PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Delineation of Urban Land Cover Changes Using Remote Sensing in the Ninh Kieu District, Can Tho Province, Viet Nam

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The study aimed to determine how changes in land cover and surface water are being made using stratified objectoriented analysis based on the interpretation of remote sensing images. It is the first step toward managing the region’s annual land-use inventories projects. The study used Sentinel-2 images from 2019 through 2021 to delineate the changing urban land cover in the Ninh Kieu District, Can Tho City, Viet Nam. The study used QGIS software to interpret the images and eCognition software to classify the objects based on the NDBI, NDVI, and NDWI indices. The interpretation results were checked for the accuracy, and the land cover was changed over the years. The results show that urban land cover changes with the increase of urban land and the decrease of vegetation land used for urban land, while water surface area inwards decreased from 2019 to 2020 but increased in 2021. Maps of the current state of the urban land covers in the study area were delineated. The interpretation results contribute to the preliminary method by using satellite images for the annual land use inventory project in the region, even though some difficulties still exist and need to be modified.
Twórcy
  • Land Resources Department, Environment and Natural Resources College, Can Tho University, Can Tho, 90000, Viet Nam
  • Land Resources Department, Environment and Natural Resources College, Can Tho University, Can Tho, 90000, Viet Nam
  • Land Resources Department, Environment and Natural Resources College, Can Tho University, Can Tho, 90000, Viet Nam
autor
  • Undergraduate students in Land Management at Can Tho University, Can Tho, 90000, Viet Nam
Bibliografia
  • 1. Artstein R. and Poesio M. 2008. Survey article: Inter-coder agreement for computational linguistics. Comput. Linguist. 34(4), 555–596, doi: 10.1162/coli.07-034-R2.
  • 2. Can Tho Government. 2004. Decree No. 05/2004/ND-CP: The decree establishes districts of Ninh Kieu, Binh Thuy, Cai Rang, and O Mon, districts of Phong Dien, Co Do, Vinh Thanh, and Thot Not, and communes, wards, and townships of Can Tho city directly under the Central Government. http://vanban.chinhphu.vn/default.aspx?pageid=27160&docid=13120
  • 3. Chiem P.V. 2020. Research on indicators to know water from sentinel 2 on Google Earth Engine: Apply to Sa Dec city, Dong Thap province. Research on water recognition indicators from Sentinel-2 images on Google Earth Engine: Apply to Sa Dec city, Dong Thap province, 60, 1–8. (In Vietnamese)
  • 4. Cohen J. 1960. A coefficient of agreement for nominal scales’, Educ. Psychol. Meas., 20, 37–46, 1960, doi: 10.1177/001316446002000104.
  • 5. De Baan L., Alkemade R., Koellner T. 2013. Land use impacts on biodiversity in LCA: a global approach. Int. J. Life Cycle Assess., 18(6), 1216–1230, Jul. doi: 10.1007/s11367-012-0412-0.
  • 6. Dongping M., Tianyu C., Hongye, C., Li L., Qiao C., Du J. 2012. Semivariogram-based spatial bandwidth selection for remote sensing image segmentation with mean-shift algorithm. IEEE Geosci. Remote Sens. Lett., 9(5), 813–817, doi: 10.1109/LGRS.2011.2182604.
  • 7. Feizizadeh B., Darabi S., Blaschke T., Lakes T. 2022. QADI as a new method and alternative to kappa for accuracy assessment of remote sensingbased image classification. Sensors. 22(12), doi: 10.3390/s22124506.
  • 8. Ha M.C., Vu P.L., Nguyen H.D., Hoang T.P., Dang D.D., Dinh T.B.H., Şerban G., Rus I., Brețcan P. 2022. Machine learning and remote sensing application for extreme climate evaluation: Example of flood susceptibility in the Hue Province, Central Vietnam Region. Water, 14(10), doi: 10.3390/w14101617.
  • 9. Linh N.V., Diem P.K. 2023. Inventorying and mapping current land use at the district level: A case study in Thap Muoi District, Dong Thap Province. CTU Journal of Science, 59, 193–202. https://doi.org/10.22144/ctu.jvn.2023.121
  • 10. Ming D., Yang J., Li L., Song Z. 2011. Modified ALV for selecting the optimal spatial resolution and its scale effect on image classification accuracy, Math. Comput. Model., 54(3), 1061–1068, doi: 10.1016/j.mcm.2010.11.036.
  • 11. MONRE. 2014. Circular No. 35/2014/TT-BTNMT, dated June 30, 2014, of the Ministry of Natural Resources and Environment regulating land investigation and assessment.
  • 12. Myint S., Gober P., Brazel A., Grossman-Clarke S., Weng Q. 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ., 115(5), 1145–1161, doi: 10.1016/j.rse.2010.12.017.
  • 13. People’s Committee of Can Tho City. 2023. Directive on correcting and strengthening state management of land in Can Tho city. No: 08/CT-UBND.
  • 14. Tan Q.L., Liu Z.G., Shen W. 2007. An algorithm for object-oriented multiscale remote sensing image segmentation. J. Beijing Jiaotong Univ., 31(4), 111–114.
  • 15. Tang W., Hu J., Zhang H., Wu P., He, H. 2015. Kappa coefficient: a popular measure of rater agreement. Shanghai Arch. Psychiatry, 27(1), 62–67, doi: 10.11919/j.issn.1002-0829.215010.
  • 16. Tuan V.A., Hang L.T.T., Quang N.H. 2018. Monitoring urban surface water fluctuations by MNDWI index from enhanced resolution satellite images. Sci. J.-Ho Chi Minh City Univ. Educ., 15(11b), 29–36.
  • 17. Vatandaşlar C., Yavuz M. 2017. Modeling the cover management factor of RUSLE using very highresolution satellite imagery in a semiarid watershed. Environ. Earth Sci., 76(2), 65, doi: 10.1007/s12665-017-6388-0.
  • 18. Xu N., Tian J., Tian Q., Xu K., Tang S. 2019. Analysis of vegetation red edge with different illuminated/shaded canopy proportions and to construct normalized difference canopy shadow index. Remote Sens., 11(10), doi: 10.3390/rs11101192.
  • 19. Zhao W., Du S., Emery W. 2017. Object-based convolutional neural network for high-resolution imagery classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 3386–3396, doi: 10.1109/JSTARS.2017.2680324.
  • 20. Zhou W., Ming D., Xu L., Bao H., Wang M. 2018. Stratified object-oriented image classification based on remote sensing image scene division. J. Spectrosc., 2018, e3918954, doi: 10.1155/2018/3918954.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-45a38bef-3f1c-4b02-b2b4-137e8ae867ee
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.