Most of the scientific methods devised for forest planning support timber production ignoring the existence of forest functions other than wood production. Fortunately, the realisation that the forest planning methods available today do not correspond to the needs of today's forestry has activated forest researchers to develop and adopt new methodologies and approaches, which are specifically aimed at multi-objective situations. This book is about the quantitative approach to multi-objective forest planning. The emphasis is on topics that are rather new and not yet systematically applied in forest planning practice. The topics and methodologies discussed in this book include: measurement of preferences, multiple criteria decision analysis, use of GIS to support multi-objective forest management, heuristic optimization, spatial optimisation, and the measurement of non-wood forest outputs. By reading the book, a planning specialist, student or a researcher will get an insight into some of the current developments in forest planning research.

This book describes the changing landscape of European forest ownership and explores the impact a new, often urban, generation of forest owners may have on the future of one of our most basic resources – forests. Forests have not played a major role in rural studies thus far, however they constitute an important part of many rural areas. Drawing on Swedish cases and comparison cases from various other areas of Europe, the authors present these ‘new forest owners’ as a pivotal factor in the changing relationships between urban and rural life. The chapters explore how forest production, the relationship to the environment, urban-rural relations and local communities have already changed as well as discussing what might be expected for the future. A result of work in the Swedish research programme PLURAL and related projects, such as the EU Cost Action FACESMAP, this volume will be of interest to scholars of forestry and rural studies, as well as to researchers in environmental, population and globalization studies more broadly.

Map packet : Modified Alternative 8 -- Series normalized Sierra Nevada Ecosystem Project late seral old growth ranks by forest type -- Herger-Feinstein Quincy Library Group land allocations.

The goal of Kangas, Kangas and Kurttila's Decision Support for Forest Management is to provide students and researchers with a toolbox of methods for approaching the different planning situations that may arise in practice. It draws together a wide range of methods used in planning forest management regimes and presents a systematic overview of current methodological approaches. While earlier books concerning forest planning have tended to focus on linear programming, economic aspects, or specific multi-criteria decision aid tools, this book provides a much broader range of tools to meet a variety of planning situations. The methods themselves cover a range of decision situations – from cases involving single decision makers, through group decision making, to participatory planning. They include traditional decision support tools, from optimization to utility functions, as well as methods that are just gaining ground in forest planning – such as problem structuring methods and social choice theory. Including examples which illustrate the application of each technique to specific management planning problems, the book offers an invaluable resource for both researchers and advanced students specializing in management and planning issues relating to forestry.

Here is the first systematic handbook treatment of quantitative modeling natural resource problems, their allocated efficient use, and societal and economic impact. Andrés Weintraub is the very top person in Natural Resource research. He has selected co-editors who are at the top of the sub-fields in natural resources: agriculture, fisheries, forestry, and mining. The book covers these areas with contributions from researchers on, among others, modeling natural research problems, quantifying data, and developing algorithms.