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E-grāmata: Forest Growth and Yield Modeling

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(University of Maine), (Southern Cross University), (University of New Brunswick), (Oregon State University)
  • Formāts: EPUB+DRM
  • Izdošanas datums: 15-Jul-2011
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119971504
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Forest Growth and Yield ModelingPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 15-Jul-2011
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119971504
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"Completely updated and expanded new edition of this widely cited book, Modelling Forest Growth and Yield, 2nd Edition synthesizes current scientific literature, provides insights in how models are constructed, gives suggestions for future developments, and outlines keys for successful implementation of models.The book describes current modeling approaches for predicting forest growth and yield and explores the components that comprise the various modeling approaches. It provides the reader with the tools for evaluating and calibrating growth and yield models and outlines the steps necessary for developing a forest growth and yield model"--

"The book describes current modeling approaches for predicting forest growth and yield and explores the components that comprise the various modeling approaches"--

Provided by publisher.

Completely updated and expanded new edition of this widely cited book, Modelling Forest Growth and Yield, 2nd Edition synthesizes current scientific literature, provides insights in how models are constructed, gives suggestions for future developments, and outlines keys for successful implementation of models.

The book describes current modeling approaches for predicting forest growth and yield and explores the components that comprise the various modeling approaches. It provides the reader with the tools for evaluating and calibrating growth and yield models and outlines the steps necessary for developing a forest growth and yield model.

Preface xi
Acknowledgements xiii
1 Introduction
1(14)
1.1 Model development and validation
7(1)
1.2 Important uses
8(5)
1.3 Overview of the book
13(2)
2 Indices of competition
15(22)
2.1 Introduction
15(1)
2.2 Two-sided competition
16(6)
2.2.1 Distance-independent
16(6)
2.2.2 Distance-dependent
22(1)
2.3 One-sided competition
22(9)
2.3.1 Distance-independent
23(1)
2.3.2 Distance-dependent
24(7)
2.4 Limitations
31(4)
2.4.1 Low predictive power
32(1)
2.4.2 Distance-independent vs. distance-dependent
32(2)
2.4.3 Influence of sampling design
34(1)
2.5 Summary
35(2)
3 Forest site evaluation
37(16)
3.1 Introduction
37(1)
3.2 Phytocentric measures of site quality
38(9)
3.2.1 Site index
38(6)
3.2.2 Plant indicators
44(1)
3.2.3 Other phytocentric measures
45(2)
3.3 Geocentric measures of site productivity
47(5)
3.3.1 Physiographic measures
48(1)
3.3.2 Climatic measures
49(1)
3.3.3 Soil measures
50(2)
3.4 Summary
52(1)
4 Whole-stand and size-class models
53(16)
4.1 Introduction
53(1)
4.2 Whole-stand models
53(8)
4.2.1 Yield tables and equations
54(2)
4.2.2 Compatible growth and yield equations
56(3)
4.2.3 Systems of equations
59(1)
4.2.4 State-space models
59(2)
4.2.5 Transition matrix models
61(1)
4.3 Size-class models
61(7)
4.3.1 Stand table projection
61(3)
4.3.2 Matrix models
64(2)
4.3.3 Diameter-class models
66(1)
4.3.4 Cohort models
67(1)
4.4 Summary
68(1)
5 Tree-level models
69(16)
5.1 Introduction
69(1)
5.2 Single-tree distance-dependent models
70(7)
5.2.1 Example models
72(5)
5.3 Tree-list distance-independent models
77(6)
5.3.1 Example models
81(2)
5.4 Summary
83(2)
6 Components of tree-list models
85(30)
6.1 Introduction
85(2)
6.2 Diameter increment
87(14)
6.2.1 Potential diameter increment equations with multiplicative modifiers
89(3)
6.2.2 Realized diameter increment equations
92(9)
6.3 Height increment
101(7)
6.3.1 Potential height increment equations with multiplicative modifiers
101(4)
6.3.2 Realized height increment equations
105(3)
6.4 Crown recession
108(6)
6.4.1 Individual-tree crown recession models
108(3)
6.4.2 Branch-level crown recession models
111(3)
6.5 Summary
114(1)
7 Individual-tree static equations
115(24)
7.1 Introduction
115(1)
7.2 Total height
115(4)
7.3 Crown length
119(4)
7.4 Crown width and profile
123(4)
7.5 Stem volume and taper
127(3)
7.6 Biomass
130(2)
7.7 Use of static equations to predict missing values
132(5)
7.8 Summary
137(2)
8 Mortality
139(18)
8.1 Introduction
139(1)
8.2 Stand-level mortality
140(2)
8.3 Individual-tree-level mortality
142(6)
8.4 Mechanistic models of mortality
148(1)
8.5 Development and application of mortality equations
148(6)
8.6 Summary
154(3)
9 Seeding, regeneration, and recruitment
157(12)
9.1 Introduction
157(1)
9.2 Seeding
158(3)
9.2.1 Flowering and pollination
158(1)
9.2.2 Seed production
158(2)
9.2.3 Seed dispersal
160(1)
9.2.4 Seed germination
160(1)
9.3 Regeneration
161(2)
9.4 Recruitment
163(3)
9.4.1 Static
163(1)
9.4.2 Dynamic
164(2)
9.5 Summary
166(3)
10 Linking growth models of different resolutions
169(14)
10.1 Introduction
169(1)
10.2 Linked stand- and size-class models
169(5)
10.2.1 Parameter recovery
170(3)
10.2.2 Modified stand table projection
173(1)
10.3 Linked stand- and tree-models
174(8)
10.3.1 Disaggregation
174(7)
10.3.2 Constrained
181(1)
10.3.3 Combined
181(1)
10.4 Summary
182(1)
11 Modeling silvicultural treatments
183(44)
11.1 Introduction
183(5)
11.2 Genetic improvements
188(3)
11.2.1 Stand-leveL
188(1)
11.2.2 Tree-leveL
189(2)
11.3 Early stand treatments
191(2)
11.3.1 Stand-level
191(1)
11.3.2 Tree-leveL
192(1)
11.4 Thinning
193(15)
11.4.1 Stand-Level
194(10)
11.4.2 Tree-leveL
204(4)
11.5 Fertilization
208(13)
11.5.1 Stand-Level
209(8)
11.5.2 Tree-level
217(4)
11.6 Combined thinning and fertiLization
221(1)
11.6.1 Stand-level
221(1)
11.6.2 Tree-level
222(1)
11.7 Harvesting
222(2)
11.7.1 Stand-Level
223(1)
11.7.2 Tree-Level
223(1)
11.8 Summary
224(3)
12 Process-based models
227(26)
12.1 Introduction
227(1)
12.2 Key physiological processes
228(12)
12.2.1 Light interception
228(3)
12.2.2 Photosynthesis
231(2)
12.2.3 Stomatal conductance
233(2)
12.2.4 Respiration
235(1)
12.2.5 Carbon allocation
236(2)
12.2.6 Soil water and nutrients
238(2)
12.3 Example models
240(7)
12.3.1 Forest-BGC
240(4)
12.3.2 CenW
244(1)
12.3.3 BALANCE
245(2)
12.4 Limitations
247(5)
12.4.1 Initialization
247(2)
12.4.2 Parameterization
249(1)
12.4.3 Scale
250(1)
12.4.4 Sensitivity
250(2)
12.5 Summary
252(1)
13 Hybrid models of forest growth and yield
253(12)
13.1 Introduction
253(1)
13.2 Types of hybrid models
254(9)
13.2.1 Statistical growth equations with physiologically derived covariate
254(4)
13.2.2 Statistical growth equations with physiologically derived external modifier
258(1)
13.2.3 Allometric models
259(4)
13.3 Comparison to statistical models
263(1)
13.4 Summary
264(1)
14 Model construction
265(30)
14.1 Introduction
265(1)
14.2 Data requirements
266(13)
14.2.1 Stem analysis
266(1)
14.2.2 Temporary plots
267(4)
14.2.3 Permanent plots
271(8)
14.3 Model form
279(2)
14.4 Parameter estimation
281(13)
14.4.1 Regression
282(3)
14.4.2 Quantile regression
285(1)
14.4.3 Generalized linear regression models
285(2)
14.4.4 Mixed models
287(2)
14.4.5 Generalized algebraic difference approach
289(1)
14.4.6 System of equations
290(2)
14.4.7 Bayesian
292(1)
14.4.8 Nonparametric
292(1)
14.4.9 Annualization
293(1)
14.5 Summary
294(1)
15 Model evaluation and calibration
295(16)
15.1 Introduction
295(1)
15.2 Model criticism
296(9)
15.2.1 Model form and parameterization
298(1)
15.2.2 Variable selection and model simplicity
298(1)
15.2.3 Biological realism
299(4)
15.2.4 Compatibility
303(1)
15.2.5 Reliability
303(1)
15.2.6 Adaptability
304(1)
15.3 Model benchmarking
305(2)
15.3.1 Statistical tests
305(2)
15.3.2 Model error characterization
307(1)
15.4 Model calibration
307(1)
15.5 Summary
308(3)
16 Implementation and use
311(10)
16.1 Introduction
311(1)
16.2 Collection of appropriate data
312(2)
16.3 Generation of appropriate data
314(1)
16.4 Temporal scale
315(1)
16.5 Spatial scale
316(1)
16.6 Computer interface
317(1)
16.7 Visualization
318(1)
16.8 Output
319(1)
16.9 Summary
320(1)
17 Future directions
321(6)
17.1 Improving predictions
321(2)
17.2 Improving input data
323(1)
17.3 Improving software
324(1)
17.4 Summary
324(3)
Bibliography 327(70)
Appendix 1 List of species used in the text 397(2)
Appendix 2 Expanded outline for ORGANON growth and yield model 399(6)
Index 405
Jerry Vanclay, Professor for Sustainable Forestry and Head, School of Environmental Science and Management, Southern Cross University, Australia

Aaron Weiskittel, Assistant Professor of Forest Biometrics and Modelling, School of Forest Resources, University of Maine, Orono, USA

John A. Kershaw, Jr., Professor of Forest Mensuration/Biometrics, Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, Canada
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