精密な飼料作物管理に向けた多段階リモートセンシングとシミューレーションモデルの応用

内容の要約広島大学(Hiroshima University)博士(農学)Doctor of Agriculturedoctora

Yield prediction in ryegrass with UAV-based RGB and multispectral imaging

Forage grass breeding is time-consuming and costly, with the need for special knowledge and experience to make the right decisions for future forage grass production. All measurements for decision-making require manual labor and hands-on inspections. For the yield trait, the traditional method of measurement is cutting and weighing the grass. New methods for yield prediction and measurement with Unmanned Aerial Vehicle (UAV) have been tested on different crops with good results. For perennial ryegrass (Lolium perenne L.) yield prediction has earlier been performed on plots with a flight altitude for image capturing at 20 meters and which has yielded promising results for our study. This study has been exploring different flight altitudes for ryegrass yield prediction using UAV imagery. The sensors that have been used in this study are multispectral and RGB cameras integrated in the UAVs. Our study consists of two trials with pre-selected varieties of perennial ryegrass, one with diploid varieties and one with tetraploid varieties and mixtures between diploid and tetraploid varieties, were investigated. Both trials were seeded at two different locations in Norway. Varieties were planted as rows for the first location (Vollebekk, Ås, Norway) while for the second location (Arneberg, Ilseng, Norway) the two trials were planted as both rows and plots. The dry matter yield (DMY) data were collected with traditional harvest four times for the rows, and three times for the plots. The UAV-images were collected at different flight altitudes with both multispectral and RGB cameras. The full data processing routine was conducted on the first and second cut for both locations. Multivariate regression model was applied for DMY prediction based on UAV imagery. The results correlated well with the predictions at Ås for both multispectral images as well as RGB methods of image acquisition. Our results indicated a high correlation between the actual DMY and the predicted DMY from both RGB images as well as multispectral images with a correlation coefficient on 0.92 for both, but at different assessment dates. The maximum correlation was acquired for the first cut from location Ås. For location Arneberg, the acquired images could not yield results of sufficient quality, and thus, no predictions could be made

Normalized difference vegetation index (NDVI) for soybean biomass and nutrient uptake estimation in response to production systems and fertilization strategies

The system fertilization approach emerged to improve nutrient use eciency in croplands. This new fertilization concept aims at taking advantage of nutrient cycling within an agroecosystem to obtain maximum production from each nutrient unit. To monitor this e ect, methodologies such as the Normalized Di erence Vegetation Index (NDVI) are promising to evaluate plant biomass and nutrient content. We evaluated the use of NDVI as a predictor of shoot biomass, P and K uptake, and yield in soybean. Treatments consisted of two production systems [integrated crop-livestock system (ICLS) and cropping system (CS)] and two periods of phosphorus (P) and potassium (K) fertilization (crop fertilization—P and K applied at soybean sowing—and system fertilization—P and K applied in the pasture establishment). NDVI was evaluated weekly from the growth stage V2 up to growth stage R8, using the Greenseeker® canopy sensor. At the growth stages V4, V6, R2, and R4, plants were sampled after NDVI evaluation for chemical analysis. Soybean yield and K uptake were similar between production systems and fertilization strategies (P > 0.05). Soybean shoot biomass and P uptake were, respectively, 25.3% and 29.7% higher in ICLS compared to CS (P < 0.05). For NDVI, an interaction between the production system and days after sowing (P < 0.05) was observed. NDVI increased to 0.95 at 96 days after sowing in CS and to 0.92 at 92 days after sowing in ICLS. A significant relationship between NDVI and shoot biomass, and P and K uptake was observed (P < 0.05). Our results show that the vegetation index NDVI can be used for estimating shoot biomass and P and K uptake in the early growth stages of soybean crops, providing farmers with a new tool for evaluating the spatial variability of soybean growth and nutrition

Just-in-time Pastureland Trait Estimation for Silage Optimization, under Limited Data Constraints

To ensure that pasture-based farming meets production and environmental targets for a growing population under increasing resource constraints, producers need to know pastureland traits. Current proximal pastureland trait prediction methods largely rely on vegetation indices to determine biomass and moisture content. The development of new techniques relies on the challenging task of collecting labelled pastureland data, leading to small datasets. Classical computer vision has already been applied to weed identification and recognition of fruit blemishes using morphological features, but machine learning algorithms can parameterise models without the provision of explicit features, and deep learning can extract even more abstract knowledge although typically this is assumed to be based around very large datasets. This work hypothesises that through the advantages of state-of-the-art deep learning systems, pastureland crop traits can be accurately assessed in a just-in-time fashion, based on data retrieved from an inexpensive sensor platform, under the constraint of limited amounts of labelled data. However the challenges to achieve this overall goal are great, and for applications such as just-in-time yield and moisture estimation for farm-machinery, this work must bring together systems development, knowledge of good pastureland practice, and also techniques for handling low-volume datasets in a machine learning context. Given these challenges, this thesis makes a number of contributions. The first of these is a comprehensive literature review, relating pastureland traits to ruminant nutrient requirements and exploring trait estimation methods, from contact to remote sensing methods, including details of vegetation indices and the sensors and techniques required to use them. The second major contribution is a high-level specification of a platform for collecting and labelling pastureland data. This includes the collection of four-channel Blue, Green, Red and NIR (VISNIR) images, narrowband data, height and temperature differential, using inexpensive proximal sensors and provides a basis for holistic data analysis. Physical data platforms built around this specification were created to collect and label pastureland data, involving computer scientists, agricultural, mechanical and electronic engineers, and biologists from academia and industry, working with farmers. Using the developed platform and a set of protocols for data collection, a further contribution of this work was the collection of a multi-sensor multimodal dataset for pastureland properties. This was made up of four-channel image data, height data, thermal data, Global Positioning System (GPS) and hyperspectral data, and is available and labelled with biomass (Kg/Ha) and percentage dry matter, ready for use in deep learning. However, the most notable contribution of this work was a systematic investigation of various machine learning methods applied to the collected data in order to maximise model performance under the constraints indicated above. The initial set of models focused on collected hyperspectral datasets. However, due to their relative complexity in real-time deployment, the focus was instead on models that could best leverage image data. The main body of these models centred on image processing methods and, in particular, the use of the so-called Inception Resnet and MobileNet models to predict fresh biomass and percentage dry matter, enhancing performance using data fusion, transfer learning and multi-task learning. Images were subdivided to augment the dataset, using two different patch sizes, resulting in around 10,000 small patches of size 156 x 156 pixels and around 5,000 large patches of size 240 x 240 pixels. Five-fold cross validation was used in all analysis. Prediction accuracy was compared to older mechanisms, albeit using hyperspectral data collected, with no provision made for lighting, humidity or temperature. Hyperspectral labelled data did not produce accurate results when used to calculate Normalized Difference Vegetation Index (NDVI), or to train a neural network (NN), a 1D Convolutional Neural Network (CNN) or Long Short Term Memory (LSTM) models. Potential reasons for this are discussed, including issues around the use of highly sensitive devices in uncontrolled environments. The most accurate prediction came from a multi-modal hybrid model that concatenated output from an Inception ResNet based model, run on RGB data with ImageNet pre-trained RGB weights, output from a residual network trained on NIR data, and LiDAR height data, before fully connected layers, using the small patch dataset with a minimum validation MAPE of 28.23% for fresh biomass and 11.43% for dryness. However, a very similar prediction accuracy resulted from a model that omitted NIR data, thus requiring fewer sensors and training resources, making it more sustainable. Although NIR and temperature differential data were collected and used for analysis, neither improved prediction accuracy, with the Inception ResNet model’s minimum validation MAPE rising to 39.42% when NIR data was added. When both NIR data and temperature differential were added to a multi-task learning Inception ResNet model, it yielded a minimum validation MAPE of 33.32%. As more labelled data are collected, the models can be further trained, enabling sensors on mowers to collect data and give timely trait information to farmers. This technology is also transferable to other crops. Overall, this work should provide a valuable contribution to the smart agriculture research space

Multitemporal assessment of crop parameters using multisensorial flying platforms

UAV sensors suitable for precision farming (Sony NEX-5n RGB camera; Canon Powershot modified to infrared sensitivity; MCA6 Tetracam; UAV spectrometer) were compared over differently treated grassland. The high resolution infrared and RGB camera allows spatial analysis of vegetation cover while the UAV spectrometer enables detailed analysis of spectral reflectance at single points. The high spatial and six-band spectral resolution of the MCA6 combines the opportunities of spatial and spectral analysis, but requires huge calibration efforts to acquire reliable data. All investigated systems were able to provide useful information in different distinct research areas of interest in the spatial or spectral domain. The UAV spectrometer was further used to assess multiangular reflectance patterns of wheat. By flying the UAV in a hemispherical path and directing the spectrometer towards the center of this hemisphere, the system acts like a large goniometer. Other than ground based goniometers, this novel method allows huge diameters without any need for infrastructures on the ground. Our experimental results shows good agreement with models and other goniometers, proving the approach valid. UAVs are capable of providing airborne data with a high spatial and temporal resolution due to their flexible and easy use. This was demonstrated in a two year survey. A high resolution RGB camera was flown every week over experimental plots of barley. From the RGB imagery a time series of the barley development was created using the color values. From this analysis we could track differences in the growth of multiple seeding densities and identify events of plant development such as ear pushing. These results lead towards promising practical applications that could be used in breeding for the phenotyping of crop varieties or in the scope of precision farming. With the advent of high endurance UAVs such as airships and the development of better light weight sensors, an exciting future for remote sensing from UAV in agriculture is expected

Monitoring of Plant Chlorophyll and Nitrogen Status Using the Airborne Imaging Spectrometer AVIS

Airborne hyperspectral remote sensing enables not only spatial monitoring of vegetation cover, but also the derivation of individual plant constituents such as chlorophyll and nitrogen content. These are important parameters for optimised agricultural management on a field basis through the possibility of spatially differentiated fertilisation and for hydrological and vegetation yield modelling. The use of existing airborne imaging spectrometers is cost-intensive. Moreover, it is difficult to obtain these sensors for multitemporal applications. The imaging spectrometer AVIS (Airborne Visible/Near Infrared Imaging Spectrometer) was built at the Chair of Geography and Geographical Remote Sensing of the Ludwig Maximilians University Munich, Germany, to overcome these difficulties. AVIS is designed as a cost-effective tool for environmental monitoring using commonly available components. AVIS enables the deployment of a hyperspectral sensor for both scientific research and educational purposes. It is based on a direct sight spectrograph coupled to a standard B/W CCD camera. The signal received by the CCD is read out and sent via a frame grabber card to a personal computer, where the data is stored on the hard disc together with additional GPS data. The radiometric, spectral and geometric properties of AVIS resulting from the calibration procedure are summarised in Table 7-1. Table 7-1: AVIS characteristics Parameter Description Spectral range 553-999nm Spectral resolution 6nm Spectral sampling rate / resampling 2nm / 6nm Number of used bands 74 SNR 45dB (year 1999), 47dB (year 2000) Spatial resolution 300 pixels per image line Spatial sampling rate 390 pixels per image line FOV 1.19rad IFOV across track 3.1mrad IFOV along track 2.98mrad One aim of this thesis was to test the potential of AVIS for the purpose of environmental monitoring, especially of the chlorophyll and nitrogen status of plants. The land cover types under investigation were grassland, maize ( Zea mays L.) and winter wheat ( Triticum aestivum L.). Within this scope, a total of 21 AVIS flights were carried out during the vegetation periods of the years 1999 and 2000. The AVIS data were preprocessed before analysis, including dark current and flat field correction, resampling as well as atmospheric correction and reflectance calibration. The test area chosen for the validation of the AVIS data is located in the northern Bavarian foothills, 25km southwest of Munich, Germany (48° 6’ N, 11° 17’ E). It is situated between the Ammersee in the west and the Starnberger See in the east. The municipalities Gilching and Andechs define the northern and southern borders respectively. Within this area, three water protection areas were chosen as test sites. In these test sites, most of the farmers are under contract to the local agricultural office “ Amt für Landwirtschaft” resulting in detailed management data for each field. This data include useful information for the interpretation of ground and AVIS data. Two weather stations of the Bavarian network of agro-meteorological stations, namely No.72 (Gut Hüll) and No.80 (Rothenfeld), are located in the test area and provide information about precipitation, temperature and radiation. Ten and thirteen stands were selected as test fields in 1999 and 2000 respectively, including three fields each of maize and wheat in 1999 as well as three fields of maize and six fields of wheat in 2000. During both years, four meadows were investigated belonging to the same plant community ( Arrhenatherion elatioris). The meadows differ in the utilisation intensity (non-fertilised meadow with one cut, meadow with one cut, meadow with rotational grazing and meadow with four to five cuts). The ground truth campaigns included weekly measurements of plant parameters, such as height, dry and wet biomass, phenological stage, chlorophyll and nitrogen content, as well as a photographic documentation for each field. The chlorophyll and nitrogen measurements, which were derived from the sampling on ground, are available in contents per area [g/m²] and in contents per mass ([mg/g] for chlorophyll and [%DM] for nitrogen). The former can be used to evaluate the photosynthetic capacity or productivity of a canopy, which is an important input parameter for hydrological or vegetation models; the latter may be an indicator for plant physiological status or level of stress, which is a valuable source of information for optimising field management. The relationship between chlorophyll and nitrogen based on the ground measurements showed that a differentiation of the land cover types is necessary for significant correlation. When the plant species are investigated separately, the chlorophyll and nitrogen content per area are always highly correlated, especially for chlorophyll a and total chlorophyll content (r²≥0.8). For all investigated land cover types, the nitrogen and chlorophyll contents per mass are uncorrelated. For wheat, the results improve when the phenological state and the cultivar are considered (r²>0.67). For maize, distinct variations in the chlorophyll content per mass during the vegetation period reduced correlation with these parameters. The use of a fitted chlorophyll trend curve instead of the original measurements does not lead to a significant improvement of the results. For grassland, no significant correlation above r²=0.67 could be observed except for chlorophyll and nitrogen, both per area, where a decreasing strength of correlation could be monitored with increasing fertilisation level. These results lead to the conclusion that the chlorophyll and nitrogen contents per mass of the investigated land covers are decoupled when the compensation point for effective photosynthesis is exceeded. Beyond this limit the nitrogen in the plants is no longer incorporated into chlorophylls, but mainly into proteins, alkaloids and nucleic acids, whereas the proteins especially are used for internal storage of nitrogen. The derivation of the chlorophyll and nitrogen content of the plant leaves on a mean field basis was conducted using three hyperspectral spectral approaches, namely the hyperspectral NDVI (hNDVI), the Optimised Soil Adjusted Vegetation Index OSAVI as well as the relatively unknown Chlorophyll Absorption Integral CAI. The multispectral NDVITM was simulated as established reference. The results of the derivation of both chlorophyll and nitrogen content of plants with the investigated approaches depend strongly on a priori knowledge about the canopies monitored. In general, the use of contents per area rather than contents per mass has been found more suitable for the investigated remote sensing applications. A significant correlation between any index and the chlorophyll or nitrogen content for the whole sample size could not be derived. The optimal spectral approach for derivation is species-dependent, but also dependent on the cultivar. The chlorophyll and nitrogen level of the plants under observation as well as their temperature sensitivity mainly caused this dependence. The NDVITM, hNDVI and OSAVI became insensitive for high chlorophyll content above about 1g/m² (1.5mg/g) chlorophyll a and 0.2g/m² (0.4mg/g) chlorophyll b, respectively. A saturation of the indices was also found for nitrogen content above 2.5g/m². The saturation limit of nitrogen in percentage of dry matter could be rated at about 4%. The positive correlation between the indices and this parameter for wheat leads to insensitivity at values above this limit, while the negative correlation for maize results in saturation for values below 2.5%. The CAI is not affected by saturation as much as the other spectral approaches, leading to higher coefficients of determination, especially for contents per area. The CAI becomes insensitive at chlorophyll contents per area above 2g/m². The results lead to the assumption, that the flattening and narrowing of the chlorophyll absorption feature at 680nm most probably causes the saturation of the NDVITM, hNDVI and OSAVI. The ratios are directly affected by an increase in reflectance in the red wavelength region. The high correlations between the CAI and contents per area can be ascribed to the fact that the CAI is based on an integrated measurement over an area and therefore is less affected by an increase of reflectance in the red wavelengths. The CAI probably becomes insensitive at the point where the narrowing of the absorption feature leads to a shift of the red edge position towards the blue wavelength region. This saturation limit lies at approximately 2g chlorophyll per m². In contrast, the chlorophyll content per mass, which indicates the plant’s physiological status or level of stress, could be estimated more accurately using spectral indices such as hNDVI and OSAVI, especially for wheat. The low correlations derived for maize are caused by its higher temperature dependence, leading to daily variations in the chlorophyll content per mass. The chlorophyll and nitrogen contents of the grassland canopies could not be derived with the spectral approaches investigated. When the meadows were investigated separately, correlations could only be found between the CAI and the chlorophyll content per area for the most intensely utilised meadow (four to five cuts), which on the one side is characterised by the highest level of fertilisation, but on the other side is affected by the highest nutrient offtake. The low potential of the investigated indices can be mainly assigned to the fact that the chlorophyll and nitrogen values of the meadows mostly exceeded the saturation limits of the applied indices. The possibility of deriving chlorophyll and nitrogen accurately enough to map within field heterogeneities was discussed on the basis of a wheat field, which was analysed separately at three sampling points for chlorophyll and nitrogen content. The approaches found to be most suitable for the parameter estimation of wheat were applied. The CAI was used for the estimation of the chlorophyll content per area and mass as well as for the nitrogen content per area. The hNDVI was applied to estimate the canopy’s nitrogen content per mass. Both approaches were able to reproduce the chlorophyll contents of the different sampling points accurately enough to derive the differences between the measurement points when the saturation limits were not exceeded. Beyond these limits the index values decreased with increasing measurement values. The spatial pattern of the nutrient supply was discussed by comparing nitrogen pattern images, which were derived from CAI measurements acquired in 2000 with the yield measurement map of the same field. The phenological stage of stem elongation (EC 30) turned out to be most suitable for the derivation of the nitrogen pattern. On the one hand, the crop condition at these stages determine yield and on the other hand the nitrogen pattern images were able to map the inner field patterns of nitrogen supply. After anthesis the nitrogen images can map areas with different degrees of maturity. Therefore they can be used for the monitoring of maturity stages for the determination of the most favourable harvest date. As described here, AVIS is still in its early stages. It has the potential to become a costeffectiveAVIS2, which covers the spectral range of 400-900nm, has been in commercial use since 2001. tool for the monitoring of the environment. A modification of AVIS, namelyDie Arbeit mit hyperspektralen Fernerkundungssensoren ermöglicht nicht nur eine flächenhafte Aufnahme der Vegetationsdecke, sondern vor allem auch die Beurteilung des phänologischen und gesundheitlichen Zustandes der Pflanzen. Dies geschieht über die Ableitung einzelner Pflanzeninhaltsstoffe, wie z. B. Chlorophyll und Stickstoff, beides bedeutende Parameter für ein optimales Feldmanagement . Daneben spielen diese Pflanzeninhaltsstoffe eine bedeutende Rolle als Inputparameter für hydrologische und pflanzenkundliche Modelle. Da sich derzeit noch keine operationell arbeitenden, satellitengestützten Spektrometer im Orbit befinden, beschränkt sich die flächenhafte Anwendung von hyperspektralen Fernerkundungssensoren auf den Einsatz flugzeuggetragener Spektrometer. Die Arbeit mit kommerziellen Sensoren, wie AVIRIS, DAIS, HYMAP oder ROSIS, ist aber mit einem hohen finanziellen Aufwand verbunden. Eine für das Vegetationsmonitoring erforderliche multitemporale Anwendung wird sowohl durch die hohen Kosten als auch durch die limitierte Verfügbarkeit dieser Systeme erschwert. Diese Einschränkungen gaben am Institut für Geographie der Ludwig-Maximilians-Universität München den Anlass für die Entwicklung und den Bau eines institutseigenen flugzeuggetragenen abbildenden Spektrometers. Das vorrangige Ziel dabei war ein kostengünstiges System für Forschung und Lehre. Diese Vorgaben führten zur Entwicklung des flugzeuggetragenen abbildenden Spektrometers AVIS (Airborne Visible/near Infrared imaging Spektrometer). Diese Arbeit beschäftigt sich sowohl mit der Kalibrierung als auch dem Einsatz von AVIS im Rahmen eines von der Deutschen Forschungsgemeinschaft DFG geförderten Projektes „Bestimmung des Stickstoffgehaltes von Vegetation – ein Beitrag zur deutschen BAHC Forschung“ (DFG MA 875 6). Die Kalibrierung von AVIS beinhaltet eine Beschreibung des Aufbaus mit den daraus resultierenden radiometrischen, spektralen und geometrischen Eigenschaften des Systems: AVIS ist ein Zeilenscanner, d.h. eine Bildzeile repräsentiert eine Aufnahme. Durch die Bewegung des Sensors über der Erdoberfläche hinweg entsteht durch die Aneinanderreihung mehrerer Aufnahmen ein Bildstreifen. Der Kern von AVIS ist ein direct sight Spektrograph, der zwischen ein Objektiv und eine Standard schwarz-weiß Videokamera montiert ist. Das einfallende Licht wird im Objektiv gebündelt und passiert dann den Spektrographen, wo es entlang einer spektralen Achse in verschiedene Wellenlängen dispergiert wird. Im Fall von AVIS wird für jeden Bildpunkt einer Zeile die spektrale Information in 240 einzelnen Wellenlängen oder Kanälen abgebildet. Die Information wird auf dem CCD der Videokamera als elektrische Ladung registriert und über eine Frame-Grabber-Karte auf der Festplatte eines angeschlossenen PCs gespeichert. Die Daten eines an AVIS gekoppelten GPS-Gerätes, wie z.B. geographische Länge und Breite, Flughöhe über NN und Zeitpunkt der Aufnahme, werden in einem header für jede Bildzeile gespeichert. Die radiometrischen, spektralen und geometrischen Eigenschaften, welche sich aus der Kalibrierung von AVIS ergeben, sind in Tabelle 8-1 zusammengefasst. Tabelle 8-1: AVIS Spezifikationen Parameter Beschreibung Spektralbereich 553-999nm Spektrale Auflösung 6nm Spektrale Abtastrate / Resamplingrate 2nm / 6nm Anzahl verwendeter Kanäle 74 Signal-Rausch-Verhältnis 45dB (Jahr 1999), 47dB (Jahr 2000) Räumliche Auflösung 300 Pixel pro Bildzeile Räumliche Abtastrate 390 Pixel pro Bildzeile FOV 1.19rad IFOV across track 3.1mrad IFOV along track 2.98mrad Der Einsatz von AVIS in der Vegetationsaufnahme, und hier speziell die Bestimmung des Chlorophyll- und Stickstoffgehaltes von Pflanzen, wird anhand drei verschiedener Landnutzungstypen erprobt, nämlich Mais ( Zea mays L.), (Winter-) Weizen ( Triticum aestivum L.) und Grünland. Dabei beschränken sich die Untersuchungen auf die Blätter der Pflanzen. Die Untersuchung der Landnutzungstypen erfolgte während der Vegetationsperioden der Jahre 1999 und 2000 in einem Testgebiet im nördlichen Alpenvorland, 25km südwestlich von München. Das Untersuchungsgebiet erstreckt sich von der Stadt Gilching im Norden bis zur Gemeinde Andechs im Süden. Die westliche bzw. östliche Grenze bilden der Ammersee und der Starnberger See. Innerhalb dieses Untersuchungsgebietes wurden drei Wasserschutzgebiete gewählt, in welchen die Testfelder liegen. Diese Gebiete zeichnen sich dadurch aus, dass die Mehrzahl der Landwirte vertraglich an das örtliche Landwirtschaftsamt gebunden ist. Diese Verträge beinhalten u.a. die genaue Aufzeichnung der Bewirtschaftung im Rahmen der sog. Schlagkartei und stellen damit eine wertvolle Informationsquelle dar. Des weiteren ermöglichen zwei Wetterstationen des Bayerischen agrarmeteorologischen Messnetzes (Nr.72 „Gut Hüll“ und Nr.80 „Rothenfeld“) die Erfassung der meteorologischer Daten innerhalb des Untersuchungsgebietes in einer stündlichen Auflösung. Im Jahr 1999 wurden insgesamt zehn Testfelder untersucht, wobei je drei Felder mit Winterweizen (Sorte Bussard) und Mais (Sorte Narval und Sortenmischung Bristol/Korus) einbezogen waren. Im Jahr 2000 wurden sechs Weizenfelder (Sorten Bussard und Capo) und drei Maisfelder (Sorte Magister) untersucht. Außerdem wurden über beide Jahre hinweg vier Felder mit der Nutzung als permanentes Grünland bearbeitet (einschürig ungedüngt, einschürig gedüngt, vier- bis fünfschürig und Mähweide). Im Laufe der Vegetationsperioden von 1999 und 2000 wurden im Untersuchungsgebiet insgesamt 21 AVIS Überflüge durchgeführt. Dabei wurden die Testgebiete aus einer Höhe von 4000ft bzw. 10000ft über NN erfasst, was bei einer mittleren Geländehöhe von 680m zu einer räumlichen Pixelauflösung von 3 bzw. 10m führt. Vor der quantitativen Auswertung der hyperspektralen Daten mussten die Rohdaten vorprozessiert werden. Dies beinhaltete folgende Korrekturen: a) die Korrektur des Dunkelstromes und den Ausgleich von Inhomogenitäten des CCD’s (Flatfield); b) ein Resampling der ursprünglich 240 Kanäle mit einer Abtastrate von 2nm zu einem 80-kanaligem Datensatz mit einer Abtastrate von 6nm, welche der spektralen Auflösung von AVIS entspricht; c) Atmosphärenkorrektur und Reflexionskalibrierung. Die bodengestützte Geländekampagne beinhaltete wöchentlich durchgeführte Messungen verschiedener Pflanzenparameter wie Höhe des Triebes und der Blätter, feuchte und trockene Biomasse, phänologischer Zustand, Chlorophyll- und Stickstoffgehalt getrennt nach Blatt, Stängel und Frucht. Außerdem wurde jedes Feld zu Dokumentationszwecken wöchentlich fotografiert. Die Chlorophyll- und Stickstoffgehalte, welche von den bodengestützten Messungen abgeleitet wurden, liegen in Gehalten pro Fläche [g/m²] und in Gehalten pro Masse (bei Chlorophyll [mg/g] und bei Stickstoff [% der trockenen Biomasse]). Mit Hilfe des Gehaltes pro Fläche können Aussagen über die photosynthetische Produktivität oder Kapazität eines Bestandes getroffen werden – ein wichtiger Eingabeparameter für hydrologische oder vegetationskundliche Modelle. Gehalte pro Masse dagegen geben Aufschluss über den physiologischen Zustand der Pflanzen sowie über Auswirkungen von Stress oder Krankheiten – wichtige Informationen für ein optimales Feldmanagement durch den Landwirt. Der in den Pflanzen befindliche Stickstoff weist im sichtbaren und nahen infraroten Wellenlängenbereich keine spezifischen Absorptions- oder Reflexionsmuster auf. Aufgrund seines engen Zusammenhanges mit dem Pflanzenchlorophyll (jedes Chlorophyllmolekül enthält vier Stickstoffatome) wird sein Gehalt über die Menge des Chlorophylls abgeleitet. Der erste Teil der Auswertungen beschäftigte sich deshalb mit dem Zusammenhang des Gehaltes an Chlorophyll und Stickstoff in den Blättern. Dabei konnte bei der gemeinsamen Analyse der drei Landnutzungsarten kein signifikanter Zusammenhang zwischen dUntersuchung konnte ein signifikant hoher Zusammenhang (r²≥0.67) zwischen dem Stickstoff und Chlorophyll gefunden werden, wenn beide Parameter in Gehalten pro Fläche vorliegen. Dabei korreliert insbesondere Chlorophyll a stark mit dem Stickstoffgehalt bei den untersuchten Mais-, Weizen- und Grünlandpflanzen (r²≥0.8). Dagegen konnten bei allen drei Landnutzungstypen keine signifikanten Beziehungen zwischen dem Chlorophyll- und Stickstoffgehalt pro Masse nachgewiesen werden. Im Fall von Weizen verbesserten sich die Ergebnisse nach der Trennung in die unterschiedlichen Sorten (r²≥0.67). Eine Unterscheidung der Wachstumsphasen ergab ebenfalls eine Verbesserung der Ergebnisse, wenn die Zeiten vor und nach der Blüte getrennt untersucht wurden (r²≥0.67). Die untersuchten Maissorten sind dagegen durch auffällige Schwankungen im Chlorophyllgehalt pro Masse geprägt. Diese Schwankungen werden von den aktuell herrschenden Temperaturen im Untersuchungsgebiet beeinflusst. Der Mais als ursprünglich tropische Pflanze stellt bei Temperaturen unter 15° das Wachstum ein und reduziert seinen Stoffwechsel erheblich, was Auswirkungen auf den Gehalt an aktivem Chlorophyll in den Pflanzen hat. Bei steigenden Temperaturen erholt sich der Stoffwechsel und die Pflanzen beginnen wieder zu wachsen. Diese Erkältungssymptome ebenso wie die Erholungszeiten sind bei den verschiedenen Maissorten unterschiedlich ausgeprägt. Diese Temperaturabhängigkeit führt im Untersuchungsgebiet, in dem während der Sommermonate des öfteren Temperaturen unter 15°C erreicht werden, zu Variationen im Chlorophyllgehalt pro Masse, welche die Beziehung zum Stickstoff vermindern. Bei der Analyse der Graslandflächen ergab sich, außer bei den oben bereits erwähnten Gehalten pro Fläche, kein signifikanter Zusammenhang zwischen Chlorophyll und Stickstoff. Die Analyse dieser Resultate führen zu dem Schluss, dass die Stickstoff- und Chlorophyllgehalte pro Masse der untersuchten Landnutzungsarten ab einem bestimmten Level, dem Kompensationspunkt, entkoppelt sind. Dieser Kompensationspunkt wird dann erreicht, wenn das in der Luft enthaltene CO2 limitierend auf die Photosyntheserate wirkt. Wird dieses Limit überschritten, hat ein weiterer Aufbau von Chlorophyllmolekülen keine Erhöhung der Photosyntheserate der Pflanze zur Folge. Eventuell vorhandener pflanzenverfügbarer Stickstoff wird somit nicht mehr für den Einbau in Chlorophylle verwendet, sondern vermehrt für die Synthese von Speicherproteinen genutzt. Ein weiterer Schwerpunkt dieser A

Source-tracking cadmium in New Zealand agricultural soils: a stable isotope approach

Cadmium (Cd) is a toxic heavy metal, which is accumulated by plants and animals and therefore enters the human food chain. In New Zealand (NZ), where Cd mainly originates from the application of phosphate fertilisers, stable isotopes can be used to trace the fate of Cd in soils and potentially the wider environment due to the limited number of sources in this setting. Prior to 1997, extraneous Cd added to soils in P fertilisers was essentially limited to a single source, the small pacific island of Nauru. Analysis of Cd isotope ratios (ɛ114/110Cd) in Nauru rock phosphate, pre-1997 superphosphate fertilisers, and Canterbury (Lismore Stony Silt Loam) topsoils (Winchmore Research Farm) has demonstrated their close similarity with respect to ɛ114/110Cd. We report a consistent ɛ114/110Cd signature in fertiliser-derived Cd throughout the latter twentieth century. This finding is useful because it allows the application of mixing models to determine the proportions of fertiliser-derived Cd in the wider environment. We believe this approach has good potential because we also found the ɛ114/110Cd in fertilisers to be distinct from unfertilised Canterbury subsoils. In our analysis of the Winchmore topsoil series (1949-2015), the ɛ114/110Cd remained quite constant following the change from Nauru to other rock phosphate sources in 1997, despite a corresponding shift in fertiliser ɛ114/110Cd at this time. We can conclude that to the present day, the Cd in topsoil at Winchmore still mainly originates from historical phosphate fertilisers. One implication of this finding is that the current applications of P fertiliser are not resulting in further Cd accumulation. We aim to continue our research into Cd fate, mobility and transformations in the NZ environment by applying Cd isotopes in soils and aquatic environments across the country

MULTISPECTRAL AND HYPERSPECTRAL SENSINGFOR NITROGEN MANAGEMENT IN AGRICULTURE

Negli ultimi anni, l\u2019attenzione dell\u2019agricoltura \ue8 stata rivolta alla ricerca di tecniche di coltivazione che permettessero un utilizzo pi\uf9 efficiente degli input ottimizzando cos\uec le rese e diminuendo l\u2019impatto ambientale del sistema produttivo. Grazie alle recenti tecnologie, l\u2019agricoltura di precisione costituisce un\u2019interessante risposta al problema. Essa sfrutta le recenti tecnologie per il monitoraggio della variabilit\ue0 di campo (e tra campi) perch\ue9 confluiscano in un sistema integrato di supporto alle decisioni agronomiche inoltre, traduce le informazioni raccolte attraverso macchinari adatti alla distribuzione sito-specifica degli input agronomici. La fertilizzazione, tra gli altri, rappresenta un importante costo di produzione per l\u2019agricoltore e, se mal gestita, costituisce una fonte di inquinamento ambientale, soprattutto in un territorio come la Lombardia, a rischio di lisciviazione dei nitrati in falda. Lo sviluppo delle recenti tecnologie, sia di monitoraggio della vegetazione che di distribuzione degli input, ha aperto la possibilit\ue0 di studiare la variabilit\ue0 di campo cos\uec da poter essere usata come guida alla distribuzione sito-specifica dei fertilizzanti. Il monitoraggio della vegetazione attraverso sensori ottici tramite telerilevamento, in particolare, ha attratto l\u2019interesse della ricerca perch\ue9 \ue8 il pi\uf9 adatto per le applicazioni in pieno campo. Il progetto di ricerca presentato ha quindi avuto inizio con uno studio approfondito della letteratura, i cui risultati sono stati presentati nel primo capitolo. Lo studio si \ue8 concentrato sull\u2019utilizzo di sensori ottici per la stima di variabili colturali legate allo stato di nutrizione azotata del mais (dose di azoto applicata, concentrazione di clorofilla, concentrazione di azoto nelle piante, LAI (leaf area index), biomassa aerea, azoto assorbito, resa, dose ottimale di fertilizzante). La ricerca si \ue8 concentrata sulle tecniche di telerilevamento con sensori ottici applicati al mais, principale coltura in Lombardia. Sono stati raccolti 91 articoli pubblicati tra il 1992 e il 2016. I risultati sono stati influenzati dallo stadio di sviluppo della coltura, dal target dello strumento, dalle bande spettrali studiate e dagli indici vegetazionali ricavati. Le stime delle variabili colturali indagate sono molto variabili (R2 = 0.6-0.97) e che ogni esperimento ha prodotto regressioni specifiche per posizione geografica, anno, cultivar, e fase di sviluppo. Questo empirismo rappresenta una limitazione all\u2019utilizzo su vasta di scala di algoritmi generici per la stima degli apporti azotati. In conclusione, lo studio della letteratura ha evidenziato la possibilit\ue0 di utilizzare con successo sensori ottici per la stima delle variabili colturali legate alla nutrizione azotata pur evidenziando alcuni limiti, generalizzabili perch\ue9 indipendenti dalla coltura oggetto di studio. Tali limiti possono essere connessi sia alle piattaforme su cui sono montati i sensori, ad esempio: la bassa risoluzione spaziale e temporale delle informazioni ottiche ricavate da satellite e la bassa risoluzione temporale e spettrale dei sensori montati su trattore; sia al sensore in s\ue9. I sensori maggiormente utilizzati sono infatti multispettrali, caratterizzati dalla possibilit\ue0 di acquisire un numero ristretto di larghe bande spettrali. Contemporaneamente, dallo studio della letteratura sono emerse due recenti tecnologie che potrebbero superare i limiti mostrati dalle piattaforme e dai sensori ottici pi\uf9 comuni: il drone (come nuova piattaforma) e i sensori di imaging iperspettrali. Il primo pu\uf2 potenzialmente sorvolare il campo in qualsiasi momento del ciclo colturale ad altezze di volo e velocit\ue0 tali da poter montare sensori ad altissima risoluzione spaziale, mentre i secondi forniscono un\u2019informazione spazializzata ad alta risoluzione spettrale (centinaia di lunghezze d\u2019onda) che permette di studiare pi\uf9 a fondo gli effetti di pi\uf9 stress combinati sulle propriet\ue0 ottiche della coltura. Questo punto \ue8 infatti un fronte di ricerca aperto, dal momento che nei nostri ambienti non \ue8 infrequente che lo stress nutrizionale per carenza di azoto si sovrapponga allo stress idrico. Il capitolo 2 presenta quindi un esperimento in serra per stimare gli stati azotato e idrico di una coltura modello (Spinacia oleracea) attraverso modelli di regressione multivariata partial least squared (PLS) su dato iperspettrale, quando i fattori di crescita azoto e acqua sono limitanti (disegno sperimentale a randomizzazione completa: due livelli idrici x quattro livelli azotati x due repliche). La riflettanza della canopy \ue8 stata acquisita in 121 lunghezze d'onda, tra 339 e 1094 nm, da un sistema di imaging iperspettrale. Lo spettro medio e l\u2019iperspettrogramma, tecnica sviluppata in ambito delle scienze alimentai, sono stati calcolati per ogni vaso e usati come predittori del contenuto idrico e della concentrazione di azoto. Le performance in cross-validazione sono risultate migliori nella stima del contenuto idrico che della concentrazione di azoto, sia da spettro medio che da iperspettrogramma. L\u2019iperspettrogramma ha portato a performance leggermente migliori: R2cv=0.82 e RMSECV=0.86 % pf per la stima del contenuto idrico e R2cv=0.57 e RMSECV=0.19% ps per la stima della concentrazione di azoto. Le migliori performance nella stima del contenuto idrico sono ascrivibili ad una maggior influenza dello stress idrico sia sulla geometria della canopy che sulla sua risposta spettrale. Questo risultato sottolinea come l'effetto combinato di pi\uf9 fattori di stress sulla struttura e sulla riflettanza della canopy debba essere ancora approfondito. Infine l\u2019imaging iperspettrale si \ue8 rivelata una tecnica molto interessante cos\uec come l\u2019estrazione degli iperspettrogrammi, aprendo prospettive per l\u2019uso della tecnica in pieno campo. Dando seguito, infine, all\u2019interesse crescente per le tecniche di rilevamento ottico da drone, i Capitoli 3-4-5 contengono due casi studio in cui il monitoraggio da drone, per la stima della variabilit\ue0 in esperimenti di pieno campo su mais e frumento, \ue8 stato applicato con due camere: una fotocamera commerciale modificata ed una ad uso professionale. Il campo sperimentale di mais \ue8 stato monitorato in due annate (2014-2015). Il monitoraggio \ue8 avvenuto tramite drone con una fotocamera digitale commerciale (Canon\uae Powershot SX260 HS) modificata per acquisire la riflettanza nel visibile (canali blu e verde) e nel vicino infrarosso. I campionamenti sono avvenuti su mais a stadio fenologico V6 e V9 (due momenti utili per l\u2019applicazione della concimazione di copertura). La biomassa aerea, la concentrazione di azoto e l\u2019azoto asportato sono stati determinati analiticamente, mentre gli indici vegetazionali BNDVI, GNDVI delle parcelle e della sola vegetazione sono stati calcolati dall\u2019immagine aerea. L\u2019alta risoluzione della camera ha permesso di stimare anche la copertura vegetale. Il miglior predittore della biomassa aerea \ue8 risultato essere la copertura vegetale stimata da BNDVI: l\u2019equazione di regressione costruita sui due anni di sperimentazione (solo V9) \ue8 risultata avere un R2 = 0.87 e rRMSE del 17%. Il sistema di imaging a basso costo ha portato ad ottime prestazioni nella stima della biomassa grazie all\u2019altissima risoluzione spaziale che compensa la mancanza di un\u2019adeguata risoluzione spettrale, limite emerso da un confronto con la camera multispettrale ad uso professionale presentato nel Capitolo 4. Il campo sperimentale di frumento \ue8 stato monitorato nell\u2019anno 2016 in tre stadi fenologici 25, 31 e 45 BBCH con l'obiettivo di individuare il momento migliore per fare la ricognizione aerea e di classificare il campo in zone omogenee per la gestione dell'azoto (Capitolo 5). Il monitoraggio \ue8 avvenuto tramite fotocamera multispettrale per uso professionale (MicaSense RedEdge\u2122) che acquisisce l\u2019informazione spettrale in cinque canali: blu, verde, rosso, red-edge e vicino infrarosso. Tre indici vegetazionali sono stati calcolati dalle immagini aeree (NDVI, GNDVI e NDRE). L\u2019indice NDRE \ue8 risultato essere il miglior indice per la stima sia della resa in granella (R2 da 0.76 a 0.91) che della biomassa aerea (R2 da 0.37 a 0.9) in tutte le fasi fenologiche. Il momento pi\uf9 adatto per il monitoraggio delle colture \ue8 risultato essere a 31 BBCH, compromesso tra la miglior stima della resa e le necessit\ue0 della coltura in termini di nutrizione azotata. Infine, sulla base dell\u2019errore di stima, sono state identificate tre zone omogenee di cui \ue8 stata stimata la produzione media di biomassa e il suo assorbimento di azoto, mettendo le basi per la creazione di un\u2019accurata mappa di prescrizione per le applicazioni di fertilizzante. Gli esperimenti condotti, hanno confermato l\u2019applicabilit\ue0 dei sensori ottici, sia multispettrali che iperspettrali, per il monitoraggio della vegetazione ai fini della concimazione azotata, quando l\u2019azoto \ue8 il principale fattore limitante. Il drone si \ue8 rivelato uno strumento utile e affidabile per le applicazioni di tali tecniche in pieno campo. \uc8 infine emerso che, a causa della mancata univocit\ue0 della relazione tra le propriet\ue0 ottiche della canopy e le variabili colturali inerenti la nutrizione azotata, il monitoraggio ottico della variabilit\ue0 di campo deve essere visto come parte di un sistema integrato che unisca pi\uf9 informazioni legate alla variabilit\ue0 del suolo, del meteo ecc. in modo da costruire un sistema di supporto alle decisioni che tenga in considerazione la complessit\ue0 della coltura, cos\uec da dare informazioni accurate riguardo alla sola risposta alla concimazione, purificate da elementi di rumore quali le interazioni con altri stress.In the recent years, agriculture increasingly searched for new techniques that would allow a more efficient use of the agronomic inputs in order to optimize yields while decreasing environmental impact. Precision farming can play a key role to fulfil these requirements. Precision farming uses the newest technologies to monitor within-field and between-field crop variability to support agronomic decisions. Moreover, precision agriculture adopts machines for site-specific distribution of agronomic inputs in order to optimize their efficiency. Among agronomic inputs, fertilizers represent a great cost for farmers and can be a source of environmental pollution if not properly managed. This is particularly true in Lombardy, a region characterized by a high risk of nitrate leaching into the groundwater. In this context, vegetation monitoring to support fertilization is very interesting. Researchers, in particular, have focused on the application of remote sensing with optical sensors, because they are considered the most suitable for in-field applications. Thus, this research project began with a literature survey, whose results are presented in Chapter 1. The literature survey focused on the use of optical sensors for the estimation of crop variables related to maize nitrogen status: applied nitrogen rate, chlorophyll concentration, plant nitrogen concentration, LAI (leaf area index), above ground biomass, nitrogen uptake, grain yield, and optimal nitrogen rate. Maize was chosen as the target crop because it is the main crop cultivated in Lombardy. Ninety-one papers, published between 1992 and 2016, were identified. Relevant information describing the performance of various sensors was extracted from the papers. The performances of estimation were highly variable (R2 = 0.60-0.97). Moreover, each experiment produced specific regression equations for location, year, cultivar and development stage. This empiricism is the stronger limitation to the large-scale application of optical sensors for the estimation of nitrogen demands. The literature survey of Chapter 1 highlighted the successful local use of optical sensors to estimate crop variables related to nitrogen nutrition. However, it showed some limitations, irrespective of the studied crop. Limitations are in fact connected to the platforms on which the sensors are mounted i.e., the low spatial and temporal resolution of the optical information obtained by satellite sensors or the low temporal and spectral resolution of the tractor-mounted sensors. Another limitation of multispectral sensors is their ability to acquire only a small number of broad spectral bands. At the same time, the literature highlighted possible solutions to these issues: the use of unmanned aerial vehicles (UAV) and the use of hyperspectral imaging sensors. The former could fly over the field at any time of the growing season carrying sensors characterized by very high spatial resolution, while the latter could provide high spectral resolution (hundreds of wavelengths) images, which would allow to investigate the effects of combined stressors. Indeed, nitrogen stress is often combined with water stress in the Italian environment, but their on optical sensor responses were not often studied in the literature. Chapter 2 reports the results of a greenhouse experiment to estimate nitrogen- and water- related variables of a model crop (Spinacia oleracea L.) using multivariate partial least squared regression models (PLS) on hyperspectral data. A completely randomized experimental design was arranged with two water levels x four nitrogen levels in two replicates. The reflectance of the canopy was acquired in 121 wavelengths, between 339 and 1094 nm, using a hyperspectral imaging system. For each pot, the average spectrum and the modified hyperspectrograms (a technique to compress the raw spectra, originally proposed in food science) were calculated and used as predictors of plant water content and plant nitrogen concentration. The best performances in cross-validation were reached in the estimation of the water content, both from the average spectrum and the hyperspectrograms. The hyperspectrograms led to slightly better performance than the average spectra: R2cv (cross validation) = 0.82 and RMSECV (Root Mean Square Error in Cross Validation) = 0.86% FM for the estimation of the water content and R2cv = 0.57 and RMSECV = 0.19% DM for the estimation of the nitrogen concentration. The better performances in the estimation of the water content (compared to nitrogen concentration) can be attributed to a greater influence of water stress on the geometry of the canopy and on its spectral properties. This result emphasizes that the combined effect of multiple stressors on the structure and the reflectance of the canopy should be further studied. In conclusion, hyperspectral imaging proved to be a very interesting technique as well as hyperspectrograms extraction, opening new opportunities for the in-field applications of this technique. Finally, knowing the great interest of UAV-based remote sensing applications, Chapters 3, 4 and 5 report the results obtained in two case studies in the field. The UAV-based optical monitoring was applied to estimate the in-field variability of maize and winter wheat using two multispectral sensors: a modified commercial camera and a professional one. The experimental maize field (Chapter 3) was monitored during two years (2014-2015) with a commercial digital camera (Canon\uae Powershot SX260 HS), modified to acquire reflectance in two visible channels (blue and green) and one near-infrared channel. Crop samples were taken at V6 and V9 (sixth and ninth unfolded leaves) phenological stages. These stages are adequate to carry out an N diagnosis of the field, because these are the stages when normally top dressing fertilization is carried out. The plant above ground biomass was determined analytically, while the vegetation indices BNDVI and GNDVI of the entire plots (soil + vegetation) and of the vegetation alone were calculated from the optical images. The very high spatial resolution of the digital camera allowed to estimate also the vegetation fraction cover. The best predictor of the above ground biomass was found to be the estimated vegetation fraction cover: the regression equation built on the two years of experimentation (V9 only) gained R2= 0.87 and rRMSE (relative RMSE, i.e. the RMSE expressed as a percentage of the measured average) of 17%. The low cost digital camera led to very good performances in the estimation of the above ground biomass thanks to its high spatial resolution, which compensated the lack of an adequate spectral resolution, as revealed also by the comparison made with the professional camera (presented in Chapter 4). The experimental wheat field (Chapter 5) was monitored in the year 2016 on three phenological stages (25, 31 and 45 BBCH) to identify the best time to make the UAV survey and to classify the field in homogeneous areas for nitrogen management. The camera used was a MicaSense RedEdge\u2122, which measures reflectance in five channels: blue, green, red, red-edge and near-infrared. Three vegetation indices were calculated from the aerial images (NDVI, GNDVI and NDRE). The NDRE index was found to be the best estimator of grain yield (R2= 0.76 to 0.91) and above ground biomass (R2 from 0.37 to 0.90), in all phenological stages. The most suitable time for crop monitoring was found to be 31 BBCH. At this phenological stage, in fact, the crop monitoring guaranteed a satisfactory estimation of wheat above ground biomass which was also found to be closely related to the grain yield. Moreover, three homogeneous zones have been identified, based on the errors in biomass estimation. Finally, the average above ground biomass and nitrogen uptake were calculated for each homogeneous zone, putting the basis for an accurate prescription map for fertilizer applications. All the experiments carried out during this PhD project confirmed the reliability of optical sensors (multispectral and hyperspectral) to monitor vegetation for fertilization purposes when nitrogen is the main limiting factor. The UAV was found to be a useful and reliable tool for in-field applications. Finally, it was also found that, due to the non-univocal relationships between canopy optical properties and nitrogen-related crop variables, optical monitoring of within-field variability should be conceived as part of an integrated system that combines additional information related to the variability of soil and weather. Only in this way it would be possible to build a decision support system able to take into account agroecosystem complexity in order to provide accurate fertilization rate prescriptions