VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

Imran, Hafiz Ali; Gianelle, Damiano; Rocchini, Duccio; Dalponte, Michele; Martín, M. Pilar; Sakowska, Karolina; Wohlfahrt, Georg; Vescovo, L.

doi:10.3390/rs12142254

Cited by 23 publications

(25 citation statements)

References 76 publications

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“…Consequently, the reduced NIR reflectance observed in the canopy of root rot-affected plants may be attributed to leaf structural changes induced by wilting processes [79]. Moreover, the NIR response is often related to the leaf water content [80], despite the fact that, in our results, we did not observe relevant differences in the WBI index between the plant groups. This indicates that leaf structure deterioration associated with the Armillaria root was not associated with the leaf water status modifications.…”

Section: Discussioncontrasting

confidence: 65%

Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

et al. 2021

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Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. Prompt recognition of diseased plants is crucial to control the pathogen. However, the current disease detection methods are limited at a field scale. Therefore, an alternative approach is needed. In this study, we investigated the potential of hyperspectral techniques to identify fungi-infected vs. healthy plants of Vitis vinifera. We used the hyperspectral imaging sensor Specim-IQ to acquire leaves’ reflectance data of the Teroldego Rotaliano grapevine cultivar. We analyzed three different groups of plants: healthy, asymptomatic, and diseased. Highly significant differences were found in the near-infrared (NIR) spectral region with a decreasing pattern from healthy to diseased plants attributable to the leaf mesophyll changes. Asymptomatic plants emerged from the other groups due to a lower reflectance in the red edge spectrum (around 705 nm), ascribable to an accumulation of secondary metabolites involved in plant defense strategies. Further significant differences were observed in the wavelengths close to 550 nm in diseased vs. asymptomatic plants. We evaluated several machine learning paradigms to differentiate the plant groups. The Naïve Bayes (NB) algorithm, combined with the most discriminant variables among vegetation indices and spectral narrow bands, provided the best results with an overall accuracy of 90% and 75% in healthy vs. diseased and healthy vs. asymptomatic plants, respectively. To our knowledge, this study represents the first report on the possibility of using hyperspectral data for root rot disease diagnosis in woody plants. Although further validation studies are required, it appears that the spectral reflectance technique, possibly implemented on unmanned aerial vehicles (UAVs), could be a promising tool for a cost-effective, non-invasive method of Armillaria disease diagnosis and mapping in-field, contributing to a significant step forward in precision viticulture.

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Section: Discussioncontrasting

confidence: 65%

Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

et al. 2021

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“…Consequently, the reduced NIR reflectance observed in the canopy of root rot affected plants may be attributed to leaf structural changes induced by wilting processes [68]. Moreover, the NIR response is also related to the leaf water content [69], despite, in our results, we did not observe relevant differences in WBI index between the plant groups. Overall, according to the considered VIs, we can assume that diseased plants are characterized by lower vegetation vigour and leaf pigment content.…”

Section: Discussioncontrasting

confidence: 72%

Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

Calamita¹,

Imran²,

Vescovo³

et al. 2021

Preprint

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The Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. The disease damage prompt assessment is crucial for pest management. However, the disease detection current methods are limited at the field scale. Therefore, an alternative approach that can enhance or supplement traditional techniques is needed. In this study, we investigated the potential of hyperspectral methods to identify the changes between fungi-infected and uninfected plants of Vitis vinifera in early detecting the Armillaria disease. The hyperspectral imaging sensor Specim-IQ was used to acquire images of leaves of the Teroldego Rotaliano grapevine cultivar. We analysed three groups of plants: healthy, asymptomatic, and diseased. Highly significant differences were found in the Near infrared (NIR) spectral region with a decreasing pattern from healthy to diseased plants attributable to internal leaf structure changes. Asymptomatic plants emerged from the other groups due to a smaller reflectance in the red-edge spectrum (around 705nm). Hypothetically associated with the presence of secondary metabolites involved in plant defence strategy. Furthermore, significant differences were observed in the wavelengths close to 550 nm in diseased plants versus asymptomatic. We used linear discriminant analysis from a machine learning context to classify the leaves based on the most significant variables (vegetation indices and single bands), with resulting overall accuracies of 85% and 84% respectively in healthy vs. diseased and healthy vs. asymptomatic. To our knowledge, this study represents the first report on the possibility of using hyperspectral data for root rot disease diagnosis on woody plants. Although further validation studies are required, it appears that the spectral reflectance technique, possibly implemented on unmanned aerial vehicles (UAV), could be a promising tool for a cost-effective, non-destructive method of Armillaria disease early diagnosis and mapping in the field, contributing to a significant step forward in precision viticulture.

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“…However, the data acquired at the semi-natural subalpine grassland at IT-MBo with the same methodology and analyzed with the same approach provided much weaker correlations. Such results may be due to the very high level of biodiversity (up to 17 species in a 0.25 × 0.25 m plot) and to the rather complex structure of the IT-MBo grasslands [44] compared to the low-diversity turf canopy at the IT-PD site. Our main findings questioned the applicability of the optical diversity method to estimate biodiversity in highly diverse grasslands, such as the ones at the IT-MBo site.…”

Section: Discussionmentioning

confidence: 99%

“…It is cut once a year around mid-July at the green biomass peak time, and it is characterized by very high plant diversity [42]. Several different vegetation types can be found in the area with extremely varying canopy structure and biomass [43,44]. The Sieverso-Nardetum strictae association covers a high portion of the plateau characterized by short canopies.…”

Section: Study Areamentioning

confidence: 99%

Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery

Imran

Gianelle

Scotton³

et al. 2021

Remote Sensing

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Plant biodiversity is an important feature of grassland ecosystems, as it is related to the provision of many ecosystem services crucial for the human economy and well-being. Given the importance of grasslands, research has been carried out in recent years on the potential to monitor them with novel remote sensing techniques. In this study, the optical diversity (also called spectral diversity) approach was adopted to check the potential of using high-resolution hyperspectral images to estimate α-diversity in grassland ecosystems. In 2018 and 2019, grassland species composition was surveyed and canopy hyperspectral data were acquired at two grassland sites: Monte Bondone (IT-MBo; species-rich semi-natural grasslands) and an experimental farm of the University of Padova, Legnaro, Padua, Italy (IT-PD; artificially established grassland plots with a species-poor mixture). The relationship between biodiversity (species richness, Shannon’s, species evenness, and Simpson’s indices) and optical diversity metrics (coefficient of variation-CV and standard deviation-SD) was not consistent across the investigated grassland plant communities. Species richness could be estimated by optical diversity metrics with an R = 0.87 at the IT-PD species-poor site. In the more complex and species-rich grasslands at IT-MBo, the estimation of biodiversity indices was more difficult and the optical diversity metrics failed to estimate biodiversity as accurately as in IT-PD probably due to the higher number of species and the strong canopy spatial heterogeneity. Therefore, the results of the study confirmed the ability of spectral proxies to detect grassland α-diversity in man-made grassland ecosystems but highlighted the limitations of the spectral diversity approach to estimate biodiversity when natural grasslands are observed. Nevertheless, at IT-MBo, the optical diversity metric SD calculated from post-processed hyperspectral images and transformed spectra showed, in the red part of the spectrum, a significant correlation (up to R = 0.56, p = 0.004) with biodiversity indices. Spatial resampling highlighted that for the IT-PD sward the optimal optical pixel size was 1 cm, while for the IT-MBo natural grassland it was 1 mm. The random pixel extraction did not improve the performance of the optical diversity metrics at both study sites. Further research is needed to fully understand the links between α-diversity and spectral and biochemical heterogeneity in complex heterogeneous ecosystems, and to assess whether the optical diversity approach can be adopted at the spatial scale to detect β-diversity. Such insights will provide more robust information on the mechanisms linking grassland diversity and optical heterogeneity.

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VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

Cited by 23 publications

References 76 publications

Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery

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