Mapping intertidal vegetation in the wash estuary using remote sensing techniques

The mapping and monitoring of the intertidal zone of the East Coast of England is of considerable interest to conservationists and coastal managers. Intertidal vegetation offers natural protection against coastal erosion and considerably reduces the cost of man made sea defences. Monitoring of intertidal vegetation may also be of value in providing an early warning of sea-level change. This thesis considers the most effective way of classifying and monitoring the intertidal zone using remote sensing techniques and incorporating the results into a coastal monitoring Geographic Information System (GIS). The coastal monitoring GIS is used to model the advantages and disadvantages of different classification strategies. The Wash Estuary forms the principal study area. This study uses multi-temporal data which requires atmospheric and radiometric correction. All Landsat 5 TM images used in the present study were referenced to a common image, based on the techniques of F.G.Hall of NASA, to allow meaningful comparisons to be made. The data sets were geometrically corrected to allow incorporation into the coastal monitoring GIS. Two strategies were used to classify the intertidal zone: a conventional maximum likelihood classifier and a fully constrained mixture model using the least squares technique. The results of the classifications were incorporated into the coastal monitoring GIS along with information acquired from previous ground based surveys. Statistical analysis of the classifications was carried out by cross tabulation with the ground based surveys in order to determine the accuracy of the methods. Detailed, reliable information arrived at cheaply, objectively and at regular intervals would provide a valuable resource for the management and monitoring of the coastal environment

A Combined Noise Reduction and Partial Volume Estimation Method for Image Quantitation

Abstract- The partial volume effect is a corrupting artifact that affects nuclear imaging data such as PET and SPECT data, manifest as a blurring action on the resultant image data. This artifact is a result of the image acquisition process, where voxels in the PET or SPECT images are typically composed of a mixture of activity concentrations. This prevents accurate localization and quantitation of the target region activity. A further well-known image artifact found in most types of signal and image data is additive noise which is caused by limited photon count statistics for PET or SPECT imaging data. This work presents a novel methodology for statistically combining image noise reduction and partial volume estimation with particular application to low contrast to noise ratio image data, e.g. image data with poor target localization. Each possible partial volume mixture is modeled as a Gaussian distribution and neighborhood statistical information is also incorporated in the form of the voxel neighborhood intensity mean, which has previously been shown to also be Gaussian distributed. This leads to an analytical solution of the optimal expected mean (thus minimizing the mean square loss), providing an equation that can iteratively and adaptively reduce the noise in each image voxel. Once the noise is reduced a further step that estimates the partial volume mixtures using an adaptive Markov Chain Monte Carlo method is found to improve the partial volume estimates in comparison to existing partial volume estimation techniques without a noise reduction step

Combining global and local information for the segmentation of MR images of the brain

Magnetic resonance imaging can provide high resolution volumetric images of the brain with exceptional soft tissue contrast. These factors allow the complex structure of the brain to be clearly visualised. This has lead to the development of quantitative methods to analyse neuroanatomical structures. In turn, this has promoted the use of computational methods to automate and improve these techniques. This thesis investigates methods to accurately segment MRI images of the brain. The use of global and local image information is considered, where global information includes image intensity distributions, means and variances and local information is based on the relationship between spatially neighbouring voxels. Methods are explored that aim to improve the classification and segmentation of MR images of the brain by combining these elements. Some common artefacts exist in MR brain images that can be seriously detrimental to image analysis methods. Methods to correct for these artifacts are assessed by exploring their effect, first with some well established classification methods and then with methods that combine global information with local information in the form of a Markov random field model. Another characteristic of MR images is the partial volume effect that occurs where signals from different tissues become mixed over the finite volume of a voxel. This effect is demonstrated and quantified using a simulation. Analysis methods that address these issues are tested on simulated and real MR images. They are also applied to study the structure of the temporal lobes in a group of patients with temporal lobe epilepsy. The results emphasise the benefits and limitations of applying these methods to a problem of this nature. The work in this thesis demonstrates the advantages of using global and local information together in the segmentation of MR brain images and proposes a generalised framework that allows this information to be combined in a flexible way

Remote sensing of the environmental impacts of utility-scale solar energy plants

Solar energy has many environmental benefits compared with fossil fuels but solar farming can have environmental impacts especially during construction and development. Thus, in order to enhance environmental sustainability, it is imperative to understand the environmental impacts of utility-scale solar energy (USSE) plants. During recent decades, remote sensing techniques and geographic information systems have become standard techniques in environmental applications. In this study, the environmental impacts of USSE plants are investigated by analyzing changes to land surface characteristics using remote sensing. The surface characteristics studied include land cover, land surface temperature, and hydrological response whereas changes are mapped by comparing pre-, syn-, and post- construction conditions. In order to study the effects of USSE facilities on land cover, the changes in the land cover are measured and analyzed inside and around two USSE facilities. The principal component analysis (PCA), minimum noise fraction (MNF), and spectral mixture analysis (SMA) of remote sensing images are used to estimate the subpixel fraction of four land surface endmembers: high-albedo, low-albedo, shadow, and vegetation. The results revealed that USSE plants do not significantly impact land cover outside the plant boundary. However, land-cover radiative characteristics within the plant area are significantly affected after construction. During the construction phase, site preparation practices including shrub removal and land grading increase high-albedo and decrease low-albedo fractions. The thermal effects of USSE facilities are studied by the time series analysis of remote sensing land surface temperature (LST). A statistical trend analysis of LST, with seasonal trends removed is performed with a particular consideration of panel shadowing by analyzing sun angles for different times of year. The results revealed that the LST outside the boundary of the solar plant does not change, whereas it significantly decreases inside the plant at 10 AM after the construction. The decrease in LST mainly occurred in winters due to lower sun’s altitude, which casts longer shadows on the ground. In order to study the hydrological impacts of PV plants, pre- and post-installation hydrological response over single-axis technology is compared. A theoretical reasoning is developed to explain flows under the influence of PV panels. Moreover, a distributed parametric hydrologic model is used to estimate runoff before and after the construction of PV plants. The results revealed that peak flow, peak flow time, and runoff volume alter after panel installation. After panel installation, peak flow decreases and is observed to shift in time, which depends on orientation. Likewise, runoff volume increases irrespective of panel orientation. The increase in the tilt angle of panel results in decrease in the peak flow, peak flow time, and runoff. This study provides an insight into the environmental impacts of USSE development using remote sensing. The research demonstrates that USSE plants are environmentally sustainable due to minimal impact on land cover and surface temperature in their vicinity. In addition, this research explains the role of rainfall shadowing on hydrological behavior at USSE plants

Procedures for the analysis and use of multiple view angle image data

It is recognised that the majority of vegetative cover types have anisotropic reflectance characteristics that are largely a function of their canopy geometry. Several studies have made attempts at formulating methods for the use of data remotely sensed from off-nadir directions. The best of these methods attempt to utilise the "extra" information implicitly contained in off-nadir image datasets. In this study, an attempt is made to extract information concerning agro-physical parameters of a number of vegetative cover types using imagery acquired by an airborne sensor, the Daedalus Airborne Thematic Mapper (ATM). It is also recognised in the literature that the nature of spatial variance in images is related to the size and distribution of the objects in the scene and the sampling characteristics of the sensor. In previous work this relationship has been explored by examining scenes using images of varying spatial resolutions, using a number of measurements of spatial variance. The underlying trend of these measurements is then used to interpret the nature of the objects in the scene. No previous work exists which attempts to utilise the change in variance of images acquired at different off-nadir view angles. In this study, the understanding of this relationship is developed by examining the change in variance of a number of vegetative cover types from multiple view angle image datasets. The geometry of the ATM sensor is derived to allow an understanding of the sampling characteristics of the instrument. Two important geometric factors are established: first, the area of the ground resolution element increases with view angle, which effectively reduces spatial resolution at off-nadir angles; and second, overlap between adjacent ground resolution elements increases with view angle, increasing the spatial auto-correlation between these samples. The effects of illumination, atmosphere and topography can all influence variance in an image. A parametric procedure for normalising multiple view angle (and therefore multitemporal) datasets for these factors is developed, based upon the production of reflectance images using a sky radiance model of the spectral and spatial distributions of irradiance, ground measurements of irradiance, and a digital terrain model of the study site. Finally, it is shown that image variance is likely to decrease at off-nadir view angles, the magnitude of this decrease being related to the sensor geometry and (more importantly) the geometry of the canopy. By a simple statistical analytical procedure it is possible to construct broad classes within which the nature of the canopy can be classified

Image Restoration from Multiple Sources

This paper proposes a new method of image restoration. The proposed method allows to combine information from several sources, taking the perceived credibility of each into account. It is applicable to both ordinal (e.g., gray level image) and non-ordinal (e.g., classified forest map) categorized images. The accuracy checks have shown the method to be robust with respect to the prior information and the accuracy of the sources. Two application examples are provided

Two complementary approaches in refining the search for liquid water and habitable environments on present-day Mars

All known active life requires liquid water. The correlation between liquid water and the presence of life on Earth has guided the search for life on other planets. For terrestrial-like life to exist in the harsh conditions that dominate the surfaces of other rocky planets, the minimum fundamental requirements of liquid water, nutrients, and chemical energy must be met. Within our solar system, Mars is a strong candidate for hospitable environments able to support life, due to the reservoirs of water within its crust and the strong likelihood of liquid water. The aim of this thesis is to refine the search for liquid water and environments that may be hospitable to life on Mars. Two complementary methodologies are developed and utilised to achieve this aim. Water requires a relatively narrow range of pressures and temperatures to occur in the liquid phase. The first approach of this thesis compares this range with the pressure-temperature conditions that occur within the Earth, the Earth’s active biosphere, and Mars. Temperature, pressure and water activity are examined to determine the extent to which they restrict life from some liquid water environments. The relevant thresholds are then applied to Mars and compared to models of where liquid water environments are likely to occur under present-day martian conditions. Extensive regions of the Earth may be inhospitable despite lying within the hydrosphere. Life is likely restricted from ~ 81% of the volume of the hydrosphere of Earth due to high temperature and/or low water activity. In contrast, the fraction of Mars that can support liquid water is five times larger than that of Earth, given estimates of an average martian brine. Many environments within the martian crust can potentially support life, with perennially habitable conditions extending from approximately 10 to 37 km beneath the surface. The surface and shallow regolith may also be habitable in the warmest regions of the planet. The second approach focuses on the shallow subsurface of Mars within the top ~20 m. The thermal behaviour of surface materials determines the occurrence of transient shallow liquid water and habitable temperatures for life. Ten classes of surface materials are identified from analysis of global martian thermal inertia and albedo, through the technique of algorithmic classification. These classes are interpreted as mixtures of dust, sand, duricrust, rocks and ice on the surface, and validated through comparisons with independent datasets. Low latitude locations of dark sand, duricrust and pebbles in Syrtis Major, Oxia Palus, Mawrth Vallis and eastern Meridiani Planum are identified as having high potential for hospitable liquid water environments at < 10 m depth. Dark, coarse, sand dominated surfaces are found in Syrtis Major and Aram Chaos and are predicted to be locations of low volume flows of liquid water, potentially analogous to the observed martian recurring slope lineae. This thesis identifies where habitable liquid water environments may occur on Mars, strengthening the astrobiological significance of the planet and providing direction for future robotic and satellite missions searching for life

Forest cover and its change in Unguja Island, Zanzibar

Tropical forests are sources of many ecosystem services, but these forests are vanishing rapidly. The situation is severe in Sub-Saharan Africa and especially in Tanzania. The causes of change are multidimensional and strongly interdependent, and only understanding them comprehensively helps to change the ongoing unsustainable trends of forest decline. Ongoing forest changes, their spatiality and connection to humans and environment can be studied with the methods of Land Change Science. The knowledge produced with these methods helps to make arguments about the actors, actions and causes that are behind the forest decline. In this study of Unguja Island in Zanzibar the focus is in the current forest cover and its changes between 1996 and 2009. The cover and changes are measured with often used remote sensing methods of automated land cover classification and post-classification comparison from medium resolution satellite images. Kernel Density Estimation is used to determine the clusters of change, sub-area –analysis provides information about the differences between regions, while distance and regression analyses connect changes to environmental factors. These analyses do not only explain the happened changes, but also allow building quantitative and spatial future scenarios. Similar study has not been made for Unguja and therefore it provides new information, which is beneficial for the whole society. The results show that 572 km2 of Unguja is still forested, but 0,82–1,19% of these forests are disappearing annually. Besides deforestation also vertical degradation and spatial changes are significant problems. Deforestation is most severe in the communal indigenous forests, but also agroforests are decreasing. Spatially deforestation concentrates to the areas close to the coastline, population and Zanzibar Town. Biophysical factors on the other hand do not seem to influence the ongoing deforestation process. If the current trend continues there should be approximately 485 km2 of forests remaining in 2025. Solutions to these deforestation problems should be looked from sustainable land use management, surveying and protection of the forests in risk areas and spatially targeted self-sustainable tree planting schemes.Siirretty Doriast