Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images

The global demand for fossil energy is triggering oil exploration and production projects in remote areas of the world. During the last few decades hydrocarbon production has caused pollution in the Amazon forest inflicting considerable environmental impact. Until now it is not clear how hydrocarbon pollution affects the health of the tropical forest flora. During a field campaign in polluted and pristine forest, more than 1100 leaf samples were collected and analysed for biophysical and biochemical parameters. The results revealed that tropical forests exposed to hydrocarbon pollution show reduced levels of chlorophyll content, higher levels of foliar water content and leaf structural changes. In order to map this impact over wider geographical areas, vegetation indices were applied to hyperspectral Hyperion satellite imagery. Three vegetation indices (SR, NDVI and NDVI705) were found to be the most appropriate indices to detect the effects of petroleum pollution in the Amazon forest

Detection, identification, and quantification of fungal diseases of sugar beet leaves using imaging and non-imaging hyperspectral techniques

Plant diseases influence the optical properties of plants in different ways. Depending on the host pathogen system and disease specific symptoms, different regions of the reflectance spectrum are affected, resulting in specific spectral signatures of diseased plants. The aim of this study was to examine the potential of hyperspectral imaging and non-imaging sensor systems for the detection, differentiation, and quantification of plant diseases. Reflectance spectra of sugar beet leaves infected with the fungal pathogens Cercospora beticola, Erysiphe betae, and Uromyces betae causing Cercospora leaf spot, powdery mildew, and sugar beet rust, respectively, were recorded repeatedly during pathogenesis. Hyperspectral data were analyzed using various methods of data and image analysis and were compared to ground truth data. Several approaches with different sensors on the measuring scales leaf, canopy, and field have been tested and compared. Much attention was paid on the effect of spectral, spatial, and temporal resolution of hyperspectral sensors on disease recording. Another focus of this study was the description of spectral characteristics of disease specific symptoms. Therefore, different data analysis methods have been applied to gain a maximum of information from spectral signatures. Spectral reflectance of sugar beet was affected by each disease in a characteristic way, resulting in disease specific signatures. Reflectance differences, sensitivity, and best correlating spectral bands differed depending on the disease and the developmental stage of the diseases. Compared to non-imaging sensors, the hyperspectral imaging sensor gave extra information related to spatial resolution. The preciseness in detecting pixel-wise spatial and temporal differences was on a high level. Besides characterization of diseased leaves also the assessment of pure disease endmembers as well as of different regions of typical symptoms was realized. Spectral vegetation indices (SVIs) related to physiological parameters were calculated and correlated to the severity of diseases. The SVIs differed in their sensitivity to the different diseases. Combining the information from multiple SVIs in an automatic classification method with Support Vector Machines, high sensitivity and specificity for the detection and differentiation of diseased leaves was reached in an early stage. In addition to the detection and identification, the quantification of diseases was possible with high accuracy by SVIs and Spectral Angle Mapper classification, calculated from hyperspectral images. Knowledge from measurements under controlled condition was carried over to the field scale. Early detection and monitoring of Cercospora leaf spot and powdery mildew was facilitated. The results of this study contribute to a better understanding of plant optical properties during disease development. Methods will further be applicable in precision crop protection, to realize the detection, differentiation, and quantification of plant diseases in early stages.Nachweis, Identifizierung und Quantifizierung pilzlicher Blattkrankheiten der Zuckerrübe mit abbildenden und nicht-abbildenden hyperspektralen Sensoren Pflanzenkrankheiten wirken sich auf die optischen Eigenschaften von Pflanzen in unterschiedlicher Weise aus. Verschiedene Bereiche des Reflektionsspektrums werden in Abhängigkeit von Wirt-Pathogen System und krankheitsspezifischen Symptomen beeinflusst. Hyperspektrale, nicht-invasive Sensoren bieten die Möglichkeit, optische Veränderungen zu einem frühen Zeitpunkt der Krankheitsentwicklung zu detektieren. Ziel dieser Arbeit war es, das Potential hyperspektraler abbildender und nicht abbildender Sensoren für die Erkennung, Identifizierung und Quantifizierung von Pflanzenkrankheiten zu beurteilen. Zuckerrübenblätter wurden mit den pilzlichen Erregern Cercospora beticola, Erysiphe betae bzw. Uromyces betae inokuliert und die Auswirkungen der Entwicklung von Cercospora Blattflecken, Echtem Mehltau bzw. Rübenrost auf die Reflektionseigenschaften erfasst und mit optischen Bonituren verglichen. Auf den Skalenebenen Blatt, Bestand und Feld wurden Messansätze mit unterschiedlichen Sensoren verglichen. Besonders berücksichtigt wurden hierbei Anforderungen an die spektrale, räumliche und zeitliche Auflösung der Sensoren. Ein weiterer Schwerpunkt lag auf der Beschreibung der spektralen Eigenschaften von charakteristischen Symptomen. Verschiedene Auswerteverfahren wurden mit dem Ziel angewendet, einen maximalen Informationsgehalt aus spektralen Signaturen zu gewinnen. Jede Krankheit beeinflusste die spektrale Reflektion von Zuckerrübenblättern auf charakteristische Weise. Differenz der Reflektion, Sensitivität sowie Korrelation der spektralen Bänder zur Befallsstärke variierten in Abhängigkeit von den Krankheiten. Eine höhere Präzision durch die pixelweise Erfassung räumlicher und zeitlicher Unterschiede von befallenem und gesundem Gewebe konnte durch abbildende Sensoren erreicht werden. Spektrale Vegetationsindizes (SVIs), mit Bezug zu pflanzenphysiologischen Parametern wurden aus den Hyperspektraldaten errechnet und mit der Befallsstärke korreliert. Die SVIs unterschieden sich in ihrer Sensitivität gegenüber den drei Krankheiten. Durch den Einsatz von maschinellem Lernen wurde die kombinierte Information der errechneten Vegetationsindizes für eine automatische Klassifizierung genutzt. Eine hohe Sensitivität sowie eine hohe Spezifität bezüglich der Erkennung und Differenzierung von Krankheiten wurden erreicht. Eine Quantifizierung der Krankheiten war neben der Detektion und Identifizierung mittels SVIs bzw. Klassifizierung mit Spektral Angle Mapper an hyperspektralen Bilddaten möglich. Die Ergebnisse dieser Arbeit tragen zu einem besseren Verständnis der optischen Eigenschaften von Pflanzen unter Pathogeneinfluss bei. Die untersuchten Methoden bieten die Möglichkeit in Anwendungen des Präzisionspflanzenschutzes implementiert zu werden, um eine frühzeitige Erkennung, Differenzierung und Quantifizierung von Pflanzenkrankheiten zu ermöglichen

Extrapolating hyperspectral anthocyanin indices to multispectral satellite sensors---applications to fall foliage in New England

Anthocyanin, thought to be a universal indicator of plant stress, is a red pigment found in many plant species and can seen in New England autumns. Detecting its presence is useful for ecosystem analysis and monitoring changes during autumn senescence. Currently fall foliage is subjectively measured; creation of a satellite-based anthocyanin index will provide an objective measurement and enhance understanding of the distribution of plant stress and senescence over large areas. Anthocyanin indices were tested hyperspectrally in a laboratory setting, then indices were simulated for Hyperion, MERIS, MODIS, and Landsat TM/ETM+ to see which most accurately represents changes in anthocyanin concentration, and finally indices were applied to actual imagery. Results of this study found that (1/R564)-(1/R697) was the best approximation for anthocyanin; the red:green ratio was the best overall estimator of anthocyanin using simulated satellite bands; and real imagery from MODIS and MERIS satellite sensors can detect a fall foliage signal

Early identification of root rot disease by using hyperspectral reflectance: the case of pathosystem grapevine/Armillaria

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 viticultur

Evaluation of soil bacteria treatments on some physiological parameters of crops by spectral vegetation indices

The effects of soil bacteria treatments on barley and wheat (1. treatment: stubble decomposers + soil inoculators; 2. stubble decomposers + soil regenerators; 3. control) were studied at the Agricultural Research Institute in Kompolt on spectral vegetation indices which are closely related to photochemical processes and photosynthetic pigments in barley and wheat leaves. We applied in vivo field measurements: SPAD 502 relative chlorophyll meter and ASD Field Spec Pro 3 spectroradiometer. This work presents the results of the experiments in 2019, moreover, we compared them with our previously published investigations on several other crops (maize, sunflower, rape, barley) carried out in 2017 and 2018. In 2019 despite the significant level of the standard deviation of data in field conditions, treated (mainly with stubble decomposers + soil inoculators) wheat leaves could be characterized by significantly higher chlorophyll and water content, higher photochemical efficiency, and lower carotenoid content. In the case of barley due to the large standard deviation of data, we couldn’t reveal the beneficial effects of treatments by these methods. Due to the very rainy spring in 2019 some experimental plots - like K9 with barley – wre covered by inland water, which negatively influenced living conditions of soil bacteria. Despite this unfavourable conditions, the first treatment resulted in an 18.9% higher yield of barley and 27.8% higher yield of wheat, while the second treatment increased barley yield with 28.9% and wheat yield with 27.7%. In the case of wheat, spectral vegetation indices could indicate beneficial effects of soil bacteria treatments at the beginning of flowering, similarly to our results in 2017 and 2018 in case of maize, sunflower, rape, and barley

Effects of fungicides on physiological parameters and yield formation of wheat assessed by non-invasive sensors

Apart from fungicidal effects, some fungicide classes have been reported to induce physiological changes in crops such as increased tolerance to abiotic stress, delayed senescence of the photosynthetic leaf area and modifications in the balance of plant growth regulators. The aim of this study was to investigate the effects of different fungicidal groups on physiological parameters of wheat through the use of non-invasive sensors and imaging techniques. Experiments were conducted under field and also under disease-free conditions in the greenhouse. Under field conditions, application of the azole, carboxamide and strobilurin compounds resulted in low disease incidence. All fungicide treatments delayed the senescence of the uppermost leaf layers; treatments with longer leaf life and lower disease incidence resulted in higher chlorophyll content. The effect of the fungicides on wheat senescence was positively correlated to grain yield and the thousand-kernel weight. However, under field conditions, the presence of the main foliar pathogens of wheat influenced the green leaf area duration as well as the yield, generating a disadvantage for the fungicide treatments with low disease control efficacy. Under disease-free conditions, an effect produced by the pyrazole carboxamide fungicide bixafen was observed. Bixafen delayed the senescence of leaves and ears resulting in a significantly extended green leaf area duration compared to untreated plants. In addition, an effect produced by this compound on morphogenesis was observed. The combination of the positive effects on physiology and morphogenesis of wheat resulted in a yield advantage of bixafen-treated plants. Furthermore, bixafen had a positive effect on plant tolerance to water stress conditions. Different non-invasive sensors and imaging techniques were used and compared to measure the effects of fungicidal compounds on wheat physiology. By using ground-based optical sensors it was possible to detect the influence of fungicidal compounds in crop physiology, i.e. degradation of photosynthetic pigments, photosynthetic activity, leaf reflectance, and transpiration of plant tissue earlier than with destructive and visual methods. Chlorophyll fluorescence of leaves was useful to measure differences in the effective quantum yield of photosystem II. Reflectance measurements of wheat leaves were highly sensitive to changes in plant vitality. The spectral vegetation indices were useful to determine differences between treatments in terms of leaf senescence, pigments and water content. Digital infrared images revealed significant differences between untreated and fungicide-treated plants at different growth stages. Moreover, thermography proved to be a suitable technique for distinguishing the beneficial effects of fungicides on plant senescence under different water supply conditions. Through the use of an image analysis software program, leaf senescence differences were successfully detected, thus allowing an early detection of the effect produced by the fungicide on the senescence status of flag leaves. Using hyperspectral imaging, it was possible to study differences in the senescence status of flag leaves. Furthermore, through the analysis of hyperspectral images it was achievable to study the pattern of the senescence process in flag leaves and to determine a delay of senescence of wheat produced by fungicides. The results of this study demonstrated that non-invasive sensors and imaging techniques are excellent alternatives to conventional screening methods for detecting the beneficial effects of fungicides on plant physiology. Furthermore, among this innovative group of sensors and techniques it was spectrometry, which proved to be the most sensitive and specific method with a high potential for large-scale fungicide screening. Sensors can be incorporated in automatic and reproducible screening of new active ingredients with high efficiency and accuracy. The recent development of hyperspectral imaging techniques will improve future studies to additionally explore plant physiology with high spatial and temporal resolution

A New Approach for the Analysis of Hyperspectral Data: Theory and Sensitivity Analysis of the Moment Distance Method

We present the Moment Distance (MD) method to advance spectral analysis in vegetation studies. It was developed to take advantage of the information latent in the shape of the reflectance curve that is not available from other spectral indices. Being mathematically simple but powerful, the approach does not require any curve transformation, such as smoothing or derivatives. Here, we show the formulation of the MD index (MDI) and demonstrate its potential for vegetation studies. We simulated leaf and canopy reflectance samples derived from the combination of the PROSPECT and SAIL models to understand the sensitivity of the new method to leaf and canopy parameters. We observed reasonable agreements between vegetation parameters and the MDI when using the 600 to 750 nm wavelength range, and we saw stronger agreements in the narrow red-edge region 720 to 730 nm. Results suggest that the MDI is more sensitive to the Chl content, especially at higher amounts (Chl \u3e 40 mg/cm2) compared to other indices such as NDVI, EVI, and WDRVI. Finally, we found an indirect relationship of MDI against the changes of the magnitude of the reflectance around the red trough with differing values of LAI