The use of red edge indices and water indices from hyperspectral data from EO-1 Hyperion for land cover classification

1Lyalko, VI, 2Shportjuk, ZM, 1Sakhatsky, OI, 1Sibirtseva, ОN
1State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine», Kyiv, Ukraine
2State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine», Kyiv
Kosm. nauka tehnol. 2008, 14 ;(3):055-068
https://doi.org/10.15407/knit2008.03.055
Publication Language: Ukrainian
Abstract: 
Our earlier results concerning the possibility to use vegetation indices of red edge and water indices from hyperspectral data of EO-1 Hyperion for land cover classification are presented. The experimental evaluation of the use of the indices for land cover classification was carried out within Kyiv region oblast. The classification of vegetation cover using images calculated on the basis of identification of red edge and water indices gives better results that with reflectance. The combination of reflectance and indices images is useful for classification of industrial objects and water bodies. The investigation results show big potential for monitoring of the vegetation cover with the help of the combination of both indices.
Keywords: indices, red edge, vegetation cover
References: 
1. Lyalko V. I., Sakhatsky A. I., Shportiuk Z. M., et al. Analysis of the Chornobyl zone’s forests state based on the red edge position with use of the  multispectral SPOT-4 images and ground-based reflectance and fluorescence spectra research. In: Zb. nauk. prac' In-tu jadernyh doslidzhen' NAN Ukrai'ny, No. 1 (14), 105—112 (2005) [in Ukrainian].
2. Lyalko V. I., Shportyuk Z. M., Sakhatskyi O. I., Sybirtseva O. M. Land cover classification in Ukrainian Carpathians using the MERIS Terrestrial Chlorophyl Index and red edge position from ENVISAT MERIS data. Kosm. nauka tehnol., 12 (5-6), 10—14 (2006) [in Ukrainian].
3. Lyalko V. I., Shportyuk Z. M.,  Sybirtseva O. M., et al. Investigation of the condition of forests using the analogue of the position of the red edge according to the SPOT-4 data. In: Information technologies for management of ecological safety, resources and measures in emergency situations: Intern. Sci. and practical conf.: Abstracts, September 8—11, 2002, 47—49 (Kyiv; Kharkiv; Crimea, 2002) [in Ukrainian].
4. Sakhats’kyi O. I. The use of satellite data in solving the problems of water exchange in geosystems. Reports of the National Academy of Sciences of Ukraine, No. 4, 118—126 (2006) [in Ukrainian].
5. Clevers J., Bartholomeus H., Mucher C., de Wit A. Land cover classification with the Medium Resolution Imaging Spectrometer (MERIS). In: New Strategies for European Remote Sensing, Ed. by Oluic, 687—694 (Millpress, Rotterdam, 2005).
6. Collins W., Chang S.-H., Raines G., et al. Airborne Biogeophysical Mapping of Hidden Mineral Deposits. Economic Geol., 4 (78), 737—749 (1983).
https://doi.org/10.2113/gsecongeo.78.4.737
7. Danson F. M., Plummer S. E. Red-edge responce to forest lief area index. Int. J. Remote Sensing, 16, 183—188 (1995).
https://doi.org/10.1080/01431169508954387
8. Dash J., Curran P. J. The MERIS terrestrial chlorophyll index. Int. J. Remote Sensing, 25, 5403— 5413 (2004).
https://doi.org/10.1080/0143116042000274015
9. Gao B. C. NDWI — a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257— 266 (1996).
https://doi.org/10.1016/S0034-4257(96)00067-3
10. Griffin M. K., Hsu S. M., Burke H. K., et al. Examples of EO-1 Hyperion Data Analysis. Lincoln Laboratory J., 15, 271—296 (2005).
11. Hu B., Miller J. R., Zarco-Tejada P., et al. Land Cover Mappingwith MERIS at the BOREAS Study Area. In: MERIS and AATSR Calibration and Geophysical Validation (MAVT-2003), 20—24 October 2003, 10 p. (ESRIN., Fraskati, Itali, 2003).
12. Jago R. A., Curran P. J. Estimating canopy chlorophyll concentration from field and airborne spectra to infer levels of land contamination. RSS'97; Observations and Interactions (Reading: Rem. Sens. Soc.), 274—279 (1997).
13. Lacaze B. Remotely-sensed optical and thermal indicators of land degradation. In: New Strategies for European Remote Sensing, Ed. by M. Oluic, 211—217 (Millpress, Rotterdam, 2005).
14. Liu L., Zhang B., Xu G., et al. Vegetation classification and soil moisture calculation using land surface temperature (LST) and vegetation index (VI). Proc. SPIE, 4730, 319—323 (2002).
https://doi.org/10.1117/12.460242
15. Lyalko V. I., Fedorovsky A. D., et al. Ahalysis of plant state an the «red edge» position of reflektive Signls. Space Research in Ukraine 1998—2000, 56—57 (NSAU, Kyiv, 2001).
16. Pearlman J. S., Barry P. S., Segal C. C., et al. Hyperion, a Space Borne Imaging Spectrometer. IEEE Trans. Geosci. Remote Sens., 41 (6), 1160—1173 (2003).
https://doi.org/10.1109/TGRS.2003.815018
17. Pu R., Gong P., Biging G., et al. Extraction of Red Edge Optical Parameters from Hyperion Data for Estimation of Forest Leaf Area Index. IEEE Trans. Geosci. Remote Sens., 41 (4), 916—921 (2003).
https://doi.org/10.1109/TGRS.2003.813555
18. Shportyuk Z. M., Sakhatsky A. I., Sibirtseva O. N. Land cover classification in Ukrainian Carpathians using the MERIS Terrestrial Chlorophyl Index and Red Edge Position from Envisat Meris data. In: Remote Sensing: From Pixels to Processes: Proc. of Mid-Term Symposium ISPRS, Enschede, the Netherlands (8—11 May 2006).
19. Xiao X., Boles S., Liu J. Y., et al. Characterization of forest type in Northeastern Chine, using multitemporal SPOT-4 Vegetation sensor data. Remote Sensing of Environmerit, 82 , 335—348 (2002).
https://doi.org/10.1016/S0034-4257(02)00051-2
20. Xin J., Tian G., Liu Q., Chen L. Combining vegetation index and remotely sensed temperature for estimation of soil moisture in China. Int. J. Remote Sensing, 27 (9), 2071—2076 (2006).
https://doi.org/10.1080/01431160500497549

21. Zarco-Tejada P. L. J., Rueda C. A., Ustin S. L. Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85, 109—124 (2003).