Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Estimation of Aboveground Forest Biomass Carbon Stock by Satellite Remote Sensing - A Comparison between k-Nearest Neighbor and Regression Tree Analysis -

위성영상을 활용한 지상부 산림바이오매스 탄소량 추정 - k-Nearest Neighbor 및 Regression Tree Analysis 방법의 비교 분석 -

  • Jung, Jaehoon (School of Civil and Environmental Engineering, Yonsei University) ;
  • Nguyen, Hieu Cong (School of Civil and Environmental Engineering, Yonsei University) ;
  • Heo, Joon (School of Civil and Environmental Engineering, Yonsei University) ;
  • Kim, Kyoungmin (Center for Forest & Climate Change, Korea Forest Research Institute) ;
  • Im, Jungho (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
  • 정재훈 (연세대학교 토목환경공학과) ;
  • 우엔 콩 효 (연세대학교 토목환경공학과) ;
  • 허준 (연세대학교 토목환경공학과) ;
  • 김경민 (국립산림과학원 기후변화연구센터) ;
  • 임정호 (울산과학기술대학교 도시환경공학부)
  • Received : 2014.09.30
  • Accepted : 2014.10.18
  • Published : 2014.10.31
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Abstract

Recently, the demands of accurate forest carbon stock estimation and mapping are increasing in Korea. This study investigates the feasibility of two methods, k-Nearest Neighbor (kNN) and Regression Tree Analysis (RTA), for carbon stock estimation of pilot areas, Gongju and Sejong cities. The 3rd and 5th ~ 6th NFI data were collected together with Landsat TM acquired in 1992, 2010 and Aster in 2009. Additionally, various vegetation indices and tasseled cap transformation were created for better estimation. Comparison between two methods was conducted by evaluating carbon statistics and visualizing carbon distributions on the map. The comparisons indicated clear strengths and weaknesses of two methods: kNN method has produced more consistent estimates regardless of types of satellite images, but its carbon maps were somewhat smooth to represent the dense carbon areas, particularly for Aster 2009 case. Meanwhile, RTA method has produced better performance on mean bias results and representation of dense carbon areas, but they were more subject to types of satellite images, representing high variability in spatial patterns of carbon maps. Finally, in order to identify the increases in carbon stock of study area, we created the difference maps by subtracting the 1992 carbon map from the 2009 and 2010 carbon maps. Consequently, it was found that the total carbon stock in Gongju and Sejong cities was drastically increased during that period.

최근 주기적이고 정확한 산림바이오매스 탄소저장량 추정에 대한 필요성이 한국에서도 점차 증가하고 있다. 본 연구에서는 k-Nearest Neighbor (kNN) 및 Regression Tree Analysis (RTA) 알고리즘을 대상으로 공주 및 세종시를 대상으로 한 탄소량 변화 탐지를 통해 그 효용성을 비교 분석 하고자 하였다. 현장 자료로는 제 3차 및 제 5, 6차 국가산림자원조사 자료를 이용하였으며, 위성영상자료는 1992년, 2010년에 취득된 Landsat TM과 2009년에 취득된 Aster 영상을 이용하였다. 또한, 추정정확도를 향상시키기 위해 각 영상으로부터 다양한 식생지수를 생성하였다. 두 방법론의 비교를 위해 RMSE 및 평균편의(mean bias)를 포함한 각종 탄소통계량을 계산하였으며, 대상지역에 대한 탄소분포지도를 생성하고 비교를 수행하였다. 그 결과, kNN 알고리즘은 영상에 상관없이 보다 안정적인 추정결과를 나타낸 반면, 스무딩 효과로 인해 탄소의 공간분포가 뚜렷하지 않은 단점이 발견되었다. RTA의 경우 평균편의 결과 및 탄소의 공간분포가 명확히 나타나는 장점이 있으나, 위성영상에 따라 탄소추정량에서 큰 차이를 나타내었다. 최종적으로 2009년 및 2010년 탄소지도에서 1992년 탄소지도를 차분한 탄소차분지도를 생성을 통해 공주시 및 세종시 지역의 산림 탄소저장량이 급격히 증가했음을 확인하였다.

Keywords

References

  1. Brockhaus, J.A. and S. Khorram, 1992. A comparison of SPOT and Landsat-TM data for use in conducting inventories of forest resources, International Journal of Remote Sensing, 13(16): 3035-3043. https://doi.org/10.1080/01431169208904100
  2. Chander, G. and B. Markham, 2003. Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges, IEEE Transactions on Geoscience and Remote Sensing, 41(11): 2674-2677. https://doi.org/10.1109/TGRS.2003.818464
  3. Cho, K., J. Heo, J. Jung, C. Kim and K. Kim, 2011. Review and Comparative Analysis of Forest Biomass Estimation using Remotely Sensed Data: from Five Different Perspectives. Korean Society for Geospatial Information System, 19(1): 87-96 (in Korean with English abstract).
  4. Chung, S.Y., J.S. Yim, H.K. Cho, J.H. Jeong, S.H. Kim and M.Y. Shin, 2009. Estimation of Forest Biomass for Muju County using Biomass Conversion Table and Remote Sensing Data, Journal of Korean Forest Society, 98(4): 409-416 (in Korean with English abstract).
  5. Colby, J.D., 1991. Topographic normalization in rugged terrain, Photogrammetric Engineering and Remote Sensing, 57(5): 531-537.
  6. Coomes, O.T., F. Grimard, C. Potvin and P. Sima, 2008. The fate of the tropical forest: Carbon or cattle?, Ecological Economics, 65(2): 207-212. https://doi.org/10.1016/j.ecolecon.2007.12.028
  7. Crist, E.P. and R.J. Kauth, 1986. The Tasseled Cap De-Mystified, Photogrammetric Engineering and Remote Sensing, 52: 81-86.
  8. Fazakas, Z., M. Nilsson and H. Olsson, 1999. Regional forest biomass and wood volume estimation using satellite data and ancillary data, Agricultural and Forest Meteorology, 98: 417-425.
  9. Franco-Lopez, H., A.R. Ek and M.E. Bauer, 2001. Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method, Remote Sensing of Environment, 77(3): 251-274. https://doi.org/10.1016/S0034-4257(01)00209-7
  10. Fuchs, H., P. Magdon, C. Kleinn and H. Flessa, 2009. Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory, Remote Sensing of Environment, 113(3): 518-531. https://doi.org/10.1016/j.rse.2008.07.017
  11. Gao, B.-c., 1996. NDWI?A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sensing of Environment, 58(3): 257-266. https://doi.org/10.1016/S0034-4257(96)00067-3
  12. Gemmell, F.M., 1995. Effects of forest cover, terrain, and scale on timber volume estimation with Thematic Mapper data in a rocky mountain site, Remote Sensing of Environment, 51(2): 291-305. https://doi.org/10.1016/0034-4257(94)00056-S
  13. Gjertsen, A.K., 2007. Accuracy of forest mapping based on Landsat TM data and a kNN-based method, Remote Sensing of Environment, 110(4): 420-430. https://doi.org/10.1016/j.rse.2006.08.018
  14. Gonzalez, P., G.P. Asner, J.J. Battles, M.A. Lefsky, K.M. Waring and M. Palace, 2010. Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California, Remote Sensing of Environment, 114(7): 1561-1575. https://doi.org/10.1016/j.rse.2010.02.011
  15. Goodenough, D.G., A. Bhogal, A. Dyk, M. Apps, R. Hall, P. Tickle, H. Chen, K. Butler and M. Gim. 2000. Determination of above ground carbon in Canada's forests-a multi-source approach, Proc. of 2000 Geoscience and Remote Sensing Symposium, Honolulu Hawaii, USA. Jul. 24-28, pp. 949-953.
  16. Heo, J., J.H. Kim, J.S. Park and H.G. Sohn, 2006a. Timber age verification using historical satellite image analysis, Forest ecology and management, 236(2): 315-323. https://doi.org/10.1016/j.foreco.2006.09.023
  17. Heo, J., J.S. Park, Y.S. Song, S.K. Lee and H.G. Sohn, 2008. An integrated methodology for estimation of forest fire-loss using geospatial information, Environmental monitoring and assessment, 144(1): 285-299. https://doi.org/10.1007/s10661-007-9992-8
  18. Holopainen, M., R. Haapanen, M. Karjalainen, M. Vastaranta, J. Hyyppa, X. Yu, S. Tuominen and H. Hyyppa, 2010. Comparing accuracy of airborne laser scanning and TerraSAR-X radar images in the estimation of plot-level forest variables, Remote Sensing, 2(2): 432-445. https://doi.org/10.3390/rs2020432
  19. Homer, C., J. Dewitz, J. Fry, M. Coan, N. Hossain, C. Larson, N. Herold, A. McKerrow, J.N. VanDriel and J. Wickham, 2007. Completion of the 2001 National Land Cover Database for the Counterminous United States, Photogrammetric Engineering and Remote Sensing, 73(4): 337.
  20. Huang, F., 2006. Child development production functions. Available at SSRN: http://ssrn.com/abstract=889005 or http://dx.doi.org/10.2139/ssrn.889005.
  21. Huete, A., K. Didan, T. Miura, E.P. Rodriguez, X. Gao and L.G. Ferreira, 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment, 83: 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2
  22. Huete, A.R., 1988. A soil-adjusted vegetation index, SAVI, Remote Sensing of Environment, 25(3): 295-309. https://doi.org/10.1016/0034-4257(88)90106-X
  23. Huete, A.R., H. Liu, K. Batchily and W.J.D. Leeuwen, 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS, Remote Sensing of Environment, 59(3): 440-451. https://doi.org/10.1016/S0034-4257(96)00112-5
  24. Hyyppa, J., H. Hyyppa, M. Inkinen, M. Engdahl, S. Linko and Y.H. Zhu, 2000. Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes, Forest Ecology and Management, 128(1): 109-120. https://doi.org/10.1016/S0378-1127(99)00278-9
  25. Jung, J.H., J. Heo, S.H. Yoo, K.M. Kim and J.B. Lee, 2010. Estimation of Aboveground Biomass Carbon Stock in Danyang Area using kNN Algorithm and Landsat TM Seasonal Satellite Images, Journal of the Korean Society for GeoSpatial Information System, 18(4): 119-129 (in Korean with English abstract).
  26. Jung, J.H., S.P. Kim, S.C. Hong, K.M. Kim, E.S. Kim, J.H. Im and J. Heo, 2013. Effects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm, ISPRS Journal of Photogrammetry and Remote Sensing, 81: 82-92. https://doi.org/10.1016/j.isprsjprs.2013.04.008
  27. Katila, M. and E. Tomppo, 2001. Selecting estimation parameters for the Finnish multisource National Forest Inventory, Remote Sensing of Environment, 76(1): 16-32. https://doi.org/10.1016/S0034-4257(00)00188-7
  28. Kim, C., J. Heo, J.B. Lee, S. Han, J.H. Jung and S. Jayakumar, 2012. A synergetic approach to estimating timber age using integrated remotely sensed optical image and InSAR height data, International Journal of Remote Sensing, 33(1): 243-260. https://doi.org/10.1080/01431161.2011.591443
  29. Kim, K.M., 2012. Spatially explicit estimation and the uncertainty analysis of carbon stocks in pine forest using growth model and GIS: The case of Danyang area, Chungcheongbuk-do, doctoral dissertation, Seoul National University, Korea (in Korean with English abstract).
  30. Kim, S., J.H. Heo, J. Heo and S. Kim, 2008. Impervious Surface Estimation of Jungnangcheon Basin using Satellite Remote Sensing and Classification and Regression Tree, Korean Society of Civil Engineers, 28(6D): 915-922 (in Korean with English abstract).
  31. Korea Forest Research Institute, 2009. The 5th national forest inventory surveying: field surveying guide book ver. 1.3 37. Korea Forest Research Institute (in Korean).
  32. Labrecque, S., R. Fournier, J. Luther and D. Piercey, 2006. A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland, Forest Ecology and Management, 226(1): 129-144. https://doi.org/10.1016/j.foreco.2006.01.030
  33. Lu, D., 2006. The potential and challenge of remote sensing based biomass estimation, International Journal of Remote Sensing, 27(7): 1297-1328. https://doi.org/10.1080/01431160500486732
  34. Lu, D., P. Mausel, E. Brondı'zio and E. Moran, 2004. Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin, Forest Ecology and Management, 198(1-3): 149-167. https://doi.org/10.1016/j.foreco.2004.03.048
  35. Luther, J., R. Fournier, D. Piercey, L. Guindon and R. Hall, 2006. Biomass mapping using forest type and structure derived from Landsat TM imagery, International journal of applied earth observation and geoinformation, 8(3): 173-187. https://doi.org/10.1016/j.jag.2005.09.002
  36. Major, D.J., F. Baret and G. Guyot, 1990. A ratio vegetation index adjusted for soil brightness, International Journal of Remote Sensing, 11: 727-740. https://doi.org/10.1080/01431169008955053
  37. McVey, G.R. and G.S. Gerald, 1968. Measuring the Color of Growing Turf with a Reflectance Spectrophotometer, Agronomy Journal, 60: 640-643. https://doi.org/10.2134/agronj1968.00021962006000060016x
  38. Muukkonen, P., 2006. Forest inventory-based largescale forest biomass and carbon budget assessment: new enhanced methods and use of remote sensing for verification, doctoral dissertation, University of Helsinki, Finland.
  39. Myeong, S., D.J. Nowak and M.J. Duggin, 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
  40. Penman, J., M. Gytarsky, T. Hiraishi, T. Krug, D. Kruger, R. Pipatti, L. Buendia, K. Miwa, T. Ngara and K. Tanabe, 2003. Good practice guidance for land use, land-use change and forestry, IPCC National Greenhouse Gas Inventories Programme and Institute for Global Environmental Strategies, IGES), Kanagawa, Japan.
  41. Pinty, B. and M. Verstraete, 1992. GEMI: A Non-Linear Index to Monitor Global Vegetation from Satellites, Vegetatio, 101: 15-20. https://doi.org/10.1007/BF00031911
  42. Prasad, A.M., L.R. Iverson and A. Liaw, 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction, Ecosystems, 9(2): 181-199. https://doi.org/10.1007/s10021-005-0054-1
  43. Qi, J., A. Chehbouni, A.R. Huete, Y.H. Kerr and S. Sorooshian, 1994. A modified soil adjusted vegetation index, Remote Sensing of Environment, 48(2): 119-126. https://doi.org/10.1016/0034-4257(94)90134-1
  44. Reese, H., M. Nilsson, T.G. Pahlen, O. Hagner, S. Joyce, U. Tingelof, M. Egberth and H. Olsson, 2003. Countrywide estimates of forest variables using satellite data and field data from the National Forest Inventory, AMBIO: A Journal of the Human Environment, 32(8): 542-548. https://doi.org/10.1579/0044-7447-32.8.542
  45. Ripple, W.J., S. Wang, D.L. Isaacson and D.P. Paine, 1991. A preliminary comparison of Landsat Thematic Mapper and SPOT-1 HTV multispectral data for estimating coniferous forest volume, International Journal of Remote Sensing, 12: 1971-1977. https://doi.org/10.1080/01431169108955230
  46. Rondeaux, G., M. Steven and F. Baret, 1996. Optimization of soil-adjusted vegetation indices, Remote Sensing of Environment, 55(2): 95-107. https://doi.org/10.1016/0034-4257(95)00186-7
  47. Roujean, J.L. and F.M. Breon, 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements, Remote Sensing of Environment, 51(3): 375-384. https://doi.org/10.1016/0034-4257(94)00114-3
  48. Rouse, J.W., R.H. Haas, J.A. Schell and D.W. Deering. 1974. Monitoring vegetation systems in the Great Plains with ERTS, NASA special publication, USA.
  49. Roy, P. and S.A. Ravan, 1996. Biomass estimation using satellite remote sensing data?an investigation on possible approaches for natural forest, Journal of Biosciences, 21(4): 535-561. https://doi.org/10.1007/BF02703218
  50. Rulequest. 2012. Rulequest research data mining tools.
  51. Santilli, M., P. Moutinho, S. Schwartzman, D. Nepstad, L. Curran and C. Nobre, 2005. Tropical deforestation and the Kyoto Protocol, Climatic Change, 71(3): 267-276. https://doi.org/10.1007/s10584-005-8074-6
  52. Tomppo, E., C. Goulding and M. Katila, 1999. Adapting Finnish Multi-Source Forest Inventory Techniques to the New Zealand Preharvest Inventory, Scandinavian Journal of Forest Research, 14: 182-192. https://doi.org/10.1080/02827589950152917
  53. Tomppo, E. and M. Halme, 2004. Using coarse scale forest variables as ancillary information and weighting of variables in k-NN estimation: a genetic algorithm approach, Remote Sensing of Environment, 92(1): 1-20. https://doi.org/10.1016/j.rse.2004.04.003
  54. Tomppo, E., H. Olsson, G. St?hl, M. Nilsson, O. Hagner and M. Katila, 2008. Combining national forest inventory field plots and remote sensing data for forest databases, Remote Sensing of Environment, 112(5): 1982-1999. https://doi.org/10.1016/j.rse.2007.03.032
  55. Tou, J.T. and R.C. Gonzalez, 1974. Pattern recognition principles.
  56. Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing of Environment, 8(2): 127?150. https://doi.org/10.1016/0034-4257(79)90013-0
  57. Yim, J.S., W.S. Han, J.H. Hwang, S.Y. Chung, H.K. Cho and M.Y. Shin, 2009. Estimation of Forest Biomass upon Satellite Data and National Forest Inventory Data, Korean Journal of Remote Sensing, 25(4): 311-320. https://doi.org/10.7780/kjrs.2009.25.4.311
  58. Yoo, S.H., J. Heo, J.H. Jung, S.H. Han and K.M. Kim, 2011. Estimation of Aboveground Biomass Carbon Stock using Landsat TM and Ratio Images - kNN Algorithm and Regression Model Priority, Korea Society for Geospatial Information System, 19(2): 39-48 (in Korean with English abstract).

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