Abstract
Sulfur dioxide (SO2) exhibits a powerful implication on the air condition and responsible for increasing the acidity of rainfall which plays negative effects on plant growth. It is a big problem to quantitatively access the stress degrees of sulfur dioxide on landscape plants. This study aims to find a non-destructive way to detect the degrees of SO2 stress by using the spectral reflectance data. Five different landscape plants were selected and a simulated SO2 stress environment by using fumigation box was built in this experiment. Landscape plants were grown on at this simulated SO2 environment, and the leaf reflectance, chlorophyll and sulfur concentration were measured at 0, 2, 4, 6, 8, 10 and 12 h respectively. The spectral, chlorophyll response of five different plants were examined and the red edge position (REP) shift obtained from the reflectance were used to evaluate the SO2 stress degrees at this paper. The results showed leaf chlorophyll content generally decreased and leaf sulfur content generally increased of all of these five landscape plants as though the chlorophyll and sulfur content disturbing during the whole stress time. However, compared with the sulfur content changed in leaves, chlorophyll content did not significantly changed when suffering from SO2. The shift of REP performed well to indicate the severity of SO2 fumigation stress and different species showed the different REP shift. The determined coefficient R2 of REP shift and the relative changed sulfur content in leaves can up to 0.85. And the results also indicated that the different species maintained different resistance to SO2.
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Acknowledgements
We would like to express our respects and gratitude to the anonymous reviewers and editors for their valuable comments and suggestions on improving the quality of the paper. This work is supported by National Natural Science Foundation of China (Grant No. 41501365).
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Wang, D., Liu, Z., Dian, Y. et al. Potential of Detecting the Sulfur Dioxide Stress on Landscape Plants in Spectral Reflectance Data. J Indian Soc Remote Sens 46, 561–568 (2018). https://doi.org/10.1007/s12524-017-0717-3
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DOI: https://doi.org/10.1007/s12524-017-0717-3