Abel Ramoelo – författare

This book places an emphasis upon statistical methodology, data interpretation, and futureproofing with the intention of advancing statistics for the environment, ecology, and environmental health, in addition to environmental theory and practice, via the application of reliable statistics.With a focus on advances in statistical methodology and application within the environmental sciences, the overarching purpose of this volume is to illuminate current trends, stimulate a focus on, and connect multidisciplinary domains originating from and within statistical analysis, development, and research. Given that the contributions consist of current improvements and new innovations in climate and environmental science research that are based on statistical theory, researchers can derive inspiration for future advancements or similar analyses on other environmental data.Authored by internationally renowned scholars, this book is organized into three parts: Part I on Supervised and Unsupervised Learning, Part II on Directional Statistics, and Part III on Spatial and Temporal Modeling. Primarily intended as a reference book for academic researchers and graduate-level students in statistics as well as multidisciplinary domains, the chapters reflect a shared commitment to advancing methodological rigor while addressing real-world environmental concerns. They illustrate how environmental complexity drives the evolution of statistical thinking—and how statistical insight, in turn, informs meaningful action.Key FeaturesEmphasizes the ongoing necessity to progress basic statistical theory and explores its relevance to environmental researchEmploys multidisciplinary approaches and applications, appealing to a broader audience beyond solely theoretical statisticsEnhances the collective understanding of the ecosystem’s diverse perspectives to ensure the welfare of present and future generationsWritten by renowned subject matter experts and researchers, making it appealing to scholars from diverse fieldsThe statistical framework is not limited to a single methodology based on data complexity but promotes different techniques

This book places an emphasis upon statistical methodology, data interpretation, and futureproofing with the intention of advancing statistics for the environment, ecology, and environmental health, in addition to environmental theory and practice, via the application of reliable statistics.With a focus on advances in statistical methodology and application within the environmental sciences, the overarching purpose of this volume is to illuminate current trends, stimulate a focus on, and connect multidisciplinary domains originating from and within statistical analysis, development, and research. Given that the contributions consist of current improvements and new innovations in climate and environmental science research that are based on statistical theory, researchers can derive inspiration for future advancements or similar analyses on other environmental data.Authored by internationally renowned scholars, this book is organized into three parts: Part I on Supervised and Unsupervised Learning, Part II on Directional Statistics, and Part III on Spatial and Temporal Modeling. Primarily intended as a reference book for academic researchers and graduate-level students in statistics as well as multidisciplinary domains, the chapters reflect a shared commitment to advancing methodological rigor while addressing real-world environmental concerns. They illustrate how environmental complexity drives the evolution of statistical thinking-and how statistical insight, in turn, informs meaningful action.Key Features Emphasizes the ongoing necessity to progress basic statistical theory and explores its relevance to environmental research Employs multidisciplinary approaches and applications, appealing to a broader audience beyond solely theoretical statistics Enhances the collective understanding of the ecosystem's diverse perspectives to ensure the welfare of present and future generations Written by renowned subject matter experts and researchers, making it appealing to scholars from diverse fields The statistical framework is not limited to a single methodology based on data complexity but promotes different techniques

This book places an emphasis upon statistical methodology, data interpretation, and futureproofing with the intention of advancing statistics for the environment, ecology, and environmental health, in addition to environmental theory and practice, via the application of reliable statistics.With a focus on advances in statistical methodology and application within the environmental sciences, the overarching purpose of this volume is to illuminate current trends, stimulate a focus on, and connect multidisciplinary domains originating from and within statistical analysis, development, and research. Given that the contributions consist of current improvements and new innovations in climate and environmental science research that are based on statistical theory, researchers can derive inspiration for future advancements or similar analyses on other environmental data.Authored by internationally renowned scholars, this book is organized into three parts: Part I on Supervised and Unsupervised Learning, Part II on Directional Statistics, and Part III on Spatial and Temporal Modeling. Primarily intended as a reference book for academic researchers and graduate-level students in statistics as well as multidisciplinary domains, the chapters reflect a shared commitment to advancing methodological rigor while addressing real-world environmental concerns. They illustrate how environmental complexity drives the evolution of statistical thinking-and how statistical insight, in turn, informs meaningful action.Key Features Emphasizes the ongoing necessity to progress basic statistical theory and explores its relevance to environmental research Employs multidisciplinary approaches and applications, appealing to a broader audience beyond solely theoretical statistics Enhances the collective understanding of the ecosystem's diverse perspectives to ensure the welfare of present and future generations Written by renowned subject matter experts and researchers, making it appealing to scholars from diverse fields The statistical framework is not limited to a single methodology based on data complexity but promotes different techniques

This edited volume focuses on the use of remote sensing techniques to assess and monitor mountainous ecosystems in Africa, with a focus on tracking changes related to climate change and human activity. The book is timely, as the interaction of mountain environmental dynamics with conservation and sustainability is an under-researched issue. The chapters in this volume use remotely sensed data to study a variety of topics related to mountains and their ecosystems, including but not limited to vegetation, energy systems, environmental hazards, ecosystem services, diseases, climatic shifts, geological formations and geomorphological dynamics. The ability to monitor, assess and analyze mountainous regions is aided by the availability of remote sensing products such as optical and microwave sensors and low-cost unmanned aerial vehicles (UAVs). The works presented here push the frontier of knowledge on mountain studies and will help shape local, national and global assessments and policies, including efforts toward the achievement of the African Agenda 2063. The book will be of interest to researchers and students in remote sensing, geography, ecology and sustainability, as well as to government organizations and conservation specialists.