Forest inventory : methodology and applications
著者
書誌事項
Forest inventory : methodology and applications
(Managing forest ecosystems, 10)
Springer, c2009
- : pbk
大学図書館所蔵 全1件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references
内容説明・目次
内容説明
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
目次
- 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.-
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