Browse > Article

Estimation of Forest Biomass for Muju County using Biomass Conversion Table and Remote Sensing Data  

Chung, Sang Young (Department of Forest Resources, College of Forest Science, Kookmin University)
Yim, Jong Su (Department of Forest Resources, College of Forest Science, Kookmin University)
Cho, Hyun Kook (Division of Forest Resource Information, Korea Forest Research Institute)
Jeong, Jin Hyun (Division of Forest Resource Information, Korea Forest Research Institute)
Kim, Sung Ho (Division of Forest Resource Information, Korea Forest Research Institute)
Shin, Man Yong (Department of Forest Resources, College of Forest Science, Kookmin University)
Publication Information
Journal of Korean Society of Forest Science / v.98, no.4, 2009 , pp. 409-416 More about this Journal
Abstract
Forest biomass estimation is essential for greenhouse gas inventories and terrestrial carbon accounting. Remote sensing allows for estimating forest biomass over a large area. This study was conducted to estimate forest biomass and to produce a forest biomass map for Muju county using forest biomass conversion table developed by field plot data from the 5th National Forest Inventory and Landsat TM-5. Correlation analysis was carried out to select suitable independent variables for developing regression models. It was resulted that the height class, crown closure density, and age class were highly correlated with forest biomass. Six regression models were used with the combination of these three stand variables and verified by validation statistics such as root mean square error (RMSE) and mean bias. It was found that a regression model with crown closure density and height class (Model V) was better than others for estimating forest biomass. A biomass conversion table by model V was produced and then used for estimating forest biomass in the study site. The total forest biomass of the Muju county was estimated about 8.8 million ton, or 128.3 ton/ha by the conversion table.
Keywords
forest biomass estimation; biomass conversion table; regression model; remote sensing; national forest inventory;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 무주군, 2007. 무주군 통계연보. http://www.muju.org/statistics/ index.html
2 IPCC. 2003. Good Practice guidance for land use, landuse change and forestry, Institute for Global Environment Strategies
3 Jakubauskas, M.E. and Price, K.P. 1997. Empirical relationships between structural and spectral factors of Yellowstone lodgepole pine forests. Photo-grammetric Engineering and Remote Sensing 63(12): 1375-1381   ScienceOn
4 Luther, J.E., Fournier, R.A., Piercey, D.E., Guindon, L. and Hall, R.J. 2006. Biomass mapping using forest type and structure derived from Landsat TM imagery. International Journal of Applied Earth Observation and Geoinformation 8(2006): 173-187   DOI   ScienceOn
5 Malhi, Y. Meir, P. and Brown, S. 2002. Forests, carbon and global climate. Philosophical transactions. Series A, Mathematical, Physical, and Engineering Sciences 360(1797): 1567-91   DOI   ScienceOn
6 손영모, 이경학, 김래현. 2007. 우리나라 산림 바이오매스 추정. 한국임학회지 96(4): 477-482
7 Bortolot, Z.J. and Wynne, R.H. 2005. Estimating forest biomass using small footprint LiDAR data : An individual tree-based approach that incorporates training data. ISPRS Journal of Photogrammetry and Remote Sensing 59(6): 342-360   DOI   ScienceOn
8 국립산림과학원, 2008. 제5차 국가산림자원조사-현지조사 매뉴얼-서울. pp. 54
9 Brown, S. 1997. Estimating biomass and biomass change in tropical forests: a primer. FAO Forestry Paper, vol. 134. Food and Agriculture Organization of the United Nations. Rome. pp. 55
10 Fazakas, Z., Nilsson, M. and Olsson, H. 1999. Regional forest biomass and wood volume estimation using satellite data and ancillary data. Agricultural and Forest Meteorology 98-99: 417-425   DOI   ScienceOn
11 Katila, M. and Tomppo, E. 2002. Stratification by ancillary data in multisource forest inventories employing knearest neighbor estimation. Canadian Journal of Forest Research 32(9): 1548-1561   DOI   ScienceOn
12 Salvador, R. and Pons, X. 1998. On the reliability of Landsat TM for estimation forest variables by regression techniques: A methodological analysis. IEEE Transactions on Geoscience & Remote Sensing 36(6): 1888-1897   DOI   ScienceOn
13 Fehrmann, L., Lehtonen, A., Kleinn, C. and Tomppo, E. 2008. Comparison of linear and mixed-effect regression models and k-nearest neighbor approach for estimation of single-tree biomass. Canadian Journal of Forest Research 38(1): 1-9   DOI   ScienceOn
14 Labrecque, S., Fournier, R.A., Luther, J.E. and Piercey, D. 2006. A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland. Forest Ecology and Management 226: 129- 144   DOI   ScienceOn
15 Fournier, R.A., Luther, J.E., Guindon, L., Lambert, M.C., Piercey, D., Hall, R.J. and Wulder, M.A. 2003. Mapping aboveground tree biomass at the stand level from inventory information: test cases in Newfoundland and Quebec. Canadian Journal of Forest Research 33(10): 1846- 1863   DOI   ScienceOn
16 Hardin, P.J. 1994. Parametric and Nearest Neighbor methods for hybrid classification: a comparison of pixel assignment accuracy. Photo-grammetric Engineering and Remote Sensing 60(12): 1439-1448   ScienceOn