USING IN SITU HYPERSPECTRAL MEASUREMENTS AND HIGH RESOLUTION SATELLITE IMAGERY TO DETECT STRESS IN WHEAT IN EGYPT | ||
| Misr Journal of Agricultural Engineering | ||
| Article 49, Volume 27, Issue 4, October 2010, Pages 1811-1822 PDF (582.92 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/mjae.2010.105351 | ||
| Authors | ||
| A.H Elmetwalli1; A.A. Derbala2; T.Z. Fouda3 | ||
| 1Lec. of Agric. Eng.Dept., Fac. of Agric., Tanta U., Egypt. | ||
| 2Assoc. Prof. of Agric. Eng.Dept., Fac. of Agric., Tanta U., Egypt. | ||
| 3Prof., and Head of Agric. Eng.Dept., Fac. of Agric., Tanta U., Egypt. | ||
| Abstract | ||
| Mapping and detecting stress at both local and regional scales are very important in site specific management. Launching the first generation of high spatial and spectral resolution remote sensing satellite at the beginning of the 21st century provides the opportunity to have better understanding of crop stress and the extent of stress in a specific environment. This work was carried out to assess the ability of hyperspectral and high spatial resolution remote sensing imagery to detect stress in wheat in the Nile Delta of Egypt. A field work visit was undertaken during winter season of 2007 in March (5-30: wheat) to collect ground reference data including soil samples, vegetation samples, water samples, chlorophyll estimates, reflectance measurements and GPS coordinates. The work visit was timed to coincide with the acquisition of QuickBird satellite imagery (7 April, 2007). The results further showed that the QuickBird image successfully detected stress within field and local scales, and therefore can be a robust tool in identifying issues of crop management at a local scale. a strong linear relation between RVI derived from in situ and RVI derived from satellite data (R = 0.75; p = 0.000). The results further showed that MLC is an effective classification algorithm for differentiating different crops within the study area. | ||
| References | ||
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