Analysis of Land Cover Changes in Northern Vietnam Using High Resolution Remote Sensing Data

  • Thanh Tung Hoang
  • Kenlo Nishida Nasahara
  • Jin Katagi
Conference paper

Abstract

This study attempts to produce 15-meter resolution land cover maps over Northern Vietnam in 2007 and 2015 using multi-temporal and multi-sensor data including ASTER, Landsat, and PALSAR mosaic based on a kernel-based probabilistic classification method. Other ancillary such as SuomiNPP nightlight image, OpenStreetMap road network and SRTM30 were applied for additional information supplement. A number of about 60,000 reference data was built by field GPS photos as well as visual interpretation using Google Earth for training and validation. Results showed that the overall accuracy of the land cover maps is 81% and 89% in 2007, 2015 respectively. The results indicated many changes in areas of land cover types between 2007 and 2015 in Son La hydropower dam area and in selected sites for forest gain detection. The analysis showed that water area demonstrated an increasing trend while cropland area presented a decreasing trend in Son La hydropower dam area; and forest area experienced a rising trend whereas grassland area indicated a declining trend in the other selected sites. The results introduced a new high-resolution regional land cover data in Northern Vietnam for environmental modeling or other regional studies.

Keywords

Land cover Kernel density estimation Multi-temporal High resolution High accuracy Northern Vietnam 

Notes

Acknowledgements

The authors thank the USGS, AIST, JAXA, NASA, NOAA and OpenStreetMap Foundation for the distribution of the data used. We would like to thank Dr. Takeo Tadono for providing necessary hardware for classification running. We thank members of ecosystem group in JAXA/EORC for technical support.

References

  1. 1.
    Friedl, M.A., McIver, D.K., Hodges, J.C.F., Zhang, X.Y., Muchoney, D., Strahler, A.H.: Global land cover mapping from MODIS: algorithms and early results. Remote Sens. Environ. 83, 287–302 (2002)CrossRefGoogle Scholar
  2. 2.
    Yu, L., Liang, L., Wang, J., Zhao, Y., Cheng, Q., Hu, L., Liu, S., Yu, L., Wang, X., Zhu, P., Li, X., Xu, Y., Li, C., Fu, W., Li, X., Li, W., Liu, C., Cong, N., Zhang, H., Sun, F., Bi, X., Xin, Q., Li, D., Yan, D., Zhu, Z., Goodchild, M.F., Gong, P.: Meta-discoveries from a synthesis of satellite-based land-cover mapping research. Int. J. Remote Sens. 35, 4573–4588 (2014)CrossRefGoogle Scholar
  3. 3.
    Gong, P., Wang, J., Yu, L.L., Zhao, Y.Y.Y.Y., Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X., Cheng, Q., Hu, L., Yao, W., Zhang, H.H., Zhu, P., Zhao, Z., Zheng, Y., Ji, L., Zhang, Y., Chen, H., Yan, A., Guo, J., Wang, L., Liu, X., Shi, T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C., Clinton, N., Zhu, Z., Chen, J.J.: Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. Int. J. Remote Sens. 34, 2607–2654 (2013)CrossRefGoogle Scholar
  4. 4.
    Running, S.W.: Climate change. Ecosystem disturbance, carbon, and climate. Science 321, 652–653 (2008)CrossRefGoogle Scholar
  5. 5.
    Bontemps, S., Herold, M., Kooistra, L., Van Groenestijn, A., Hartley, A., Arino, O., Moreau, I., Defourny, P.: Revisiting land cover observation to address the needs of the climate modeling community. Biogeosciences 9, 2145–2157 (2012)CrossRefGoogle Scholar
  6. 6.
    Pielke, 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., de Noblet, N.: Land use/land cover changes and climate: modeling analysis and observational evidence. Wiley Interdiscip. Rev. Clim. Change 2, 828–850 (2011)CrossRefGoogle Scholar
  7. 7.
    Streets, D.G., Canty, T., Carmichael, G.R., de Foy, B., Dickerson, R.R., Duncan, B.N., Edwards, D.P., Haynes, J.A., Henze, D.K., Houyoux, M.R., Jacob, D.J., Krotkov, N.A., Lamsal, L.N., Liu, Y., Lu, Z., Martin, R.V., Pfister, G.G., Pinder, R.W., Salawitch, R.J., Wecht, K.J.: Emissions estimation from satellite retrievals: a review of current capability. Atmos. Environ. 77, 1011–1042 (2013)CrossRefGoogle Scholar
  8. 8.
    Jung, M., Henkel, K., Herold, M., Churkina, G.: Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sens. Environ. 101, 534–553 (2006)CrossRefGoogle Scholar
  9. 9.
    Houghton, R.A.: How well do we know the flux of CO2 from land-use change? Tellus Ser. B Chem. Phys. Meteorol. 62, 337–351 (2010)CrossRefGoogle Scholar
  10. 10.
    Shevliakova, E., Pacala, S.W., Malyshev, S., Hurtt, G.C., Milly, P.C.D., Caspersen, J.P., Sentman, L.T., Fisk, J.P., Wirth, C., Crevoisier, C.: Carbon cycling under 300 years of land use change: importance of the secondary vegetation sink. Global Biogeochem. Cycles 23, 1–16 (2009)CrossRefGoogle Scholar
  11. 11.
    Van Tuyl, S., Law, B.E., Turner, D.P.: Gitelman, a.I.: Variability in net primary production and carbon storage in biomass across Oregon forests—an assessment integrating data from forest inventories, intensive sites, and remote sensing. For. Ecol. Manage. 209, 273–291 (2005)CrossRefGoogle Scholar
  12. 12.
    Avitabile, V., Herold, M., Henry, M., Schmullius, C.: Mapping biomass with remote sensing: a comparison of methods for the case study of Uganda. Carbon Balance Manage. 6, 7 (2011)CrossRefGoogle Scholar
  13. 13.
    Pham, T.D., Yoshino, K.: Aboveground biomass estimation of mangrove species using ALOS-2 PALSAR imagery in Hai Phong City, Vietnam. J. Appl. Remote Sens. 11, 026010 (2017)CrossRefGoogle Scholar
  14. 14.
    Miller, S.N., Guertin, D.P., Goodrich, D.C.: Hydrologic Modeling Uncertainty Resulting, 43 (2008)Google Scholar
  15. 15.
    Shi, Z.H., Ai, L., Li, X., Huang, X.D., Wu, G.L., Liao, W.: Partial least-squares regression for linking land-cover patterns to soil erosion and sediment yield in watersheds. J. Hydrol. 498, 165–176 (2013)CrossRefGoogle Scholar
  16. 16.
    Newbold, T., Hudson, L.N., Hill, S.L., Contu, S., Lysenko, I., Senior, R.A., Borger, L., Bennett, D.J., Choimes, A., Collen, B., Day, J., De Palma, A., Diaz, S., Echeverria-Londono, S., Edgar, M.J., Feldman, A., Garon, M., Harrison, M.L., Alhusseini, T., Ingram, D.J., Itescu, Y., Kattge, J., Kemp, V., Kirkpatrick, L., Kleyer, M., Correia, D.L., Martin, C.D., Meiri, S., Novosolov, M., Pan, Y., Phillips, H.R., Purves, D.W., Robinson, A., Simpson, J., Tuck, S.L., Weiher, E., White, H.J., Ewers, R.M., Mace, G.M., Scharlemann, J.P., Purvis, A.: Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015)CrossRefGoogle Scholar
  17. 17.
    Ishihara, M., Tadono, T.: Land cover changes induced by the great east Japan earthquake in 2011. Sci. Rep. 7, 45769 (2017)CrossRefGoogle Scholar
  18. 18.
    Foley, J.A., Defries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., Daily, G.C., Gibbs, H.K., Helkowski, J.H., Holloway, T., Howard, E.A., Kucharik, C.J., Monfreda, C., Patz, J.A., Prentice, I.C., Ramankutty, N., Snyder, P.K.: Global consequences of land use. Science 309, 570–574 (2005)CrossRefGoogle Scholar
  19. 19.
    IPCC: Climate Change 2013 The Physical Science Basis - Summary for Policymakers, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge (2013)Google Scholar
  20. 20.
    Mayaux, P., Eva, H., Gallego, J., Strahler, A.H., Herold, M., Agrawal, S., Naumov, S., De Miranda, E.E., Di Bella, C.M., Ordoyne, C., Kopin, Y., Roy, P.S.: Validation of the global land cover 2000 map. IEEE Trans. Geosci. Remote Sens. 44, 1728–1737 (2006)CrossRefGoogle Scholar
  21. 21.
    Bontemps, S., Defourny, P., Bogaert, E.V., Kalogirou, V., Perez, J.R.: GLOBCOVER 2009 Products Description and Validation Report, p. 53 (2011)Google Scholar
  22. 22.
    Scepan, J., Estes, J.E.: Thematic validation of global land cover data sets-procedures and interpretation methods. In: IEEE 2001 International Geoscience and Remote Sensing Symposium, IGARSS 2001, vol. 1113, pp. 1119–1121 (2001)Google Scholar
  23. 23.
    FAO: Global Forest Resources Assessment 2015 – Country Report - Vietnam. Food and Agriculture Organization of the United Nations (2015)Google Scholar
  24. 24.
    Stibig, H.J., Achard, F., Carboni, S., Raši, R., Miettinen, J.: Change in tropical forest cover of Southeast Asia from 1990 to 2010. Biogeosciences 11, 247–258 (2014)CrossRefGoogle Scholar
  25. 25.
    Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.: High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013)CrossRefGoogle Scholar
  26. 26.
    Decision No.196/2004/QD-TTg: Decision of the Prime Minister of Vietnam on regulations of compensation, displacement and resettlement of the Son La Hydropower Project (in Vietnamese). The Government of Viet Nam (2004)Google Scholar
  27. 27.
    Duong, N.D.: Land cover mapping of Vietnam using modis 500M 32-Day. In: International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences 2004, pp. 1–6 (2004)Google Scholar
  28. 28.
    Hoan, N.T., Duong, N.D., Tateishi, R.: Combination of ADEOS II - GLI and MODIS 250m data for land cover mapping of indochina Peninsula. In: Asian Association on Remote Sensing - 26th Asian Conference on Remote Sensing and 2nd Asian Space Conference, ACRS 2005, pp. 417–424 (2005)Google Scholar
  29. 29.
    Hashimoto, S., Tadono, T., Onosato, M., Hori, M., Moriyama, T.: Probabilistic land cover classification approach toward knowledge-based satellite data interpretations. In: 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1513–1516 (2012)Google Scholar
  30. 30.
    Hashimoto, S., Tadono, T., Onosato, M., Hori, M.: Land use and land cover inference in large areas using multi-temporal optical satellite images. In: 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3333–3336. IEEE (2013)Google Scholar
  31. 31.
    Hashimoto, S., Tadono, T., Onosato, M., Hori, M., Shiomi, K.: A New method to derive precise land-use and land-cover maps using multi-temporal optical data. J. Remote Sens. Jpn. 34, 102–112 (2014)Google Scholar
  32. 32.
    Gregorio, A.D.: Land Cover Classification System (LCCS), version 2: Classification concepts and user manual. FAO (2005)Google Scholar
  33. 33.
    Decision No.272/QD-TTg: Decision by The Prime Minister on Ratifying The Results of The 2005 General Land Inventory (in Vietnamese). The Government of Viet Nam (2007)Google Scholar
  34. 34.
    National Land Use Status Quo Classified by Geographical and Economic Regions in 2014. http://thongke.monre.gov.vn
  35. 35.
    Bui, T.M.H., Schreinemachers, P., Berger, T.: Hydropower development in Vietnam: involuntary resettlement and factors enabling rehabilitation. Land Use Policy 31, 536–544 (2013)CrossRefGoogle Scholar
  36. 36.
    Rutten, M., van Dijk, M., van Rooij, W., Hilderink, H.: Land use dynamics, climate change, and food security in Vietnam: a global-to-local modeling approach. World Dev. 59, 29–46 (2014)CrossRefGoogle Scholar
  37. 37.
    Van, T.T., Wilson, N., Thanh-Tung, H., Quisthoudt, K., Quang-Minh, V., Xuan-Tuan, L., Dahdouh-Guebas, F., Koedam, N.: Changes in mangrove vegetation area and character in a war and land use change affected region of Vietnam (Mui Ca Mau) over six decades. Acta Oecologica 63, 71–81 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Thanh Tung Hoang
    • 1
  • Kenlo Nishida Nasahara
    • 2
  • Jin Katagi
    • 2
  1. 1.Viet Nam Institute of MeteorologyHydrology and Climate ChangeHanoiVietnam
  2. 2.University of TsukubaTsukubaJapan

Personalised recommendations