Connecting infrared spectra with plant traits to identify species : abstract + powerpoint

Plant traits are used to define species, but also to evaluate the health status of forests, plantations and crops. Conventional methods of measuring plant traits (e.g. wet chemistry), although accurate, are inefficient and costly when applied over large areas or with intensive sampling. Spectroscopic methods, as used in the food industry and mineralogy, are nowadays applied to identify plant traits, however, most studies analysed visible to near infrared, while infrared spectra of longer wavelengths have been little used for identifying the spectral differences between plant species. This study measured the infrared spectra (1.4–16.0 mm) on individual, fresh leaves of 19 species (from herbaceous to woody species), as well as 14 leaf traits for each leaf. The results describe at which wavelengths in the infrared the leaves’ spectra can differentiate most effectively between these plant species. A Quadratic Discrimination Analysis (QDA) shows that using five bands in the SWIR or the LWIR is enough to accurately differentiate these species (Kappa: 0.93, 0.94 respectively), while the MWIR has a lower classification accuracy (Kappa: 0.84). This study also shows that in the infrared spectra of fresh leaves, the identified speciesspecific features are correlated with leaf traits as well as changes in their values. Spectral features in the SWIR (1.66, 1.89 and 2.00 mm) are common to all species and match the main features of pure cellulose and lignin spectra. The depth of these features varies with changes of cellulose and leaf water content and can be used to differentiate species in this region. In the MWIR and LWIR, the absorption spectra of leaves are formed by key species-specific traits including lignin, cellulose, water, nitrogen and leaf thickness. The connection found in this study between leaf traits, features and spectral signatures are novel tools to assist when identifying plant species by spectroscopy and remote sensing

Auto-correcting for atmospheric effects in thermal hyperspectral measurements

Correct estimation of soil and vegetation thermal emissivities is of huge importance in remote sensing studies. It has been shown that the emissivity of leaves retrieved from field observations show subtle spectral features that are related to leaf water content. However, such field measurements provide additional challenges before leaf water content can be successfully obtained, specifically atmospheric correction. The aim of this research was to investigate how information within hyperspectral thermal observations can be used to auto-correct the atmospheric influence. Hyperspectral thermal measurements were taken over a large variety of soil and vegetation types (including vineyard and barley) during ESA’s REFLEX campaign in 2012 using a MIDAC FTIR radiometer. Using MODTRAN simulations, a simple quadratic model was constructed that emulates the atmosphere radiative transfer between the target and the sensor. Afterwards, this model was used to estimate the concentrations of H20 (g) and CO2 (g) while simultaneously correcting for these gas absorptions. Finally, a temperature-emissivity separation was applied to estimate the emissivities of the different land surface components. The uncertainty of the approach was evaluated by comparing the retrieved gas concentrations against parallel measurements of a LICOR 7500. It was found that most measurements of gas concentrations were successfully retrieved, with uncertainties lower than 25%. However, absolute correction of the absorption features proved more difficult and resulted in overestimations of the correction-terms. This was mainly due to overlapping of spectral features with the observations in the simulations that proved troublesome

Infrared spectroscopic determination of leaf traits : abstract + powerpoint.

Plant traits are features that characterise and differentiate between species. From these, certain leaf traits, such as broadleaf or coniferous, have been used to characterise the whole plant, especially when assessing large areas of vegetation. Leaf traits do not only differentiate species but also provide information on plant health. Conventional methods of measuring leaf traits, especially at the molecular level (e.g. water, lignin, and cellulose content), are often expensive and time-consuming. Spectroscopic methods allowing the estimation of leaf traits through empirical models are becoming a tool for accurate estimations of leaf traits. This study identifies the most important bands in the infrared related to biochemical and morphological leaf traits. We generated regression models for eleven leaf traits (including organic and morphological traits), using an optimisation method of partial least squares regression models. This study used spectroscopic data with 6612 bands from the short to the long wave infrared (1.4-16.0μm) from 20 plant species including herbaceous, woody, temperate and tropical plants. Fourteen leaf traits were measured in each fresh leaf, including chemical (e.g., leaf water content, nitrogen, cellulose) and morphological (e.g., leaf area and leaf thickness) traits. Optimized partial least squares regression models were fitted for each leaf trait with a few bands (4-10) as explanatory variables. For these remaining bands, we identified a physiological explanation. From the original pool of leaf traits, cuticle thickness, bundle area and stomata size resulted in R-squared values lower than 70%. These traits were therefore not further considered in the analysis. Eleven leaf traits resulted in optimized models with high R-squared values. The models selected bands from the SWIR where the leaf spectra have features correlated to lignin and cellulose content, which account on average for 28% of the fresh weight of the leaves. The biochemical traits have high R-squares when using the whole spectra (6612 bands) and also in optimized models with five to seven bands. The selected bands match with known molecular bonds of the molecules analysed (water, lignin, cellulose and nitrogen bearing molecules, such as proteins). Morphological leaf traits models have in general good R-squares, especially leaf thickness which has a high correlation with most bands of the SWIR (1.4-2.5 μm) and a specific feature at 7.38μm

Detecting temperature and water stress in plants with Thermal Infrared Spectroscopy : abstract + powerpoint

Stress in plants generates changes in leaves from decreasing water content to changes in the microstructure and the internal composition of the leave, and changes in the structure of the whole community. Although physiological changes such as water content, relocalization of micro molecules, and macro structural changes such as smaller leaves and canopies are known, the effect of these changes on the thermal properties of plants, and the spectral detection by remote sensors has not been demonstrated yet. This research shows the results of a series of laboratory experiments with an FTIR system (Bruker Vertex70) as a proxy for the remote detection of plant stress in a deciduous and an evergreen species (European beech Fagus sylvatica and Rhododendron Rhododendron sp.) in the Thermal Infrared (TIR). Four groups of fifteen plants each were separated and treated with cold and warm temperatures (±10°C and 20°C), and poor and well watered conditions. Five leaves of each plant were measured with the FTIR at the beginning and re-measured three months later. These preliminary results show that plants exposed to water and temperature stress have different thermal spectra compared to plants with optimal growing conditions for several sections of the thermal Infrared. Plants under limited water regime showed lower emissivity in regions related to water content (4-6um), but also at longer wavelengths probably associated with adaptations leaf structural traits. Furthermore the evergreen plants (Rhododendron sp.) showed less effect to water stress compared to the deciduous plants (Fagus sp.), suggesting that Rhododendron sp. has more intrinsic resilience to extreme growing conditions