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Hyperspectral leaf reflectance of Carpinus betulus L. saplings for urban air quality estimation.

Author information

Affiliations

  • 1. Lab. of Environmental and Urban Ecology, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium; iMinds, Vision Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium.
      Authors
    • Brackx M 1
    (1 author)
  • 2. Lab. of Environmental and Urban Ecology, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium.
      Authors
    • Van Wittenberghe S 2
    • Verhelst J 2
    • Samson R 2
    (3 authors)
  • 3. iMinds, Vision Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium.
      Authors
    • Scheunders P 3
    (1 author)

ORCIDs linked to this article

Environmental Pollution (Barking, Essex : 1987), 06 Oct 2016, 220(Pt A):159-167
https://doi.org/10.1016/j.envpol.2016.09.035 PMID: 27720547 

Abstract 

In urban areas, the demand for local assessment of air quality is high. The existing monitoring stations cannot fulfill the needs. This study assesses the potential of hyperspectral tree leaf reflectance for monitoring traffic related air pollution. Hereto, 29 Carpinus betulus saplings were exposed to an environment with either high or low traffic intensity. The local air quality was estimated by leaf saturation isothermal remanent magnetization (SIRM). The VIS-NIR leaf reflectance spectrum (350-2500 nm) was measured using a handheld AgriSpec spectroradiometer (ASD Inc.). Secondary, leaf chlorophyll content index (CCI), specific leaf area (SLA) and water content (WC) were determined. To gain insight in the link between leaf reflectance and air quality, the correlation between SIRM and several spectral features was determined. The spectral features that were tested are plain reflectance values, derivative of reflectance, two-band indices using the NDVI formula and PCA components. Spectral reflectance for wavelength bands in the red and short wave IR around the red edge, were correlated to SIRM with Pearson correlations of up to R = -0.85 (R2 = 0.72). Based on the spectral features and combinations thereof, binomial logistic regression models were trained to classify trees into high or low traffic pollution exposure, with classification accuracies up to 90%. It can be concluded that hyperspectral reflectance of C. betulus leaves can be used to detect different levels of air pollution within an urban environment.

Full text links 

Read article at publisher's site: https://doi.org/10.1016/j.envpol.2016.09.035

References 

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