Spectral unmixing of multiple lichen species and underlying substrate

Background The quality of forage plants is a crucial component of animal performance and a limiting factor in pasture based production systems. Key forage attributes that may require improvement include the sugar, lipid, protein and energy contents of the vegetative parts of these plants. The aim of this study was to evaluate the potential capacity of hyperspectral imaging (HSI) for non-invasive assessment of forage chemical composition. Hyperspectral image data within the visible near-infrared range into the extended near-infrared covering 550–1700 nm wavelengths were obtained from 185 accessions of ryegrass ( Lolium perenne ), which were also analysed for 13 forage quality attributes. Results Medium to high predictive power was observed for the HSI models of total sugars (R 2 validation of 0.58), high molecular weight sugars (R 2 validation of 0.63), %Ash (R 2 validation of 0.50) and %Nitrogen (R 2 validation of 0.70). Significant HSI models with low R 2 validation of 0.1–0.5 were also obtained for low molecular weight sugars, NDF (%), ADF (%), DOMD (% DM), ME (MJ/kg DM), DM (%), Ca (mg/g) and OM (%). We also observed significant differences in the chemical composition between the pseudostems and leaves of the plants for each accession. The power of HSI for prediction of these differences within plants was also demonstrated. Conclusion This study paves the way for the HSI technology to be used for in-field estimation of forage composition attributes in perennial ryegrass. This will allow more rapid genetic-based selection and breeding for a trait that is normally expensive to measure providing a cheaper, non-destructive and high throughput screening tool.

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“…only in the leaves). It can also be used for spectral un-mixing procedures for forage imaged at greater distances from the ground [34].…”

Section: Discussionmentioning

confidence: 99%

Predicting the quality of ryegrass using hyperspectral imaging

et al. 2019

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“…Remote sensing community intends to use reflectance based methods to estimate net or gross primary productivity, to manage forest and/ or agricultural lands, and to monitor effects of environmental changes (Kiang et al 2007, Malenovský et al 2009, Granlund et al 2018; it has been also used for mapping lichen cover (Théau et Duguay 2004). Further, spectral reflectance, together with many other non-invasive methods, are integrated into plant phenotyping research for selection of stress tolerant genotypes (see review Mishra et al 2016), and also in lichen research (Ager et Milton 1987, Solheim et al 2000, Bechtel et al 2002, Van Der Veen et Csatho 2005, Rees et al 2004, Morison et al 2014, Mishra et al 2019. Reflectance in visible region, 400-800 nm, is highly absorbed by photosynthetic pigments (chlorophylls and carotenoids), and, thus, it also reflects associated photosynthetic activity.…”

Section: Introductionmentioning

confidence: 99%

Spectral reflectance indices sense desiccation induced changes in the thalli of Antarctic lichen Dermatocarpon polyphyllizum

Barták¹,

Mishra²,

Marečková³

2018

Czech Polar Rep.

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Lichens, in polar and alpine regions, pass through repetitive dehydration and rehydration events over the years. The harsh environmental conditions affect the plasticity of lichen’s functional and structural features for their survival, in a species-specific way, and, thus, their optical and spectral characteristics. For an understanding on how dehydration affects lichens spectral reflectance, we measured visible (VIS) and near infrared (NIR) reflectance spectra of Dermatocarpon polyphyllizum, a foliose lichen species, from James Ross Island (Antarctica), during gradual dehydration from fully wet (relative water content (RWC) = 100%) to dry state (RWC = 0%), under laboratory conditions, and compared several derived reflectance indices (RIs) to RWC. We found a curvilinear relationship between RWC and range of RIs: water index (WI), photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), modified chlorophyll absorption in reflectance indices (MCARI and MCARI1), simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and a new NIR shoulder region spectral ratio index (NSRI). The index NDVI was initially increased with maxima around 70% RWC and it steadily declined with further desiccation, whereas PRI in-creased with desiccation and steeply falls when RWC was below 10%. The curvilinear relationship, for RIs versus RWC, was best fitted by polynomial regressions of second or third degree, and it was found that RWC showed very high correlation with WI (R2 = 0.94) that is followed by MCARI (R2 = 0.87), NDVI (R2 = 0.83), and MCARI (R2 = 0.81). The index NSRI, proposed for accessing structural deterioration, was almost invariable during dehydration with the least value of the coefficient of determination (R2 = 0.28). This may mean that lichen, Dermatocarpon polyphyllizum, activates protection mechanisms initially in response to the progression of dehydration; however, severe dehydration causes deactivation of photosynthesis and associated pigments without much affecting its structure.

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“…In the tropics, this technique is revolutionizing the study of biodiversity through the mapping of forest species and processes associated with them (Sanchez-Azofeifa et al, 2017;Shiklomanov et al, 2019). Several studies have examined the discrimination of lichens based on their spectral features (e.g., Bechtel, Rivard & Sánchez-Azofeifa 2002;Rees, Tutubalina & Golubeva 2004;Morison, Cloutis & Mann 2014), but we know of no such studies for tropical lichens.…”

Section: Introductionmentioning

confidence: 99%

“…This optical property implies that lichen signatures should not be affected by their substrate, allowing their accurate detection and discrimination. Three studies have addressed the classification of lichens based on their spectra: the first conducted in the Canadian Rockies (Bechtel et al, 2002), the second in subarctic Sweden (Rees et al, 2004) and the third in Manitoba, Alberta and Nunavut in Canada (Morison et al, 2014). These studies characterize the spectral properties of lichens based on a relatively few (5-10) rock-encrusting species (Bechtel et al, 2002, Rees et al, 2004 or attempt to discriminate lichens based on the colors of 16 species (Morison et al, 2014).…”

Section: Introductionmentioning

confidence: 99%