Identifying leaf traits that signal stress in TIR spectra

Monitoring Plant Functional Diversity Using the Reflectance and Echo from Space

“…Signatures from the visible to shortwave infrared spectral region are related to the biophysical and biochemical properties of top canopy leaves [10,11]. In the thermal infrared region, instead, one can infer canopy surface temperatures from which canopy transpiration rates can be further estimated [12]. In electromagnetic wavelengths longer than the thermal infrared region (microwave region), one can detect even deeper structural properties and the vertically integrated water content [13].…”

Infrared spectroscopy of leaf traits: Detecting plant stress and identifying plant species

“…The LWIR has a similarly high classification accuracy for species classification (Kappa: 0.94), with the particularity of being a region of the electromagnetic spectrum, where the spectral features are correlated to changes in leaf composition and leaf traits, as shown in the present study (Appendix 1 and 2) and previous investigations (e.g. Buitrago Acevedo et al 2017;Ribeiro da Luz and Crowley 2007;Salisbury and Milton 1988). The selected bands are located at features highly correlated with cellulose and nitrogen content (6.91µm), water and lignin (8.54, 9.78, 12.10, and 12.80µm).…”

“…So although the SWIR has deep spectral features at 1.66, 1.89 and 2.20µm (spectral contrast > 0.4), common to all species and matching the spectra of pure cellulose (Figure 3.2) (e.g. Card et al 1988;Curran 1989), the spectral signatures in the LWIR appear related to species-specific structural and organic traits (e.g., leaf thickness and cellulose), as was suggested by Elvidge (1988), Ribeiro da Luz and Crowley (2007) and Buitrago Acevedo et al (2017). This study highlights that there is indeed a high correlation between leaf traits and certain spectral bands (Figure 3.10a, Appendix 3.2).…”

Remote sensing of European spruce (lps typographus, L.) bark beetle green attack

“…In general, the length of time required for a moisture deficit to produce visible symptoms in foliage can vary from weeks to several months. However, these changes are detectable with spectral data, particularly if acquired in the shortwave and thermal infrared spectra, where water absorption features in vegetation are well documented (Berni et al, 2009;Buitrago Acevedo et al, 2017;Jang et al, 2006;Sepulcre-Cantó et al, 2006).…”

“…They also identified the wavelength range between 950 nm and 1390 nm as a good spectral region able to separate healthy from green attacked trees. In general, moisture stress in vegetation may result in non-visual symptoms that are detectable with remote sensing data, particularly in the shortwave and thermal infrared regions, where water absorption features exist (Berni et al, 2009;Buitrago Acevedo et al, 2017;Jang et al, 2006;Sepulcre-Cantó et al, 2006). However, the majority of the studies on bark beetle (either mountain pine beetle or spruce bark beetle) green attack detection with remotely sensed data have mainly utilised optical remote sensing data ( Fig.…”