A drone-based study on the possibilities of agricultural crop assessment using reflectance index in NIR
Asparuh Atanasov
Abstract: Тhrough the reflective vegetation indices to assess plant stress, we can accurately determine the condition of the crops and plan the necessary actions. The research aims to evaluate the performance of the NIRI (Near InfraRed Index) index using a NIR camera mounted on a drone. The vegetation index NIRI compared to NDVI was studied. The results show good estimation capabilities and similar trends of change. The results of regression analysis of the relationships between NDVI and NIRI for wheat crops are Multiple R = 0.979; R Square = 0.959.
Keywords: NDVI; NIR; NIRI; vegetation indices
Citation: Atanasov, A. (2024). A drone-based study on the possibilities of agricultural crop assessment using reflectance index in NIR. Bulg. J. Agric. Sci., 30(6), 1136–1140
References: (click to open/close) | Bannari, A., Morin, D., Hue A. R. & Bonn, F. (1995). A review of vegetation indices. Remote Sensing Reviews, 13(1-2), 95-120. Baret, F., Jacquemoud, S. & Hanocq, J. F. (1993). The soil line concept in remote sensing. Remote Sensing Reviews, 7(1), 65-82. Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J. & Bareth, G. (2015). Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 39, 79–87. Burgess, D. W., Lewis, P. & Muller, J-P. A. L. (1995). Topographic effects in AVHRR NDVI data. Remote Sensing of Environment, 54(3), 223-232. Gitelson, A. A., Kaufman, Y. J., Stark, R. & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1), 76–87. Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309. Louhaichi, M., Borman, M. M. & Johnson, D. E. (2001). Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 16(1), 65–70. Myneni, R. B. & Asrar, G. (1994). Atmospheric effects and spectral vegetation indices. Remote Sensing of Environment, 47(3), 390-402. NIMH (2023). https://weather.bg/. Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W. & Harlan, J. C. (1974). Monitoring the vernal advancement of retrogradation of natural vegetation, 371. Greenbelt, MD: NASA/GSFC (Type III, Final Report). Teillet, P. M., Staenz, K. & Williams, D. J. (1997). Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions. Remote Sensing of Environment, 61(1), 139-149. Vincini, M., Frazzi, E. & D' Alessio, P. (2008). A broad-band leaf chlorophyll vegetation index at the canopy scale. Yang, Z., Willis, P. & Mueller, R. (2008). Impact of band-ratio enhanced AWIFS image to crop classification accuracy. Pecora, 17(1), 1-11. Zhangyan, J., Alfredo, R. H., Kamel, D. & Tomoaki, M. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10), 3833-3845. |
|
| Date published: 2024-12-16
Download full text