Retrieval of grassland biophysical parameters using multitemporal optical and radar satellite data

The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.

Uncertainty in wind energy forecasting

Wind energy is the energy source that contributes most to the renewable energy mix of European countries. While there are good wind resources throughout Europe, the intermittency of the wind represents a major problem for the deployment of wind energy into the electricity networks. To ensure grid security a Transmission System Operator needs today for each kilowatt of wind energy either an equal amount of spinning reserve or a forecasting system that can predict the amount of energy that will be produced from wind over a period of 1 to 48 hours. In the range from 5m/s to 15m/s a wind turbine’s production increases with a power of three. For this reason, a Transmission System Operator requires an accuracy for wind speed forecasts of 1m/s in this wind speed range. Forecasting wind energy with a numerical weather prediction model in this context builds the background of this work. The author’s goal was to present a pragmatic solution to this specific problem in the ”real world”. This work therefore has to be seen in a technical context and hence does not provide nor intends to provide a general overview of the benefits and drawbacks of wind energy as a renewable energy source. In the first part of this work the accuracy requirements of the energy sector for wind speed predictions from numerical weather prediction models are described and analysed. A unique set of numerical experiments has been carried out in collaboration with the Danish Meteorological Institute to investigate the forecast quality of an operational numerical weather prediction model for this purpose. The results of this investigation revealed that the accuracy requirements for wind speed and wind power forecasts from today’s numerical weather prediction models can only be met at certain times. This means that the uncertainty of the forecast quality becomes a parameter that is as important as the wind speed and wind power itself. To quantify the uncertainty of a forecast valid for tomorrow requires an ensemble of forecasts. In the second part of this work such an ensemble of forecasts was designed and verified for its ability to quantify the forecast error. This was accomplished by correlating the measured error and the forecasted uncertainty on area integrated wind speed and wind power in Denmark and Ireland. A correlation of 93% was achieved in these areas. This method cannot solve the accuracy requirements of the energy sector. By knowing the uncertainty of the forecasts, the focus can however be put on the accuracy requirements at times when it is possible to accurately predict the weather. Thus, this result presents a major step forward in making wind energy a compatible energy source in the future.

Analysis of institutional arrangements and common pool resources governance: the case of Lake Tana sub-basin, Ethiopia

Although Common Pool Resources (CPRs) make up a significant share of total income for rural households in Ethiopia and elsewhere in developing world, limited access to these resources and environmental degradation threaten local livelihoods. As a result, the issues of management, governance of CPRs and how to prevent their over-exploitation are of great importance for development policy. This study examines the current state and dynamics of CPRs and overall resource governance system of the Lake Tana sub-basin. This research employed the modified form of Institutional Analysis and Development (IAD) framework. The framework integrates the concept of Socio-Ecological Systems (SES) and Interactive Governance (IG) perspectives where social actors, institutions, the politico-economic context, discourses and ecological features across governance and government levels were considered. It has been observed that overexploitation, degradation and encroachment of CPRs have increased dramatically and this threatens the sustainability of Lake Tana ecosystem. The stakeholder analysis result reveals that there are multiple stakeholders with diverse interest in and power over CPRs. The analysis of institutional arrangements reveals that the existing formal rules and regulations governing access to and control over CPRs could not be implemented and were not effective to legally bind and govern CPR user’s behavior at the operational level. The study also shows that a top-down and non-participatory policy formulation, law and decision making process overlooks the local contexts (local knowledge and informal institutions). The outcomes of examining the participation of local resource users, as an alternative to a centralized, command-and-control, and hierarchical approach to resource management and governance, have called for a fundamental shift in CPR use, management and governance to facilitate the participation of stakeholders in decision making. Therefore, establishing a multi-level stakeholder governance system as an institutional structure and process is necessary to sustain stakeholder participation in decision-making regarding CPR use, management and governance.

High-current inductors for high-power automotive DC-DC converters

This thesis is focused on the investigation of magnetic materials for high-power dcdc converters in hybrid and fuel cell vehicles and the development of an optimized high-power inductor for a multi-phase converter. The thesis introduces the power system architectures for hybrid and fuel cell vehicles. The requirements for power electronic converters are established and the dc-dc converter topologies of interest are introduced. A compact and efficient inductor is critical to reduce the overall cost, weight and volume of the dc-dc converter and optimize vehicle driving range and traction power. Firstly, materials suitable for a gapped CC-core inductor are analyzed and investigated. A novel inductor-design algorithm is developed and automated in order to compare and contrast the various magnetic materials over a range of frequencies and ripple ratios. The algorithm is developed for foil-wound inductors with gapped CC-cores in the low (10 kHz) to medium (30 kHz) frequency range and investigates the materials in a natural-convection-cooled environment. The practical effects of frequency, ripple, air-gap fringing, and thermal configuration are investigated next for the iron-based amorphous metal and 6.5 % silicon steel materials. A 2.5 kW converter is built to verify the optimum material selection and thermal configuration over the frequency range and ripple ratios of interest. Inductor size can increase in both of these laminated materials due to increased airgap fringing losses. Distributing the airgap is demonstrated to reduce the inductor losses and size but has practical limitations for iron-based amorphous metal cores. The effects of the manufacturing process are shown to degrade the iron-based amorphous metal multi-cut core loss. The experimental results also suggest that gap loss is not a significant consideration in these experiments. The predicted losses by the equation developed by Reuben Lee and cited by Colonel McLyman are significantly higher than the experimental results suggest. Iron-based amorphous metal has better preformance than 6.5 % silicon steel when a single cut core and natural-convection-cooling are used. Conduction cooling, rather than natural convection, can result in the highest power density inductor. The cooling for these laminated materials is very dependent on the direction of the lamination and the component mounting. Experimental results are produced showing the effects of lamination direction on the cooling path. A significant temperature reduction is demonstrated for conduction cooling versus natural-convection cooling. Iron-based amorphous metal and 6.5% silicon steel are competitive materials when conduction cooled. A novel inductor design algorithm is developed for foil-wound inductors with gapped CC-cores for conduction cooling of core and copper. Again, conduction cooling, rather than natural convection, is shown to reduce the size and weight of the inductor. The weight of the 6.5 % silicon steel inductor is reduced by around a factor of ten compared to natural-convection cooling due to the high thermal conductivity of the material. The conduction cooling algorithm is used to develop high-power custom inductors for use in a high power multi-phase boost converter. Finally, a high power digitally-controlled multi-phase boost converter system is designed and constructed to test the high-power inductors. The performance of the inductors is compared to the predictions used in the design process and very good correlation is achieved. The thesis results have been documented at IEEE APEC, PESC and IAS conferences in 2007 and at the IEEE EPE conference in 2008.

The Hurst exponent as an indicator of the behaviour of a model monopile in an ocean wave testing basin

With the importance of renewable energy well-established worldwide, and targets of such energy quantified in many cases, there exists a considerable interest in the assessment of wind and wave devices. While the individual components of these devices are often relatively well understood and the aspects of energy generation well researched, there seems to be a gap in the understanding of these devices as a whole and especially in the field of their dynamic responses under operational conditions. The mathematical modelling and estimation of their dynamic responses are more evolved but research directed towards testing of these devices still requires significant attention. Model-free indicators of the dynamic responses of these devices are important since it reflects the as-deployed behaviour of the devices when the exposure conditions are scaled reasonably correctly, along with the structural dimensions. This paper demonstrates how the Hurst exponent of the dynamic responses of a monopile exposed to different exposure conditions in an ocean wave basin can be used as a model-free indicator of various responses. The scaled model is exposed to Froude scaled waves and tested under different exposure conditions. The analysis and interpretation is carried out in a model-free and output-only environment, with only some preliminary ideas regarding the input of the system. The analysis indicates how the Hurst exponent can be an interesting descriptor to compare and contrast various scenarios of dynamic response conditions.

CloudNeg: An autonomous multi issue negotiation system, with preference elicitation component, for trading cloud services

Cloud services provide its users with flexible resource provisioning. But in the current market, a user has to choose from a limited set of configurations at a fixed price. This paper presents an autonomous negotiation system termed CloudNeg for negotiating cloud services. CloudNeg provides buyers and sellers of cloud services with autonomous agents to negotiate on the specifications of a cloud instance, including price, on their behalf. These agents elicit their buyers’ time preferences and use them in negotiations. Further, this paper presents two artifacts: a negotiation algorithm and a prototype which together form CloudNeg.