share
Published
How to Cite
Issue
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
