The thesis describes Random Coefficient Autoregressive time series mo- dels (RCA models). In first chapter we introduce different types of estimati- ons for coefficients of RCA model. Main part is in second chapter, where we describe detection changes procedures for all methods mentioned in chapter one, here the thesis expands the current theory about change detection of wei- ghted least square method and functional estimation. In last chapter we sum- marize results of simulation study. 1

<p>Accurate measurements from sensors measuring the vehicle's lateral behavior are vital in todays vehicle dynamic control systems such as the Electronic Stability Program (ESP). This thesis concerns accurate plausibilisation of two of these sensors, namely the yaw rate sensor and the lateral acceleration sensor. The estimation is based on Kalman filtering and culminates in the use of a 2 degree-of-freedom nonlinear two-track model describing the vehicle lateral dynamics. The unknown and time-varying cornering stiffnesses are adapted while the unknown yaw moment of inertia is estimated. The Kalman filter transforms the measured signals into a sequence of residuals that are then investigated with the aid of various change detection methods such as the CuSum algorithm. An investigation into the area of adaptive thresholding has also been made.</p><p>The change detection methods investigated successfully detects faults in both the yaw rate and the lateral acceleration sensor. It it also shown that adaptive thresholding can be used to improve the diagnosis system. All of the results have been evaluated on-line in a prototype vehicle with real-time fault injection.</p>

<p>The longitudinal controller at DaimlerChrysler AG consists of two cascade controllers. The outer control loop contains the driver assistance functions such as speed limiter, cruise control, etc. The inner control loop consists of a PID-controller and an observer. The task of the observer is to estimate the part of the vehicle's acceleration caused by large disturbances, for example by a changed vehicle mass or the slope of the road.</p><p>As observer the Kalman filter is selected. It is the optimal filter when the process model is linear and the process noise and measurement noise can be modeled as Gaussian noise. In this Master's thesis the theory for the Kalman filter is presented and it is shown how to choose the filter parameters. Simulated annealing is a global optimization technique which can be used when autotuning, i.e., automatically find the optimal parameter settings. To be able to perform autotuning for the longitudinal controller one has to model the environment and driving situations.</p><p>In this Master's thesis it is verified that the parameter choice is a compromise between a fast but jerky, or a slow but smooth estimate. As the output from the Kalman filter is directly added to the control value for the engine and brakes, it is important that the output is smooth. It is shown that the Kalman filter implemented in the test vehicles today can be exchanged with a first-order lag function, without loss in performance. This makes the filter tuning easier, as there is only one parameter to choose.</p><p>Change detection is a method that can be used to detect large changes in the signal, and react accordingly - for example by making the filter faster. A filter using change detection is implemented and simulations show that it is possible to improve the estimate using this method. It is suggested to implement the change detection algorithm in a test vehicle and evaluate it further.</p>

<p>This project analyzes satellite images from 1976, 1985 and 2000 of Dutse, Jigawa state, in north-western Nigeria. The analyzed satellite images were used to determine land-use and vegetation changes that have occurred in the land-use from 1976 to 2000 will help recommend possible planning measures in order to protect the vegetation from further deterioration.</p><p>Studying land-use change in north-western Nigeria is essential for analyzing various ecological and developmental consequences over time. The north-western region of Nigeria is of great environmental and economic importance having land cover rich in agricultural production and livestock grazing. The increase of population over time has affected the land-use and hence agricultural and livestock production.</p><p>On completion of this project, the possible land use changes that have taken place in Dutse will be analyzed for future recommendation. The use of supervised classification and change detection of satellite images have produced an economic way to quantify different types of landuse and changes that has occurred over time.</p><p>The percentage difference in land-use between 1976 and 2000 was 37%, which is considered to be high land-use change within the period of study. The result in this project is being used to propose planning strategies that could help in planning sustainable land-use and diversity in Dutse.</p>

Forest ecosystems have recently received worldwide attention due to their biological diversity and their major role in the global carbon balance. Detecting forest cover change is crucial for reporting forest status and assessing the evolution of forested areas. However, existing change detection approaches based on satellite remote sensing are not quite appropriate to rapidly process the large volume of earth observation data. Recent advances in image segmentation have led to new opportunities for a new object-based monitoring system. <br> <br> This thesis aims at developing and evaluating an automated object-based change detection method dedicated to high spatial resolution satellite images for identifying and mapping forest cover changes in different ecosystems. This research characterized the spectral reflectance dynamics of temperate forest stand cycle and found the use of several spectral bands better for the detection of forest cover changes than with any single band or vegetation index over different time periods. Combining multi-date image segmentation, image differencing and a dedicated statistical procedure of multivariate iterative trimming, an automated change detection algorithm was developed. This process has been further generalized in order to automatically derive an up-to-date forest mask and detect various deforestation patterns in tropical environment.<br> <br> Forest cover changes were detected with very high performances (>90 %) using 3 SPOT-HRVIR images over temperate forests. Furthermore, the overall results were better than for a pixel-based method. Overall accuracies ranging from 79 to 87% were achieved using SPOT-HRVIR and Landsat ETM imagery for identifying deforestation for two different case studies in the Virunga National Park (DRCongo). Last but not least, a new multi-scale mapping solution has been designed to represent change processes using spatially-explicit maps, i.e. deforestation rate maps. By successfully applying these complementary conceptual developments, a significant step has been done toward an operational system for monitoring forest in various ecosystems.

Ground penetrating radar (GPR) is a promising technique for humanitarian demining applications as it permits providing useful information about the subsurface based on wave reflections produced by electromagnetic (EM) contrasts. Yet, landmine detection using GPR can suffer from: (1) clutter, i.e, undesirable effects from antenna coupling, system ringing and soil surface and subsurface reflections; (2) false alarms, e.g., reflections from buried mine-like objects such as stones or metallic debris; (3) effects of soil properties on the GPR performance, such as attenuation. This thesis addresses these topics in an integrated approach aiming at reducing clutter, identifying landmines from false alarms and analysing GPR performance. For subtracting undesirable reflections, a new physically-based filtering algorithm is developed, which takes into account major antenna effects and soil surface reflection. It is applied in conjunction with a change detection algorithm for enhancing landmine detection. Landmine identification is performed using discriminant characteristics extracted from the pre-filtered data by a novel feature extraction approach in the time-frequency domain. For analysing the effects of soil properties, in particular soil dielectric permittivity, an EM model is coupled to pedotransfer functions for estimating the GPR performance on a given soil. The developed algorithms are validated using data acquired by two different hand-held GPR systems. Promising results are obtained under laboratory and outdoor conditions, where different types of soil (including real mine-affected soils) and landmines (including improvised explosive devices) are considered.

Ground penetrating radar (GPR) is a promising technique for humanitarian demining applications as it permits providing useful information about the subsurface based on wave reflections produced by electromagnetic (EM) contrasts. Yet, landmine detection using GPR can suffer from: (1) clutter, i.e, undesirable effects from antenna coupling, system ringing and soil surface and subsurface reflections; (2) false alarms, e.g., reflections from buried mine-like objects such as stones or metallic debris; (3) effects of soil properties on the GPR performance, such as attenuation. This thesis addresses these topics in an integrated approach aiming at reducing clutter, identifying landmines from false alarms and analysing GPR performance. For subtracting undesirable reflections, a new physically-based filtering algorithm is developed, which takes into account major antenna effects and soil surface reflection. It is applied in conjunction with a change detection algorithm for enhancing landmine detection. Landmine identification is performed using discriminant characteristics extracted from the pre-filtered data by a novel feature extraction approach in the time-frequency domain. For analysing the effects of soil properties, in particular soil dielectric permittivity, an EM model is coupled to pedotransfer functions for estimating the GPR performance on a given soil. The developed algorithms are validated using data acquired by two different hand-held GPR systems. Promising results are obtained under laboratory and outdoor conditions, where different types of soil (including real mine-affected soils) and landmines (including improvised explosive devices) are considered.

Hurricane Hugo struck the coast of South Carolina on September 21, 1989 as a category 4 hurricane on the Saffir-Simpson Scale. Landsat Thematic mapper was utilized to determine the extent of damage experienced at the Santee Experimental Forest (SEF) (a part of Francis Marion National Forest) in South Carolina. Normalized Difference Vegetation Index (NDVI) and the change detection techniques were used to determine initial forest damage and to monitor the recovery over a 22-year period following Hurricane Hugo. According to the results from the NDVI analysis the SEF made a full recovery after a 10-year period. The remote sensing techniques used were effective in identifying the damage as well as the recovery.

Fine-scale resolution digital elevation models (DEMs) created from data collected using high precision instruments have become ubiquitous in fluvial geomorphology. They permit a diverse range of spatially explicit analyses including hydraulic modeling, habitat modeling and geomorphic change detection. Yet, the intercomparison of survey technologies across a diverse range of wadeable stream habitats has not yet been examined. Additionally, we lack an understanding regarding the precision of DEMs derived from ground-based surveys conducted by different, and inherently subjective, observers. This thesis addresses current knowledge gaps with the objectives i) to intercompare survey techniques for characterizing instream topography, and ii) to characterize observer variability in instream topographic surveys. To address objective i, we used total station (TS), real-time kinematic (rtk) GPS, terrestrial laser scanner (TLS), and infrared airborne laser scanning (ALS) topographic data from six sites of varying complexity in the Lemhi River Basin, Idaho. The accuracy of derived bare earth DEMs was evaluated relative to higher precision TS point data. Significant DEM discrepancies between pairwise techniques were calculated using propagated DEM errors thresholded at a 95% confidence interval. Mean discrepancies between TS and rtkGPS DEMs were relatively low (≤ 0.05 m), yet TS data collection time was up to 2.4 times longer than rtkGPS. ALS DEMs had lower accuracy than TS or rtkGPS DEMs, but ALS aerial coverage and floodplain topographic representation was superior to all other techniques. The TLS bare earth DEM accuracy and precision were lower than other techniques as a result of vegetation returns misinterpreted as ground returns. To address objective ii, we used a case study where seven field crews surveyed the same six sites to quantify the magnitude and effect of observer variability on DEMs interpolated from the survey data. We modeled two geomorphic change scenarios and calculated net erosion and deposition volumes at a 95% confidence interval. We observed several large magnitude elevation discrepancies across crews, however many of these i) tended to be highly localized, ii) were due to systematic errors, iii) did not significantly affect DEM-derived metric precision, and iv) can be corrected post-hoc.

Rapid urban growth and unprecedented rural to urban transition, along with a huge population growth are new phenomena for both high and low income countries, which started in the mid-20th century. However, urban growth rates and patterns are different in developed countries and developing ones. In less developed countries, urbanization and rural to urban transition usually takes place in an unmanaged way and they are associated with a series of socioeconomical and environmental issues and problems. Identification of the city growth trends in past decades can help urban planners and managers to minimize these negative impacts. In this research, urban growth in the city of Isfahan, Iran, is the subject of study. Isfahan the third largest city in Iran has experienced a huge urban growth and population boom during the last three decades. This transition led to the destruction of natural and agricultural lands and environmental pollutions. Historical and recent remotely sensed data, along with different remote sensing techniques and methods have been used by researchers for urban land use and land cover change detection. In this study three Landsat TM and ETM+ images of the study site, acquired in 1985, 2000 and 2009 are used. Before starting processing, radiometric normalization is done to minimize the atmospheric effects. Then, processing methods including principal component analysis (PCA), vegetation indices and supervised classification are implemented on the images. Accuracy assessment of the PCA method showed that the first PC was responsible for more than 81% of the total variance, and therefore used for analysis of PCA differencing. ΔPC1t1-t2 shows the amount of changes in land use and land cover during the period of study. In this study ten vegetation indices were selected to be applied to the 1985 image. Accuracy assessments showed that Transformed Differencing Vegetation Index (TDVI) is the most sensitive and accurate index for mapping vegetation in arid and semi-arid urban areas. Hence, TDVI was applied to the 2000 and 2009 images. ΔTDVIt1-t2 showed the changes in land use and land cover especially the land use transformation from vegetation cover into the urban class. Supervised classification is the last method applied to the images. Training sites were assigned for the selected classes and accuracy was monitored during the process of training site selection. The results of classification show the expansion of urban class and diminishment in natural and agricultural lands.