Understanding and predicting the behaviour of natural and human environmental systems is crucial for the effective management of the Earth’s limited resources. Recently, great advances have been made through spatial modelling. This book provides a snapshot of the latest research in modelling technologies and methodologies within five environmental fields; the cryosphere, hydrology, geomorphology, vegetation interfaces and urban environments. Spatial Modelling of the Terrestrial Environment deals with the use of remote sensing, numerical models and GIS in addressing important natural and human environmental sciences issues, focusing on the theory and application of modelling remotely sensed data within the context of environmental processes. Extensive case material exemplifies the latest research and modelling paradigms presented in the book.
Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.
People are constantly changing the land surface through construction, agriculture, energy production, and other activities. Changes both in how land is used by people (land use) and in the vegetation, rock, buildings, and other physical material that cover the Earth's surface (land cover) can be described and future land change can be projected using land-change models (LCMs). LCMs are a key means for understanding how humans are reshaping the Earth's surface in the past and present, for forecasting future landscape conditions, and for developing policies to manage our use of resources and the environment at scales ranging from an individual parcel of land in a city to vast expanses of forests around the world. Advancing Land Change Modeling: Opportunities and Research Requirements describes various LCM approaches, suggests guidance for their appropriate application, and makes recommendations to improve the integration of observation strategies into the models. This report provides a summary and evaluation of several modeling approaches, and their theoretical and empirical underpinnings, relative to complex land-change dynamics and processes, and identifies several opportunities for further advancing the science, data, and cyberinfrastructure involved in the LCM enterprise. Because of the numerous models available, the report focuses on describing the categories of approaches used along with selected examples, rather than providing a review of specific models. Additionally, because all modeling approaches have relative strengths and weaknesses, the report compares these relative to different purposes. Advancing Land Change Modeling's recommendations for assessment of future data and research needs will enable model outputs to better assist the science, policy, and decisionsupport communities.
At the convergence of the land and sea, coastal environments are some of the most dynamic and populated places on Earth. This book explains how the many varied forms of spatial analysis, including mapping, monitoring and modelling, can be applied to a range of coastal environments such as estuaries, mangroves, seagrass beds and coral reefs. Presenting empirical geographical approaches to modelling, which draw on recent developments in remote sensing technology, geographical information science and spatial statistics, it provides the analytical tools to map, monitor and explain or predict coastal features. With detailed case studies and accompanying online practical exercises, it is an ideal resource for undergraduate courses in spatial science. Taking a broad view of spatial analysis and covering basic and advanced analytical areas such as spatial data and geostatistics, it is also a useful reference for ecologists, geomorphologists, geographers and modellers interested in understanding coastal environments.
Authored by world-class scientists and scholars, The Handbook of Natural Resources, Second Edition, is an excellent reference for understanding the consequences of changing natural resources to the degradation of ecological integrity and the sustainability of life. Based on the content of the bestselling and CHOICE-awarded Encyclopedia of Natural Resources, this new edition demonstrates the major challenges that the society is facing for the sustainability of all well-being on the planet Earth. The experience, evidence, methods, and models used in studying natural resources are presented in six stand-alone volumes, arranged along the main systems of land, water, and air. It reviews state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of remote sensing and geospatial data with field-based measurements in the study of natural resources. Volume 1, Terrestrial Ecosystems and Biodiversity, provides fundamental information on terrestrial ecosystems, approaches to monitoring, and impacts of climate change on natural vegetation and forests. New to this edition are discussions on biodiversity conservation, gross and net primary production, soil microbiology, land surface phenology, and decision support systems. This volume demonstrates the key processes, methods, and models used through many case studies from around the world. Written in an easy-to-reference manner, The Handbook of Natural Resources, Second Edition, as individual volumes or as a complete set, is an essential reading for anyone looking for a deeper understanding of the science and management of natural resources. Public and private libraries, educational and research institutions, scientists, scholars, and resource managers will benefit enormously from this set. Individual volumes and chapters can also be used in a wide variety of both graduate and undergraduate courses in environmental science and natural science at different levels and disciplines, such as biology, geography, earth system science, and ecology.
This book examines the impacts of global change on terrestrial ecosystems. Emphasis is placed on impacts of atmospheric, climate and land use change, and the book discusses the future challenges and the scientific frameworks to address them. Finally, the book explores fundamental new research developments and the need for stronger integration of natural and human dimensions in addressing the challenge of global change.
Expanding the risk assessment toolbox, this book provides a comprehensive and practical evaluation of specific ecological models for potential use in risk assessment. Ecological Modeling in Risk Assessment: Chemical Effects on Populations, Ecosystems, and Landscapes goes beyond current risk assessment practices for toxic chemicals as applied to individual-organism endpoints to describe ecological effects models useful at the population, ecosystem, and landscape levels. The authors demonstrate the utility of a set of ecological effects models, eventually improving the ecological relevance of risk assessments and making data collection more cost effective.
Spatial predictive modeling (SPM) is an emerging discipline in applied sciences, playing a key role in the generation of spatial predictions in various disciplines. SPM refers to preparing relevant data, developing optimal predictive models based on point data, and then generating spatial predictions. This book aims to systematically introduce the entire process of SPM as a discipline. The process contains data acquisition, spatial predictive methods and variable selection, parameter optimization, accuracy assessment, and the generation and visualization of spatial predictions, where spatial predictive methods are from geostatistics, modern statistics, and machine learning. The key features of this book are: •Systematically introducing major components of SPM process. •Novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods. •Novel predictive accuracy-based variable selection techniques for spatial predictive methods. •Predictive accuracy-based parameter/model optimization. •Reproducible examples for SPM of various data types in R. This book provides guidelines, recommendations, and reproducible examples for developing optimal predictive models by considering various components and associated factors for quality-improved spatial predictions. It provides valuable tools for researchers, modelers, and university students not only in SPM field but also in other predictive modeling fields. Dr Li has produced over 100 various publications in spatial predictive modelling, statistical computing, ecological and environmental modelling, and ecology, developed a number of hybrid methods for SPM, and published four R packages for variable selections as well as SPM.
This is a book about how ecologists can integrate remote sensing and GIS in their daily work. It will allow ecologists to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. All practical examples in this book rely on OpenSource software and freely available data sets. Quantum GIS (QGIS) is introduced for basic GIS data handling, and in-depth spatial analytics and statistics are conducted with the software packages R and GRASS. Readers will learn how to apply remote sensing within ecological research projects, how to approach spatial data sampling and how to interpret remote sensing derived products. The authors discuss a wide range of statistical analyses with regard to satellite data as well as specialised topics such as time-series analysis. Extended scripts on how to create professional looking maps and graphics are also provided. This book is a valuable resource for students and scientists in the fields of conservation and ecology interested in learning how to get started in applying remote sensing in ecological research and conservation planning.
