Managing Forest Ecosystems

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.- 12. The Finnish multi-source national forest inventory -- small area estimation and map production; E. Tomppo. 12.1 Introduction. 12.1.1 Background. 12.1.2 Progress in the Finnish multi-source inventory. 12.2 Input data sets for the basic and improved k-NN methods. 12.2.1 Processing of field data for multi-source calculations. 12.2.2 Satellite images. 12.2.3 Digital map data. 12.2.4 Large-area forest resource data. 12.3 Basic k-NN estimation. 12.4 The improved k-NN, (ik-NN) method. 12.4.1 Simplified sketch of the genetic algorithm. 12.4.2 Application of the algorithm. 12.4.3 Reductions of the bias and standard error of the estimates at the pixel level and regional level. 12.5 Conclusions. References.- 13. Correcting map errors in forest inventory estimates for small areas; M. Katila. 13.1 Introduction. 13.2 Land use class areas. 13.3 Calibrated plot weights. References.- 14. Multiphase sampling; S. Tuominen et al. 14.1 Introduction. 14.2 Double sampling for stratification when estimating population parameters. 14.3 Double sampling for regression. 14.4 Forest inventory applications of two-phase sampling. 14.4.1 Grouping method - two-phase sampling for stratification with one second-phase unit per stratum. 14.4.2 Stratification with mean vector estimation. 14.4.3 K nearest neighbor method with mean vector estimation. 14.5 Multi-phase sampling with more than two phases. 14.6 Estimation testing. 14.7 Concluding remarks. References.- 15. Segmentation; A. Pekkarinen, M. Holopainen. 15.1 Introduction. 15.2 Image segmentation. 15.2.1 General. 15.2.2 Image segmentation techniques. 15.2.3 Segmentation software. 15.3 Segmentation in forest inventories. 15.4 Segmentation examples. 15.4.1 General. 15.4.2 Example material. 15.4.3 Example 1: pixel-based segmentation. 15.4.4 Example 2: edge detection. 15.4.5 Example 3: region segmentation. References.- 16. Inventory by compartments; J. Koivuniemi, K.T. Korhonen. 16.1 Basic concepts and background. 16.2 History of the inventory method in Finland. 16.3. Inventory by compartments today. 16.3.1 The inventory method. 16.3.2 Estimation methods. 16.4 Accuracy in the inventory by compartments method and sources of error. References.- 17. Assessing the world's forests; A. Kangas. 17.1 Global issues. 17.1.1 Issues of interest. 17.1.2 Forest area. 17.1.3. Wood volume and woody biomass. 17.1.4 Biodiversity and conservation. 17.2 Methodology. 17.2.1 Global forest resources assessment. 17.2.2 Temperate and boreal forest assessment. 17.2.3 Pan-tropical remote sensing survey. 17.2.4 Global mapping. 17.2.5 Forest information database. References.- Part III: Cases. 18. Europe; T. Tokola. 18.1 Sweden. 18.1.1 Swedish national forest inventory. 18.1.2 Inventory for forest management planning. 18.2 Germany. 18.2.1 National forest inventory: natural forests. 18.2.2 Regional inventories. 18.2.3 Forest management planning: compartment level inventory. 18.3 Other European areas. References.- 19. Asia; T. Tokola. 19.1 India. 19.1.1 Forest cover mapping. 19.1.2 Forest inventory. 19.1.3 Trees outside the forest (TOF) and the household survey. 19.1.4 Forest management planning. 19.2 Indonesia. 19.2.1 The national forest inventory. 19.2.2 Concession renewal mapping. 19.2.3 Forest management planning: compartment-level inventories of natural forests. 19.2.4 Forest management planning: compartment-level inventories of plantation forests. 19.3 China. 19.3.1 National forest inventory: natural forests. 19.3.2 Forest management planning: compartment-level inventories. 19.4 Other Asian areas. References.- 20. North America; T. Tokola. 20.1 Canada. 20.1.1 Provincial-level management inventories. 20.1.2 National forest inventories, national aggregation. 20.1.3 Industrial forest management inventories. 20.2 The United States of America. 20.2.1 The national forest inventory. 20.2.2 Industrial forest management planning: stand-level inventory. 20.2.3 Cruising, scaling and volume estimation. 20.3 Mexico. References.- Part IV: Future. 21. Modern data acquisition for forest inventories; M. Holopainen, J. Kalliovirta. 21.1 Introduction. 21.2 Remote sensing. 21.2.1 Digital aerial photos. 21.2.2 Spectrometer imagery. 21.2.3 High-resolution satellite imagery. 21.2.4 Microwave radars. 21.2.5 Profile imaging. 21.2.6 Laser scanning. 21.3 Use of modern remote sensing in forest inventories. 21.3.1 Accuracy of remote sensing in forest inventories. 21.3.2 Stand-, plot- and tree-level measurements on digital aerial photographs. 21.3.3 Stand-, plot- and tree-level measurements using laser scanning. 21.3.4 Integration of laser scanning and aerial imagery. 21.4 Improving the quality of ground-truth data in remote sensing analysis. 21.4.1 Development of field measuring devices. 21.4.1.1 Terrestrial lasers. 21.4.1.2 Laser-relascope. 21.4.1.3 Digital cameras. 21.4.2 Field data acquisition by logging machines. References.-