O inventário florestal é uma das principais ferramentas na gestão dos recursos florestais, uma vez que as informações geradas por ele são utilizadas ao longo de toda a cadeia produtiva do setor. Desta forma, erros nas estimativas volumétricas dos inventários florestais devem ser controlados. Inúmeras informações podem ser obtidas a partir de imagens orbitais ou aerotransportadas, uma vez que podem cobrir facilmente toda a área de interesse, e estão comumente disponíveis em empresas florestais ou ao usuário final. A utilização de preditores derivados das imagens pode trazer benefícios para as estimativas do inventário florestal. Desta forma, a aplicação de técnicas de regressão linear múltipla (RLM) ganhou espaço no setor devido a sua facilidade de aplicação. Porém, a RLM não leva em consideração a dependência espacial entre as unidades amostrais, sendo que a geoestatística pode ser utilizada para predizer a distribuição espacial do estoque de madeira (VTCC) para uma dada região. A modelagem geoestatística mais simples como a krigagem ordinária (KO), por considerar apenas a dependência espacial entre os pontos não amostrados, pode apresentar erros de predição nestes locais. Tais erros podem ser reduzidos com a aplicação de técnicas mais robustas como a Krigagem com Deriva Externa (KDE), pois esta agrega as informações obtidas das imagens com a distribuição espacial do volume. Buscando-se avaliar as vantagens da integração do Sensoriamento Remoto (SR) ao inventário florestal foram testados 4 tipos diferentes de imagens; as oriundas dos satélites LANDSAT8, RAPIDEYE e GEOEYE, e as provenientes de aeronaves (Imagens Aerotransportadas). Avaliou-se também diferentes tipos de estimativas para a predição volumétrica sendo estas RLM, KDE e KO. A melhor estimativa serviu de variável auxiliar para o estimador de regressão (ER), sendo os resultados comparados com a abordagem tradicional da amostragem aleatória simples (AAS). Os resultados demonstraram por meio da validação cruzada que as estimativas da KDE foram mais eficientes que as estimativas da KO e da RLM. Os melhores preditores (variáveis auxiliares) foram aqueles derivados do satélite LANDSAT8 e do satélite RAPIDEYE. Obteve-se como produto das estimativas de KDE e RLM mapas capazes de detectar áreas com mortalidade ou anomalias em meio a formação florestal. A utilização de uma estimativa de KDE utilizando imagens LANDSAT8 como medida auxiliar para o ER permitiu reduzir o erro amostral da AAS de 3,87% para 2,34%. Da maneira tradicional, tal redução de erro apenas seria possível com um aumento de mais 99 unidades amostrais. / Forest Inventory (FI) is one of the main tools for managing forest resources, once the information derived from FI is used along the sector production chain. When estimating volume, errors resulting from FI are common, therefore these errors must be controlled. Once orbital or airborne imaging data are easily acquired for an entire area, and are commonly available in forest companies or for the end user, much information can be obtained from these products. The use of predictor derived from images can be of significant benefits to forest inventory estimates. For that reason, the application of linear multiple regression (LMR) techniques have taken place in the forest sector, due to the facilities of its application. However, the LMR technique does not take the spatial dependence among sample units in consideration, the geostatistics utilized to predict the spatial distribution of the wood stock (VTCC) for a specific region. Simpler geostatistical modeling as the ordinary kriging (OK), just takes in consideration the spatial dependence among non-sampled points, because of that, prediction errors can be found. Such errors can be reduced when techniques that are more robust are applied, such as the kriging with external drift (KED) approach. This technique aggregates the information obtained from the images with the spatial distribution of the volume. In order to evaluate the advantages of Remote Sensing and Forest Inventory integration, we considered 4 different types of images, from the satellites LANSAT 8, RAPIDEYE, GEOEYE and from airborne images. When predicting volume, three different approaches were evaluated: LMR, EDK, OK. The best model among those evaluated, served as auxiliary variable for the regression estimator (RE). The result were then compared to the traditional approach, simple random sampling (SRS).This approach showed, through a cross-validation, that the KDE estimates were more efficiently than the OK and the LMR. The best predictor model (auxiliary variables) were derived from LADNSAT 8 and RAPIDEYE satellites. There is a significant advantage to using the KDE and LMR approaches, as it allows for a spatial representation of areas with mortality or anomalies, in a forest environment. The combination of KDE approach and LANDSAT 8 images as an auxiliary method for the RE, abled the decrease of the sampling error of SRS from 3.87% to 2.34%.The traditional approaches to conduct plantation inventories would allow for this error reduction, only if there were an increase of 99 more sampling units.

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Ostlänken är en planerad höghastighetsjärnväg mellan Järna och Linköping med stegvis byggstart från 2017, ett omfattande projekt som inkluderar totalt cirka 20 kilometer tunnel, varav en tunnel planeras att korsa Getåravinen. Undermarksbyggande är komplicerat och kontakt med svaghetszoner i berg innebär ökade risker för anläggning, miljö och omgivande fastigheter. För att åstadkomma en riskbedömning, dimensionering och ekonomisk kalkyl utförs undersökningar och ingenjörsgeologiska prognoser av bergkvalité och geologisk struktur. Studier har visat ett samband mellan naturliga lineament och svaghetszoner i berg. Därför var uppsatsens syfte att bedöma geologiska byggbarhetsfaktorer genom en strukturgeologisk karaktärisering av lineamentet Getåravinen. Arbetet fokuserade på ett avgränsat område inom Getåravinen där Sweco, på uppdrag av Trafikverket, har utfört ingenjörsgeologiska undersökningar inför den planerade anläggningen av Ostlänken.Arbetet utgick från en studie av lineament genom geologiska kartor, kärnborrprov och hällkarteringar kring Getåravinen, dessa analyserades sedan för att identifiera strukturgeologiska samband. Metoderna Inverse Distance Weighted och kriging användes för att prediktera berget i borrkärnornas omgivning utifrån kvalitéklassningarna Rock Mass Rating, Q-systemet och Rock Quality Designation. För att visa resultatet pedagogiskt och förbättra underlaget inför byggteknisk diskussion visualiserades resultatet i en Building Information Model i mjukvaruprogrammet AutoCAD Civil 3D. Resultatet visade cirka 20° avvikelse mellan lineamentet och huvudsprickriktningen i Getåravinen, det bekräftade ett sprickmönster som då generellt kan antas gälla för lineament vilket är användbar information vid undermarksbyggande. Prediktionerna visade en rumslig beskrivning av bergets klassificeringar och samband med lineamentet. Nyttan av prediktiva metoder var därmed god då även effektiv kvantifiering av bergklasserna möjliggjordes vilket kan implementeras vid undermarksbyggande. / The East Link is a planned high-speed rail link between Järna and Linköping with a gradual construction start in 2017, a comprehensive project covering a total of approximately 20 kilometers of tunnel, of which one tunnel is planned to cross the Getå ravine. Subsurface construction is complicated, and contact with weak zones in the rock implies increased risks for the tunnel construction, the environment and the surrounding real estates. In order to accomplish a risk assessment, dimensioning and economic calculation, geological engineering investigations of the rock’s quality and geological structure are carried out. Studies have shown a relationship between natural lineaments and weaknesses in the rock. Therefore, the purpose of this thesis was to assess geological constructability factors in the Getå ravine by using a characterization of structural geology. The focus was within a demarcated area where the consulting firm Sweco, on behalf of the Swedish Transport Administration, had performed engineering geological surveys for the planned construction of the East link.The thesis was based on a study of lineament in geological maps, core drill samples and bedrock mapping in the surroundings of the Getå ravine, which were later analysed to identify structural geological relations. The methods Inverse Distance Weighted and Kriging were used to predict the rock ambient to the core kernels based on the quality ratings Rock Mass Rating, Q System and Rock Quality Designation. In order to visualize the results pedagogically and enhance the basis for technical discussion regarding civil engineering, the results were visualized in a Building Information Model in the software AutoCAD Civil 3D. The result showed approximately 20° deviation between the lineament and the primary joint strike in the Getå ravine, it confirmed a joint pattern that can be generally assumed for natural lineaments which is useful information in subsurface construction. The predictions showed a spatial distribution of the rock's different quality ratings and relationship with the lineament. The utility of the predictive methods was therefore certain because of efficient quantification of the rock quality was made possible, which can be implemented when characterizing the rock into quality domains prior subsurface construction.

In the journey toward optimal spectrum usage, techniques and concepts such as Cognitive Radio and Dynamic Spectrum Access have enjoyed increasing attention in many research projects. Dynamic Spectrum Access introduces the need for real-time RF spectrum information in the form of Radio Environment Maps. This need motivates an investigation into a hybrid approach of sample measurements and spatial interpolation as opposed to using conventional propagation models. Conventional propagation models, both path-general and path-specific, require information of transmitters within the area of interest. Irregular Terrain Models such as the Longley-Rice model, further require topographic information in order to consider the effects of obstacles. The proposed spatial interpolation technique, kriging, requires no information regarding transmitters. Furthermore, Ordinary Kriging requires nothing other than measured samples whereas other kriging variants such as Universal Kriging and Regression Kriging can use additional information such as topographic data to aid in prediction accuracy. This dissertation investigates the performance of the three aforementioned kriging variants in producing Radio Environment Maps of received power. For practical and financial reasons, the received power measurement samples are generated using the Longley-Rice Irregular Terrain Model and are, therefore, simulated measurements. The experimental results indicate that kriging shows great promise as a tool to generate Radio Environment Maps. It is found that Ordinary Kriging produces the most accurate predictions of the three kriging methods and that prediction errors of less than 10 dB can be achieved even when using very low sampling densities. / MSc (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015

Kriging is proposed as a tool for Wi-Fi signal strength estimation for complex indoor environments. This proposal is based on two studies suggesting that kriging might be suitable for this application. Both of these studies have shortcomings in supporting this proposal, but their results encourage a more in depth investigation into this. Even though kriging is a geostatistical method developed for geographical interpolation, it has been used successfully in a wide range of other applications as well. This further suggests that kriging might be a versatile method to overcome some of the difficul- ties of existing signal strength estimation methods. Two main types of signal strength estimation are deterministic methods and empirical methods. Deterministic methods are generally very complex and requires input parameters that are difficult to obtain. Empirical methods are known to have low accuracy which makes them unreliable for practical use. Three main investigations are presented in order to evaluate the use of kriging for this application. A sampling plan is proposed as part of a generic application protocol for the practical use of kriging for Wi-Fi signal strength. It is concluded that kriging can be conffidently used as an estimation technique for Wi-Fi signal strength in complex indoor environments. Kriging is recommended for practical applications, especially where in- sufficient information is available about a building or where time consuming site surveys are not feasible. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015

In the journey toward optimal spectrum usage, techniques and concepts such as Cognitive Radio and Dynamic Spectrum Access have enjoyed increasing attention in many research projects. Dynamic Spectrum Access introduces the need for real-time RF spectrum information in the form of Radio Environment Maps. This need motivates an investigation into a hybrid approach of sample measurements and spatial interpolation as opposed to using conventional propagation models. Conventional propagation models, both path-general and path-specific, require information of transmitters within the area of interest. Irregular Terrain Models such as the Longley-Rice model, further require topographic information in order to consider the effects of obstacles. The proposed spatial interpolation technique, kriging, requires no information regarding transmitters. Furthermore, Ordinary Kriging requires nothing other than measured samples whereas other kriging variants such as Universal Kriging and Regression Kriging can use additional information such as topographic data to aid in prediction accuracy. This dissertation investigates the performance of the three aforementioned kriging variants in producing Radio Environment Maps of received power. For practical and financial reasons, the received power measurement samples are generated using the Longley-Rice Irregular Terrain Model and are, therefore, simulated measurements. The experimental results indicate that kriging shows great promise as a tool to generate Radio Environment Maps. It is found that Ordinary Kriging produces the most accurate predictions of the three kriging methods and that prediction errors of less than 10 dB can be achieved even when using very low sampling densities. / MSc (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015

Kriging is proposed as a tool for Wi-Fi signal strength estimation for complex indoor environments. This proposal is based on two studies suggesting that kriging might be suitable for this application. Both of these studies have shortcomings in supporting this proposal, but their results encourage a more in depth investigation into this. Even though kriging is a geostatistical method developed for geographical interpolation, it has been used successfully in a wide range of other applications as well. This further suggests that kriging might be a versatile method to overcome some of the difficul- ties of existing signal strength estimation methods. Two main types of signal strength estimation are deterministic methods and empirical methods. Deterministic methods are generally very complex and requires input parameters that are difficult to obtain. Empirical methods are known to have low accuracy which makes them unreliable for practical use. Three main investigations are presented in order to evaluate the use of kriging for this application. A sampling plan is proposed as part of a generic application protocol for the practical use of kriging for Wi-Fi signal strength. It is concluded that kriging can be conffidently used as an estimation technique for Wi-Fi signal strength in complex indoor environments. Kriging is recommended for practical applications, especially where in- sufficient information is available about a building or where time consuming site surveys are not feasible. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015

This is a thesis about the spatial distribution of willow warbler, nightingale, blackbird, robin and grey flycatcher in Svealand and Götaland, that is the southern third of Sweden. It explores the possibilities of using statistics to describe the distribution and variation of birds in a given region.The data was collected by observation of birds on sites called standard routes, with 25 kilometres between them. The standard routes are the points in a grid net placed upon the map of Sweden. The purpose of standard routes is to represent the birds in Sweden both geographic and biotopological.The thesis compare the results from kriging, variogram and four alternative poisson regressions. In the end I come up with the information provided by kriging and variogram and which poisson regression that bests estimates the population sizes of the birds at a given site with information about year, mean temperature from January to May and what kind of environment or habitat the site consist of.

To adapt to climate change which results in increasing flood frequency and intensity, the European Community has proposed Flood Directive 2007/60/EC. It requires member states to conduct risk assessments of all river basins and coastal areas and to establish Flood Risk Management Plans focused on prevention, protection and preparedness by 2015. Sustainable Flood Retention Basins (SFRB) that impound water are a new concept that arose in 2006. They can have a pre-defined or potential role in flood defense and were supposed to facilitate the implementation of the Flood Directive. Early and preliminary studies of SFRB were derived from case studies in Southern Baden, Germany. In Scotland, there are a relatively high number of SFRB which could contribute to flood management control. This research aimed to produce a guidance manual for the rapid survey of SFRB and to propose a series of frameworks for comprehensive analysis and assessment of SFRB. Precisely 372 SFRB in central Scotland and 202 SFRB in Southern Baden were investigated and characterized by 43 holistic variables. Based on this practical experience, a detailed guidance manual was created, guiding users to conduct a SFRB survey in a standardized and straightforward way. To explore the hidden data structure of data arising from the SFRB survey, various widely used machine learning algorithms and geo-statistical techniques were applied. For instance, cluster analysis showed intrinsic groupings of SFRB data, assisting with SFRB categorization. Principal Component Analysis (PCA) was applied to reduce the dimensions of SFRB data from the original 43 to 23, simplifying the SFRB system. Self-organizing Maps (SOM) visualized the relationships among variables and predicted certain variables as well as the types of SFRB by using the highly related variables. Three feature-selection techniques (Information Gain, Mutual Information and Relief) and four benchmark classifiers (Support Vector Machine, K-Nearest Neighbours, C4.5 Decision Tree and Naive Bayes) were used to select and verify the optimal subset of variables, respectively. Findings indicated that only nine important variables were required to accurately classify SFRB. Three popular multi-label classifiers (Multi-Label Support Vector Machine (MLSVM), Multi-Label K-Nearest Neighbour (MLKNN) and Back- Propagation for Multi-Label Learning (BP-MLL)) were applied to classify SFRB with multiple types. Experiments demonstrated that the classification framework achieved promising results and outperformed traditional single-label classifiers. Ordinary Kriging was used to estimate the spatial properties of the flood-related variables across the research area, while Disjunctive kriging was used to assess the probability of these individual variables exceeding specific management thresholds. The results provided decision makers with an effective tool for spatial planning of flood risk management. To assess dam failure hazards and risks of SFRB, a rapid screening tool was proposed based on expert judgement. It demonstrated that the levels of Dam Failure Hazard and Dam Failure Risk varied for different SFRB types and in different regions of central Scotland. In all, this thesis provided a guidance manual for rapid survey of SFRB and presented various effective, efficient and comprehensive frameworks for SFRB analysis and assessment, helping to promote the understanding and management of SFRB and thus to contribute to Flood Risk Management Plans in the context of the Flood Directive.

Spatial statistics is one of the youngest trends in the science of statistics. First, it has been applied in mining, during the fifth decade of the last century. In fifty years after this trend of science had been discovered, the circle of the scientists involved in it has grown drastically as well as areas of application. Also, a wide range of theoretical and practical material has been issued. Nowadays, spatial statistics methods are used in: ecology, quantity geology, image processing and analysis, epidemiology, studying global climate change and even cosmology. However, in Lithuania, the methodology of spatial data analysis has been studied only from the beginning of this Millennium. Since only few scientists (Dumbrauskas, A.; Kumetaitis, A.; Kumetaitienė, A. and others) are involved, it is very important to expand this area and develop the existing methods. Also it is essential to study the spatial dada modelling methods throughly and provide general spatial data modelling methodology. In order to apply the methods of spatial statistics, it is necessary to know the location of data in space, which is usually expressed in geographic coordinates. Thus, one of the main distinctions of spatial statistics which makes it different from the classical is the ability to model both spatial trend and spatial autocorrelation. One of the main objectives of spatial statistics is creating a mathematical model of spatial data, which can be used for interpolation (extrapolation) or for... [to full text] / Disertacijoje nagrinėjama erdvinių duomenų su stacionariomis klaidomis modeliavimo per semivariogramas ir tiesinio prognozavimo metodika. Erdvinių duomenų skiriamasis bruožas – jų išsidėstymas erdvėje, kuris dažniausiai aprašomas geografinėmis koordinatėmis. Tokių duomenų modeliavimas semivariogramomis, ir prognozavimas krigingu yra vienas iš svarbių geostatistikos mokslo uždavinių. Krigingas yra stochastinis prognozavimo metodas, kuris prie tam tikrų salygų pateikia geriausią tiesinę nepaslinktą prognozę. Krigingo rezultatų paklaidos priklauso nuo to kaip tiksliai erdvinių duomenų sklaida aprašoma kovariacine funkcija arba semivariograma. Darbe dėmesys skiriamas semivariogramoms, nes jos aprašo platesnę erdvinių procesų klasę. Pagrindinis disertacijos tikslas yra apibendrinti ir realizuoti vieningą erdvinių duomenų su stacionariomis klaidomis modeliavimo metodiką, pagrįstą semivariogramomis. Darbo objektai yra semivariogramos, jų modeliai, įvairūs erdvinių duomenų prognozavimo metodai bei erdvinių duomenų modeliavimo, prognozavimo etapai. Šių objektų analizė bei interpretacija prie tam tikrų sąlygų leidžia gauti geriausius erdvinių duomenų modeliavimo bei prognozavimo rezultatus. Taip pat disertaciniame darbe empiriniam Materon‘o semivariogramų įvertiniui MoM pateikta dispersijų-kovariacijų matricos išraiška per teorines semivariogramas stacionaraus Gauso duomenų modelio atvejui. Tiriami erdvinių duomenų vidurkio modelio parametrų bei semivariogramų vertinimo metodai... [toliau žr. visą tekstą]