Książki

This book has been developed as a forest inventory textbook for students and could also serve as a handbook for practical foresters. We have set out to keep the mathematics in the book at a fairly non-technical level, and therefore, although we deal with many issues that include highly sophisticated methodology, we try to present first and foremost the ideas behind them. For foresters who need more details, references are given to more advanced scientific papers and books in the fields of statistics and biometrics. Forest inventory books deal mostly with sampling and measurement issues, as found here in section I, but since forest inventories in many countries involve much more than this, we have also included material on forestry applications. Most applications nowadays involve remote sensing technology of some sort, so that section II deals mostly with the use of remote sensing material for this purpose. Section III deals with national inventories carried out in different parts of world, and section IV is an attempt to outline some future possibilities of forest inventory methodologies. The editors, Annika Kangas Professor of Forest Mensuration and Management, Department of Forest Resource Management, University of Helsinki. Matti Maltamo Professor of Forest Mensuration, Faculty of Forestry, University of Joensuu. ACKNOWLEDGEMENTS

Forest Inventory: Methodology and Applications

Preface. Acknowledgements. List of contributing authors. Part I: Theory. 1. Introduction; A. Kangas et al. 1.1 General. 1.2 Historical background of sampling theory. 1.3 History of forest inventories. References.- 2. Design-based sampling and inference; A. Kangas. 2.1 Basis for probability sampling. 2.2 Simple random sampling. 2.3 Determining the sample size. 2.4 Systematic sampling. 2.5 Stratified sampling. 2.6 Cluster sampling. 2.7 Ratio and regression estimators. 2.8 Sampling with probability proportional to size. 2.9 Non-linear estimators. 2.10 Resampling. 2.11 Selecting the sampling method. References.- 3. Model-based inference; A. Kangas. 3.1 Foundations of model-based inference. 3.2 Models. 3.3 Applications of model-based methods to forest inventory. 3.4 Model-based versus design-based inference. References.- 4. Mensurational aspects; A. Kangas. 4.1 Sample plots. 4.1.1 Plot size. 4.1.2 Plot shape. 4.2 Point sampling. 4.3 Comparison of fixed-sized plots and points. 4.4 Plots located on an edge or slope. 4.4.1 Edge corrections. 4.4.2 Slope corrections. References.- 5. Change monitoring with permanent sample plots; S. Poso. 5.1 Concepts and notations. 5.2 Choice of sample plot type and tree measurement. 5.3 Estimating components of growth at the plot level. 5.4 Monitoring volume and volume increment over two or more measuring periods at the plot level. 5.5 Estimating population parameters. 5.6 Concluding remarks. References.- 6. Generalizing sample tree information; J. Lappi et al. 6.1 Estimation of tally tree regression. 6.2 Generalizing sample tree information in a small subpopulation. 6.2.1 Mixed estimation. 6.2.2 Applying mixed models. 6.3 A closer look at the three-level model structure. References.- 7. Use of additional information; J. Lappi, A. Kangas. 7.1 Calibration estimation. 7.2 Small area estimates. References.- 8. Sampling rare populations; A. Kangas. 8.1 Methods for sampling rare populations. 8.1.1 Principles. 8.1.2 Strip sampling. 8.1.3 Line intersect sampling. 8.1.4 Adaptive cluster sampling. 8.1.5 Transect and point relascope sampling. 8.1.6 Guided transect sampling. 8.2 Wildlife populations. 8.2.1 Line transect sampling. 8.2.2 Capture-recapture methods. 8.2.3 The wildlife triangle scheme. References.- 9. Inventories of vegetation, wild berries and mushrooms; M. Maltamo. 9.1 Basic principles. 9.2 Vegetation inventories. 9.2.1 Approaches to the description of vegetation. 9.2.2 Recording of abundance. 9.2.3 Sampling methods for vegetation analysis. 9.3 Examples of vegetation surveys. 9.4 Inventories of mushrooms and wild berries. References.- 10. Assessment of uncertainty in spatially systematic sampling; J. Heikkinen. 10.1 Introduction. 10.2 Notation, definitions and assumptions. 10.3 Variance estimators based on local differences. 10.3.1 Restrictions of SRS-estimator. 10.3.2 Development of estimators based on local differences. 10.4 Variance estimation in the national forest inventory in Finland. 10.5 Model-based approaches. 10.5.1 Modelling spatial variation. 10.5.2 Model-based variance and its estimation. 10.5.3 Descriptive versus analytic inference. 10.5.4 Kriging in inventories. 10.6 Other sources of uncertainty. References.- Part II: Applications. 11. The Finnish national forest inventory; E. Tomppo. 11.1 Introduction. 11.2 Field sampling system used in NFI9. 11.3 Estimation based on field data. 11.3.1 Area estimation. 11.3.2 Volume estimation. 11.3.2.1 Predicting sample tree volumes and volumes by timber assortment classes. 11.3.2.2 Predicting volumes for tally trees. 11.3.3.3 Computing volumes for computation units. 11.4 Increment estimation. 11.5 Conclusions. References.-