Assessment of land use and land cover changes during the last 50 years in oases and surrounding rangelands of Xinjiang, NW China

An analysis of historical Corona images, Landsat images, recent radar and Google Earth® images was conducted to determine land use and land cover changes of oases settlements and surrounding rangelands at the fringe of the Altay Mountains from 1964 to 2008. For the Landsat datasets supervised classification methods were used to test the suitability of the Maximum Likelihood Classifier with subsequent smoothing and the Sequential Maximum A Posteriori Classifier (SMAPC). The results show a trend typical for the steppe and desert regions of northern China. From 1964 to 2008 farmland strongly increased (+ 61%), while the area of grassland and forest in the floodplains decreased (- 43%). The urban areas increased threefold and 400 ha of former agricultural land were abandoned. Farmland apparently affected by soil salinity decreased in size from 1990 (1180 ha) to 2008 (630 ha). The vegetated areas of the surrounding rangelands decreased, mainly as a result of overgrazing and drought events.The SMAPC with subsequent post processing revealed the highest classification accuracy. However, the specific landscape characteristics of mountain oasis systems required labour intensive post processing. Further research is needed to test the use of ancillary information for an automated classification of the examined landscape features.

Changes of traditional farming systems and their effects on land degradation and socio-economic conditions in the Inle Lake region, Myanmar

At many locations in Myanmar, ongoing changes in land use have negative environmental impacts and threaten natural ecosystems at local, regional and national scales. In particular, the watershed area of Inle Lake in eastern Myanmar is strongly affected by the environmental effects of deforestation and soil erosion caused by agricultural intensification and expansion of agricultural land, which are exacerbated by the increasing population pressure and the growing number of tourists. This thesis, therefore, focuses on land use changes in traditional farming systems and their effects on socio-economic and biophysical factors to improve our understanding of sustainable natural resource management of this wetland ecosystem. The main objectives of this research were to: (1) assess the noticeable land transformations in space and time, (2) identify the typical farming systems as well as the divergent livelihood strategies, and finally, (3) estimate soil erosion risk in the different agro-ecological zones surrounding the Inle Lake watershed area. GIS and remote sensing techniques allowed to identify the dynamic land use and land cover changes (LUCC) during the past 40 years based on historical Corona images (1968) and Landsat images (1989, 2000 and 2009). In this study, 12 land cover classes were identified and a supervised classification was used for the Landsat datasets, whereas a visual interpretation approach was conducted for the Corona images. Within the past 40 years, the main landscape transformation processes were deforestation (- 49%), urbanization (+ 203%), agricultural expansion (+ 34%) with a notably increase of floating gardens (+ 390%), land abandonment (+ 167%), and marshlands losses in wetland area (- 83%) and water bodies (- 16%). The main driving forces of LUCC appeared to be high population growth, urbanization and settlements, a lack of sustainable land use and environmental management policies, wide-spread rural poverty, an open market economy and changes in market prices and access. To identify the diverse livelihood strategies in the Inle Lake watershed area and the diversity of income generating activities, household surveys were conducted (total: 301 households) using a stratified random sampling design in three different agro-ecological zones: floating gardens (FG), lowland cultivation (LL) and upland cultivation (UP). A cluster and discriminant analysis revealed that livelihood strategies and socio-economic situations of local communities differed significantly in the different zones. For all three zones, different livelihood strategies were identified which differed mainly in the amount of on-farm and off-farm income, and the level of income diversification. The gross margin for each household from agricultural production in the floating garden, lowland and upland cultivation was US$ 2108, 892 and 619 ha-1 respectively. Among the typical farming systems in these zones, tomato (Lycopersicon esculentum L.) plantation in the floating gardens yielded the highest net benefits, but caused negative environmental impacts given the overuse of inorganic fertilizers and pesticides. The Revised Universal Soil Loss Equation (RUSLE) and spatial analysis within GIS were applied to estimate soil erosion risk in the different agricultural zones and for the main cropping systems of the study region. The results revealed that the average soil losses in year 1989, 2000 and 2009 amounted to 20, 10 and 26 t ha-1, respectively and barren land along the steep slopes had the highest soil erosion risk with 85% of the total soil losses in the study area. Yearly fluctuations were mainly caused by changes in the amount of annual precipitation and the dynamics of LUCC such as deforestation and agriculture extension with inappropriate land use and unsustainable cropping systems. Among the typical cropping systems, upland rainfed rice (Oryza sativa L.) cultivation had the highest rate of soil erosion (20 t ha-1yr-1) followed by sebesten (Cordia dichotoma) and turmeric (Curcuma longa) plantation in the UP zone. This study indicated that the hotspot region of soil erosion risk were upland mountain areas, especially in the western part of the Inle lake. Soil conservation practices are thus urgently needed to control soil erosion and lake sedimentation and to conserve the wetland ecosystem. Most farmers have not yet implemented soil conservation measures to reduce soil erosion impacts such as land degradation, sedimentation and water pollution in Inle Lake, which is partly due to the low economic development and poverty in the region. Key challenges of agriculture in the hilly landscapes can be summarized as follows: fostering the sustainable land use of farming systems for the maintenance of ecosystem services and functions while improving the social and economic well-being of the population, integrated natural resources management policies and increasing the diversification of income opportunities to reduce pressure on forest and natural resources.

Classification of recycled sands and their applications as fine aggregates for concrete and bituminous mixtures

As a result of the drive towards waste-poor world and reserving the non-renewable materials, recycling the construction and demolition materials become very essential. Now reuse of the recycled concrete aggregate more than 4 mm in producing new concrete is allowed but with natural sand a fine aggregate while. While the sand portion that represent about 30\% to 60\% of the crushed demolition materials is disposed off. To perform this research, recycled concrete sand was produced in the laboratory while nine recycled sands produced from construction and demolitions materials and two sands from natural crushed limestone were delivered from three plants. Ten concrete mix designs representing the concrete exposition classes XC1, XC2, XF3 and XF4 according to European standard EN 206 were produced with partial and full replacement of natural sand by the different recycled sands. Bituminous mixtures achieving the requirements of base courses according to Germany standards and both base and binder courses according to Egyptian standards were produced with the recycled sands as a substitution to the natural sands. The mechanical properties and durability of concrete produced with the different recycled sands were investigated and analyzed. Also the volumetric analysis and Marshall test were performed hot bituminous mixtures produced with the recycled sands. According to the effect of replacement the natural sand by the different recycled sands on the concrete compressive strength and durability, the recycled sands were classified into three groups. The maximum allowable recycled sand that can be used in the different concrete exposition class was determined for each group. For the asphalt concrete mixes all the investigated recycled sands can be used in mixes for base and binder courses up to 21\% of the total aggregate mass.

Development of thermo-analytical prediction and classification models for food in thermal devices using a multi sensor system and artificial neural networks

Die thermische Verarbeitung von Lebensmitteln beeinflusst deren Qualität und ernährungsphysiologischen Eigenschaften. Im Haushalt ist die Überwachung der Temperatur innerhalb des Lebensmittels sehr schwierig. Zudem ist das Wissen über optimale Temperatur- und Zeitparameter für die verschiedenen Speisen oft unzureichend. Die optimale Steuerung der thermischen Zubereitung ist maßgeblich abhängig von der Art des Lebensmittels und der äußeren und inneren Temperatureinwirkung während des Garvorgangs. Das Ziel der Arbeiten war die Entwicklung eines automatischen Backofens, der in der Lage ist, die Art des Lebensmittels zu erkennen und die Temperatur im Inneren des Lebensmittels während des Backens zu errechnen. Die für die Temperaturberechnung benötigten Daten wurden mit mehreren Sensoren erfasst. Hierzu kam ein Infrarotthermometer, ein Infrarotabstandssensor, eine Kamera, ein Temperatursensor und ein Lambdasonde innerhalb des Ofens zum Einsatz. Ferner wurden eine Wägezelle, ein Strom- sowie Spannungs-Sensor und ein Temperatursensor außerhalb des Ofens genutzt. Die während der Aufheizphase aufgenommen Datensätze ermöglichten das Training mehrerer künstlicher neuronaler Netze, die die verschiedenen Lebensmittel in die entsprechenden Kategorien einordnen konnten, um so das optimale Backprogram auszuwählen. Zur Abschätzung der thermische Diffusivität der Nahrung, die von der Zusammensetzung (Kohlenhydrate, Fett, Protein, Wasser) abhängt, wurden mehrere künstliche neuronale Netze trainiert. Mit Ausnahme des Fettanteils der Lebensmittel konnten alle Komponenten durch verschiedene KNNs mit einem Maximum von 8 versteckten Neuronen ausreichend genau abgeschätzt werden um auf deren Grundlage die Temperatur im inneren des Lebensmittels zu berechnen. Die durchgeführte Arbeit zeigt, dass mit Hilfe verschiedenster Sensoren zur direkten beziehungsweise indirekten Messung der äußeren Eigenschaften der Lebensmittel sowie KNNs für die Kategorisierung und Abschätzung der Lebensmittelzusammensetzung die automatische Erkennung und Berechnung der inneren Temperatur von verschiedensten Lebensmitteln möglich ist.

Einsatz von rückgekoppelten mehrstufigen selbstorganisierenden Karten in der Windenergie Leistungsprognose

In dieser Arbeit wird ein Verfahren zum Einsatz neuronaler Netzwerke vorgestellt, das auf iterative Weise Klassifikation und Prognoseschritte mit dem Ziel kombiniert, bessere Ergebnisse der Prognose im Vergleich zu einer einmaligen hintereinander Ausführung dieser Schritte zu erreichen. Dieses Verfahren wird am Beispiel der Prognose der Windstromerzeugung abhängig von der Wettersituation erörtert. Eine Verbesserung wird in diesem Rahmen mit einzelnen Ausreißern erreicht. Verschiedene Aspekte werden in drei Kapiteln diskutiert: In Kapitel 1 werden die verwendeten Daten und ihre elektronische Verarbeitung vorgestellt. Die Daten bestehen zum einen aus Windleistungshochrechnungen für die Bundesrepublik Deutschland der Jahre 2011 und 2012, welche als Transparenzanforderung des Erneuerbaren Energiegesetzes durch die Übertragungsnetzbetreiber publiziert werden müssen. Zum anderen werden Wetterprognosen, die der Deutsche Wetterdienst im Rahmen der Grundversorgung kostenlos bereitstellt, verwendet. Kapitel 2 erläutert zwei aus der Literatur bekannte Verfahren - Online- und Batchalgorithmus - zum Training einer selbstorganisierenden Karte. Aus den dargelegten Verfahrenseigenschaften begründet sich die Wahl des Batchverfahrens für die in Kapitel 3 erläuterte Methode. Das in Kapitel 3 vorgestellte Verfahren hat im modellierten operativen Einsatz den gleichen Ablauf, wie eine Klassifikation mit anschließender klassenspezifischer Prognose. Bei dem Training des Verfahrens wird allerdings iterativ vorgegangen, indem im Anschluss an das Training der klassenspezifischen Prognose ermittelt wird, zu welcher Klasse der Klassifikation ein Eingabedatum gehören sollte, um mit den vorliegenden klassenspezifischen Prognosemodellen die höchste Prognosegüte zu erzielen. Die so gewonnene Einteilung der Eingaben kann genutzt werden, um wiederum eine neue Klassifikationsstufe zu trainieren, deren Klassen eine verbesserte klassenspezifisch Prognose ermöglichen.