References
- Asner, G.P., J.A. Hicke and D.B. Lobell. 2003. Per-pixel analysis of forest structure-vegetation indices, spectral mixture analysis, and canopy reflectance modeling. In: M.A. Wulder and S.E. Franklin(ed.). Remote Sensing of Forest Environments: Concepts and Case Studies. Kluwer Academic Publishers, Stanford, California, USA. pp.209-254.
- Asrar, G., M. Fuchs, E.T. Kanemasu and J.L. Hatfield. 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agronomy Journal 76(2):300-306. https://doi.org/10.2134/agronj1984.00021962007600020029x
- Avery, T.E., and G.L. Berlin. 1992. Fundamentals of Remote Sensing and Airphoto Interpretation(5th). Macmillan Publishing Company, New York, 476pp.
- Bannari, A., D. Morin, F. Bonn and A.R. Hute. 1995. A review of vegetation indices. Remote Sensing Review 13 (1-2):95-120. https://doi.org/10.1080/02757259509532298
- Broge, N.H. and E. Leblanc. 2000. Comparing prediction power an stability of broadband and hyperspectral vegetation indices for estimating of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment 76(2):156-172.
- De Jong, S.M., E.J. Pebesma and B. Lacaze. 2003. Aboveground biomass assessment of Mediterranean forest using airborne imagine spectrometry : the DAIS Peyne experiment. Journal of Remote Sensing 24(7):1505-1520. https://doi.org/10.1080/01431160210145560
- Gao, X., A.R. Huete, W. Ni and T. Miura. 2000. Optical-biophysical relationships of vegetation spectra without background contamination. Remote Sensing of Environment. 74(3):609-620. https://doi.org/10.1016/S0034-4257(00)00150-4
- Haboudane, D., J.R. Miller, E. Pattey, P. Zarco-Tejada and I.B. Strachan. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90(3):337-352. https://doi.org/10.1016/j.rse.2003.12.013
- Huang, F., J. Ling, A. Shi and L. Xu. 2010. A band selection method for hyperspectral images using choquet fuzzy integral. Journal of Computers 5(7):1019-1026.
- Jang, G.S., K.A. Sudduth, S.Y. Hong, N.R. Kitchen and H.L. Palm. 2006. Relating hyperspectral image bands and vegetation indices to corn and soybean yield. Korean Journal of Remote Sensing 22(3):183-197. https://doi.org/10.7780/kjrs.2006.22.3.183
- Jensen, J.R. 2005. Introductory Digital Image Processing: A Remote Sensing Perspective(3rd). SIGMA Press, Seoul, 580pp.
- Kim, G.S. and T.K. Yim, 2005. Analysis of the spatial and temporal variability of NDVI time series in South Korea. The Proceeding of Korea Water Resources Association 2005 Conference. pp.1-4 (김광섭, 임태경. 2005. 남한지역 정규식생지수의 시공간 변화도 분석. 한국수자원학회학술발표대회 1-4쪽).
- Kim, T.W., D.J. Choi, G.J. We and Y.C. Suh. 2013. Detection of small green space in an urban area using airborne hyperspectral imagery and spectral angle mapper. Journal of Korean Association of geographic information Studies 16(2):88-100 (김태우, 최돈정, 위광재, 서용철. 2013. 분광각매퍼 기법을 적용한 항공기 탑재 초분광영상의 소규모 녹지공간 탐지. 한국지리정보학회지 16(2):88-100). https://doi.org/10.11108/kagis.2013.16.2.088
- Kim, T.W., G.J. We and Y.C. Suh. 2012. Correlation analysis with vegetation indices and vegetation- endmembers from airborne hyperspectral data in forest area. Journal of Korean Association of geographic information Studies 15(3):52-65 (김태우, 위광재, 서용철. 2012. 산림지역의 항공기 탑재 하이퍼스펙트럴 영상에 대한 식생 endmember와 식생지수의 상관분석. 한국지리정보학회지 15(3):52-65). https://doi.org/10.11108/kagis.2012.15.3.052
- Myneni, R.B., F.G. Hall, P.J. Sellers and A.L. Marshark. 1995. The interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing 33(2):481-486. https://doi.org/10.1109/36.377948
- Roujean, J.L. and F.M. Breon. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment 51:375-384. https://doi.org/10.1016/0034-4257(94)00114-3
- Rouse, J.W., R.H. Haas, J.A. Schell and D.W. Deering. 1974. Monitoring vegetation systems in the Great Plains with ERTS. Proceeding of Third ERTS-1 Symposium, NASA Goddard, NASA SP-351 pp.309-317.
- Sellers, P.J., J.A. Berry, G.J. Collatz, C.B. Field and F.G. Hall. 1992. Canopy reflectance, photosysnthetisis and transpiration, III a reanalysis using improved leaf models and a new canopy integration scheme. Remote Sensing of Environment 42:187-216. https://doi.org/10.1016/0034-4257(92)90102-P
- Shin. S.H., G.H. Koh, D.S. Kim and M.W. Pyeon. 2011. Band aggregation of hyperspectral images to detect vegetation information for U-City, proceeding of ICCC. pp.393-394.
- Sritakae, A. 2006. Predictive relations of forest stand parameters from hyperspectral remote sensing at Thetford forest, the UK. Master Thesis, International institute for Geo-Information Science and Earth Obsevationm Enschede, The Netherlands. 75pp.
- Thenkabali, P.S., R.B. Smith and E. De Pauw. 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment 71(2):158-182. https://doi.org/10.1016/S0034-4257(99)00067-X
- Tucker, C.J. 1979. Red and photographic infrared linear combination for monitoring vegetation. Remote Sensing of Environment 8(2):127-150. https://doi.org/10.1016/0034-4257(79)90013-0
- Zarco-Tejada, P.J., A. Berjon and J.R. Miller. 2004. Stress detection in crops with hyperspectral remote sensing and physical simulation models. Airborne Imaging Spectroscopy Workshop, 8 October 2004, Bruges, Belgium.
- Zarco-Tejada, P.J., J.R. Miller, T.L. Noland, G.H. Mohanmmed and P.H. Sampson. 2001. Scaling-up and model inverson methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geosciences and Remote Sensing 39(7):1491-1507. https://doi.org/10.1109/36.934080