DOI QR코드

DOI QR Code

Estimation of Carbon Absorption Distribution by Land Use Changes using RS/GIS Method in Green Land

RS/GIS를 이용한 토지이용변화에 의한 녹지의 이산화탄소 (CO2) 흡착량 분포 추정

  • 나상일 (충북대학교 농업생명환경대학 지역건설공학과) ;
  • 박종화 (충북대학교 농업생명환경대학 지역건설공학과) ;
  • 박진기 (충북대학교 농업생명환경대학 지역건설공학과)
  • Received : 2010.04.02
  • Accepted : 2010.05.06
  • Published : 2010.05.31

Abstract

Quantification of carbon absorption and understanding the human induced land use changes (LUC) forms one of the major study with respect to global climatic changes. An attempt study has been made to quantify the carbon absorption by LUC through remote sensing technology. The Landsat imagery four time periods was classified with the hybrid classification method in order to quantify carbon absorption by LUC. Thereafter, for estimating the amount of carbon absorption, the stand biomass of forest was estimated with the total weight, which was the sum of individual tree weight. Individual tree volumes could be estimated with the crown width extracted from digital forest cover type map. In particular, the carbon conversion index and the ratio of the $CO_2$ molecular weight to the C atomic weight, reported in the IPCC guideline, was used to convert the stand biomass into the amount of carbon absorption. Total carbon absorption has been modeled by taking areal estimates of LUC of four time periods and carbon factors for land use type and standing biomass. Results of this study, through LUC suggests that over a period of construction, 7.10 % of forest and 9.43 % of barren were converted into urban. In the conversion process, there has been a loss of 6.66 t/ha/y (7.94 %) of carbon absorption from the study area.

Keywords

References

  1. Federico, G. A., Asuncion, R. Z., and Jose, C. G., 2006. Assessing forest carbon sinks in Spain using satellite images. Geo-science and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International: 1721-1723.
  2. Journel, A. G., 1986. Constrainted interpolation and qualitative information - the soft Kriging approach, Math. Geol. 18(3): 269-286. https://doi.org/10.1007/BF00898032
  3. Kim, S. R., 2007. Forest cover classification by optimal segmentation of IKONOS imagery. Thesis for Master, Korea University, Seoul.
  4. Kim, S. R., Lee, W. K., Kwak, H. B., and Ghoi, S. H., 2009. Estimating carbon sequestration in forest using KOMPSAT-2 imagery, Journal of Korean Forest Society 98(3): 324-330 (in Korean).
  5. Krishna, V. P., Yogesh, K., and Badarinath, V. S., 2002. Land use changes and modeling carbon fluxes from satellite data, Adv. Space Res. 30(11): 2511- 2516. https://doi.org/10.1016/S0273-1177(02)80324-6
  6. Myeong, S., Nowak, D. J., and Duggin, M. J., 2006. A temporal analysis of urban forest carbon storage using remote sensing. Remote Sensing of Environment 101(2): 277-282. https://doi.org/10.1016/j.rse.2005.12.001
  7. Nicholas Stern, 2006. Stern Review: The Economics of Climate Change.
  8. Ranson, K. J., Nelson, R., Kimes, D., Sun, G., Kharuk, V. and Montesano, P., 2007. Using MODIS and GLAS data to develop timber volume estimates in dentralSiberia. Geo-science and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International: 2306-2309.
  9. Richards, J. A., and Jia, X., 1999. Remote sensing digital image analysis, 3rd Ed., Springer-Verlag, Berlin.
  10. Son, Y. M., Lee, K. H., and Kim, R. H., 2007. Estimation of forest biomass in Korea, Journal of Korean Forest Society 96(4): 477-482 (in Korean).
  11. STATISTICS KOREA, 2006. Chungbuk statistical yearbook. http://www.kos-tat.go.kr/. Accessed 3 Mar. 2010.
  12. Thenkabail, P. S., Stucky, N., Grisscom, B. W., Ashton, M. S., Djels, J., van der Meer, B. and Enclona, E., 2004. Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data. International Journal of Remote Sensing 25(23): 5477-5472.