Multi-Source National Forest Inventory [electronic resource] : Methods and Applications /
Tomppo, Erkki.author.
Haakana, Markus.author.
Katila, Matti.author.
Peräsaari, Jouni.author.
Building on more than a decade of innovative research into multi-source forest inventory (MS-NFI) this book presents full details of the development, outputs and applications of the improved k-NN method. The method, which was pioneered in Finland in 1990, is rapidly becoming a world standard in forest inventory, having been adopted as standard in Finland and Sweden, and recently introduced in Austria and across the US. The book describes in detail the full MS-NFI process, and the input data used – including field data, satellite images, and digital map data, as well as coarse-scale variation of forest variables. It also presents comprehensive information on the types of outputs which can be derived, including maps and statistics, describing, for example, stock volumes and development, dominant tree species, age-class distribution, and large and small-scale variation. The book will provide an invaluable resource for those involved in forest inventory, including government departments and bodies involved in forest policy, management and monitoring, forest managers, and researchers and graduate students interested in forest inventory, modelling and analysis. It will find an additional market among those interested in Earth observation, ecology and broader areas of environmental and natural resource management. Erkki Tomppo was the winner of the 1997 Marcus Wallenberg Prize for his work on the k-NN method.
Description based upon print version of record.
Includes bibliographical references and index.
List of abbreviations -- Preface -- 1. Introduction -- 2. Materials. 2.1 Field data. 2.2 Satellite images. 2.3 Digital map data. 2.4 Digital elevation model. 2.5 Large area forest resource data -- 3. Methods. 3.1 Image rectification and pre-processing of data. 3.2 Estimation -- 4. Results. 4.1 Forest resources by municipalities. 4.2 Comparison of the results by regions and to MS-NFI8 results. 4.3 Accuracy of small-area estimates from MS-NFI8 and MS-NFI9. 4.4 Digital thematic output maps -- 5. Discussion -- Acknowledgements -- References -- Appendix 1. Forest resource tables 1–8 -- Appendix 2. Examples of forest resource maps -- Index.
Building on more than a decade of innovative research into multi-source forest inventory (MS-NFI) this book presents full details of the development, outputs and applications of the improved k-NN method. The method, which was pioneered in Finland in 1990, is rapidly becoming a world standard in forest inventory, having been adopted as standard in Finland and Sweden, and recently introduced in Austria and across the US. The book describes in detail the full MS-NFI process, and the input data used – including field data, satellite images, and digital map data, as well as coarse-scale variation of forest variables. It also presents comprehensive information on the types of outputs which can be derived, including maps and statistics, describing, for example, stock volumes and development, dominant tree species, age-class distribution, and large and small-scale variation. The book will provide an invaluable resource for those involved in forest inventory, including government departments and bodies involved in forest policy, management and monitoring, forest managers, and researchers and graduate students interested in forest inventory, modelling and analysis. It will find an additional market among those interested in Earth observation, ecology and broader areas of environmental and natural resource management. Erkki Tomppo was the winner of the 1997 Marcus Wallenberg Prize for his work on the k-NN method.
Environmental management.
Forestry.
Forestry management.
Geographical information systems.
Environmental monitoring.
Environmental sciences.
Environmental Management.
Forestry.
Forestry Management.
Geographical Information Systems/Cartography.
Monitoring/Environmental Analysis.
Math. Appl. in Environmental Science.
Managing Forest Ecosystems