참고문헌
- Azizia, Z., A. Najafi, and H. Sohrabia, 2008. Forest Canopy Density Estimating Using Satellite Images, Proc. of The International Society for Photogrammetry and Remote Sensing Congress Commission VIII, Beijing, China, July 3-11, XXXVII. Part B8.
- Baret, F.J., G.P.W. Clevers, and M.D. Steven, 1995. The robustness of canopy gap fraction estimates from red and near-infrared reflectances: a comparison of approaches, Remote Sensing of Environment, 54(2): 141- 151. https://doi.org/10.1016/0034-4257(95)00136-O
- Bonner, G.M., 1985. Inventory of forest biomass in Canada, Petawawa National Forestry Institute, Canadian Forest Service.
- Chandrashekhar, M.B., S. Saran, P.L.N. Raju, and P.S. Roy, 2005. Forest Canopy Density Stratification: How Relevant is Biophysical Spectral Response Modelling Approach?, Geocarto International, 20(1): 15-21. https://doi.org/10.1080/10106040508542332
- Gitelson, A.A., 2004. Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation, Journal of Plant Physiology, 161(2): 165-173. https://doi.org/10.1078/0176-1617-01176
- Gobron, N., B. Pinty, M.M. Verstraete, and J.L. Widlowski, 2000. Advanced Vegetation Indices Optimized for Up-Coming Sensors: Design, Performance, and Applications, IEEE Transactions on Geoscience and Remote Sensing, 38(6): 2489-2505. https://doi.org/10.1109/36.885197
- Heo, H.S., S.H. Kim, H. Lee, K.J. We, Y.R. Park, and S.Y. Cho, 2011. A study of Forest Species Classification using Airborne Hyperspectral Imagery, Proc. of the Korean Society of Remote Sensing Spring Conference 2011, Kyoung-Ju, Korea, March 24-25, pp. 290-295.
- Jolliffe, I.T., 2002. Principal Component Analysis, Springer-Verlag, New-York.
- Joshi, C., J.D. Leeuw, A.K. Skidmore, I.C. Duren, and H. Oosten, 2006. Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods, International Journal of Applied Earth Observation and Geoinformation, 8: 84-95. https://doi.org/10.1016/j.jag.2005.08.004
- Kurz, W.A. and M.J. Apps, 1999. A 70-Year Retrospective Analysis of Carbon Fluxes in the Canadian Forest Sector, Ecological Applications, 9(2): 526-547. https://doi.org/10.1890/1051-0761(1999)009[0526:AYRAOC]2.0.CO;2
- Makela, H. and A. Pekkarinen, 2004. Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data, Forest Ecology and Management, 196: 245-255. https://doi.org/10.1016/j.foreco.2004.02.049
- Maselli, F., C., Conese, T.D. Filippis, and S. Norcini, 1995. Estimation of forest parameters through fuzzy classification of TM data, IEEE Transactions on Geoscience and Remote Sensing, 33(1): 77-84. https://doi.org/10.1109/36.368220
- Mohd Shafri, H.Z., M.A. Mohd Salleh, and A. Ghiyamat, 2006. Hyperspectral Remote Sensing of Vegetation Using Red Edge Position Techniques, American Journal of Applied Sciences, 3(6):1864-1871. https://doi.org/10.3844/ajassp.2006.1864.1871
- Nandy, S., P.K.. Joshi, and K.K. Das, 2003. Forest Canopy Density Stratification Using Biophysical Modeling, Journal of the Indian Society of Remote Sensing, 31(4):291-297. https://doi.org/10.1007/BF03007349
- Ono, A., K., Kajiwara, and Y. Honda, 2010. Development of new vegetation indexes, shadow index (SI) and water stress trend (WST), Proc. of International Society for Photogrammetry and Remote Sensing Technical commission VIII Symposium, Kyoto, Japan, August 9-12, XXXVIII, Part 8.
- Rikimaru, A., P.S. Roy, and S. Miyatake, 2002. Tropical forest cover density mapping, Tropical Ecology, 43(1):39-47.
- Saei jamalabad, M. and A.A. Abkar, 2004. Forest Canopy Density Monitoring Using Satellite Images, Proc. of The International Society for Photogrammetry and Remote Sensing Congress, Istanbul, Turkey, July 12-23, XXXV.
- Schlerf, M., C. Atzberger, and J. Hill, 2005. Remote sensing of forest biophysical variables using HyMap imaging spectrometer data, Remote Sensing of Environment, 95: 177-194. https://doi.org/10.1016/j.rse.2004.12.016
- Shen, M., Y. Tang, J. Klein, P. Zhang, S. Gu, A. Shimono, and J. Chen, 2008. Estimation of aboveground biomass using in situ hyperspectral measurements in five major grassland ecosystems on the Tibetan Plateau, Journal of Plant Ecology, 1(4): 247-257. https://doi.org/10.1093/jpe/rtn025
- Sritakae, A., 2006. Predictive relations of forest stand parameters from hyperspectral remote sensing at Thetford Forest, the UK. Enschede, ITC
- Torbick, N. and B. Becker, 2009. Evaluating Principal Components Analysis for Identifying Optimal Bands Using Wetland Hyperspectral Measurements From the Great Lakes, USA, Remote Sensing, 1(3):408-417. https://doi.org/10.3390/rs1030408
- Tsai, F., E.K. Lin, and K.Yoshino, 2007. Spectrally segmented principal component analysis hyperspectral imagery mapping invasive plant species, International Journal of Remote Sensing, 28(5):1023-1039. https://doi.org/10.1080/01431160600887706
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