Proton translocation coupled to electron transfer reactions in terminal oxidases

Terminal oxidases are the final proteins of the respiratory chain in eukaryotes and some bacteria. They catalyze most of the biological oxygen consumption on Earth done by aerobic organisms. During the catalytic reaction terminal oxidases reduce dioxygen to water and use the energy released in this process to maintain the electrochemical proton gradient by functioning as a redox-driven proton pump. This membrane gradient of protons is extremely important for cells as it is used for many cellular processes, such as transportation of substrates and ATP synthesis. Even though the structures of several terminal oxidases are known, they are not sufficient in themselves to explain the molecular mechanism of proton pumping. In this work we have applied a complex approach using a variety of different techniques to address the properties and the mechanism of proton translocation by the terminal oxidases. The combination of direct measurements of pH changes during catalytic turnover, time-resolved potentiometric electrometry and optical spectroscopy, made it possible to obtain valuable information about various aspects of oxidase functioning. We compared oxygen binding properties of terminal oxidases from the distinct heme-copper (CcO) and cytochrome bd families and found that cytochrome bd has a high affinity for oxygen, which is 3 orders of magnitude higher than that of CcO. Interestingly, the difference between CcO and cytochrome bd is not only in higher affinity of the latter to oxygen, but also in the way that each of these enzymes traps oxygen during catalysis. CcO traps oxygen kinetically - the molecule of bound dioxygen is rapidly reduced before it can dissociate. Alternatively, cytochrome bd employs an alternative mechanism of oxygen trapping - part of the redox energy is invested into tight oxygen binding, and the price paid for this is the lack of proton pumping. A single cycle of oxygen reduction to water is characterized by translocation of four protons across the membrane. Our results make it possible to assign the pumping steps to discrete transitions of the catalytic cycle and indicate that during in vivo turnover of the oxidase these four protons are transferred, one at a time, during the P→F, F→OH, Oh→Eh, and Eh→R transitions. At the same time, each individual proton translocation step in the catalytic cycle is not just a single reaction catalyzed by CcO, but rather a complicated sequence of interdependent electron and proton transfers. We assume that each single proton translocation cycle of CcO is assured by internal proton transfer from the conserved Glu-278 to an as yet unidentified pump site above the hemes. Delivery of a proton to the pump site serves as a driving reaction that forces the proton translocation cycle to continue.

Remote sensing of leaf area index: enhanced retrieval from close-range and remotely sensed optical observations

A wide range of models used in agriculture, ecology, carbon cycling, climate and other related studies require information on the amount of leaf material present in a given environment to correctly represent radiation, heat, momentum, water, and various gas exchanges with the overlying atmosphere or the underlying soil. Leaf area index (LAI) thus often features as a critical land surface variable in parameterisations of global and regional climate models, e.g., radiation uptake, precipitation interception, energy conversion, gas exchange and momentum, as all areas are substantially determined by the vegetation surface. Optical wavelengths of remote sensing are the common electromagnetic regions used for LAI estimations and generally for vegetation studies. The main purpose of this dissertation was to enhance the determination of LAI using close-range remote sensing (hemispherical photography), airborne remote sensing (high resolution colour and colour infrared imagery), and satellite remote sensing (high resolution SPOT 5 HRG imagery) optical observations. The commonly used light extinction models are applied at all levels of optical observations. For the sake of comparative analysis, LAI was further determined using statistical relationships between spectral vegetation index (SVI) and ground based LAI. The study areas of this dissertation focus on two regions, one located in Taita Hills, South-East Kenya characterised by tropical cloud forest and exotic plantations, and the other in Gatineau Park, Southern Quebec, Canada dominated by temperate hardwood forest. The sampling procedure of sky map of gap fraction and size from hemispherical photographs was proven to be one of the most crucial steps in the accurate determination of LAI. LAI and clumping index estimates were significantly affected by the variation of the size of sky segments for given zenith angle ranges. On sloping ground, gap fraction and size distributions present strong upslope/downslope asymmetry of foliage elements, and thus the correction and the sensitivity analysis for both LAI and clumping index computations were demonstrated. Several SVIs can be used for LAI mapping using empirical regression analysis provided that the sensitivities of SVIs at varying ranges of LAI are large enough. Large scale LAI inversion algorithms were demonstrated and were proven to be a considerably efficient alternative approach for LAI mapping. LAI can be estimated nonparametrically from the information contained solely in the remotely sensed dataset given that the upper-end (saturated SVI) value is accurately determined. However, further study is still required to devise a methodology as well as instrumentation to retrieve on-ground green leaf area index . Subsequently, the large scale LAI inversion algorithms presented in this work can be precisely validated. Finally, based on literature review and this dissertation, potential future research prospects and directions were recommended.

Enhanced processing of SPOT multispectral satellite imagery for environmental monitoring and modelling

The Taita Hills in southeastern Kenya form the northernmost part of Africa’s Eastern Arc Mountains, which have been identified by Conservation International as one of the top ten biodiversity hotspots on Earth. As with many areas of the developing world, over recent decades the Taita Hills have experienced significant population growth leading to associated major changes in land use and land cover (LULC), as well as escalating land degradation, particularly soil erosion. Multi-temporal medium resolution multispectral optical satellite data, such as imagery from the SPOT HRV, HRVIR, and HRG sensors, provides a valuable source of information for environmental monitoring and modelling at a landscape level at local and regional scales. However, utilization of multi-temporal SPOT data in quantitative remote sensing studies requires the removal of atmospheric effects and the derivation of surface reflectance factor. Furthermore, for areas of rugged terrain, such as the Taita Hills, topographic correction is necessary to derive comparable reflectance throughout a SPOT scene. Reliable monitoring of LULC change over time and modelling of land degradation and human population distribution and abundance are of crucial importance to sustainable development, natural resource management, biodiversity conservation, and understanding and mitigating climate change and its impacts. The main purpose of this thesis was to develop and validate enhanced processing of SPOT satellite imagery for use in environmental monitoring and modelling at a landscape level, in regions of the developing world with limited ancillary data availability. The Taita Hills formed the application study site, whilst the Helsinki metropolitan region was used as a control site for validation and assessment of the applied atmospheric correction techniques, where multiangular reflectance field measurements were taken and where horizontal visibility meteorological data concurrent with image acquisition were available. The proposed historical empirical line method (HELM) for absolute atmospheric correction was found to be the only applied technique that could derive surface reflectance factor within an RMSE of < 0.02 ps in the SPOT visible and near-infrared bands; an accuracy level identified as a benchmark for successful atmospheric correction. A multi-scale segmentation/object relationship modelling (MSS/ORM) approach was applied to map LULC in the Taita Hills from the multi-temporal SPOT imagery. This object-based procedure was shown to derive significant improvements over a uni-scale maximum-likelihood technique. The derived LULC data was used in combination with low cost GIS geospatial layers describing elevation, rainfall and soil type, to model degradation in the Taita Hills in the form of potential soil loss, utilizing the simple universal soil loss equation (USLE). Furthermore, human population distribution and abundance were modelled with satisfactory results using only SPOT and GIS derived data and non-Gaussian predictive modelling techniques. The SPOT derived LULC data was found to be unnecessary as a predictor because the first and second order image texture measurements had greater power to explain variation in dwelling unit occurrence and abundance. The ability of the procedures to be implemented locally in the developing world using low-cost or freely available data and software was considered. The techniques discussed in this thesis are considered equally applicable to other medium- and high-resolution optical satellite imagery, as well the utilized SPOT data.