Cotton (Gossypium hirsutum L.) and corn (Zea mays L.) spectral reflectance holds promise for deriving variable rate N (VRN) treatments calibrated with red-edge inflection (REI) type vegetation indices (VIs). The objectives of this study were to define the relationships between two commercially available sensors and the suitable VIs used to predict N status. Field trials were conducted during the 2012-2013 growing seasons using fixed and variable N rates in cotton ranging from 33.6-134.4 kg N ha-1 and fixed N rates in corn ranging from 0.0 to 268.8 kg N ha-1. Leaf N concentration, SPAD chlorophyll and crop yield were analyzed for their relation to fertilizer N treatment. Sensor effects were significant and red-edge VIs most strongly correlated to N status. A theoretical ENDVI index was derived from the research dataset as an improvement and alternative to the Guyot’s Red Edge Inflection and Simplified Canopy Chlorophyll Content Index (SI).

Cotton (Gossypium hirsutum L.) reflectance has potential to drive variable rate N (VRN) applications, but more precise definitions of relationships between sensor-observed reflectance, plant height, and N status are necessary. The objectives of this study were to define effectiveness and relationships between three commercially available sensors, and examine relationships of wavelengths and indices obtained by a spectrometer to plant height and N status. Field trials were conducted during 2008-2010 growing seasons at Mississippi State, MS. Fertilizer N rates ranged from 0-135 kg N ha-1 to establish growth differences. Sensor effects were significant, but sensors monitoring Normalized Difference Vegetation Index (NDVI) failed to correlate well with early-season N status. Wavelengths and indices utilizing the red-edge correlated most strongly with N status. Both Guyot’s Red Edge Index (REI) and Canopy Chlorophyll Content Index (I) correlated consistently with N status independent of biomass status early enough in the growing season to drive VRN.

Spatial variations in soil properties can directly affect Nitrogen status of corn (Zea mays L.) and decrease efficiency of uniform fertilizer N applications. The objective of this study was to assess the spatial variations of soil properties and measure the effect on corn Nitrogen status through canopy reflectance. Field trials were conducted in 2010 and 2011 on a producer’s field west of Yazoo City, MS that contained high in field variability. Soil physical and chemical properties all exhibited moderate to high spatial dependency during both years of this study. Vegetative indices were derived from canopy reflectance values and indices utilizing the red-edge were the strongest and most consistent descriptors of tissue N percent and whole plant N uptake. The Canopy Chlorophyll Content Index (I) shows the greatest potential of assessing variations of corn Nitrogen status among the indices tested.

L’évolution rapide des technologies de détection et de caractérisation des exoplanètes depuis le début des années 1990 permet de croire que de nouveaux instruments du type Terrestrial Planet Finder (TPF) pourront prendre les premiers spectres d’exoplanètes semblables à la Terre d’ici une ou deux décennies. Dans ce contexte, l’étude du spectre de la seule planète habitée connue, la Terre, est essentielle pour concevoir ces instruments et analyser leurs résultats. Cette recherche présente les spectres de la Terre dans le visible (390-900 nm), acquis lors de 8 nuits d’observation étalées sur plus d’un an. Ces spectres ont été obtenus en observant la lumière cendrée de la Lune avec le télescope de 1.6 m de l’Observatoire du Mont-Mégantic (OMM). La surface de la Lune réfléchissant de manière diffuse la lumière provenant d’une portion de la Terre, ces spectres sont non résolus spatialement. L’évolution de ces spectres en fonction de la lumière réfléchie à différentes phases de Terre est analogue à celle du spectre d’une exoplanète, dont la phase change selon sa position autour de l’étoile. L'eau, l'oxygène et l'ozone de l’atmosphère, détectés dans tous nos spectres, sont des biomarqueurs dont la présence suggère l’habitabilité de la planète et/ou la présence d’une activité biologique. Le Vegetation Red Edge (VRE), une autre biosignature spectrale, dû aux organismes photosynthétiques à la surface, est caractérisé par l’augmentation de la réflectivité autour de 700 nm. Pour les spectres de 5 nuits, cette augmentation a été évaluée entre -5 et 15% ±~5%, après que les contributions de la diffusion de Rayleigh, des aérosols et d’une large bande moléculaire de l’ozone aient été enlevées. Les valeurs mesurées sont cohérentes avec la présence de végétation dans la phase de la Terre contribuant au spectre, mais s’étendent sur une plage de variations plus large que celles trouvées dans la littérature (0-10%). Cela pourrait s’expliquer par des choix faits lors de la réduction des données et du calcul du VRE, ou encore par la présence d’autres éléments de surface ou de l’atmosphère dont la contribution spectrale autour de 700 nm serait variable. / The rapid evolution of the detection and characterization of exoplanets since the nineties is such that instruments like the Terrestrial Planet Finder (TPF) will surely take the first spectra of exoplanets similar to the Earth in the next decades. The study of the spectrum of the only inhabited planet we know, the Earth, is thus essential to conceive these instruments and to complete pertinent analyses of their results. This research presents the optical spectra (390-900 nm) of the Earth that were secured on 8 observing nights covering more than a year. These spectra were obtained by observing the Earthshine with the 1.6 m telescope at the Observatoire du Mont-Mégantic (OMM). Because the surface of the Moon reflects diffusely the light coming from a portion of the Earth, the observation of Earthshine allow us to get spatially unresolved spectra, like those that will likely be obtained for exoplanets with the first generation of instruments. The variation of the Earth’s spectrum with the changing contributing phase of the Earth is also similar to that of an exoplanet spectrum, which changes with its position around the star. Water, oxygen and ozone of the Earth’s atmosphere, detected in all of our spectra, are biomarkers. They give clues about the habitability and the possible presence of life on a planet. The Vegetation Red Edge (VRE), another spectral biomarker, caused by photosynthetic organisms, is characterized by an increase in reflectivity around 700 nm. For the spectra of 5 nights, this increase was evaluated to be between -5 and 15% ±~5%, after the contributions of Rayleigh and aerosol scattering, as well as of a wide ozone absorption band were removed. These values are consistent with the presence of vegetation in the phase of the Earth contributing to the spectra. However, they cover a larger range than that usually found in the literature (0-10%). A possible explanation could be the few arbitrary choices that were made during data processing and VRE computation or the presence of other surface and atmospheric elements with a spectral signature varying around 700 nm.

L’évolution rapide des technologies de détection et de caractérisation des exoplanètes depuis le début des années 1990 permet de croire que de nouveaux instruments du type Terrestrial Planet Finder (TPF) pourront prendre les premiers spectres d’exoplanètes semblables à la Terre d’ici une ou deux décennies. Dans ce contexte, l’étude du spectre de la seule planète habitée connue, la Terre, est essentielle pour concevoir ces instruments et analyser leurs résultats. Cette recherche présente les spectres de la Terre dans le visible (390-900 nm), acquis lors de 8 nuits d’observation étalées sur plus d’un an. Ces spectres ont été obtenus en observant la lumière cendrée de la Lune avec le télescope de 1.6 m de l’Observatoire du Mont-Mégantic (OMM). La surface de la Lune réfléchissant de manière diffuse la lumière provenant d’une portion de la Terre, ces spectres sont non résolus spatialement. L’évolution de ces spectres en fonction de la lumière réfléchie à différentes phases de Terre est analogue à celle du spectre d’une exoplanète, dont la phase change selon sa position autour de l’étoile. L'eau, l'oxygène et l'ozone de l’atmosphère, détectés dans tous nos spectres, sont des biomarqueurs dont la présence suggère l’habitabilité de la planète et/ou la présence d’une activité biologique. Le Vegetation Red Edge (VRE), une autre biosignature spectrale, dû aux organismes photosynthétiques à la surface, est caractérisé par l’augmentation de la réflectivité autour de 700 nm. Pour les spectres de 5 nuits, cette augmentation a été évaluée entre -5 et 15% ±~5%, après que les contributions de la diffusion de Rayleigh, des aérosols et d’une large bande moléculaire de l’ozone aient été enlevées. Les valeurs mesurées sont cohérentes avec la présence de végétation dans la phase de la Terre contribuant au spectre, mais s’étendent sur une plage de variations plus large que celles trouvées dans la littérature (0-10%). Cela pourrait s’expliquer par des choix faits lors de la réduction des données et du calcul du VRE, ou encore par la présence d’autres éléments de surface ou de l’atmosphère dont la contribution spectrale autour de 700 nm serait variable. / The rapid evolution of the detection and characterization of exoplanets since the nineties is such that instruments like the Terrestrial Planet Finder (TPF) will surely take the first spectra of exoplanets similar to the Earth in the next decades. The study of the spectrum of the only inhabited planet we know, the Earth, is thus essential to conceive these instruments and to complete pertinent analyses of their results. This research presents the optical spectra (390-900 nm) of the Earth that were secured on 8 observing nights covering more than a year. These spectra were obtained by observing the Earthshine with the 1.6 m telescope at the Observatoire du Mont-Mégantic (OMM). Because the surface of the Moon reflects diffusely the light coming from a portion of the Earth, the observation of Earthshine allow us to get spatially unresolved spectra, like those that will likely be obtained for exoplanets with the first generation of instruments. The variation of the Earth’s spectrum with the changing contributing phase of the Earth is also similar to that of an exoplanet spectrum, which changes with its position around the star. Water, oxygen and ozone of the Earth’s atmosphere, detected in all of our spectra, are biomarkers. They give clues about the habitability and the possible presence of life on a planet. The Vegetation Red Edge (VRE), another spectral biomarker, caused by photosynthetic organisms, is characterized by an increase in reflectivity around 700 nm. For the spectra of 5 nights, this increase was evaluated to be between -5 and 15% ±~5%, after the contributions of Rayleigh and aerosol scattering, as well as of a wide ozone absorption band were removed. These values are consistent with the presence of vegetation in the phase of the Earth contributing to the spectra. However, they cover a larger range than that usually found in the literature (0-10%). A possible explanation could be the few arbitrary choices that were made during data processing and VRE computation or the presence of other surface and atmospheric elements with a spectral signature varying around 700 nm.

Increased near infrared (NIR) reflection, closely resembling the red edge found in leaves, has been known in frogs for many years. Whereas previously thought of as an isolated rarity, we have shown that it is likely far more prevalent than previously believed, occurring in multiple distinct family groups and world regions. To date, there are now 26 anuran species known to demonstrate increased NIR reflectance, from 12 different genera, 4 families, and 3 ecozones. The visible/NIR reflection spectra of each individual measured was found to be characteristic of its species; whether it was wild or captive bred; and its sex. A machine learning based classification system was demonstrated as a viable method of identifying these properties from a frog's reflection spectra alone. How this reflection spectra developed from a pre-metamorphosis froglet through to adult frog was tracked, with the gradual changes to the reflection spectra of both NIR reflective and other frogs identified as being most likely dominated by the reduction in epidermal melanophores, and the increasing number of dermal iridophores. A modified consumer camera was shown to be a viable method for rapid identification of increased NIR reflection in anurans, and was used to identify that salamanders also show variation in NIR reflection between ground dwelling and leaf sitting species. The overnight colour change in Hylomantis lemur was observed, and found to occur pre-emptively of the frog's future location; with the frogs regularly transitioning from pale green ‘daytime' colouration, to the dark brown ‘night time' colouration, while still on the green leaf surface before becoming active, and undertaking the reverse transition while still active, but shortly before returning to the leaf. It seems likely that this change is for protection from silhouetting whilst active. Optical coherence tomography images were taken of several species of frog, and found to be a viable method for non-invasive investigation of anuran skin structure, with structural differences observed between the two colourations of H. lemur. It was found that the most likely cause of the increased NIR reflection in frogs is a reduction in melanin, either by absence or substitution with pterorhodin. Although the true benefit to the frog is difficult to determine, it seems likely that cryptic thermoregulation plays a key role: the maintenance of body temperature for the purpose of camouflage from animals capable of far-infrared vision. This thesis demonstrates the legitimacy of several techniques and approaches for non-invasive study of anurans, but the ultimate scope of the project is fundamentally limited by the range of frogs available. Further insight is likely to arise from increasing this scope, applying these techniques to more frogs, from more species, in more regions, and the author wishes all future researchers the greatest success in this endeavour.

The diploma thesis deals with the use of unmanned monitoring in conditions of precise agriculture. The literature review provides information on the distribution of drones from a technical point of view, information on precision farming, geographic information systems and plant monitoring, and on the use of unmanned aerial vehicles in the Slovak Republic and the Czech Republic. The own work is focused on the statistical processing and evaluation of data usable for work in the ArcMap program and the economic evaluation of the efficiency of the use of unmanned aerial surveillance in the conditions of precision farming.

Detta arbete presenterar en icke-destruktiv metod för att detektera och klassificera mognadsgraden hos äpplen med användning av hyperspektrala bilder. Fastställning av mognadsgraden hos äpplen är intressant för bland annat äppelodlare och musterier vid lagring och beredning. Äpplens mognadsgrad är även intressant inom växtförädling. För att fastställa mognadsgraden idag krävs att det skärs i frukten, en så kallad destruktiv metod. Hyperspektrala bilder kan idag användas inom områden som jordbruk, miljöövervakning och militär spaning. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

Vegetation status is a key indicator of the ecosystem condition in a particular area. The study objective was about the estimation of leaf nitrogen (N) as an indicator of vegetation water stress using vegetation indices especially the red edge based ones, and how leaf N concentration is influenced by various environmental factors. Leaf nitrogen was estimated using univariate and multivariate regression techniques of stepwise multiple linear regression (SMLR) and random forest. The effects of environmental parameters on leaf nitrogen distribution were tested through univariate regression and analysis of variance (ANOVA). Vegetation indices were evaluated derived from the analytical spectral device (ASD) data, resampled to RapidEye. The multivariate models were also developed to predict leaf N. The best model was chosen based on the lowest root mean square error (RMSE) and higher coefficient of determination (R2) values. Univariate results showed that red edge based vegetation index called MERRIS Terrestrial Chlorophyll Index (MTCI) yielded higher leaf N estimation accuracy as compared to other vegetation indices. Simple ratio (SR) based on the bands red and near-infrared was found to be the best vegetation index for leaf N estimation with exclusion of red edge band for stepwise multiple linear regression (SMLR) method. Simple ratio (SR3) was the best vegetation index when red edge was included for stepwise linear regression (SMLR) method. Random forest prediction model achieved the highest leaf N estimation accuracy, the best vegetation index was Red Green Index (RGI1) based on all bands with red green index when including the red edge band. When red edge band was excluded the best vegetation index for random forest was Difference Vegetation Index (DVI1). The results for univariate and multivariate results indicated that the inclusion of the red edge band provides opportunity to accurately estimate leaf N. Analysis of variance results showed that vegetation and soil types have a significant effect on leaf N distribution with p-values<0.05. Red edge based indices provides opportunity to assess vegetation health using remote sensing techniques. / Environmental Sciences / M. Sc. (Environmental Management)

Vegetation status is a key indicator of the ecosystem condition in a particular area. The study objective was about the estimation of leaf nitrogen (N) as an indicator of vegetation water stress using vegetation indices especially the red edge based ones, and how leaf N concentration is influenced by various environmental factors. Leaf nitrogen was estimated using univariate and multivariate regression techniques of stepwise multiple linear regression (SMLR) and random forest. The effects of environmental parameters on leaf nitrogen distribution were tested through univariate regression and analysis of variance (ANOVA). Vegetation indices were evaluated derived from the analytical spectral device (ASD) data, resampled to RapidEye. The multivariate models were also developed to predict leaf N. The best model was chosen based on the lowest root mean square error (RMSE) and higher coefficient of determination (R2) values. Univariate results showed that red edge based vegetation index called MERRIS Terrestrial Chlorophyll Index (MTCI) yielded higher leaf N estimation accuracy as compared to other vegetation indices. Simple ratio (SR) based on the bands red and near-infrared was found to be the best vegetation index for leaf N estimation with exclusion of red edge band for stepwise multiple linear regression (SMLR) method. Simple ratio (SR3) was the best vegetation index when red edge was included for stepwise linear regression (SMLR) method. Random forest prediction model achieved the highest leaf N estimation accuracy, the best vegetation index was Red Green Index (RGI1) based on all bands with red green index when including the red edge band. When red edge band was excluded the best vegetation index for random forest was Difference Vegetation Index (DVI1). The results for univariate and multivariate results indicated that the inclusion of the red edge band provides opportunity to accurately estimate leaf N. Analysis of variance results showed that vegetation and soil types have a significant effect on leaf N distribution with p-values<0.05. Red edge based indices provides opportunity to assess vegetation health using remote sensing techniques. / Environmental Sciences / M. Sc. (Environmental Management)