Using the characteristic parameters of Hilbert marginal spectrum for indirectly estimating copper content in maize leaves under copper stress

References

• Asmaryan, S., T. A. Warner, and V. Muradyan. 2013. “Mapping Tree Stress Associated with Urban Pollution Using the Worldview-2 Red Edge Band.” Remote Sensing Letters 4 (2): 200–209. doi:10.1080/2150704X.2012.715771.
   Web of Science ®Google Scholar
• Behmann, J., J. Steinrücken, and L. Plümer. 2014. “Detection of Early Plant Stress Responses in Hyperspectral Images.” ISPRS Journal of Photogrammetry and Remote Sensing 93: 98–111. doi:10.1016/j.isprsjprs.2014.03.016.
   Web of Science ®Google Scholar
• Conesa, H. M., Á. Faz, and R. Arnaldos. 2006. “Heavy Metal Accumulation and Tolerance in Plants from Mine Tailings of the Semiarid Cartagena–La Unión Mining District (SE Spain).” Science of the Total Environment 366 (1): 1–11. doi:10.1016/j.scitotenv.2005.12.008.
   PubMed Web of Science ®Google Scholar
• Dong, J., M. Yu, Z. Bian, Y. Wang, and C. Di. 2011. “Geostatistical Analyses of Heavy Metal Distribution in Reclaimed Mine Land in Xuzhou, China.” Environmental Earth Sciences 62 (1): 127–137. doi:10.1007/s12665-010-0507-5.
   Web of Science ®Google Scholar
• Guo, H., K. M. Yang, W. W. Zhang, C. Liu, and T. Xia. 2018. “Recognition of Corn Leaf Spectra Pollution Degree under Heavy Metals Cu2+ Pb2+ Stress.” Spectroscopy and Spectral Analysis 38 (1): 212–217. doi:10.3964/j.issn.1000-0593(2018)01-0212-06.
   Web of Science ®Google Scholar
• Hede, A. N. H., K. Kashiwaya, K. Koike, and S. Sakurai. 2015. “A New Vegetation Index for Detecting Vegetation Anomalies Due to Mineral Deposits with Application to A Tropical Forest Area.” Remote Sensing of Environment 171: 83–97. doi:10.1016/j.rse.2015.10.006.
   Web of Science ®Google Scholar
• Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, et al. 1998. “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis.” Proceedings Mathematical Physical & Engineering Sciences 454 (1971): 903–995. doi:10.1098/rspa.1998.0193.
   Web of Science ®Google Scholar
• Khosravi, V., F. D. Ardejani, S. Yousefi, and A. Aryafar. 2018. “Monitoring Soil Lead and Zinc Contents via Combination of Spectroscopy with Extreme Learning Machine and Other Data Mining Methods.” Geoderma 318: 29–41. doi:10.1016/j.geoderma.2017.12.025.
   Web of Science ®Google Scholar
• Kooistra, L., E. A. L. Salas, J. G. P. W. Clevers, R. Wehrens, R. S. E. W. Leuven, P. H. Nienhuis, and L. M. C. Buydens. 2004. “Exploring Field Vegetation Reflectance as an Indicator of Soil Contamination in River Floodplains.” Environmental Pollution 127 (2): 281–290. doi:10.1016/S0269-7491(03)00266-5.
   PubMed Web of Science ®Google Scholar
• Liu, M., X. Liu, W. Ding, and L. Wu. 2011. “Monitoring Stress Levels on Rice with Heavy Metal Pollution from Hyperspectral Reflectance Data Using Wavelet-Fractal Analysis.” International Journal of Applied Earth Observation and Geoinformation 13 (2): 246–255. doi:10.1016/j.jag.2010.12.006.
   Web of Science ®Google Scholar
• Meer, F. D. V. D., H. M. A. V. D. Werff, F. J. A. V. Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. V. D. Meijde, et al. 2012. “Multi- and Hyperspectral Geologic Remote Sensing: A Review.” International Journal of Applied Earth Observation & Geoinformation 14 (1): 112–128. doi:10.1016/j.jag.2011.08.002.
   Web of Science ®Google Scholar
• Ren, H., D. F. Zhuang, J. J. Pan, X. Z. Shi, R. H. Shi, and H. J. Wang. 2010. “Study on Canopy Spectral Characteristics of Paddy Polluted by Heavy Metals.” Spectroscopy and Spectral Analysis 30 (2): 430–434. doi:10.3964/j.issn.1000-0593(2010)02-0430-05.
   Web of Science ®Google Scholar
• Wang, F., J. Gao, and Y. Zha. 2018. “Hyperspectral Sensing of Heavy Metals in Soil and Vegetation: Feasibility and Challenges.” ISPRS Journal of Photogrammetry and Remote Sensing 136: 73–84. doi:10.1016/j.isprsjprs.2017.12.003.
   Web of Science ®Google Scholar
• Wang, J., T. Wang, T. Shi, G. Wu, and A. K. Skidmore. 2015. “A Wavelet-Based Area Parameter for Indirectly Estimating Copper Concentration in Carex Leaves from Canopy Reflectance.” Remote Sensing 7 (11): 15340–15360. doi:10.3390/rs71115340.
   Web of Science ®Google Scholar
• Zabcic, N., B. Rivard, C. Ong, and A. Mueller. 2014. “Using Airborne Hyperspectral Data to Characterize the Surface PH and Mineralogy of Pyrite Mine Tailings.” International Journal of Applied Earth Observations & Geoinformation 32: 152–162. doi:10.1109/WHISPERS.2009.5289015.
   Web of Science ®Google Scholar
• Zhang, C., H. Ren, and X. J. Dai. 2019a. “Spectral Characteristics of Copper-stressed Vegetation Leaves and Further Understanding of the Copper Stress Vegetation Index.” International Journal of Remote Sensing 40 (12): 4473–4488. doi:10.1080/01431161.2018.1563842.
   Web of Science ®Google Scholar
• Zhang, C., H. Ren, and Z. Y. Huang. 2019b. “Assessment of the Application of Copper Stress Vegetation Index on Hyperion Image in Dexing Copper Mine, China.” Journal of Applied Remote Sensing 13 (1): 014511. doi:10.1117/1.JRS.13.014511.
   Web of Science ®Google Scholar
• Zhang, C., H. Ren, Q. Qin, and O. K. Ersoy. 2017. “A New Narrow Band Vegetation Index for Characterizing the Degree of Vegetation Stress Due to Copper: The Copper Stress Vegetation Index (CSVI).” Remote Sensing Letters 8 (6): 576–585. doi:10.1080/2150704X.2017.1306135.
   Web of Science ®Google Scholar
• Zhou, C., S. Chen, Y. Zhang, J. Zhao, D. Song, and D. Liu. 2018. “Evaluating Metal Effects on the Reflectance Spectra of Plant Leaves during Different Seasons in Post-Mining Areas, China.” Remote Sensing 10 (8): 1211–1232. doi:10.3390/rs10081211.
   Web of Science ®Google Scholar
• Zhu, Y., Y. H. Qu, S. Liu, and S. Chen. 2014. “Spectral Response of Wheat and Lettuce to Copper Pollution.” Journal of Remote Sensing 18 (2): 335–352. doi:10.11834/jrs.20143073.
   Google Scholar