Hyperspectral leaf reflectance of Carpinus betulus L. saplings for urban air quality estimation. (January 2017)

• Record Type: Journal Article
• Authors: Brackx, Melanka; Van Wittenberghe, Shari; Verhelst, Jolien; Scheunders, Paul; Samson, Roeland
• Is Part Of: Environmental pollution. Volume 220:Part A(2017)
• Page Start: 159
• Page End: 167
• Publication Date: 2017-01
• Subjects: Air pollution -- Plants -- Urban environment -- Spectral reflectance -- Spectroscopy -- Correlation analysis; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2016.09.035
• Languages: English
• ISSNs: 0269-7491

Abstract

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 (R 2 = 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. Graphical abstract: Highlights: Hornbeam saplings leaves show differences in high traffic or low traffic environment. The linear Pearson correlation between SIRM and spectral features is determined. Red and near infrared spectral reflectance are most strongly correlated to SIRM. Trees are classified into high or low traffic pollution groups, with 90% accuracy.