Dynamics of coupled modes in two-section semiconductor lasers with saturable absorption

Semiconductor lasers have the potential to address a number of critical applications in advanced telecommunications and signal processing. These include applications that require pulsed output that can be obtained from self-pulsing and mode-locked states of two-section devices with saturable absorption. Many modern applications place stringent performance requirements on the laser source, and a thorough understanding of the physical mechanisms underlying these pulsed modes of operation is therefore highly desirable. In this thesis, we present experimental measurements and numerical simulations of a variety of self-pulsation phenomena in two-section semiconductor lasers with saturable absorption. Our theoretical and numerical results will be based on rate equations for the field intensities and the carrier densities in the two sections of the device, and we establish typical parameter ranges and assess the level of agreement with experiment that can be expected from our models. For each of the physical examples that we consider, our model parameters are consistent with the physical net gain and absorption of the studied devices. Following our introductory chapter, the first system that we consider is a two-section Fabry-Pérot laser. This example serves to introduce our method for obtaining model parameters from the measured material dispersion, and it also allows us to present a detailed discussion of the bifurcation structure that governs the appearance of selfpulsations in two-section devices. In the following two chapters, we present two distinct examples of experimental measurements from dual-mode two-section devices. In each case we have found that single mode self-pulsations evolve into complex coupled dualmode states following a characteristic series of bifurcations. We present optical and mode resolved power spectra as well as a series of characteristic intensity time traces illustrating this progression for each example. Using the results from our study of a twosection Fabry-Pérot device as a guide, we find physically appropriate model parameters that provide qualitative agreement with our experimental results. We highlight the role played by material dispersion and the underlying single mode self-pulsing orbits in determining the observed dynamics, and we use numerical continuation methods to provide a global picture of the governing bifurcation structure. In our concluding chapter we summarise our work, and we discuss how the presented results can inform the development of optimised mode-locked lasers for performance applications in integrated optics.

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.