Land surface is experiencing human interference of unprecedented frequency and intensity, which impairs sustainability. Continuously monitoring the land surface and timely detecting the land cover changes become the key to understand the human-nature interaction and balance the relationship between social development and natural resources. Many Landsat time series-based change detection methods have been developed to capture fine-scale human-induced changes with the temporal accuracy as high as sub-annual level. However, time series dominated the field of continuous change detection while spatial information and spatial-temporal modelling for change detection have not been fully exploited. This research developed a quantile regression-based change detection method that incorporated both spatial information and time series to enhance the change detection performance. The algorithm was verified in central Worcester which experienced intensive human activities at the beginning of 2000s. Comparing with the pixel-based state-of-art, the proposed method reached an acceptable accuracy with 88% F1 score and 16 correctly detected changes out of 25 in total. It had the lowest temporal Root Mean Square Error (RMSE) with 10.9 days, proving its ability of early detection. Besides, it showed the best result compared to the competitors without extra cloud filter, which indicated greater robustness to outliers. The proposed method is one of the first methods that have used spatial-temporal modelling in the continuous change detection framework. Future work will be focused on improving the model development, enhancing the computational efficiency and proving the generalization.

Quantile regression analysis differs from more conventional regression models in its emphasis on distributions. Whereas standard regression procedures show how the expected value of the dependent variable responds to a change in an explanatory variable, quantile regressions imply predicted changes for the entire distribution of the dependent variable. Despite its advantages, quantile regression is still not commonly used in the analysis of spatial data. The objective of this book is to make quantile regression procedures more accessible for researchers working with spatial data sets. The emphasis is on interpretation of quantile regression results. A series of examples using both simulated and actual data sets shows how readily seemingly complex quantile regression results can be interpreted with sets of well-constructed graphs. Both parametric and nonparametric versions of spatial models are considered in detail.

Sequential change point analysis aims to detect structural change as quickly as possible when the process state changes. A good sequential change point detection procedure is expected to minimize the detection delay time and the risk of raising false alarm. Existing sequential change point detection methods cannot be applicable for high-dimensional data because they are univariate in nature and thus present challenges.In the first part of the dissertation, we develop a monitoring method to detect structural change in smoothly clipped absolute deviation (SCAD) penalized regression model for high-dimensional data after the historical sample with the sample size m. The unknown pre-change regression coefficients are replaced by the SCAD penalized estimator. The asymptotic properties of the proposed test statistics are derived. We conduct a simulation study to evaluate the performance of the propose method. The proposed method is applied to the gene expression in the mammalian eye data to detect changes sequentially.In the second part of the dissertation, we develop a sequential change point detection method to monitor structural changes in SACD penalized quantile regression (SPQR) model for high-dimensional data. We derive the asymptotic distributions of the test statistic under the null and alternative hypotheses. Furthermore, to improve the performance of the SPQR method, we propose the Post-SCAD penalized quantile regression estimator (P-SPQR) for high-dimensional data. Simulations are conducted under different scenarios to study the finite sample properties of the SPQR and P-SPQR methods. A real data application is provided to demonstrate the effectiveness of the method.In the third and fourth part of the dissertation, we investigate the change point problem for Skew-Normal distribution and three parameter Weibull distribution respectively. Besides detecting and obtaining the point estimate of a change location, we propose an estimation procedure based on the confidence distribution (CD) along with the modified information criterion (MIC) to construct the confidence set for the change location. Simulations are conducted to evaluate the performance of the proposed method in terms of powers, coverage probabilities and average lengths of confidence sets. Real data applications are provided in each part to illustrate the performance of the proposed methods.

A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and followed by applications using real data. Quantile Regression: Presents a complete treatment of quantile regression methods, including, estimation, inference issues and application of methods. Delivers a balance between methodolgy and application Offers an overview of the recent developments in the quantile regression framework and why to use quantile regression in a variety of areas such as economics, finance and computing. Features a supporting website (www.wiley.com/go/quantile_regression) hosting datasets along with R, Stata and SAS software code. Researchers and PhD students in the field of statistics, economics, econometrics, social and environmental science and chemistry will benefit from this book.

This brief addresses the estimation of quantile regression models from a practical perspective, which will support researchers who need to use conditional quantile regression to measure economic relationships among a set of variables. It will also benefit students using the methodology for the first time, and practitioners at private or public organizations who are interested in modeling different fragments of the conditional distribution of a given variable. The book pursues a practical approach with reference to energy markets, helping readers learn the main features of the technique more quickly. Emphasis is placed on the implementation details and the correct interpretation of the quantile regression coefficients rather than on the technicalities of the method, unlike the approach used in the majority of the literature. All applications are illustrated with R.

Land use and land cover (LULC) as well as its changes (LUCC) are an interplay between bio-geophysical characteristics of the landscape and climate as well as the complex human interaction including its different patterns of utilization superimposed on the natural vegetation. LULC is a core information layer for a variety of scientific and administrative tasks(e.g. hydrological modelling, climate models, land use planning).In particular in the context of climate change with its impacts on socio-economic, socio-ecologic systems as well as ecosystem services precise information on LULC and LUCC are mandatory baseline datasets required over large areas. Remote sensing can provide such information on different levels of detail and in a homogeneous and reliable way. Hence, LULC mapping can be regarded as a prototype for integrated approaches based on spaceborne and airborne remote sensing techniques combined with field observations. The book provides for the first time a comprehensive view of various LULC activities focusing on European initiatives, such as the LUCAS surveys, the CORINE land covers, the ESA/EU GMES program and its resulting Fast-Track- and Downstream Services, the EU JRC Global Land Cover, the ESA GlobCover project as well as the ESA initiative on Essential Climate Variables. All have and are producing highly appreciated land cover products. The book will cover the operational approaches, but also review current state-of-the-art scientific methodologies and recommendations for this field. It opens the view with best-practice examples that lead to a view that exceeds pure mapping, but to investigate into drivers and causes as well as future projections.

Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments. The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.