Abstract: Land cover mapping and monitoring has been widely recognized as important for understanding global change and in particular, human contributions.This research emphasizes the use of the time domain for mapping land cover and changes in land cover using satellite images. Unlike most prior methods that compare pairs or sets of images for identifying change, this research compares observations with model predictions. Moreover, instead of classifying satellite images directly, it uses coefficients from time series models as inputs for land cover mapping. The methods developed are capable of detecting many kinds of land cover change as they occur and providing land cover maps for any given time at high temporal frequency.One key processing step of the satellite images is the elimination of "noisy" observations due to clouds, cloud shadows, and snow. I developed a new algorithm called Fmask that processes each Landsat scene individually using an object-based method. For a globally distributed set of reference data, the overall cloud detection accuracy is 96%. A second step further improves cloud detection by using temporal information.The first application of the new methods based on time series analysis found change in forests in an area in Georgia and South Carolina. After the difference between observed and predicted reflectance exceeds a threshold three consecutive times a site is identified as forest disturbance. Accuracy assessment reveals that both the producers and users accuracies are higher than 95% in the spatial domain and approximately 94% in the temporal domain.The second application of this new approach extends the algorithm to include identification of a wide variety of land cover changes as well as land cover mapping. In this approach, the entire archive of Landsat imagery is analyzed to produce a comprehensive land cover history of the Boston region. The results are accurate for detecting change, with producers accuracy of 98% and users accuracies of 86% in the spatial domain and temporal accuracy of 80%. Overall, this research demonstrates the great potential for use of time series analysis of satellite images to monitor land cover change

Although the development of remote sensing techniques focuses greatly on construction of new sensors with higher spatial and spectral resolution, it is advisable to also use data of older sensors (especially, the LANDSAT-mission) when the historical mapping of land use/land cover and monitoring of their dynamics are needed. Using data from LANDSAT missions as well as from Terra (ASTER) Sensors, the authors shows in his book maps of historical land cover changes with a focus on agricultural irrigation projects. The kernel of this study was whether, how and to what extent applying the various remotely sensed data that were used here, would be an effective approach to classify the historical and current land use/land cover, to monitor the dynamics of land use/land cover during the last four decades, to map the development of the irrigation areas, and to classify the major strategic winter- and summer-irrigated agricultural crops in the study area of the Euphrates River Basin.

Land cover assessment and monitoring of its dynamics are essential requirements for the sustainable management of natural resources, environmental protection, food security, humanitarian programmes as well as core data for monitoring and modelling. Land Cover (LC) data are therefore fundamental in fulfilling the mandates of many United Nations (UN), international and national institutions and programmes. Despite the recognition of such importance, current users of LC data still lack access to sufficient reliable or comparable baseline LC data. These data are essential to tackle the increasing concerns in regard to food security, environmental degradation, and climate change. Critically, maintaining and restoring land resources plays a vital task in tackling climate change, securing biodiversity, and maintaining crucial ecosystem services, while ensuring resilient livelihoods and food security.

Mixed-Phase Clouds: Observations and Modeling presents advanced research topics on mixed-phase clouds. As the societal impacts of extreme weather and its forecasting grow, there is a continuous need to refine atmospheric observations, techniques and numerical models. Understanding the role of clouds in the atmosphere is increasingly vital for current applications, such as prediction and prevention of aircraft icing, weather modification, and the assessment of the effects of cloud phase partition in climate models. This book provides the essential information needed to address these problems with a focus on current observations, simulations and applications. - Provides in-depth knowledge and simulation of mixed-phase clouds over many regions of Earth, explaining their role in weather and climate - Features current research examples and case studies, including those on advanced research methods from authors with experience in both academia and the industry - Discusses the latest advances in this subject area, providing the reader with access to best practices for remote sensing and numerical modeling

Filling the need for a comprehensive book that covers both theory and application, Remote Sensing of Land Use and Land Cover: Principles and Applications provides a synopsis of how remote sensing can be used for land-cover characterization, mapping, and monitoring from the local to the global scale. With contributions by leading scientists from aro