Application of spectral and spatial indices for specific class identification in Airborne Prism EXperiment (APEX) imaging spectrometer data for improved land cover classification

Hyperspectral remote sensing's ability to capture spectral information of targets in very narrow bandwidths gives rise to many intrinsic applications. However, the major limiting disadvantage to its applicability is its dimensionality, known as the Hughes Phenomenon. Traditional classification and image processing approaches fail to process data along many contiguous bands due to inadequate training samples. Another challenge of successful classification is to deal with the real world scenario of mixed pixels i.e. presence of more than one class within a single pixel. An attempt has been made to deal with the problems of dimensionality and mixed pixels, with an objective to improve the accuracy of class identification. In this paper, we discuss the application of indices to cope with the disadvantage of the dimensionality of the Airborne Prism EXperiment (APEX) hyperspectral Open Science Dataset (OSD) and to improve the classification accuracy using the Possibilistic c–Means (PCM) algorithm. This was used for the formulation of spectral and spatial indices to describe the information in the dataset in a lesser dimensionality. This reduced dimensionality is used for classification, attempting to improve the accuracy of determination of specific classes. Spectral indices are compiled from the spectral signatures of the target and spatial indices have been defined using texture analysis over defined neighbourhoods. The classification of 20 classes of varying spatial distributions was considered in order to evaluate the applicability of spectral and spatial indices in the extraction of specific class information. The classification of the dataset was performed in two stages; spectral and a combination of spectral and spatial indices individually as input for the PCM classifier. In addition to the reduction of entropy, while considering a spectral-spatial indices approach, an overall classification accuracy of 80.50% was achieved, against 65% (spectral indices only) and 59.50% (optimally determined principal component

Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging

Subsoil organic carbon (OC) is generally lower in content and more heterogeneous than topsoil OC, rendering it difficult to detect significant differences in subsoil OC storage. We tested the application of laboratory hyperspectral imaging with a variety of machine learning approaches to predict OC distribution in undisturbed soil cores. Using a bias-corrected random forest we were able to reproduce the OC distribution in the soil cores with very good to excellent model goodness-of-fit, enabling us to map the spatial distribution of OC in the soil cores at very high resolution (~53 × 53 µm). Despite a large increase in variance and reduction in OC content with increasing depth, the high resolution of the images enabled statistically powerful analysis in spatial distribution of OC in the soil cores. In contrast to the relatively homogeneous distribution of OC in the plough horizon, the subsoil was characterized by distinct regions of OC enrichment and depletion, including biopores which contained ~2–10 times higher SOC contents than the soil matrix in close proximity. Laboratory hyperspectral imaging enables powerful, fine-scale investigations of the vertical distribution of soil OC as well as hotspots of OC storage in undisturbed samples, overcoming limitations of traditional soil sampling campaigns

Mehitamata õhusõiduki rakendamine põllukultuuride saagikuse ja maa harimisviiside tuvastamisel

A Thesis for applying for the degree of Doctor of Philosophy in Environmental Protection.Väitekiri filosoofiadoktori kraadi taotlemiseks keskkonnakaitse erialal.This thesis aims to examine how machine learning (ML) technologies have aided significant advancements in image analysis in the area of precision agriculture. These multimodal computing technologies extend the use of machine learning to a broader spectrum of data collecting and selection for the advancement of agricultural practices (Nawar et al., 2017) These techniques will assist complicated cropping systems with more informed decisions with less human intervention, and provide a scalable framework for incorporating expert knowledge of the PA system. (Chlingaryan et al., 2018). Complexity, on the other hand, can be seen as a disadvantage in crop trials, as machine learning models require training/testing databases, limited areas with insignificant sampling sizes, time and space-specificity, and environmental factor interventions, all of which complicate parameter selection and make using a single empirical model for an entire region impractical. During the early stages of writing this thesis, we used a relatively traditional machine learning method to address the regression problem of crop yield and biomass prediction [(i.e., random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] to predicted dry matter (DM) yields of red clover. It obtained favourable results, however, the choosing of hyperparameters, the lengthy algorithms selection process, data cleaning, and redundant collinearity issues significantly limited the way of the machine learning application. We will further discuss the recent trend of automated machine learning (AutoML) that has been driving further significant technological innovation in the application of artificial intelligence from its automated algorithm selection and hyperparameter optimization of the deployable pipeline model for unravelling substance problems. However, a present knowledge gap exists in the integration of machine learning (ML) technology with unmanned aerial systems (UAS) and hyperspectral-based imaging data categorization and regression applications. In this thesis, we explored a state-of-the-art (SOTA) and entirely open-source AutoML framework, Auto-sklearn, which was built on one of the most frequently used machine learning systems, Scikit-learn. It was integrated with two unique AutoML visualization tools to examine the recognition and acceptance of multispectral vegetation indices (VI) data collected from UAS and hyperspectral narrow-band VIs across a varied spectrum of agricultural management practices (AMP). These procedures incorporate soil tillage method (STM), cultivation method (CM), and manure application (MA), and are classified as four-crop combination fields (i.e., red clover-grass mixture, spring wheat, pea-oat mixture, and spring barley). Additionally, they have not been thoroughly evaluated and lack characteristics that are accessible in agriculture remote sensing applications. This thesis further explores the existing gaps in the knowledge base for several critical crop categories and cultivation management methods referring to biomass and yield analysis, as well as to gain a better understanding of the potential for remotely sensed solutions to field-based and multifunctional platforms to meet precision agriculture demands. To overcome these knowledge gaps, this research introduces a rapid, non-destructive, and low-cost framework for field-based biomass and grain yield modelling, as well as the identification of agricultural management practices. The results may aid agronomists and farmers in establishing more accurate agricultural methods and in monitoring environmental conditions more effectively.Doktoritöö eesmärk oli uurida, kuidas masinõppe (MÕ) tehnoloogiad võimaldavad edusamme täppispõllumajanduse valdkonna pildianalüüsis. Multimodaalsed arvutustehnoloogiad laiendavad masinõppe kasutamist põllumajanduses andmete kogumisel ja valimisel (Nawar et al., 2017). Selline täpsemal informatsioonil põhinev tehnoloogia võimaldab keerukate viljelussüsteemide puhul teha otsuseid inimese vähema sekkumisega, ja loob skaleeritava raamistiku täppispõllumajanduse jaoks (Chlingaryan et al., 2018). Põllukultuuride katsete korral on komplekssete masinõppemudelite kasutamine keerukas, sest alad on piiratud ning valimi suurus ei ole piisav; vaja on testandmebaase, kindlaid aja- ja ruumitingimusi ning keskkonnategureid. See komplitseerib parameetrite valikut ning muudab ebapraktiliseks ühe empiirilise mudeli kasutamise terves piirkonnas. Siinse uurimuse algetapis rakendati suhteliselt traditsioonilist masinõppemeetodit, et lahendada saagikuse ja biomassi prognoosimise regressiooniprobleem (otsustusmetsa regression, tugivektori regressioon ja tehisnärvivõrk) punase ristiku prognoositava kuivaine saagikuse suhtes. Saadi sobivaid tulemusi, kuid hüperparameetrite valimine, pikk algoritmide valimisprotsess, andmete puhastamine ja kollineaarsusprobleemid takistasid masinõpet oluliselt. Automatiseeritud masinõppe (AMÕ) uusimate suundumustena rakendatakse tehisintellekti, et lahendada põhiprobleemid automatiseeritud algoritmi valiku ja rakendatava pipeline-mudeli hüperparameetrite optimeerimise abil. Seni napib teadmisi MÕ tehnoloogia integreerimiseks mehitamata õhusõidukite ning hüperspektripõhiste pildiandmete kategoriseerimise ja regressioonirakendustega. Väitekirjas uuriti nüüdisaegset ja avatud lähtekoodiga AMÕ tehnoloogiat Auto-sklearn, mis on ühe enimkasutatava masinõppesüsteemi Scikit-learn edasiarendus. Süsteemiga liideti kaks unikaalset AMÕ visualiseerimisrakendust, et uurida mehitamata õhusõidukiga kogutud andmete multispektraalsete taimkatteindeksite ja hüperspektraalsete kitsaribaandmete taimkatteindeksite tuvastamist ja rakendamist põllumajanduses. Neid võtteid kasutatakse mullaharimisel, kultiveerimisel ja sõnnikuga väetamisel nelja kultuuriga põldudel (punase ristiku rohusegu, suvinisu, herne-kaera segu, suvioder). Neid ei ole põhjalikult hinnatud, samuti ei hõlma need omadusi, mida kasutatatakse põllumajanduses kaugseire rakendustes. Uurimus käsitleb biomassi ja saagikuse seni uurimata analüüsivõimalusi oluliste põllukultuuride ja viljelusmeetodite näitel. Hinnatakse ka kaugseirelahenduste potentsiaali põllupõhiste ja multifunktsionaalsete platvormide kasutamisel täppispõllumajanduses. Uurimus tutvustab kiiret, keskkonna suhtes kahjutut ja mõõduka hinnaga tehnoloogiat põllupõhise biomassi ja teraviljasaagi modelleerimiseks, et leida sobiv viljelusviis. Töö tulemused võimaldavad põllumajandustootjatel ja agronoomidel tõhusamalt valida põllundustehnoloogiaid ning arvestada täpsemalt keskkonnatingimustega.Publication of this thesis is supported by the Estonian University of Life Scieces and by the Doctoral School of Earth Sciences and Ecology created under the auspices of the European Social Fund

REMOTE SENSING OF FOLIAR NITROGEN IN CULTIVATED GRASSLANDS OF HUMAN DOMINATED LANDSCAPES

Foliar nitrogen (N) concentration of plant canopies plays a central role in a number of important ecosystem processes and continues to be an active subject in the field of remote sensing. Previous efforts to estimate foliar N at the landscape scale have primarily focused on intact forests and grasslands using aircraft imaging spectrometry and various techniques of statistical calibration and modeling. The present study was designed to extend this work by examining the potential to estimate the foliar N concentration of residential, agricultural and other cultivated grassland areas within a suburbanizing watershed. In conjunction with ground-based vegetation sampling, we developed Partial Least Squares (PLS) models for predicting mass-based foliar N across management types using input from airborne and field based imaging spectrometers. Results yielded strong predictive relationships for both ground- and aircraft-based sensors across sites that included turf grass, grazed pasture, hayfields and fallow fields. We also report on relationships between imaging spectrometer data and other important variables such as canopy height, biomass, and water content, results from which show strong promise for detection with high quality imaging spectrometry data and suggest that cultivated grassland offer opportunity for empirical study of canopy light dynamics. Finally, we discuss the potential for application of our results, and potential challenges, with data from the planned HyspIRI satellite, which will provide global coverage of data useful for vegetation N estimation

Airborne Visible/Infrared Imaging spectrometer AVIS: Design, characterization and calibration

The Airborne Visible/Infrared imaging Spectrometer AVIS is a hyperspectral imager designed for environmental monitoring purposes. The sensor, which was constructed entirely from commercially available components, has been successfully deployed during several experiments between 1999 and 2007. We describe the instrument design and present the results of laboratory characterization and calibration of the system's second generation, AVIS-2, which is currently being operated. The processing of the data is described and examples of remote sensing reflectance data are presented

Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation

The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years

Soil moisture, organic carbon, and nitrogen content prediction with hyperspectral data using regression models

Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques

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

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings