Spatial Modeling In Forest Resources Management

This book demonstrates the measurement, monitoring, mapping, and modeling of forest resources. It explores state-of-the-art techniques based on open-source software & R statistical programming and modeling specifically, with a focus on the recent trends in data mining/machine learning techniques and robust modeling in forest resources. Discusses major topics such as forest health assessment, estimating forest biomass & carbon stock, land use forest cover (LUFC), dynamic vegetation modeling (DVM) approaches, forest-based rural livelihood, habitat suitability analysis, biodiversity and ecology, and biodiversity, the book presents novel advances and applications of RS-GIS and R in a precise and clear manner. By offering insights into various concepts and their importance for real-world applications, it equips researchers, professionals, and policy-makers with the knowledge and skills to tackle a wide range of issues related to geographic data, including those with scientific, societal, and environmental implications.

At the end of the 1970s, when signs of destabilization of forests became visible in Eu rope on a large scale, it soon became obvious that the syndrome called "forest de cline" was caused by a network of interrelated factors of abiotic and biotic origin. All attempts to explain the wide-spread syndrome by a single cause, and there were many of them, failed or can only be regarded as a single mosaic stone in the network of caus es behind the phenomenon. Forest ecosystems are highly complex natural or quasi natural systems, which exhibit different structures and functions and as a conse quence different resilience to internal or external stresses. Moreover, forest ecosys tems have a long history, which means that former impacts may act as predisposing factors for other stresses. The complexity and the different history of forest ecosys tems are two reasons that make it difficult to assess the actual state and future devel opment of forests. But there are two other reasons: one is the large time scale in which forests react, the other is the idiosyncrasy of the reactions on different sites. Due to the slow reaction and the regional complexity of the abiotic environment of forest ecosys tems, a profound analysis of each site and region is necessary to identify the underly ing causes and driving forces when attempting to overcome the destruction of forest ecosystems.

Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions. The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling. Offers a clear, interdisciplinary guide to serve researchers in a variety of fields, including hazards, land surveying, remote sensing, cartography, geophysics, geology, natural resources, environment and geography Provides an overview, methods and case studies for each application Expresses concepts and methods at an appropriate level for both students and new users to learn by example

Key researchers present newly emerging approaches to computer simulation models of large, forest landscapes.

Modelling is an important tool for understanding the complexity of forest ecosystems and the variety of interactions of ecosystem components, processes and values. This book describes the hybrid approach to modelling forest ecosystems and their possible response to natural and management-induced disturbance. The book describes the FORECAST family of ecosystem management models at three different spatial scales (tree, stand and landscape), and compares them with alternative models at these three spatial scales. The book will help forest managers to understand what to expect from ecosystem-based forest models; serve as a tool for use in teaching about sustainability, scenario analysis and value trade-offs in natural resources management; and assist policy makers, managers and researches working in assessment of sustainable forest management and ecosystem management. Several real-life examples of using the FORECAST family of models in forest management and other applications are presented from countries including Canada, China, Spain and the USA, to illustrate the concepts described in the text. The book also demonstrates how these models can be extended for scenario and value trade-off analysis through visualization and educational or management games.

Stress on natural resources has recently increased due to commercialization and the need to provide livelihoods for locals. Because they are such core parts of everyday life, ensuring sustainability in resource management is of paramount importance. Only by integrating the tools of spatial information science can an effective course for preserving and protecting natural resources be created. Spatial Information Science for Natural Resource Management is a pivotal reference source that explores coordinated approaches to sustainable development and management of natural resources to keep a balance of the environment, ecology, and human livelihood. Featuring coverage on a wide range of topics including crop yield estimation, ecosystem services, and land information systems, this book covers interdisciplinary techniques in monitoring and managing natural resources. This publication is ideally designed for urban planners, environmentalists, policymakers, ecologists, researchers, academicians, students, and professionals in the fields of remote sensing, civil engineering, social science, computer science, and information technology.