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A new narrow band vegetation index for characterizing the degree of vegetation stress due to copper: the copper stress vegetation index (CSVI).
- Source :
-
Remote Sensing Letters . Jun2017, Vol. 8 Issue 6, p576-585. 10p. - Publication Year :
- 2017
-
Abstract
- This study proposed a new narrow band index to characterize the Cu (Copper) stress degree on vegetation (Copper Stress Vegetation Index, CSVI). Firstly, the spectral reflectance and biochemical data of wheat, pea, locust and ash were analysed using Pearson correlation coefficient (r) to select wavelengths sensitive to Cu stress. The calculated Pearson correlation coefficients suggested that the reflectance near 550 nm and 700 nm correlated positively with Cu contents in leaves and solutions, and negative correlation was present in the range of 800–900 nm. Secondly, the selected wavelengths of 550 nm, 700 nm, and 850 nm were used to establish CSVI, and it was compared with existing popular vegetation indices (VIs) related to heavy metal stress (Normalized Difference Vegetation Index (NDVI), Red-Edge Position (REP), Difference Vegetation Index (DVI), Photochemical Reflectance Index (PRI)) by calculating Pearson correlation coefficient between VIs and Cu contents in leaves and solutions. Thirdly, verifications of CSVI on other vegetations were conducted, and the performance of CSVI was also compared with that of NDVI, REP, DVI, and PRI. The results suggested that CSVI showed significant correlation with Cu stress degree, and the correlation of CSVI was much stronger than that of other VIs for all the tested vegetations. The proposed CSVI characterizes the Cu stress degree on vegetation with advantages of better effectiveness, straightforward calculation, and robustness for different vegetations. This study focused on the spectral reflectance at the leaf scale, so it is expected that future work extends it to canopy scale and mixed-pixel scale. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 2150704X
- Volume :
- 8
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Remote Sensing Letters
- Publication Type :
- Academic Journal
- Accession number :
- 122084435
- Full Text :
- https://doi.org/10.1080/2150704X.2017.1306135