Добірка наукової літератури з теми "Multi-temporal Data Analysi"

Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical significance of results. To explore the network structure and semantic relationships, we propose a general framework for multi-source spatio-temporal data analysis via knowledge graph embedding. The framework extracts low-dimensional feature representation from multi-source spatio-temporal data in a high-dimensional space, and recognizes the network structure and semantic relationships about multi-source spatio-temporal data. Experiment results show that the framework can not only effectively utilize multi-source spatio-temporal data, but also explore the network structure and semantic relationship. Taking real Shanghai datasets as an example, we confirm the validity of the multi-source spatio-temporal data analytical framework based on knowledge graph embedding.

LiDAR technology is rapidly evolving as various new systems emerge, providing unprecedented data to characterize forest vertical structure. Data from different LiDAR systems present distinct characteristics owing to a combined effect of sensor specifications, data acquisition strategies, as well as forest conditions such as tree density and canopy cover. Comparative analysis of multi-platform, multi-resolution, and multi-temporal LiDAR data provides guidelines for selecting appropriate LiDAR systems and data processing tools for different research questions, and thus is of crucial importance. This study presents a comprehensive comparison of point clouds from four systems, linear and Geiger-mode LiDAR from manned aircraft and multi-beam LiDAR on unmanned aerial vehicle (UAV), and in-house developed Backpack, with the consideration of different forest canopy cover scenarios. The results suggest that the proximal Backpack LiDAR can provide the finest level of information, followed by UAV LiDAR, Geiger-mode LiDAR, and linear LiDAR. The emerging Geiger-mode LiDAR can capture a significantly higher level of detail while operating at a higher altitude as compared to the traditional linear LiDAR. The results also show: (1) canopy cover percentage has a critical impact on the ability of aerial and terrestrial systems to acquire information corresponding to the lower and upper portions of the tree canopy, respectively; (2) all the systems can obtain adequate ground points for digital terrain model generation irrespective of canopy cover conditions; and (3) point clouds from different systems are in agreement within a ±3 cm and ±7 cm range along the vertical and planimetric directions, respectively.

This paper describes a statistical analysis of lineaments based on multi-temporal Landsat 8 imageries in the Fergana Valley (East Uzbekistan). The results of the statistical analysis showed that determined that the count of lineament structures changes by months. It was also noted that the Namangan region is more prone to the manifestation of lineament structures. The maximum count of lineament structures was in July. And in November, we observe a sharp decrease in lineament structures. According to the results of the rose diagrams, various orientations are observed for these months. There is a coincidence of directions.

Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characterised by a very low signal to noise ratio. Hence the activation is repeated and the three dimensional signal (multi-slice 2D) is gathered during relatively long time ranges (3-5 min). From the noisy and distorted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters). Filters applied are compared by classifications of activations.

Land fragmentation and small plots are the main features of the rural environment of Galicia (NW Spain). Smallholding limits land use management, representing a drawback in local forest planning. This study analyzes the potential use of multitemporal Sentinel-2 images to detect and control forest cuts in very small pine and eucalyptus plots located in southern Galicia. The proposed approach is based on the analysis of Sentinel-2 NDVI time series in 4231 plots smaller than 3 ha (average 0.46 ha). The methodology allowed us to detect cuts, allocate cut dates and quantify plot areas due to different cutting cycles in an uneven-aged stand. An accuracy of approximately 95% was achieved when the whole plot was cut, with an 81% accuracy for partial cuts. The main difficulty in detecting and dating cuts was related to cloud cover, which affected the multitemporal analysis. In conclusion, the proposed methodology provides an accurate estimation of cutting date and area, helping to improve the monitoring system in sustainable forest certifications to ensure compliance with forest management plans.

Oggigiorno, per poter affrontare efficacemente i problemi ambientali su larga scala, la necessità di disporre di mappe di copertura del suolo affidabili e ad alta risoluzione spaziale e temporale è più che mai urgente. Infatti, numerosi contesti potrebbero trarre beneficio da tali prodotti come, ad esempio, il cambiamento climatico, la desertificazione, l'inverdimento dell'artico, la deforestazione, l'urbanizzazione, l'erosione del suolo, il monitoraggio delle foreste, la conservazione della biodiversità, la gestione delle aree urbane, la gestione delle risorse idriche, l'agricoltura, la sicurezza alimentare e molti altri. Siccome le variabili di interesse tendono a cambiare molto rapidamente nel tempo e nello spazio, la disponibilità di mappe di copertura del suolo frequenti e di buona qualità suscita un grande interesse. Negli ultimi anni sono state prodotte diverse mappe tematiche e di copertura del suolo su scala regionale/globale le quali, tuttavia, spesso non soddisfano i requisiti imposti dalle applicazioni; ciò è dovuto principalmente al fatto che i prodotti esistenti sono stati generati da diversi sensori satellitari (ottici, radar o entrambi), diverse strategie di campionamento, diverse legende, diversi protocolli di validazione, ecc. Inoltre, la risoluzione spaziale e/o temporale di tali prodotti è spesso insufficiente per diverse applicazioni. In questo lavoro di tesi è stato studiato come sfruttare dati multitemporali di tipo ottico e SAR (Synthetic Aperture Radar) per caratterizzare un insieme molto ristretto di classi, piuttosto che un'ampia gamma di tipi di copertura del suolo. Il lavoro presentato si concentra sulla vegetazione (tra cui specie arboree, praterie, arbusti ed altri), corpi idrici (tra cui laghi, mari, fiumi ed altri) e colture biologiche (in particolare, pratiche di agricoltura biologica). Per quanto riguarda la vegetazione, la letteratura scientifica offre numerose metodologie consolidate, finalizzate alla mappatura delle coperture vegetative. Al contrario, gli approcci che sfruttano i sensori SAR come principale fonte di dati sono decisamente più rari. Per questo motivo, parte di questa tesi è dedicata all'analisi della potenzialità dei dati SAR multitemporali nel caratterizzare diversi tipi di vegetazione naturale. Per quanto riguarda la mappatura dei corpi idrici, la letteratura tecnica fornisce diverse soluzioni basate sia su dati ottici che SAR. Tuttavia, la maggioranza delle metodologie analizzate presentano alcune limitazioni legate principalmente alla mancanza di automatismo degli algoritmi, l'impossibilità di utilizzare il modello in altre regioni di interesse, alla risoluzione spaziale relativamente bassa ed altri. Dal momento che la comunità sul cambiamento climatico necessita di informazioni tempestive relative allo stato dei corpi idrici a livello non solo locale/regionale ma anche globale, e che sia indipendente dalle condizioni meteorologiche delle diverse aree del mondo, in questa tesi si propone una metodologia volta a mappare i corpi idrici sfruttando sequenze temporali di dati SAR, che sia in grado di superare le limitazioni più gravi presenti negli approcci esistenti. Infine, per quanto concerne la caratterizzazione dei terreni agricoli biologici, occorre rilevare e monitorare diversi aspetti, tra cui le operazioni di diserbo, le attività di fertilizzazione e le tecniche di lavorazione del terreno. A tal fine, sia i dati ottici multitemporali che i dati SAR vengono sfruttati per costruire piccoli blocchi che faranno parte di un sistema di monitoraggio dell'agricoltura biologica più complesso, volto a migliorare la trasparenza e la tracciabilità all'interno della catena di approvvigionamento alimentare biologica. In generale, i risultati hanno dimostrato che le sequenze temporali di dati SAR e multispettrali possono essere impiegate con successo nella classificazione dei diversi tipi di copertura del suolo di cui sopra.
Nowadays, the need for reliable, timely, high-resolution land cover maps is more than urgent if large-scale environmental problems are to be tackled effectively. Many different contexts would in fact benefit from such products, such as climate change, desertification, arctic greening, deforestation, urbanization, soil erosion, forest monitoring, conservation of biodiversity, urban area management, water resources management, agriculture, food security and many others. Due to the fact that the involved variables tend to change very rapidly in time and space, the availability of frequent and good quality global land cover products raises great interest. Several regional/global thematic and land cover maps have been delivered and other are expected, but they often do not meet the specific requirements of various applications; this is mainly due to the fact that all the existing products have been generated from different satellite sensors (optical, radar or both), different sampling strategies, different types of mapped land cover types, different validation protocols, etc. Moreover, the spatial and/or temporal resolution of these products is often insufficient for some applications. In this thesis work, we investigated how to leverage multitemporal optical and SAR data to characterize a very small set of classes rather than a full range of land cover types. Our work focuses on vegetation (including tree species, grasslands, shrublands and others), water bodies (including lakes, seas, rivers and others) and organic croplands (specifically, organic farming practices). Regarding vegetation, the technical literature offers numerous well-established methodologies aimed at mapping vegetated land covers. On the contrary, approaches that use SAR sensors as the main source of data are definitely more scarce. For this reason, part of this thesis work will be devoted to analyze the potential of multitemporal SAR data to characterize several types of natural vegetation. Regarding mapping of water bodies, the scientific literature provides several solutions based on optical and SAR data. However, almost all the analyzed methodologies have some limitations, mainly related to lack of automatism, impossibility to use the proposed method in other regions of interest, relatively low spatial resolution and others. Given the climate change community's need for timely information on the status of water bodies at the global level regardless of weather conditions, in this thesis a methodology aimed at mapping water bodies using sequences of SAR data, that is able to overcome the most severe limitations of the existing methodologies, is proposed. Finally, to characterize organic farmland, several aspects must be detected and monitored, including weed-killer operations, fertilization activities and tillage techniques. To do so, both multitemporal optical and SAR data are exploited to build small detection blocks, that will be part of a more complex organic farming monitoring system aimed at improving transparency and traceability within the organic food supply chain. In general, results showed that SAR and multispectral time series can be successfully employed to classify these land cover types.

The movement of human beings appears to respond to a complex motor system that contains signals at different hierarchical levels. For example, an action such as ``grasping a glass on a table'' represents a high-level action, but to perform this task, the body needs several motor inputs that include the activation of different joints of the body (shoulder, arm, hand, fingers, etc.). Each of these different joints/muscles have a different size, responsiveness, and precision with a complex non-linearly stratified temporal dimension where every muscle has its temporal scale. Parts such as the fingers responds much faster to brain input than more voluminous body parts such as the shoulder. The cooperation we have when we perform an action produces smooth, effective, and expressive movement in a complex multiple temporal scale cognitive task. Following this layered structure, the human body can be described as a kinematic tree, consisting of joints connected. Although it is nowadays well known that human movement and its perception are characterised by multiple temporal scales, very few works in the literature are focused on studying this particular property. In this thesis, we will focus on the analysis of human movement using data-driven techniques. In particular, we will focus on the non-verbal aspects of human movement, with an emphasis on full-body movements. The data-driven methods can interpret the information in the data by searching for rules, associations or patterns that can represent the relationships between input (e.g. the human action acquired with sensors) and output (e.g. the type of action performed). Furthermore, these models may represent a new research frontier as they can analyse large masses of data and focus on aspects that even an expert user might miss. The literature on data-driven models proposes two families of methods that can process time series and human movement. The first family, called shallow models, extract features from the time series that can help the learning algorithm find associations in the data. These features are identified and designed by domain experts who can identify the best ones for the problem faced. On the other hand, the second family avoids this phase of extraction by the human expert since the models themselves can identify the best set of features to optimise the learning of the model. In this thesis, we will provide a method that can apply the multi-temporal scales property of the human motion domain to deep learning models, the only data-driven models that can be extended to handle this property. We will ask ourselves two questions: what happens if we apply knowledge about how human movements are performed to deep learning models? Can this knowledge improve current automatic recognition standards? In order to prove the validity of our study, we collected data and tested our hypothesis in specially designed experiments. Results support both the proposal and the need for the use of deep multi-scale models as a tool to better understand human movement and its multiple time-scale nature.

My doctoral research dissertation focuses on two aspects of functional data analysis (FDA): FDA under spatial interdependence and FDA for multi-level data. The first part of my thesis focuses on developing modeling and inference procedure for functional data under spatial dependence. The methodology introduced in this part is motivated by a research study on inequities in accessibility to financial services. The first research problem in this part is concerned with a novel model-based method for clustering random time functions which are spatially interdependent. A cluster consists of time functions which are similar in shape. The time functions are decomposed into spatial global and time-dependent cluster effects using a semi-parametric model. We also assume that the clustering membership is a realization from a Markov random field. Under these model assumptions, we borrow information across curves from nearby locations resulting in enhanced estimation accuracy of the cluster effects and of the cluster membership. In a simulation study, we assess the estimation accuracy of our clustering algorithm under a series of settings: small number of time points, high noise level and varying dependence structures. Over all simulation settings, the spatial-functional clustering method outperforms existing model-based clustering methods. In the case study presented in this project, we focus on estimates and classifies service accessibility patterns varying over a large geographic area (California and Georgia) and over a period of 15 years. The focus of this study is on financial services but it generally applies to any other service operation. The second research project of this part studies an association analysis of space-time varying processes, which is rigorous, computational feasible and implementable with standard software. We introduce general measures to model different aspects of the temporal and spatial association between processes varying in space and time. Using a nonparametric spatiotemporal model, we show that the proposed association estimators are asymptotically unbiased and consistent. We complement the point association estimates with simultaneous confidence bands to assess the uncertainty in the point estimates. In a simulation study, we evaluate the accuracy of the association estimates with respect to the sample size as well as the coverage of the confidence bands. In the case study in this project, we investigate the association between service accessibility and income level. The primary objective of this association analysis is to assess whether there are significant changes in the income-driven equity of financial service accessibility over time and to identify potential under-served markets. The second part of the thesis discusses novel statistical methodology for analyzing multilevel functional data including a clustering method based on a functional ANOVA model and a spatio-temporal model for functional data with a nested hierarchical structure. In this part, I introduce and compare a series of clustering approaches for multilevel functional data. For brevity, I present the clustering methods for two-level data: multiple samples of random functions, each sample corresponding to a case and each random function within a sample/case corresponding to a measurement type. A cluster consists of cases which have similar within-case means (level-1 clustering) or similar between-case means (level-2 clustering). Our primary focus is to evaluate a model-based clustering to more straightforward hard clustering methods. The clustering model is based on a multilevel functional principal component analysis. In a simulation study, we assess the estimation accuracy of our clustering algorithm under a series of settings: small vs. moderate number of time points, high noise level and small number of measurement types. We demonstrate the applicability of the clustering analysis to a real data set consisting of time-varying sales for multiple products sold by a large retailer in the U.S. My ongoing research work in multilevel functional data analysis is developing a statistical model for estimating temporal and spatial associations of a series of time-varying variables with an intrinsic nested hierarchical structure. This work has a great potential in many real applications where the data are areal data collected from different data sources and over geographic regions of different spatial resolution.

O desenvolvimento de uma nova geração de sensores nos últimos 20 anos consolidou as imagens de sensoriamento remoto como uma importante fonte de dados para estudos ambientais e fenômenos geográficos em larga escala. É grande a variedade de resoluções (espacial, temporal e espectral) das imagens de sensoriamento remoto, desde pancromáticas até imagens polarimétricas. Apesar da grande experiência em coleta, armazenamento e distribuição de imagens e da diversidade de ferramentas computacionais para processamento e análise, ainda é difícil de se encontrar sistemas que apresentem um ambiente integrado para transformar imagens multi-temporais e de diversas resoluções em informação útil. Tendo em vista este panorama, a contribuição desta tese é dupla. Em primeiro lugar, propomos e implementamos uma nova ferramenta, seguindo os padrões de código-fonte aberto (\textit{Free and Open Source Software - FOSS}) , para integrar métodos de análise de imagens com técnicas de mineração de dados, visando produzir um ambiente computacional extensível e focado no usuário, aplicado à extração de informações e à descoberta de conhecimento em grandes bases de dados geométricos. Esta ferramenta é chamada GeoDMA - \textit{Geographic Data Mining Analyst} (mineração de dados geográficos). GeoDMA integra técnicas de sementação de imagens, extração e seleção de atributos, classificação, métricas da ecologia da paisagem, métodos de análise multi-temporal para detecção de mudanças e classificação por métodos de árvores de decisão adaptados à mineração de dados espaciais. O sistema agrega imagens de sensoriamento remoto com outros tipos de dados geográficos através do acesso a bancos de dados locais ou remotos. GeoDMA também provê métodos de simulação para avaliar a acurácia dos modelos, e ferramentas para análise espaço-temporal, incluindo um esquema de visuação de perfis temporais que auxilia os usuários a descrever padrões em eventos cíclicos. Em segundo lugar, desenvolvemos um novo método para analizar dados espaço-temporais baseados na transformação dos perfis em coordenadas polares, o que permite a geração de um novo conjunto de atributos que aumenta a acurácia da classificação de imagens multi-temporais. O sistema GeoDMA foi construído como uma extensão do SIG Terra View, e por isso mapas temáticos e demais resultados são produzidos rapidamente, aproveitando-se das funcionalidades deste SIG. Para demonstrar as ferramentas do GeoDMA, cinco (5) casos de estudo, aplicados em diferentes contextos de detecção de uso e cobertura da terra, foram realizados usando dados de diferentes domínios. A avaliação destes experimentos, do ponto de vista do usuário, mostrou que a ferramenta obteve os resultados com um nível de integração não encontrado em sistemas semelhantes.
The deployment of a new generation of sensors over the last 20 years has made satellite remotely sensed imagery a very important source of spatial data available for environmental studies of large-scale geographic phenomena. The variety of spatial, temporal and spectral resolutions for remote sensing images is large, ranging from panchromatic images to polarimetric radar images. Despite the great experience in image data gathering and distribution and a diversity of image processing and analysis toolboxes, it is still difficult to find image analysis systems that provide a straightforward fully integrated environment to transform multi-temporal and multiresolution satellite image data into meaningful information. Taking this into account, the contribution of this thesis is two-fold. Firstly, we propose and implement a new toolbox, developed under the Free and Open Source Software (FOSS) foundation, for integrating remote sensing imagery analysis methods with data mining techniques producing a user-centered, extensible, rich computational environment for information extraction and knowledge discovery over large geographic databases. The toolbox is called GeoDMA - Geographic Data Mining Analyst. It integrates techniques of segmentation, feature extraction, feature selection, classification, landscape metrics and multi-temporal methods for change detection and analysis with decision-tree based strategies adapted for spatial data mining. It gathers remotely sensed imagery with other geographic data types using access to local or remote databases. GeoDMA provides simulation methods to assess the accuracy of process mo dels as well as tools for spatio-temporal analysis, including a visualization scheme for temporal profiles that helps users to describe patterns in cyclic events. Secondly, we develop a new approach for analyzing spatio-temporal data based on a polar coordinates transformation that allows creating a new set of features which improves the classification accuracy of multi-temporal image databases. As GeoDMA was built on top of Terra View GIS, thematic maps and other results can be produced rapidly, taking advantage of the basic GIS functionalities. To demonstrate the features of GeoDMA toolbox, five (5) case studies, applied in contexts of land use and land cover change, were carried out in different application domains. Evaluations of these experiments pointed out that the GeoDMA toolbox achieved results with a level of integration, from a user perspective, that could not be found elsewhere.

Landslide hazard assessment of densely forested, remote, and difficult to access areas can be rapidly accomplished with airborne light detection and ranging (lidar) data. An evaluation of geomorphic change by lidar-derived digital elevation models (DEMs) coupled with geotechnical soils analysis, aerial photographs, ground measurements, precipitation data, and numerical modeling can provide valuable insight to the reactivation process of unstable landslides. A landslide was selected based on previous work by Mickleson (2011) and Burns et al. (2010) that identified the Madrone Landslide with significant volumetric changes. This study expands on previous work though an evaluation of the timing and causation of slope failure of the Madrone Landslide. The purpose of this study was to evaluate landslide morphology, precipitation data, historical aerial photographs, ground crack measurements, geotechnical properties of soil, numerical modeling, and elevation data (with multi-temporal lidar data), to determine the conditions associated with failure of the Madrone Landslide. To evaluate the processes involved and timing of slope failure events, a deep seated potentially unstable landslide, situated near the contact of Eocene sedimentary and volcanic rocks, was selected for a detailed analysis. The Madrone Landslide (45.298383/-123.338796) is located in Yamhill County, about 12 kilometers west of Carlton, Oregon. Site elevation ranges from 206 meters (m) North American Vertical Datum (NAVD-88) near the head scarp to 152 m at the toe. The landslide is composed of two parts, an upper more recent rotational slump landslide and a lower much older earth flow landslide. The upper slide has an area of 2,700 m2 with a head scarp of 5-7 m and a volume of 15,700 m3. The lower earth flow has an area of 2300 m2, a head scarp of 15 m, and a volume of 287,500 m3. Analysis of aerial photographs indicates the lower slide probably originated between 1956 and 1963. The landslide is located at a geologic unit contact of Eocene deep marine sedimentary rock and intrusive volcanic rock. The landslide was instrumented with 20 crack monitors established across ground cracks and measured periodically. Field measurements did not detect ground crack displacement over a 15 month period. Soil samples indicate the soil is an MH soil with a unit weight of 12 kN/m3 and residual friction angle of 28φ'r which were both used as input for slope stability modeling. Differential DEMs from lidar data were calculated to generate a DEM of Difference (DoD) raster to identify and quantify elevation changes. Historical aerial photograph review, differential lidar analysis, and precipitation data suggest the upper portion of the landslide failed as a result of the December 2007 storm.

Several decades of intensive dryland-farming in the Gadarif Region, located in the Eastern part of Sudan, has led to rapid landuse/landcover (LULC) changes mainly due to agricultural expansion, government policies and environmental calamities such as drought. The study area represents part of the African Sahel. The fundamental goal of the thesis was to assess land degradation and the impact of agriculture expansion on land cover, soil and crops production. To analyse and to monitor the LULC changes, multi-temporal Landsat data of the years 1979, 1989 and 1999 and ASTER data of the year 2009 covering an area of approximately 1200 km² were used. For this a post-classification comparison technique was applied to detect LULC changes from satellite images. Six LULC classes were identified during the classification scheme, namely cultivated land, fallow land, woodland, bare land, settlement and water. For the four dates of satellite images the overall classification accuracy ranged from 86 % to 92 %. During the three decades of the study period an extensive change of LULC patterns occurred. The cultivated areas increased significantly, covering 81 % of the previous woodland in the period 1979 – 2009. Fallow land only increased during the period 1989 – 1999. Over the three decades, urban expansion continuously increased covering an area of 23, 21 and 27 km² for the periods 1979 – 1989, 1989 – 1999 and 1999 – 2009 respectively. The detailed LULC map of the study area was obtained by using a dual polarisation (HH and HV) TerraSAR-X data of the year 2009. The different LULCs of the study area were analysed by employing an object-oriented classification approach. For that purpose, multi-resolution segmentation of the Definiens Software was used for creating the image objects. Using the feature-space optimisation tool the attributes of the TerraSAR-X images were optimised in order to obtain the best separability among classes for the LULC mapping. In addition to the classes that have been obtained by the optical data, the following LULCs resulted from SAR data: harvested land, rock, settlement 1 (local-roof buildings) and settlement 2 (concrete roof buildings). The backscattering coefficients for some classes were different along HH and HV polarisation. The best separation distance of the tested spectral, shape and textural features showed different variations among the discriminated LULC classes. An overall accuracy of 84 % with a kappa value of 0.82 was resulted from the classification scheme. Accuracy differences among the classes were kept minimal. For more than six decades in the Gadarif Region mechanised dryland farming is practised. As a result, due to continuous conventional tillage, extensive woodcutting and over-grazing, serious soil degradation occurred. To discuss the impact of LULC changes on the selected soil properties, three main LULC types were chosen to be investigated, namely: cultivated land, fallow land and woodland. In addition to the reference soil profiles, soil samples were also collected at two depths from ten sample plots for each of the LULC type. For these soil samples, various soil properties such as texture, bulk density (BD), organic matter (OM), soil pH, electrical conductivity (EC), sodium adsorption ratio (SoAR), phosphorous (P) and potassium (K) were analysed. Laboratory tests proved that soil properties were significantly affected by LULC changes. Within the different LULC types, clay content in the surface layers (0 – 5 and 5 – 15 cm) varied from 59 % to 65 %, whereas silt fractions ranged from 27 % to 37 %. Soil BD, OM and P were significantly different (p < 0·05) across the three LULC types. Soil pH was significantly different between cultivated land and woodland on one side and between fallow land and woodland on the other side. EC and SoAR values of fallow land were found to be significantly different (p < 0·05) from woodland. The dryland vertisol of the Gadarif Region in Sudan produced more than one-third of the national production of sorghum – the main food stuff in the country. Soil compaction has been recognised as one of the major problems in crop production worldwide. Soil strength and infiltration rate are important variables for understanding and predicting the soil processes. The effects of three different landuse systems (cultivated land, fallow land and woodland) on soil compaction and infiltration rate were investigated at two sites of the study area. Site 1 represents the older one of the two. The soil penetration resistance (SPR) was measured in three depths using a manually operated cone penetrometer. Infiltration rate was measured in the field using a double-ring infiltrometer. Following the cone-penetrometer sampling, soil samples were collected to determine the variables that affect SPR and infiltration rate vs. particle size, dry BD, volumetric moisture content (VMC) and organic carbon (OC) content. Field measurements and soil samples were collected for each landuse type. The measured infiltration rate data were inserted into the Kostiakov Model in order to predict the cumulative soil water infiltration. Soil compaction for the cultivated land was 65 % larger in comparison to woodland. Woodland areas showed an increase in the infiltration rate by 87 % and 74 % compared to cultivated and fallow land respectively. Both study sites showed an increase in the dry BD when SPR is increasing, while VMC decreases with increasing SPR. Also, low OC contents were observed to be associated with high SPR values. For Site 1 the average coefficient of determination (R²) for the infiltration data fit to the Kostiakov Model were 0.65, 0.73 and 0.84 for cultivated land, fallow land and woodland respectively. However, for Site 2 they were 0.63, 0.76 and 0.78. In the Gadarif Region agriculture is the main activity and practised in many forms with a variety of environmental effects and consequences. Continuous ploughing of the cultivated land coupled with inproper soil management has contributed to soil deterioration when the landuse changed from woodland to cultivated and fallow land. Therefore, the development of sustainable landuse practises in the dryland-farming of the study area need to be improved in order to reduce the amount of soil degradation in the future
Mehrere Jahrzehnte intensiven Trockenfeldbaus in der Region von Gadarif, welche sich im östlichen Teil des Sudans befindet, führten hauptsächlich aufgrund von landwirtschaftlicher Expansion, politischen Beschlüssen der Regierung und Naturkatastrophen wie Trockenheit zu einer raschen Veränderung der Landnutzung und Landbedeckung. Das wesentliche Ziel dieser Dissertation war es, die Degradation des Landes, sowie die Auswirkungen von landwirtschaftlicher Expansion auf die Landbedeckung, den Boden und den Pflanzenbau im Untersuchungsgebiet, welches Teile der afrikanischen Sahelzone beinhaltet, abzuschätzen. Zur Analyse und Beobachtung der Veränderungen der Landnutzung und Landbedeckung wurden multi-temporale Landsat-Daten der Jahre 1979, 1989 und 1999 sowie ASTER-Daten aus dem Jahr 2009 genutzt, welche eine Fläche von schätzungsweise 1200 km² abdecken. Um Veränderungen von Landnutzung und Landbedeckung aus Satellitenbilddaten zu bestimmen, wurde ein auf Post-Klassifikation basierendes Vergleichsverfahren angewandt. Sechs Landnutzungs- und Landbedeckungsklassen, welche die Namen bewirtschaftetes Land, brach liegendes Land, Waldgebiet, Ödland, besiedeltes Land und Wasserfläche tragen, wurden während des Klassifikationsprozesses bestimmt. Für die vier Aufnahmezeitpunkte der Satellitendaten lag die allgemeine Klassifikationsgenauigkeit zwischen 86 % und 92 %. Während des dreißigjährigen Untersuchungszeitraums fand eine beträchtliche Veränderung der Landnutzungs- und Landbedeckungsstruktur statt. Bewirtschaftete Flächen nahmen in ihrem Anteil signifikant zu und bedeckten innerhalb des Zeitraums von 1979 bis 2009 81 % der früheren Waldgebiete. Der Anteil von brach liegendem Land nahm lediglich während des Zeitraums von 1989 bis 1999 zu. Besiedelte Gebiete breiteten sich über die drei Jahrzehnte kontinuierlich aus und wuchsen innerhalb des Zeitraums von 1979 bis 1989 um eine Fläche von 23 km², sowie um 21 km² zwischen 1989 und 1999 und um 27 km² in dem Zeitabschnitt 1999 – 2009. Eine detaillierte Karte zur Landnutzung und Landbedeckung des Untersuchungsgebiets wurde mittels der Nutzung dual polarisierter (HH und HV) TerraSAR-X Daten aus dem Jahr 2009 erzeugt. Die verschiedenen Landnutzungen und Landbedeckungen im Beobachtungsgelände wurden durch die Anwendung eines objektorientierten Klassifikationsansatzes analysiert. Um Bildobjekte zu erzeugen, wurde für diesen Zweck die auf einer mehrfachen Auflösung basierende Segmentierung der Software Definiens genutzt. Das Werkzeug Feature Space Optimisation wurde für die Optimierung der Attribute der TerraSAR-X Bilder angewandt, damit eine ideale Unterscheidungsfähigkeit entlang der Klassen für die Kartierung der Landnutzungen und Landbedeckungen erreicht werden kann. Zusätzlich zu jenen Klassen, welche mittels optischer Daten abgeleitet wurden, ergaben sich aus SAR-Daten noch die nachfolgenden Landnutzungen und Landbedeckungen: Abgeerntetes Land, Fels, Besiedlung 1 (Gebäude mit landestypischer Bedachung) und Besiedlung 2 (Gebäude mit Betondach). Die Koeffizienten der Rückstreuung entlang der Polarisationen HH und HV waren für einige Klassen unterschiedlich. Der günstigste Trennungsabstand der getesteten spektralen, formgebenden und texturalen Features ergab verschiedene Abweichungen zwischen den bestimmten Klassen der Landnutzung und Landbedeckung. Die Klassifikationsmaßnahmen ergaben eine Gesamtgenauigkeit von 84 % mit einem Kappa-Wert von 0.82. Genauigkeitsunterschiede entlang der Klassen wurden minimal gehalten. Seit über sechs Jahrzehnten wird in der Region Gadarif maschinenbetriebener Trockenfeldbau ausgeübt. In Folge dessen fand eine beträchtliche Abholzung und Überweidung sowie eine schwerwiegende Bodendegradation aufgrund des stetigen konventionellen Feldbaus statt. Um die Auswirkungen der Veränderung von Landnutzung und Landbedeckung auf die ausgewählten Bodenbeschaffenheiten auszuwerten, wurden drei Haupttypen der Landnutzung und Landbedeckung für die weitere Untersuchung ausgewählt: Bewirtschaftetes Land, brach liegendes Land, und Waldgebiet. Zusätzlich zu den Referenzbodenprofilen wurden außerdem für jeden Landnutzungs- und Landbedeckungstyp auf je zehn Probeflächen Bodenproben in zwei Tiefen entnommen. Bei diesen Bodenproben wurden zahlreiche Bodeneigenschaften analysiert, wie etwa Textur, Bodendichte (BD), organischer Materialgehalt (OM), pH-Wert des Bodens, elektrische Leitfähigkeit (EC), Adsorptionsgeschwindigkeit von Natrium (SoAR), Phosphorgehalt (P) sowie Kaliumgehalt (K). Labortests ergaben, dass die Bodeneigenschaften signifikant durch die Veränderungen der Landnutzung und Landbedeckung beeinflusst werden. Innerhalb der verschiedenen Landnutzungs- und Landbedeckungstypen variierte der Tongehalt in den Deckschichten (0 – 5 cm und 5 – 15 cm) zwischen 59 % und 65 %, wohin gegen sich die Lehmanteile von 27 % bis 37 % bewegten. Bodendichte, organischer Materialgehalt und Phosphorgehalt zeigten signifikant unterschiedliche Werte bei den drei Typen der Landnutzung und Landbedeckung (p < 0.05). Der pH-Wert des Bodens war signifikant verschieden zwischen bewirtschaftetem Land und Waldgebiet zum einen, und zwischen brach liegendem Land und Waldgebiet zum anderen. Die Werte der elektrischen Leitfähigkeit und der Adsorptionsgeschwindigkeit von Natrium bei brach liegendem Land erwiesen sich als maßgeblich verschieden zu jenen von Waldgebieten (p < 0.05). Auf dem Trockenland-Vertisolboden der Region Gadarif im Sudan wurde mehr als ein Drittel der nationalen Hirseproduktion erwirtschaftet – dem Haupternährungserzeugnis des Landes. Bodenverdichtung erwies sich als eines der weltweiten Hauptprobleme für den Pflanzenbau. Bodenfestigkeit und Versickerungsrate sind wichtige Variabeln, um Bodenprozesse verstehen und vorhersagen zu können. Die Auswirkungen der drei verschiedenen Landnutzungssysteme (bewirtschaftetes Land, brach liegendes Land und Waldgebiet) auf die Bodenverdichtung und Versickerungsrate wurden an zwei Standorten im Beobachtungsgebiet untersucht. Standort 1 ist der ältere der beiden. Der Widerstand der Bodenpenetration (SPR) wurde in drei Tiefen durch eine manuell angewandte Rammsonde gemessen. Mittels der Nutzung eines Doppelring-Infiltrometers ist die Versickerungsrate im Feld gemessen worden. Im Anschluss an die Probenentnahme mittels Rammsonden wurden Bodenproben gesammelt, um jene Variabeln bestimmen zu können, welche den Widerstand der Bodenpenetration sowie der Versickerungsrate im Vergleich zur Partikelgröße, zur trockenen Bodendichte, zum volumetrischen Feuchtigkeitsgehalt (VMC) und zum organischen Karbongehalt (OC) beeinflussen. Für jeden Landnutzungstypen wurden Feldmessungen durchgeführt und Bodenproben entnommen. Die gemessenen Daten der Versickerungsrate wurden in das Kostiakov-Modell eingespeist, um die gesamte Bodenwasserversickerung vorhersagen zu können. Die Bodenverdichtung bei bewirtschaftetem Land war 65 % stärker als bei Waldgebiet. Für Waldgebietsflächen wurde eine Zunahme der Versickerungsrate um 87 % verglichen mit bewirtschaftetem Land und um 74 % im Vergleich zu brach liegendem Land aufgezeigt. Beide Untersuchungsstandorte zeigten eine Zunahme in der trockenen Bodendichte für den Fall, dass der Widerstand der Bodenpenetration zunimmt, während der volumetrische Feuchtigkeitsgehalt mit zunehmendem Bodenpenetrationswiderstand abnimmt. Ebenso wurde beobachtet, dass ein geringer organischer Karbongehalt in Verbindung zu hohen Widerstandswerten der Bodenpenetration steht. Bei Standort 1 passte der durchschnittliche Bestimmungskoeffizient (R²) der Versickerungsrate zum Kostiakov-Modell mit den Werten 0.65 für bewirtschaftetes Land, 0.73 für brach liegendes Land und 0.84 für Waldgebiet. Für Standort 2 indessen ergaben die Werte 0.63, 0.76 und 0.78. Landwirtschaft, die in vielen Formen ausgeübt wird, ist die Haupttätigkeit in der Region Gadarif, und geht mit verschiedenartigsten Umweltauswirkungen und Konsequenzen einher. Kontinuierliche Feldbestellung des bewirtschafteten Landes, verbunden mit ungeeigneter Bodenbewirtschaftung, hat sich seit jenem Zeitpunkt, als sich die Landnutzung von Waldgebiet zu bewirtschaftetem und brach liegendem Land änderte, zu Bodenschädigung geführt. Daher muss die Entwicklung nachhaltiger Landnutzungspraktiken beim Trockenfeldbau im Untersuchungsgebiet verbessert werden, damit in Zukunft der Umfang der Bodendegradation verringert werden kann

An integrating approach, including knowledge about whole systems of processes, is essential in order to reach both development and environmental protection goals. In this thesis Geographical Information Systems (GIS) are suggested as a tool to realise such integrated models. The main hypothesis in this work is that several natural technical and social systems that share a time-space can be compared and analysed in a GIS. My first objective was to analyze how GIS can support research, planning, and, more specifically, bring a broad scattering of competence together in an interdisciplinary process. In this process GIS was ivestigated as a tool to achieve models that give us a better overview of a problem, a better understanding for the processes involved, aid in foreseeing conflicts between interests, find ecological limits and assist in choosing countermeasures and monitor the result of different programs. The second objective concerns the requirement that models should be comparable and possible to include in other models and that they can be communicated to planners, politicians and the public. For this reason the possibilities to communicate the result and model components of multidimensional and multi-temporal data are investigated. Four examples on the possibilities and problems when using GIS in interdisciplinary studies are presented. In the examples, water plays a central role as a component in questions about development, management and environmental impact. The first articles focus on non-point source pollutants as a problem under growing attention when the big industrial and municipal point sources are brought under control. To manage non-point source pollutants, detailed knowledge about local conditions is required to facilitate precise advices on land use. To estimate the flow of metals and N(itrogen) in an area it is important to identify the soil moisture. Soil moisture changes over time but also significantly in the landscape according to several factors. Here a method is presented that calculate soil moisture over large areas. Man as a hydrologie factor has to be assessed to also understand the relative importance of anthropogen processes. To offer a supplement to direct measurements and add anthropogen factors, a GIS model is presented that takes soil-type, topography, vegetation, land-use, agricultural drainage and relative position in the watershed into account. A method to analyse and visualise development over time and space in the same model is presented in the last empirical study. The development of agricultural drainage can be discussed as a product of several forces here analyzed together and visualized with help of colour coded "Hyper pixels" and maps. Finally a discussion concerning the physiological and psychological possibilities to communicate multidimensional phenomena with the help of pictures and maps is held. The main conclusions in this theses are that GIS offer the possibilities to develop distributed models, e.g., models that calculate effects from a vide range of factors in larger areas and with a much higher spatial resolution than has been possible earlier. GIS also offer a possibility to integrate and communicate information from different disciplines to scientists, decision makers and the public.

Diss. (sammanfattning) Umeå : Umeå universitet, 1993, härtill 6 uppsatser.

digitalisering@umu

AbstractThe advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec. 2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.

AbstractUrban mobility and the transport of people have been increasing in volume inexorably for decades. Despite the advantages and opportunities mobility has brought to our society, there are also severe drawbacks such as the transport sector’s role as one of the main contributors to greenhouse-gas emissions and traffic jams. In the future, an increasing number of people will be living in large urban settings, and therefore, these problems must be solved to assure livable environments. The rapid progress of information and communication, and geographic information technologies, has paved the way for urban informatics and smart cities, which allow for large-scale urban analytics as well as supporting people in their complex mobile decision making. This chapter demonstrates how geosmartness, a combination of novel spatial-data sources, computational methods, and geospatial technologies, provides opportunities for scientists to perform large-scale spatio-temporal analyses of mobility patterns as well as to investigate people’s mobile decision making. Mobility-pattern analysis is necessary for evaluating real-time situations and for making predictions regarding future states. These analyses can also help detect behavioral changes, such as the impact of people’s travel habits or novel travel options, possibly leading to more sustainable forms of transport. Mobile technologies provide novel ways of user support. Examples cover movement-data analysis within the context of multi-modal and energy-efficient mobility, as well as mobile decision-making support through gaze-based interaction.