Informationsgehalt von ERS-1/-2 SAR-Daten zur Erfassung der Arteninventare und des Zustandes landwirtschaftlich genutzter Böden und Vegetation

Auf einer drei Anbauperioden umfassenden Ground Truth Datenbasis wird der Informationsgehalt multitemporaler ERS-1/-2 Synthetic Aperture Radar (SAR) Daten zur Erfassung der Arteninventare und des Zustandes landwirtschaftlich genutzter Böden und Vegetation in Agrarregionen Bayerns evaluiert.Dazu wird ein für Radardaten angepaßtes, multitemporales, auf landwirtschaftlichen Schlägen beruhendes Klassifizierungsverfahren ausgearbeitet, das auf bildstatistischen Parametern der ERS-Zeitreihen beruht. Als überwachte Klassifizierungsverfahren wird vergleichend der Maximum-Likelihood-Klassifikator und ein Neuronales-Backpropagation-Netz eingesetzt. Die auf Radarbildkanälen beruhenden Gesamtgenauigkeiten variieren zwischen 75 und 85%. Darüber hinaus wird gezeigt, daß die interferometrische Kohärenz und die Kombination mit Bildkanälen optischer Sensoren (Landsat-TM, SPOT-PAN und IRS-1C-PAN) zur Verbesserung der Klassifizierung beitragen. Gleichermaßen können die Klassifizierungsergebnisse durch eine vorgeschaltete Grobsegmentierung des Untersuchungsgebietes in naturräumlich homogene Raumeinheiten verbessert werden. Über die Landnutzungsklassifizierung hinaus, werden weitere bio- und bodenphysikalische Parameter aus den SAR-Daten anhand von Regressionsmodellen abgeleitet. Im Mittelpunkt stehen die Paramter oberflächennahen Bodenfeuchte vegetationsfreier/-armer Flächen sowie die Biomasse landwirtschaftlicher Kulturen. Die Ergebnisse zeigen, daß mit ERS-1/-2 SAR-Daten eine Messung der Bodenfeuchte möglich ist, wenn Informationen zur Bodenrauhigkeit vorliegen. Hinsichtlich der biophysikalischen Parameter sind signifikante Zusammenhänge zwischen der Frisch- bzw. Trockenmasse des Vegetationsbestandes verschiedener Getreide und dem Radarsignal nachweisbar. Die Biomasse-Informationen können zur Korrektur von Wachstumsmodellen genutzt werden und dazu beitragen, die Genauigkeit von Ertragsschätzungen zu steigern.

Emissions of volatile organic compounds from tropical savanna vegetation and biomass burning

This dissertation focuses on characterizing the emissions of volatile organic compounds (VOCs) from grasses and young trees, and the burning of biomass mainly from Africa and Indonesia. The measurements were performed with a proton-transfer-reaction mass spectrometer (PTR-MS). The biogenic emissions of tropical savanna vegetation were studied in Calabozo (Venezuela). Two field campaigns were carried out, the first during the wet season (1999) and the second during the dry season (2000). Three grass species were studied: T. plumosus, H. rufa and A. canescens, and the tree species B. crassifolia, C. americana and C. vitifolium. The emission rates were determined with a dynamic plant enclosure system. In general, the emissions increased exponentially with increasing temperature and solar radiation. Therefore, the emission rates showed high variability. Consequently, the data were normalized to a standard temperature of 30°C, and standard emission rates thus determined allowed for interspecific and seasonal comparisons. The range of average daytime (10:00-16:00) emission rates of total VOCs measured from green (mature and young) grasses was between 510-960 ngC/g/h. Methanol was the primary emission (140-360 ngC/g/h), followed by acetaldehyde, butene and butanol and acetone with emission rates between 70-200 ngC/g/h. The emissions of propene and methyl ethyl ketone (MEK) were <80 ngC/g/h, and those of isoprene and C5-alcohols were between 10-130 ngC/g/h. The oxygenated species represented 70-75% of the total. The emission of VOCs was found to vary by up to a factor of three between plants of the same species, and by up to a factor of two between the different species. The annual source of methanol from savanna grasses worldwide estimated in this work was 3 to 4.4 TgC, which could represent up to 12% of the current estimated global emission from terrestrial vegetation. Two of the studied tree species, were isoprene emitters, and isoprene was also their primary emission (which accounted for 70-94% of the total carbon emitted) followed by methanol and butene + butanol. The daytime average emission rate of isoprene measured in the wet season was 27 mgC/g/h for B. crassifolia, and 123 mgC/g/h for C. vitifolium. The daytime emissions of methanol and butene + butanol were between 0.3 and 2 mgC/g/h. The total sum of VOCs emission measured during the day in the wet season was between 30 and 130 mgC/g/h. In the dry season, in contrast, the methanol emissions from C. vitifolium saplings –whose leaves were still developing– were an order of magnitude higher than in the wet season (15 mgC/g/h). The isoprene emission from B. crassifolia in the dry season was comparable to the emission in the wet season, whereas isoprene emission from C. vitifolium was about a factor of three lower (~43 mgC/g/h). Biogenic emission inventories show that isoprenoids are the most prominent and best-studied compounds. The standard emission rates of isoprene and monoterpenes of the measured savanna trees were in the lower end of the range found in the literature. The emission of other biogenic VOCs has been sparsely investigated, but in general, the standard emissions from trees studied here were within the range observed in previous investigations. The biomass burning study comprised the measurement of VOCs and other trace-gas emissions of 44 fires from 15 different fuel types, primarily from Africa and Indonesia, in a combustion laboratory. The average sum of emissions (excluding CO2, CO and NO) from African fuels was ~18 g(VOC)/kg. Six of the ten most important emissions were oxygenated VOCs. Acetic acid was the major emission, followed by methanol and formaldehyde. The emission of methane was of the same order as the methanol emission (~5 g/kg), and that of nitrogen-containing compounds was ~1 g/kg. An estimate of the VOC source from biomass burning of savannas and grasslands worldwide suggests that the sum of emissions is about 56 Tg/yr, of which 34 Tg correspond to oxygenated VOCs, 14 Tg to unsaturated and aromatic compounds, 5 Tg to methane and 3 Tg to N-compounds. The estimated emissions of CO, CO2 and NO are 216, 5117 and 9.4 Tg/yr, respectively. The emission factors reported here for Indonesian fuels are the first results of laboratory fires using Indonesian fuels. Acetic acid was the highest organic emission, followed by acetol, a compound not previously reported in smoke, methane, mass 97 (tentatively identified as furfural, dimethylfuran and ethylfuran), and methanol. The sum of total emissions of Indonesian fuels was 91 g/kg, which is 5 times higher than the emissions from African fuels. The results of this study reinforces the importance of oxygenated compounds. Due to the vast area covered by tropical savannas worldwide, the biogenic and biomass burning emission of methanol and other oxygenated compounds may be important for the regional and even global tropospheric chemistry.

Flash floods and their effects on the development in El-Qaá plain area in South Sinai, Egypt

The arid regions are dominated to a much larger degree than humid regions by major catastrophic events. Although most of Egypt lies within the great hot desert belt; it experiences especially in the north some torrential rainfall, which causes flash floods all over Sinai Peninsula. Flash floods in hot deserts are characterized by high velocity and low duration with a sharp discharge peak. Large sediment loads may be carried by floods threatening fields and settlements in the wadis and even people who are living there. The extreme spottiness of rare heavy rainfall, well known to desert people everywhere, precludes any efficient forecasting. Thus, although the limitation of data still reflects pre-satellite methods, chances of developing a warning system for floods in the desert seem remote. The relatively short flood-to-peak interval, a characteristic of desert floods, presents an additional impediment to the efficient use of warning systems. The present thesis contains introduction and five chapters, chapter one points out the physical settings of the study area. There are the geological settings such as outcrop lithology of the study area and the deposits. The alluvial deposits of Wadi Moreikh had been analyzed using OSL dating to know deposits and palaeoclimatic conditions. The chapter points out as well the stratigraphy and the structure geology containing main faults and folds. In addition, it manifests the pesent climate conditions such as temperature, humidity, wind and evaporation. Besides, it presents type of soils and natural vegetation cover of the study area using unsupervised classification for ETM+ images. Chapter two points out the morphometric analysis of the main basins and their drainage network in the study area. It is divided into three parts: The first part manifests the morphometric analysis of the drainage networks which had been extracted from two main sources, topographic maps and DEM images. Basins and drainage networks are considered as major influencing factors on the flash floods; Most of elements were studied which affect the network such as stream order, bifurcation ratio, stream lengths, stream frequency, drainage density, and drainage patterns. The second part of this chapter shows the morphometric analysis of basins such as area, dimensions, shape and surface. Whereas, the third part points the morphometric analysis of alluvial fans which form most of El-Qaá plain. Chapter three manifests the surface runoff through rainfall and losses analysis. The main subject in this chapter is rainfall which has been studied in detail; it is the main reason for runoff. Therefore, all rainfall characteristics are regarded here such as rainfall types, distribution, rainfall intensity, duration, frequency, and the relationship between rainfall and runoff. While the second part of this chapter concerns with water losses estimation by evaporation and infiltration which are together the main losses with direct effect on the high of runoff. Finally, chapter three points out the factors influencing desert runoff and runoff generation mechanism. Chapter four is concerned with assessment of flood hazard, it is important to estimate runoff and tocreate a map of affected areas. Therefore, the chapter consists of four main parts; first part manifests the runoff estimation, the different methods to estimate runoff and its variables such as runoff coefficient lag time, time of concentration, runoff volume, and frequency analysis of flash flood. While the second part points out the extreme event analysis. The third part shows the map of affected areas for every basin and the flash floods degrees. In this point, it has been depending on the DEM to extract the drainage networks and to determine the main streams which are normally more dangerous than others. Finally, part four presets the risk zone map of total study area which is of high inerest for planning activities. Chapter five as the last chapter concerns with flash flood Hazard mitigation. It consists of three main parts. First flood prediction and the method which can be used to predict and forecast the flood. The second part aims to determine the best methods which can be helpful to mitigate flood hazard in the arid zone and especially the study area. Whereas, the third part points out the development perspective for the study area indicating the suitable places in El-Qaá plain for using in economic activities.

Lernen mit einer Hybride aus Lernendem Klassifizierendem System und Selbstorganisierender Karte

Im Forschungsgebiet der Künstlichen Intelligenz, insbesondere im Bereich des maschinellen Lernens, hat sich eine ganze Reihe von Verfahren etabliert, die von biologischen Vorbildern inspiriert sind. Die prominentesten Vertreter derartiger Verfahren sind zum einen Evolutionäre Algorithmen, zum anderen Künstliche Neuronale Netze. Die vorliegende Arbeit befasst sich mit der Entwicklung eines Systems zum maschinellen Lernen, das Charakteristika beider Paradigmen in sich vereint: Das Hybride Lernende Klassifizierende System (HCS) wird basierend auf dem reellwertig kodierten eXtended Learning Classifier System (XCS), das als Lernmechanismus einen Genetischen Algorithmus enthält, und dem Wachsenden Neuralen Gas (GNG) entwickelt. Wie das XCS evolviert auch das HCS mit Hilfe eines Genetischen Algorithmus eine Population von Klassifizierern - das sind Regeln der Form [WENN Bedingung DANN Aktion], wobei die Bedingung angibt, in welchem Bereich des Zustandsraumes eines Lernproblems ein Klassifizierer anwendbar ist. Beim XCS spezifiziert die Bedingung in der Regel einen achsenparallelen Hyperquader, was oftmals keine angemessene Unterteilung des Zustandsraumes erlaubt. Beim HCS hingegen werden die Bedingungen der Klassifizierer durch Gewichtsvektoren beschrieben, wie die Neuronen des GNG sie besitzen. Jeder Klassifizierer ist anwendbar in seiner Zelle der durch die Population des HCS induzierten Voronoizerlegung des Zustandsraumes, dieser kann also flexibler unterteilt werden als beim XCS. Die Verwendung von Gewichtsvektoren ermöglicht ferner, einen vom Neuronenadaptationsverfahren des GNG abgeleiteten Mechanismus als zweites Lernverfahren neben dem Genetischen Algorithmus einzusetzen. Während das Lernen beim XCS rein evolutionär erfolgt, also nur durch Erzeugen neuer Klassifizierer, ermöglicht dies dem HCS, bereits vorhandene Klassifizierer anzupassen und zu verbessern. Zur Evaluation des HCS werden mit diesem verschiedene Lern-Experimente durchgeführt. Die Leistungsfähigkeit des Ansatzes wird in einer Reihe von Lernproblemen aus den Bereichen der Klassifikation, der Funktionsapproximation und des Lernens von Aktionen in einer interaktiven Lernumgebung unter Beweis gestellt.