A Markov Chain Random Field Cosimulation-Based Approach for Land Cover Post-classification and Urban Growth Detection

The recently proposed Markov chain random field (MCRF) approach has great potential to significantly improve land cover classification accuracy when used as a post-classification method by taking advantage of expert-interpreted data and pre-classified image data. This doctoral dissertation explores the effectiveness of the MCRF cosimulation (coMCRF) model in land cover post-classification and further improves it for land cover post-classification and urban growth detection. The intellectual merits of this research include the following aspects: First, by examining the coMCRF method in different conditions, this study provides land cover classification researchers with a solid reference regarding the performance of the coMCRF method for land cover post-classification. Second, this study provides a creative idea to reduce the smoothing effect in land cover post-classification by incorporating spectral similarity into the coMCRF method, which should be also applicable to other geostatistical models. Third, developing an integrated framework by integrating multisource data, spatial statistical models, and morphological operator reasoning for large area urban vertical and horizontal growth detection from medium resolution remotely sensed images enables us to detect and study the footprint of vertical and horizontal urbanization so that we can understand global urbanization from a new angle. Such a new technology can be transformative to urban growth study. The broader impacts of this research are concentrated on several points: The first point is that the coMCRF method and the integrated approach will be turned into open access user-friendly software with a graphical user interface (GUI) and an ArcGIS tool. Researchers and other users will be able to use them to produce high-quality land cover maps or improve the quality of existing land cover maps. The second point is that these research results will lead to a better insight of urban growth in terms of horizontal and vertical dimensions, as well as the spatial and temporal relationships between urban horizontal and vertical growth and changes in socioeconomic variables. The third point is that all products will be archived and shared on the Internet

Spatial epidemiology of Rhodesian sleeping sickness in recently affected areas of central and eastern Uganda

The tsetse transmitted fatal disease of humans, sleeping sickness, is caused by two morphologically identical subspecies of the parasite T. brucei; T. b. rhodesiense and T. b. gambiense. Current distributions of the two forms of disease are not known to overlap in any area, and Uganda is the only country with transmission of both. The distribution of Rhodesian sleeping sickness in Uganda has expanded in recent years, with five districts newly affected since 1998. This movement has narrowed the gap between Rhodesian and Gambian sleeping sickness endemic areas, heightening concerns over a potential future overlap which would greatly complicate the diagnosis and treatment of the two diseases. An improved understanding of the social, environmental and climatic determinants of the distribution of Rhodesian sleeping sickness is required to allow more effective targeting of control measures and to prevent further spread and possible concurrence with Gambian sleeping sickness. The work presented in this thesis investigates the drivers of the distribution and spread of Rhodesian sleeping sickness in districts of central and eastern Uganda which form part of the recent disease focus extension. The spatial distribution of Rhodesian sleeping sickness was examined in Kaberamaido and Dokolo districts where the disease was first reported in 2004, using three different methodologies. A traditional one-step logistic regression analysis of disease prevalence was compared with a two-step hierarchical logistic regression analysis. The two-step method included the analysis of disease occurrence followed by the analysis of disease prevalence in areas with a high predicted probability of occurrence. These two methods were compared in terms of their predictive accuracy. The incorporation of a stochastic spatial effect to model the residual spatial autocorrelation was carried out using a Bayesian geostatistical approach. The geostatistical analysis was compared with the non-spatial models to assess the importance of spatial autocorrelation, to establish which method had the highest predictive accuracy and to establish which factors were the most significant in terms of the disease’s distribution. Links between Rhodesian sleeping sickness and landcover in Soroti district were also assessed using a matched case-control study design. Temporal trends in these relationships were observed using an annually stratified analysis to allow an exploration of the disease’s dispersion following its introduction to a previously unaffected area. This work expands on previous research that demonstrated the source of infection in this area to be the movement of untreated livestock from endemic areas through a local livestock market. With regards to the comparison of regression frameworks, the two-step regression compared favourably with the traditional one-step regression, but the Bayesian geostatistical analysis outperformed both in terms of predictive accuracy. Each of these regression methods highlighted the importance of distance to the closest livestock market on the distribution of Rhodesian sleeping sickness, indicating that the disease may have been introduced to this area via the movement of untreated cattle from endemic areas, despite the introduction of regulations requiring the treatment of livestock prior to sale. In addition, several other environmental and climatic variables were significantly associated with sleeping sickness occurrence and prevalence within the study area. The temporal stratification of the matched case-control analysis highlights the dispersion of sleeping sickness away from the point of introduction (livestock market) into more suitable areas; areas with higher proportions of seasonally flooding grassland, lower proportions of woodland and dense savannah and lower elevations. These findings relate to the habitat preferences of the predominant vector species in the study area; Glossina fuscipes fuscipes, which prefers riverine vegetation. The findings presented highlight the importance of the livestock reservoir as well as the climatic and environmental preferences of the tsetse fly vector for the introduction of Rhodesian sleeping sickness into previously unaffected areas, the subsequent spread of infection following an introduction and the equilibrium spatial distribution of the disease. By enhancing the knowledge base regarding the spatial determinants of the distribution of Rhodesian sleeping sickness within newly affected areas, future control efforts within Uganda may be better targeted to decrease prevalence and to prevent further spread of the disease

Coastal zone landscape classification using remote sensing and model development

Coastal zone landscape characterization and empirical model development were evaluated using multi-spectral airborne imagery. Collectively, four projects are described that address monitoring and classification issues common to the resource management community. Chapter 1 discusses opportunities for remote sensing. Chapter 2 examines spectral and spatial image resolution requirements, as well as training sample selection methods required for accurate landscape classification. Classification accuracy derived from 25nm imagery with 4m pixel sizes outperformed 70nm imagery with 1m pixel sizes. Eight natural and five cultural landscape features were tested for classification accuracy. Chapter 3 investigated the ability to characterize 1m multispectral imagery into rank-ordered categorical biomass index classes of Phragmites australis. Statistical clustering and sample membership was based upon normalized field-measurements. The red imagery channel showed highly significant correlation with field measurements (p = 0.00) and explained much of its variability (r2 = 0.79). Addition of near-infra red, green, and blue image channels in a forward stepwise regression improved the coefficient of determination (r2 = 0.98). In Chapter 4, a landscape cover map was revised by incorporating expert knowledge into a simple spatial model. Examples are provided for a barrier island environment to illustrate this post-classification methodology. A prototype selection of expert rules was sufficient to change more than 20 per cent of the originally classified landscape pixels. Chapter 5 discusses the development of an empirical model that uses vegetation community classes to estimate: (a) soil type, (b) soil compaction rate, and (c) elevation. Vegetation class proved itself a reliable surrogate for estimating these variables based upon field-based statistical measures of association and significance tests. Vegetation was highly associated with four soil types (Cramer\u27s V = 0.98) and soil compaction rates values at depths of 30 and 46cm (Cramer\u27s V \u3e 0.85), and was able to accurately estimate three decimeter-level elevation zones (r2 = 0.86, p = 0.00). A preliminary model to estimate transverse dune crest heights and locations under forest canopy was presented. Lastly, Chapter 6 offers a summary and concluding statements advocating continued use of remote sensing as an application tool for resource management needs

GIS and remote sensing as a potential tool to support digital soil mapping in the Eastern Cape province in South Africa

This study is based on assessing the potential use of GIS and Remote Sensing in trying to fill the various soil maps of selected regions at different scales with spatial soil data. A variety of processes are available for use. These include band ratios, principal component analysis as well as use of a digital elevation model (DEM). With the advent of GIS and Remote Sensing, these principles in the new niche of study are investigated to check if they can be used to augment the current processes available in soil mapping techniques. Such processes as band ratioing, principal component analysis and use of Digital Elevation Models (DEMs) are investigated to check if they can be used in soil mapping techniques. From the results produced it is evident that these processes have the potential to be used in the Digital Soil Mapping process. Despite the limitation of remote sensing to a few centimetres of the topsoil these processes can be used together with the soil mapping techniques currently being used to come up with soil maps

GIS and remote sensing as a potential tool to support digital soil mapping in the Eastern Cape province in South Africa

This study is based on assessing the potential use of GIS and Remote Sensing in trying to fill the various soil maps of selected regions at different scales with spatial soil data. A variety of processes are available for use. These include band ratios, principal component analysis as well as use of a digital elevation model (DEM). With the advent of GIS and Remote Sensing, these principles in the new niche of study are investigated to check if they can be used to augment the current processes available in soil mapping techniques. Such processes as band ratioing, principal component analysis and use of Digital Elevation Models (DEMs) are investigated to check if they can be used in soil mapping techniques. From the results produced it is evident that these processes have the potential to be used in the Digital Soil Mapping process. Despite the limitation of remote sensing to a few centimetres of the topsoil these processes can be used together with the soil mapping techniques currently being used to come up with soil maps

Time series analysis of high resolution remote sensing data to assess degradation of vegetation cover of the island of Socotra (Yemen)

The island of Socotra has long been in geographical isolation, hence nearly 30% of the plant species are believed to be endemic to the island. Until the end of 20th century there was only very little and incomplete information and literature about the vegetation on the island. This isolation broke down in 1990 with the country unification in which then the island received much attention. Subsequently the scientific knowledge of the local flora slowly increased, but many of plant species are now reported to be confined into small populations, hence being particularly vulnerable to habitat loss, overgrazing, as well as urban expansion. 1. The overall objective of this research attempted to assess and examine the trends of vegetation changes since 1972 to 2010 with the use of Landsat MSS, TM and ETM+ images and to investigate the related driving factors, such as rainfall, grazing pressure changes and underlying spatial variability of the landscape. This is to answer the overall question: Is there a trend in biomass, cover and species composition on Socotra Island over the last 40 years? If so, is that trend associated with the rainfall patterns? What are the drivers behind the vegetation change? And then how can we define changes in patterns or changes in this study area? 2. From a methodological point of view, our approach of systematically using remote sensing technology data proved scientifically an applicable tool to improve our understanding of the spatial complexity and heterogeneity of the vegetation cover as well as to provide a conceptual method with specific data for monitoring the changes over this time period. Our data obtained from these different Landsat sensors during the study period were - after many sophisticated processing steps - essentially able to provide time series information for Normalized Difference Vegetation Index (NDVI) data and to assess the long term trend in vegetation cover in the island. 3. Moreover, our approach combining supervised maximum-likelihood and unsupervised classification with the pre- and the post-classification approaches besides the knowledge based classification was table to provide sufficient results to distinguish and to map nine (9) terrestrial vegetation cover classes. The overall accuracy (compared with ground truth data) was about 91%, 77%, 70% and 72% for the images 2005, 1994, 1984 and 1972 respectively. Consecutively, the GIS analysis allowed estimates of highly valuable information as absolute areas and relative coverage of particular vegetation classes over the island with their spatial distribution and also their ecological requirements. Analysis of climatic conditions and NDVI 4. As a results of the complex topography of the study area and the wide climate range, with the guidance of prior knowledge of functional relationships between site parameters, ecosystem and the specific form of biological production, our work resulted in a division of the entire area into six variously sized ecosystem units, which were enough to properly depict the spatial heterogeneity of the rainfall and vegetation and to assist reflecting the influence and reaction between environmental parameters as well as it might have significance both for development of resources and for conservation of environment

Statistical tools to model space-time data with a focus on biodiversity applications

Statistische Modelle sind wichtige Hilfsmittel um Raum-Zeit-Daten wie Satellitenbilder und ökologische Feldmessungen zu analysieren und interpretieren. Dabei verunmöglichen komplexe Datenstrukturen und immer grössere Datenmengen den Gebrauch von herkömmlichen geostatistischen Methoden wie Kriging. Diese Unzulänglichkeit eröffnet das aktive und attraktive Forschungsgebiet der angewandten Raum-Zeit-Statistik für grosse Daten. Die in dieser Arbeit präsentierten Fortschritte auf diesem Gebiet sind hauptsächlich durch ökologische Fragestellungen betreffend die arktische Vegetation und deren Anpassungen an die globale Klimaerwärmung motiviert. Quantitative Aussagen über die arktische Vegetation beruhen hauptsächlich auf zwei fundamental verschiedenen Arten von Messungen: Die eine Art besteht aus Feldmessungen von biologisch relevanten Parametern, die andere stützt sich auf Fernerkundungsdaten und die daraus abgeleiteten Vegetationsindizes. Beide Ansätze führen zu Raum-Zeit-Daten und bringen verschiedene Probleme mit sich, welche gültige Aussagen für die ganze Arktis erschweren. Zum Beispiel gibt es relativ wenige Orte mit Feldmessungen und die Fernerkundungsdaten sind häufig beeinträchtigt durch mit Wolken, Schnee und Wasser bedeckte Landschaften. Diese Doktorarbeit präsentiert eine Reihe von statistischen und rechnerischen Entwicklungen, welche helfen die Aussagen zur Vegetation der Arktis zu präzisieren. Die Arbeit ist in fünf Manuskripte aufgeteilt: Paper I behandelt den 64-bit Ausbau der R Erweiterung spam, welche neu dünnbesetzte Matrizen mit mehr als 2 31 von Null verschiede Einträgen manipulieren kann. Besagter Ausbau ermöglichte grosse fernerkundungsbasierte Vegetationsindex Daten mit einem nicht stationären Gauss-Prozess zu modellieren. Die 64-bit Erweiterung basiert auf der R Erweiterung dotCall64, welche in Paper II detailliert diskutiert wird. Ferner beschreibt Paper III eine neue Methode um fehlende Werte in raum-zeitlichen Fernerkundungsdaten zu berechnen. Dabei berechnet die Methode jeden fehlenden Wert einzeln. Sie sucht eine geeignete Raum-Zeit-Teilmenge der Daten und wendet Sortieralgorithmen für Bilder sowie Quantilsregression an. Um auch sehr grosse Daten mit leistungsstarken Rechnern bearbeiten zu können verfügt die dazugehörige R Erweiterung gapfill über ein modulares Design mit Möglichkeiten zur parallelen Datenverarbeitung. Paper IV behandelt verschiedene Umsetzungs- und Validationsstrategien von bayesschen hierarchischen Modellen für Zähldaten. Wie in der Einleitung dieser Arbeit skizziert sind Fortschritte auf diesem Gebiet vielversprechend um Daten von verschiedenen Quellen, zum Beispiel Daten zum Vorkommen von Pflanzenarten und Vegetationsindex Daten, gemeinsam zu modellieren. Schliesslich stellt Paper V eine Fallstudie vor, welche arktische Feldmessungen der Biodiversität mit einer fernerkundungsbasierten Landschaftscharakterisierung verbindet. Genauer werden die Abhängigkeiten zwischen Biodiversitätsindizes basierend auf Daten des Arctic Vegetation Archive und Landschaftscharakterisierungen mit Vegetationsindex Daten und einem Höhenmodell untersucht. Statistical models are important means to analyze and interpret space-time data, such as satellite datasets and ecological field measurements. However, complex data structures and increasing dataset sizes make it impossible to use standard geostatistical methods like kriging. The resulting methodological gap opens up an active and attractive research area, namely the one of applied spatio-temporal statistics for large datasets. The herein presented advances in that field are mainly motivated by ecological research questions centered around the Arctic vegetation and its response to global warming. Quantitative statements about the Arctic vegetation are typically based on two fundamentally different types of measurements: field measurements of biologically relevant parameters on the one hand and remotely sensed vegetation indices on the other. Both techniques lead to spatio-temporal data and face various challenges, which make it difficult to characterize vegetation at Pan-Arctic scale. For instance, the spatial sparsity of field measurements and the fact that satellite observations are often confounded by cloud, snow, and water covered surfaces are major drawbacks. This PhD thesis presents a series of statistical and computational developments, which help to improve the quality of quantitative statements about the Arctic vegetation. The thesis is structured into five self-contained paper manuscripts: Paper I is concerned with making the sparse matrix algebra R package spam capable of handling large 64-bit matrices with 2 31 and more non-zero elements. This enabled fitting a non-stationary spatial Gaussian process model to a large remote sensing based vegetation index dataset. The 64-bit extension is based on the R package dotCall64, which is discussed in detail in Paper II. Paper III introduces a new spatio-temporal prediction method for missing values in satellite data. The method predicts each missing value separately by selecting a suitable spatio-temporal subset followed by an image sorting procedure and quantile regression. To be able to process massive amounts of data with large computer systems the corresponding R package gapfill features a modular design with an emphasis on parallel computing. Paper IV elaborates on different implementation and validation strategies for spatial Bayesian hierarchical models for count data. As sketched in the introduction of the thesis, advances in that direction are promising to jointly model data from various sources, such as Arctic plant abundance data and remotely sensed vegetation indices. Eventually, Paper V presents a case-study, in which Arctic plot scale biodiversity measurements are related to remote sensing based landscape characterizations. More precisely, relations between biodiversity indices derived from field measurements of the Arctic Vegetation Archive and landscape characterizations based on vegetation index data as well as a digital elevation model are explored