International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Classification ofWarm Temperate Vegetations and GIS-based Forest Management System

  • Cho, Sung-Min (Dept. of Landscape Architecture, Honam University)
  • Received : 2021.03.09
  • Accepted : 2021.03.14
  • Published : 2021.03.31
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Abstract

Aim of this research was to classify forest types at Wando in Jeonnam Province and develop warm temperate forest management system with application of Remote Sensing and GIS. Another emphasis was given to the analysis of satellite images to compare forest type changes over 10 year periods from 2009 to 2019. We have accomplished this study by using ArcGIS Pro and ENVI. For this research, Landsat satellite images were obtained by means of terrestrial, airborne and satellite imagery. Based on the field survey data, all land uses and forest types were divided into 5 forest classes; Evergreen broad-leaved forest, Evergreen Coniferous forest, Deciduous broad-leaved forest, Mixed fores, and others. Supervised classification was carried out with a random forest classifier based on manually collected training polygons in ROI. Accuracy assessment of the different forest types and land-cover classifications was calculated based on the reference polygons. Comparison of forest changes over 10 year periods resulted in different vegetation biomass volumes, producing the loss of deciduous forests in 2019 probably due to the expansion of residential areas and rapid deforestation.

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References

  1. KFS, "Warm-temperate Forests and Preservation," in Proc. Workshop on Temperate Forests, pp 26-31, Oct. 5. 2005.
  2. S, Kim and Jung, Hyungsup, "Classification of Forest Vertical Structure in South Korea from Aerial Orthophoto and Lidar Data using an Artificial Neural Network," Appl. Sci., Vol. 7, No 10, pp. 1046, Oct. 2017. DOI: https://doi.org/10.3390/app7101046
  3. M, Thomas and R. Kiefer, "Remote Sensing and Image Interpretation." 3 rd Ed. John Wiley, New York, pp. 98. 1994.
  4. R. Jensen, "Introductory Digital Image Processing: Remote Sensing Perspective," 3rd ed., Pearson Education Inc., Upper Saddle River. pp. 145-146. 2005.
  5. T. Lilesand, and T. M. Kiefer, "Remote Sensing and Image interpretation," 5th ed. USA: John Wiley and Sons Inc. pp. 87. 2004.
  6. A. Singh, "Spectral Separability of Forest Cover Classes," International Journal of Remote Sensing, Vol. 8, pp 971-979. June. 2007. DOI:10.1080/01431168708954741
  7. V. Rodriguez-Galiano, B. Ghimire, and J. Rogan, "An assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification," ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 67, pp 93-104,Jan. 2012. DOI: https://doi.org/10.1016/j.isprsjprs.2011.11.002.
  8. P. Thenkabail, Enclona, E. A., and Ashton. M. S. "Hyperion, IKONOS, ALI, and ETM Sensors in the Study of Rainforest." Remote Sensing of Environment, Vol. 90, No. 1, pp 23-43. March, 2004. DOI: 10.1016/j.rse.2003.11.018
  9. G. Rosenfield, and K. Fitzpatrick, "A Coefficient of Agreement as a Measure of Thematic Classification Accuracy." Photogrammetric Engineering & Remote Sensing, Vol. 52, No. 2, pp. 223-227. April, 1986. DOI: https://doi.org/10.1177/001316446002000104
  10. L Weng, "A Survey Image Classification Methods and Techniques for Improving Classification Performance," International Journal of Remote Sensing, Vol. 28, No. 5, pp. 823-870. March, 2007. DOI: http://doi.org/10.1080/ 01431160600746456.
  11. R. Laurini R and D. Thompson. "Fundamentals of Spatial Information Systems," Academic Press. San Diego, CA. pp.156. 1998.
  12. F. Gomariz-Castillo and F. Alonso-Sarria, "Improving Classification Accuracy of Multi-Temporal and Images by Assessing the Use of Different Algorithms, Textural and Ancillary Information for a Mediterranean Semiarid Area from 2000 to 2015." Journal Remote Sensing, Vol.9, No. 1058. pp 2-23. July, 2016. DOI: 10.3390/rs9101058
  13. G. Asner, "Cloud Cover in Landsat Observation of the Brazilian Amazon," InternationalJournal of Remote Sensing, Vol. 22, Issue 18, pp. 3855-3862. Nov. 2010. DOI: https://doi.org/10.1080/01431160010006926
  14. G. Foody, "Status of Land Cover Classification Accuracy Assessment," Remote Sensing of Environment. Vol. 80, No. 1, pp.185-201. April, 2002. DOI: https://doi.org/10.1016/S0034-4257(01)00295-4
  15. Oh, Kyukoon, "Modeling of Temperate Forest Management," KEOPS. Vol. 10, No. 1, pp.97-102. 2002.
  16. Lee, Soodong, Kim Jiseok, Kim Mijeong, and Kang Hyunkyong, "Plant Community Structure Characteristic of the Evergreen Forest," Korean Journal of Environment and Ecology, Vol.30, No.2 pp.228-242. April, 2018. DOI: https://doi.org/10.13047/KJEE.2016.30.2.228
  17. E. Achard and H. Eva., "Tropical Forest Mapping from Coarse Spatial Resolution Data," International Journal of Remote Sensing, Vo. 22, No 14. pp 2271-2762, Nov, 2010. DOI : https://doi.org/10.1080/01431160120548
  18. Kim, Jongyoumg, "Cultivating the Seedlings in Greenhouse for Evergreen Broad-leaved Target Species and Artificial Regeneration in Warm-temperate Forests," Ph, D Thesis. Chonnam Univ. pp. 94-96. 2010. DOI: http://www.riss.kr/link?id=T12161053&outLink=K