Cucumber Powdery Mildew Detection Using Hyperspectral Data

This study aimed to understand the spectral changes induced by Podosphaera xanthii, the causal agent of powdery mildew, in cucumber leaves from the moment of inoculation until visible symptoms are apparent. A Principal Component Analysis (PCA) was applied to the spectra to assess the spectral separability between healthy and infected leaves. A spectral ratio between infected and healthy leaf spectra was used to determine the best wavelengths for detecting the disease. Additionally, the spectra were used to compute two spectral variables, i.e., the Red-Well Point (RWP) and the Red-Edge Inflexion Point (REP). A linear Support Vector Machine (SVM) classifier was applied to certain spectral features to assess how well these features can separate the infected leaves from the healthy ones. The PCA showed that a good separability could be achieved from 4 DPI. The best model to fit the RWP and REP wavelengths corresponded to a linear model. The linear model had a higher adjusted R2 for the infected leaves than for the healthy leaves. The SVM trained with five first principal components scores achieved an overall accuracy of 95% at 4 DPI, i.e., two days before the visible symptoms. With the RWP and REP parameters, the SVM accuracy increased as a function of the day of inoculation, reaching 89% and 86%, respectively, when symptoms were visible at 6 DPI. Further research must consider a higher number of samples and more temporal repetitions of the experiment.

Multi-Scale Spectral Separability of Submerged Aquatic Vegetation Species in a Freshwater Ecosystem

Optical remote sensing has been suggested as a preferred method for monitoring submerged aquatic vegetation (SAV), a critical component of freshwater ecosystems that is facing increasing pressures due to climate change and human disturbance. However, due to the limited prior application of remote sensing to mapping freshwater vegetation, major foundational knowledge gaps remain, specifically in terms of the specificity of the targets and the scales at which they can be monitored. The spectral separability of SAV from the St. Lawrence River, Ontario, Canada, was therefore examined at the leaf level (i.e., spectroradiometer) as well as at coarser spectral resolutions simulating airborne and satellite sensors commonly used in the SAV mapping literature. On a Leave-one-out Nearest Neighbor criterion (LNN) scale of values from 0 (inseparable) to 1 (entirely separable), an LNN criterion value between 0.82 (separating amongst all species) and 1 (separating between vegetation and non-vegetation) was achieved for samples collected in the peak-growing season from the leaf level spectroradiometer data. In contrast, samples from the late-growing season and those resampled to coarser spectral resolutions were less separable (e.g., inter-specific LNN reduction of 0.25 in late-growing season samples as compared to the peak-growing season, and of 0.28 after resampling to the spectral response of Landsat TM5). The same SAV species were also mapped from actual airborne hyperspectral imagery using target detection analyses to illustrate how theoretical fine-scale separability translates to an in situ, moderate-spatial scale application. Novel radiometric correction, georeferencing, and water column compensation methods were applied to optimize the imagery analyzed. The SAV was generally well detected (overall recall of 88% and 94% detecting individual vegetation classes and vegetation/non-vegetation, respectively). In comparison, underwater photographs manually interpreted by a group of experts (i.e., a conventional SAV survey method) tended to be more effective than target detection at identifying individual classes, though responses varied substantially. These findings demonstrated that hyperspectral remote sensing is a viable alternative to conventional methods for identifying SAV at the leaf level and for monitoring at larger spatial scales of interest to ecosystem managers and aquatic researchers.

Identifying the Spectral Signatures of Invasive and Native Plant Species in Two Protected Areas of Pakistan through Field Spectroscopy

Globally, biological invasions are considered as one of the major contributing factors for the loss of indigenous biological diversity. Hyperspectral remote sensing plays an important role in the detection and mapping of invasive plant species. The main objective of this study was to discriminate invasive plant species from adjacent native species using a ground-based hyperspectral sensor in two protected areas, Lehri Reserve Forest and Jindi Reserve Forest in Punjab, Pakistan. Field spectral measurements were collected using an ASD FieldSpec handheld2TM spectroradiometer (325–1075 nm) and the discrimination between native and invasive plant species was evaluated statistically using hyperspectral indices as well as leaf wavelength spectra. Finally, spectral separability was calculated using Jeffries Matusita distance index, based on selected wavebands. The results reveal that there were statistically significant differences (p < 0.05) between the different spectral indices of most of the plant species in the forests. However, the red-edge parameters showed the highest potential (p < 0.001) to discriminate different plant species. With leaf spectral signatures, the mean reflectance between all plant species was significantly different (p < 0.05) at 562 (75%) wavelength bands. Among pairwise comparisons, invasive Leucaena leucocephala showed the best discriminating ability, with Dodonaea viscosa having 505 significant wavebands showing variations between them. Jeffries Matusita distance analysis revealed that band combinations of the red-edge region (725, 726 nm) showed the best spectral separability (85%) for all species. Our findings suggest that it is possible to identify and discriminate invasive species through field spectroscopy for their future monitoring and management. However, the upscaling of hyperspectral measurements to airborne and satellite sensors can provide a reliable estimation of invasion through mapping inside the protected areas and can help to conserve biodiversity and environmental ecosystems in the future.

Seasonal Spectral Separation of Western Snowberry and Wolfwillow in Grasslands with Field Spectroradiometer and Simulated Multispectral Bands

Woody plant encroachment (WPE), the expansion of native and non-native trees and shrubs into grasslands, has led to degradation worldwide. In the Canadian prairies, western snowberry and wolfwillow shrubs are common encroachers, whose cover is currently unknown. As the use of remote sensing in grassland monitoring increases, opportunities to detect and map these woody species are enhanced. Therefore, the purpose of this study is to identify the optimal season for detection of the two shrubs, to determine the sensitive wavelengths and bands that allow for their separation, and to investigate differences in separability potential between a hyperspectral and broadband multispectral approach. We do this by using spring, summer, and fall field-based spectra of both shrubs for the calculation of spectral separability metrics and for the simulation of broadband spectra. Our results show that the summer offers higher discrimination between the two species, especially when using the red and blue spectral regions and to a lesser extent the green region. The fall season fails to provide significant spectral separation along the wavelength spectrum. Moreover, there is no significant difference in the results from the hyperspectral or broadband approach. Nevertheless, cross-validation with satellite imagery is needed to confirm the current results.

Identification of the Optimal Season and Spectral Regions for Shrub Cover Estimation in Grasslands

Woody plant encroachment (WPE), the expansion of native and non-native trees and shrubs into grasslands, is a less studied factor that leads to declines in grassland ecosystem health. With the increasing application of remote sensing in grassland monitoring and measuring, it is still difficult to detect WPE at its early stages when its spectral signals are not strong enough. Even at late stages, woody species have strong vegetation characteristics that are commonly categorized as healthy ecosystems. We focus on how shrub encroachment can be detected through remote sensing by looking at the biophysical and spectral properties of the WPE grassland ecosystem, investigating the appropriate season and wavelengths that identify shrub cover, testing the spectral separability of different shrub cover groups and by revealing the lowest shrub cover that can be detected by remote sensing. Biophysical results indicate spring as the best season to distinguish shrubs in our study area. The earliest shrub encroachment can be identified most likely only when the cover reaches between 10% and 25%. A correlation between wavelength spectra and shrub cover indicated four regions that are statistically significant, which differ by season. Furthermore, spectral separability of shrubs increases with their cover; however, good separation is only possible for pure shrub pixels. From the five separability metrics used, Transformed divergence and Jeffries-Matusita distance have better interpretations. The spectral regions for pure shrub pixel separation are slightly different from those derived by correlation and can be explained by the influences from land cover mixtures along our study transect.

Indoor spectroradiometric characterization of plastic litters commonly polluting the Mediterranean Sea: toward the application of multispectral imagery

Around 350 million tonnes of plastics are annually produced worldwide. A remarkable percentage of these products is dispersed in the environment, finally reaching and dispersed in the marine environment. Recent field surveys detected microplastics’ concentrations in the Mediterranean Sea. The most commonly polymers found were polyethylene, polypropylene and viscose, ethylene vinyl acetate and polystyrene. In general, the in-situ monitoring of microplastic pollution is difficult and time consuming. The main goals of this work were to spectrally characterize the most commonly polymers and to quantify their spectral separability that may allow to determine optimal band combinations for imaging techniques monitoring. The spectral signatures of microplastics have been analysed in laboratory, both in dry condition and on water surface, using a full spectrum spectroradiometer. The theoretical use of operational satellite images for remote sensing monitoring was investigated by quantifying the spectral separability achievable by their sensors. The WorldView-3 sensor appears the most suitable for the monitoring but better average spectral separability are expected using the recently released PRISMA images. This research was preparatory to further outdoor experiments needed to better simulate the real acquisition condition.

SEASONAL SPECTRAL SEPARABILITY OF SELECTED GRASSES: CASE STUDY FROM THE KRKONOŠE MTS. TUNDRA ECOSYSTEM

This paper focuses on spectral separability of closed alpine grasslands dominated with Nardus stricta and competitive grasses Calamagrostis villosa and Molinia caerulea in the relict arctic-alpine tundra located in the Krkonoše Mountains National Park, Czech Republic. The spectral data were acquired and compared at three levels: spectra of a single layer of leaves measured with the ASD FieldSpec4 Wide-Res spectroradiometer coupled with a contact probe in a laboratory (leaf level), canopy spectra measured in a field with the same spectroradiometer using the fiber optic cable with a pistol grip (canopy level), and hyperspectral image data acquired by Nano-Hyperspec® fastened to the DJI Matrice 600 Pro drone (image level). All the measurements were repeated three times during the 2019 vegetation season – in June, July and August. Using the methods of analysis of variance and Welch's (unpaired) t-test, it was proven that there were differences in the results for all three spectra sources. But in general, for each combination of species and each data source a suitable date and intervals of the spectral bands for species separation exist. The most suitable term for data acquisition in order to differentiate all the species is July. At the leaf level, the best species separability was observed in the near-infrared and shortwave infrared spectral ranges. At the canopy and image levels, the visible bands are of higher importance for discriminating the species.