Browse > Article
http://dx.doi.org/10.7780/kjrs.2013.29.5.7

Mapping and estimating forest carbon absorption using time-series MODIS imagery in South Korea  

Cha, Su-Young (Asian Institute for Energy, Environment & Sustainability, Seoul National University)
Pi, Ung-Hwan (High Performance Device Group, Samsung Advanced Institute of Technology)
Park, Chong-Hwa (Graduate School of Environmental Studies, Seoul National University)
Publication Information
Korean Journal of Remote Sensing / v.29, no.5, 2013 , pp. 517-525 More about this Journal
Abstract
Time-series data of Normal Difference Vegetation Index (NDVI) obtained by the Moderate-resolution Imaging Spectroradiometer(MODIS) satellite imagery gives a waveform that reveals the characteristics of the phenology. The waveform can be decomposed into harmonics of various periods by the Fourier transformation. The resulting $n^{th}$ harmonics represent the amount of NDVI change in a period of a year divided by n. The values of each harmonics or their relative relation have been used to classify the vegetation species and to build a vegetation map. Here, we propose a method to estimate the annual amount of carbon absorbed on the forest from the $1^{st}$ harmonic NDVI value. The $1^{st}$ harmonic value represents the amount of growth of the leaves. By the allometric equation of trees, the growth of leaves can be considered to be proportional to the total amount of carbon absorption. We compared the $1^{st}$ harmonic NDVI values of the 6220 sample points with the reference data of the carbon absorption obtained by the field survey in the forest of South Korea. The $1^{st}$ harmonic values were roughly proportional to the amount of carbon absorption irrespective of the species and ages of the vegetation. The resulting proportionality constant between the carbon absorption and the $1^{st}$ harmonic value was 236 tCO2/5.29ha/year. The total amount of carbon dioxide absorption in the forest of South Korea over the last ten years has been estimated to be about 56 million ton, and this coincides with the previous reports obtained by other methods. Considering that the amount of the carbon absorption becomes a kind of currency like carbon credit, our method is very useful due to its generality.
Keywords
Carbon estimation; Forest; Biomass; Fourier Transform Analysis; Harmonics; MODIS; Time-series data;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Blackard, J.A., M.V. Finco, E.H. Helmer, G.R. Holden, M.L. Hoppus, D.M. Jacobs, A.J. Lister, G.G. Moisen, M.D. Nelson, R. Riemann, B. Ruefenacht, D. Salajanu, D.L Weyermann, K.C. Winterberger, T.J. Brandeis, R.L. Czaplewski, R.E. McRoberts, P.L. Patterson, and R.P. Tymcio, 2008. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information, Remote Sensing of Environment, 112:1658-1677.   DOI   ScienceOn
2 DeFries, R., F. Achard, S. Brown, M. Herold, D. Murdiyarso, B. Schlamadinger, and C. de Souza, 2007. Earth observations for estimating greenhouse gas emissions from deforestation in developing countries, Environmental Science and Policy, 10(4):385-394.   DOI   ScienceOn
3 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.   DOI   ScienceOn
4 Gibbs, H.K., S. Brown, J.O. Niles, and J.A. Foley, 2007. Monitoring and estimating tropical forest carbon stocks: making REDD a reality, Environmental Research Letters, 2(4): 045023.   DOI   ScienceOn
5 Goetz S., A. Baccini, N. Laporte, T. Johns, W. Walker, J. Kellndorfer, R. A Houghton, and M. Sun, 2009. Mapping and monitoring carbon stocks with satellite observations: a comparison of methods, Carbon Balance and Management, 4(1):2.   DOI   ScienceOn
6 Jakubauskas, M.E., D.R. Legates, and J.H. Kastens, 2002. Crop identification using harmonic analysis of time-series AVHRR NDVI data, Computers and Electronics in Agriculture, 37(1):127-139.   DOI   ScienceOn
7 Herold, M. and T. Johns, 2007. Linking requirements with capabilities for deforestation monitoring in the context of the UNFCCC-REDD process, Environmental Research Letters, 2(4): 045025.   DOI   ScienceOn
8 Huiyan, G., D. Limin, W. Gang, X. Dong, W. Shunzhong, and W. Hui, 2006. Estimation of forest volumes by integrating Landsat TM imagery and forest inventory data, Science in China, Series E Technological Sciences, 49(I):54-62.   DOI
9 IPCC, 2003. Good Practice Guidance for Land Use, Land-Use Change and Forestry, Institute for Global Environment Strategies.
10 Niels, A. and J. Sathaye, 2008. Reducing deforestation and trading emissions: Economic implications for the post-Kyoto carbon market, ZEW-Centre for European Economic Research Discussion Paper, 08-016.
11 Otsuka T., W. Mo, T. Satomura, M. Inatomi, and H. Koizumi, 2007. Biometric based carbon flux measurements and net ecosystem production (NEP) in a temperate deciduous broad-leaved forest beneath a flux tower, Ecosystems, 10:324-334.   DOI
12 Patenaude G., R. Milne, and T.P. Dawson, 2005. Synthesis of remote sensing approaches for forest carbon estimation: reporting to the Kyoto Protocol, Environmental Science and Policy, 8(2): 161-178.   DOI   ScienceOn
13 Rahman, M.M., E. Csaplovics, and B. Koch, 2008. Satellite estimation of forest carbon using regression models, International Journal of Remote Sensing, 29(23): 6917-6936.   DOI   ScienceOn
14 Reed, B.C., J.F. Brown, D. VanderZee, T.R. Loveland, J.W. Merchant, and D.O. Ohlen, 1994. Measuring phonological variability from satellite imagery, Journal of Vegetation Science, 5(5): 703-714.   DOI   ScienceOn
15 Smith, B., W. Knorr, J.L. Widlowski, B. Pinty, and N. Gobron, 2008. Combining remote sensing data with process modelling to monitor boreal conifer forest carbon balances, Forest Ecology and Management, 255(12): 3985-3994.   DOI   ScienceOn
16 Son Y.M., K.H. Lee, and R.H. Kim, 2007. Estimation of Forest Biomass in Korea, Journal of Korean Forestry Society, 96(4): 477-482.   과학기술학회마을
17 Wulder, M.A., J.C. White, R.A. Fournier, J.E. Luther, and S. Magnussen, 2008. Spatially explicit large area biomass estimation: three approaches using forest inventory and remotely sensed imagery in a GIS, Sensors, 8:529-560.   DOI
18 Wylie, B.K., D.J. Meyer, L.L. Tieszen, and S.?Mannel, 2002. Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands - A case study, Remote Sensing of Environment, 79(2-3): 266-278.   DOI   ScienceOn
19 Yim J.S., W.S. Han, J.H. Hwang, S.Y. Chung, H.K. Cho, and M.Y. Shin, 2009. Estimation of Forest Biomass based upon Satellite Data and National Forest Inventory Data, Korean Journal of Remote Sensing, 25(4): 311-320.   과학기술학회마을   DOI
20 Ministry of Environment, 2007, http://egis.me.go.kr/
21 Korea Forest Service, 2009, http://www.forest.go.kr/
22 Korea Forest Service, 2010, http://www.klaw.go.kr/