Computer Applications in Sustainable Forest Management - Shao, Guofan / Reynolds, Keith M. (eds.)
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This book is the most comprehensive and up-to-date treatment of computer applications in forestry. It is the first text on software for forest management to emphasize integration of computer applications. It also offers important new insights on how to continue advancing computational technologies in forest management.
The authors are internationally-recognized authorities in the subjects presented.
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Produktbeschreibung
This book is the most comprehensive and up-to-date treatment of computer applications in forestry. It is the first text on software for forest management to emphasize integration of computer applications. It also offers important new insights on how to continue advancing computational technologies in forest management.

The authors are internationally-recognized authorities in the subjects presented.

  • Produktdetails
  • Managing Forest Ecosystems 11
  • Verlag: Springer / Springer Netherlands
  • 2006
  • Seitenzahl: 312
  • Erscheinungstermin: 15. September 2006
  • Englisch
  • Abmessung: 241mm x 160mm x 24mm
  • Gewicht: 688g
  • ISBN-13: 9781402043055
  • ISBN-10: 1402043058
  • Artikelnr.: 20946372
Autorenporträt
Forests provide goods and services essential to human societies and the natural environment, but they are disappearing faster today than ever before. What should forestry do in the information era? How can we use computer technologies to better manage forests in a sustainable manner? Computer Applications in Sustainable Forest Management provides critical information needed for forestry professionals to address such questions. Computer Applications in Sustainable Forest Management presents state-of-the-art computer applications in a variety of specialty areas of forestry, including inventory, remote sensing, information management, modelling and visualization, biometrics, forest and harvest planning, bioeconomics and marketing, and decision science for management. This book emphasizes integration, or collaborative use, of computer technologies across different disciplines through interdisciplinary research and development in North America, China, and Europe. It also offers important new insights on how to continue advancing computational technologies in forest management to better achieve the basic goal of sustainable forest management. This book will be a valuable technical resource for resource managers, planners, administrators, researchers, educators, graduate students, and senior undergraduate students in the field of forestry. Case studies demonstrate integration of, or collaboration among, multiple computer applications for sustainable forest management.
Inhaltsangabe
Contributing authors. Preface. Part I: Introduction. 1. Introduction. 1.1 What is digital forestry? 1.2 Contemporary computer applications in forestry. 1.2.1 Remote sensing 1.2.2 Geographic information systems. 1.2.3 Modeling and simulation. 1.2.4 Visualization. 1.2.5 Decision making. 1.3. Overview of chapters. 1.4. Goals and objectives of this volume.- Part II: Core technologies. 2. High-spatial-resolution remote sensing. 2.1 Introduction.2.2 Tree delineation approaches. 2.2.1 Local-maxima approaches. 2.2.2 Boundary-seeking approaches. 2.2.3 Region-based segmentation. 2.2.4 Template matching. 2.2.5 Model-based approach in 3D. 2.3 Identifying species. 2.3.1 Spectral features and tree polygons. 2.3.2 Spatial features. 2.3.3 Temporal information for classification. 2.4 Developing stand maps. 2.5 Tree health. 2.6 Future directions and issues.- 3. Active remote sensing. 3.1 Introduction 3.2 Active, high-resolution airborne remote sensing technologies for precision forestry. 3.3 Principles of airborne laser scanning. 3.4 Lidar terrain mapping in forested areas. 3.5 Lidar for forest inventory applications. 3.6 Principles of interferometric synthetic aperture radar. 3.7 IFSAR Terrain mapping in forested areas. 3.8 Multi-frequency IFSAR for forest inventory applications. 3.9 Conclusions.- 4. Forest information systems. 4.1 Introduction. 4.2 The nature of information. 4.3 The nature of forest information systems. 4.4 A typology of forest information systems. 4.4.1 Monitoring and control systems. 4.4.2 Conventional information systems. 4.4.3 Evaluation and analysis systems. 4.4.4 Decision-support systems. 4.4.5 Integrated information systems. 4.5 Methodological components of information systems. 4.5.1 Database systems and geographic information systems. 4.5.2 Knowledge-based systems. 4.5.3 Modeling and simulation. 4.5.4 User interfaces and software ergonomics. 4.5.5 Computer graphicsand visualization. 4.5.6 Artificial neural networks and fuzzy logic. 4.5.7 Integration. 4.5.8 Other relevant methods. 4.6 Conclusions.- 5. Road and harvesting planning and operations. 5.1 Introduction. 5.2 Forest road design and location planning. 5.3 Harvest planning. 5.4 Harvesting operations. 5.4.1 Computer simulation. 5.4.2 Real-time decision making - optimizing in-woods log processing. 5.5 Road operations. 5.6 Concluding comments.- 6. Forest simulation models. 6.1 Introduction. 6.2 Forest simulation models. 6.2.1 Forest growth and yield models. 6.2.2 Forest succession models (gap models). 6.2.3 Forest process-based models. 6.2.4 Hybrid models. 6.3. Application of forest simulation models: Four case studies in Canada. 6.3.1 Case I: Red pine (Pinus resinosa) density management diagram for Ontario. 6.3.2 Case II: Simulating effects of climate change on species composition of boreal ecosystem using FORSKA 2.0. 6.3.3 Case III: Simulating effect of climate change and fire disturbances on carbon dynamics of boreal forests using CENTURY 4.0. 6.3.4 Case IV: Predicting forest growth and yield of boreal forests in Northern Ontario using TRIPLEX1.0. 6.4 Challenges and directions. 6.4.1 Modeling ecosystem sustainability. 6.4.2 Diversified forest modeling approaches.- 7. Visualization with spatial data. 7.1 Introduction. 7.2 Visualization techniques. 7.3 Contemporary 3D visualization. 7.4 Visualization in forest planning. 7.4.1 Application areas of visualization tools. 7.4.2 Spatial data and visualization. 7.5 Examples of visualization. 7.6 Concluding remarks. 8. Computer-aided decision making. 8.1 Introduction. 8.2 Mathematical programming. 8.3 Expert systems. 8.4 Network-based models. 8.4.1 Artificial neural networks. 8.4.2 Bayesian belief networks. 8.4.3 Fuzzy logic networks. 8.5 Multicriteria methods. 8.5.1 Multi-attribute utility theory. 8.5.2 Analytic hierarchy process.