Muutke küpsiste eelistusi

Modeling Forest Trees and Stands 2012 ed. [Pehme köide]

4.00/5 (2 hinnangut Goodreads-ist)
,
  • Formaat: Paperback / softback, 458 pages, kõrgus x laius: 235x155 mm, kaal: 718 g, biography
  • Ilmumisaeg: 09-May-2014
  • Kirjastus: Springer
  • ISBN-10: 9400795165
  • ISBN-13: 9789400795167
Teised raamatud teemal:
  • Pehme köide
  • Hind: 122,77 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 144,43 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
Modeling Forest Trees and Stands 2012 ed.Suurem pilt
  • Formaat: Paperback / softback, 458 pages, kõrgus x laius: 235x155 mm, kaal: 718 g, biography
  • Ilmumisaeg: 09-May-2014
  • Kirjastus: Springer
  • ISBN-10: 9400795165
  • ISBN-13: 9789400795167
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789400795167.html
Märksõnad:

Drawing upon a wealth of past research and results, this book provides a comprehensive summary of state-of-the-art methods for empirical modeling of forest trees and stands. It opens by describing methods for quantifying individual trees, progresses to a thorough coverage of whole-stand, size-class and individual-tree approaches for modeling forest stand dynamics, growth and yield, moves on to methods for incorporating response to silvicultural treatments and wood quality characteristics in forest growth and yield models, and concludes with a discussion on evaluating and implementing growth and yield models.

Ideal for use in graduate-level forestry courses, this book also provides ready access to a plethora of reference material for researchers working in growth and yield modeling.



This book details state-of-the-art methods for empirical modeling of forest trees and stands. It includes methods for quantifying site quality as well as stand and point density, and features applied topics, including silvicultural treatment and wood quality.

1 Introduction
1(8)
1.1 Quantifying Forest Trees and Stands
1(1)
1.2 Modeling Approaches
2(1)
1.3 Empirical Modeling of Forests
3(1)
1.4 Organization of Book Contents
4(1)
1.5 Abbreviations and Symbols Used
5(4)
References
7(2)
2 Tree Form and Stem Taper
9(34)
2.1 Form and Taper
9(1)
2.2 Stem Taper Functions
10(14)
2.2.1 Simple Equations
12(4)
2.2.2 Segmented Functions
16(4)
2.2.3 Variable-Exponent Functions
20(4)
2.3 Inclusion of Additional Predictor Variables
24(5)
2.3.1 Crown Dimensions
25(1)
2.3.2 Site and Stand Variables
26(2)
2.3.3 Upper-Stem Diameters
28(1)
2.4 Compatible Prediction of Inside and Outside Bark Diameters
29(1)
2.5 Taper-Volume Compatible Functions
30(1)
2.6 Statistical Considerations
31(12)
2.6.1 Model Assumptions
31(1)
2.6.2 Multicollinearity
32(1)
2.6.3 Retransformation Bias
33(2)
2.6.4 Mixed-Effects Approach
35(3)
References
38(5)
3 Tree-Stem Volume Equations
43(22)
3.1 Developing Volume Equations
43(2)
3.2 Equations for Total Stem Volume
45(5)
3.2.1 Combined Variable Equations
45(3)
3.2.2 Logarithmic Volume Equations
48(2)
3.2.3 Honer Volume Equation
50(1)
3.3 Estimating Merchantable Stem Volume
50(9)
3.3.1 Volume Ratio Equations
51(3)
3.3.2 Deriving Taper Functions from Volume Equations
54(1)
3.3.3 Compatible Stem Volume and Taper Functions
55(4)
3.4 Inclusion of Variables in Addition to dbh and Total Height
59(1)
3.5 Volume Prediction for Irregular Stems
60(1)
3.6 Stem Quality Assessment and Prediction
61(4)
References
62(3)
4 Tree Weight and Biomass Estimation
65(20)
4.1 Estimating Green Weight of Stems
65(2)
4.2 Estimating Dry Weight of Stems
67(4)
4.3 Biomass Estimation
71(14)
4.3.1 Models for Biomass Estimation
71(1)
4.3.2 Additivity of Linear Biomass Equations
72(3)
4.3.3 Additivity of Nonlinear Biomass Equations
75(3)
4.3.4 Inclusion of Additional Predictor Variables
78(1)
References
79(6)
5 Quantifying Tree Crowns
85(26)
5.1 Approximating Tree Crowns with Geometric Shapes
85(1)
5.2 Modeling Crown Profiles
86(7)
5.2.1 Incorporating Stochastic Variation
90(3)
5.2.2 Additional Techniques for Describing Tree Crowns
93(1)
5.3 Modeling Crown Morphology
93(7)
5.4 Tree Crowns and Growth
100(11)
5.4.1 Modeling Crown Ratio
101(5)
5.4.2 Crown Relationships for Open-Grown Trees
106(1)
References
106(5)
6 Growth Functions
111(20)
6.1 Introduction
111(1)
6.2 Empirical Versus Mechanistic or Theoretical Growth Functions
112(4)
6.3 Growth Functions of the Lundqvist-Korf Type
116(2)
6.3.1 Schumacher Function
116(1)
6.3.2 Lundqvist-Korf Function
116(2)
6.4 Growth Functions of the Richards Type
118(5)
6.4.1 Monomolecular Function
118(1)
6.4.2 Logistic and Generalized Logistic Functions
119(1)
6.4.3 Gompertz Function
120(1)
6.4.4 Richards Function
120(3)
6.5 Functions of the Hossfeld IV Type
123(4)
6.5.1 The Hossfeld IV Function
123(1)
6.5.2 McDill-Amateis/Hossfeld IV Function
124(2)
6.5.3 Generalizations of the Hossfeld IV Function
126(1)
6.6 Other Growth Functions
127(1)
6.7 Zeide Decomposition of Growth Functions
127(1)
6.8 Formulating Growth Functions Without Age Explicit
128(3)
References
129(2)
7 Evaluating Site Quality
131(44)
7.1 Need to Quantify Site Quality
131(1)
7.2 Computing Top Height
132(1)
7.3 Data Sources for Developing Site Index Curves
133(4)
7.3.1 Temporary Plots
133(1)
7.3.2 Permanent Plots
133(1)
7.3.3 Stem Analysis
134(3)
7.4 Fitting Site Index Guide Curves
137(3)
7.4.1 Comparisons of Stem-Analysis and Guide-Curve Based Site Index Equations
139(1)
7.5 Site Index Equations Using Age and Height at Index Age
140(1)
7.6 Segmented Models for Site Index Curves
141(1)
7.7 Differential Equation Approach
142(2)
7.8 Difference Equation Approach
144(13)
7.8.1 Algebraic Difference Approach
145(3)
7.8.2 Generalized Algebraic Difference Approach for Dynamic Site Equations
148(6)
7.8.3 Estimating Parameters in ADA- and GADA-Type Formulations
154(3)
7.9 Mixed-Effects Models for Height Prediction
157(6)
7.9.1 Varying Parameter Model
158(1)
7.9.2 Mixed-Effects Models with Multiple Random Components
159(1)
7.9.3 Accounting for Serial Correlation
160(1)
7.9.4 Calibration of Nonlinear Mixed-Effects Models
161(1)
7.9.5 Evaluation of Population-Averaged and Subject-Specific Predictions
161(2)
7.10 Comparison of Subject-Specific Approaches for Modeling Dominant Height
163(2)
7.11 Including Concomitant Information in Height-Age Models
165(1)
7.12 Effect of Stand Density on Height Growth
166(9)
References
167(8)
8 Quantifying Stand Density
175(26)
8.1 Stocking and Stand Density
175(2)
8.1.1 Trees Per Unit Area
176(1)
8.1.2 Basal Area Per Unit Area
176(1)
8.2 Size-Density Relationships
177(4)
8.2.1 Reineke's Stand Density Index
177(2)
8.2.2 3/2 Rule of Self-thinning
179(1)
8.2.3 Relative Spacing
180(1)
8.3 Methods for Fitting Maximum Size-Density Relationships
181(6)
8.3.1 Data Screening
181(1)
8.3.2 Free Hand Fitting
181(1)
8.3.3 Reduced Major Axis Regression
182(1)
8.3.4 Frontier Functions
182(2)
8.3.5 Mixed Models
184(1)
8.3.6 Curvilinear Size-Density Boundaries
184(1)
8.3.7 Segmented Regression
185(2)
8.4 Applying Maximum Size-Density Concepts to Complex Stand Structures
187(1)
8.5 Incorporating Size-Density Relationships in Models of Stand Dynamics
188(2)
8.6 Other Proposed Measures of Stand Density
190(2)
8.6.1 Tree-Area Ratio
190(1)
8.6.2 Crown Competition Factor
191(1)
8.7 Similarity of Stand Density Measures
192(1)
8.8 Efficacy of Various Stand Density Measures for Growth and Yield Prediction
193(3)
8.9 Evaluation of Concepts Underlying Stand Density Measures
196(5)
References
197(4)
9 Indices of Individual-Tree Competition
201(32)
9.1 Distance-Independent Indices
202(2)
9.2 Distance-Dependent Indices
204(20)
9.2.1 Selection of Competitors
204(3)
9.2.2 Formulation of the Competition Index
207(8)
9.2.3 Asymmetric/One-Sided Versions of the Competition Indices
215(3)
9.2.4 Interspecific Competition
218(1)
9.2.5 Clumping, Differentiation and Mingling
219(2)
9.2.6 Using Change in Competition Indices to Model Thinning Effects
221(1)
9.2.7 Edge Bias in Competition Indices Computation
222(1)
9.2.8 Modeling and Simulating the Spatial Pattern of Forest Stands
223(1)
9.3 Evaluation and Comparison of Competition Measures
224(9)
9.3.1 Simple Correlations with Tree Growth or Models with the Competition Index as the Unique Independent Variable
224(1)
9.3.2 Contribution of Competition Indices to Tree Growth Models in Which Tree Size and/or Stand Variables Are Already Included
225(3)
9.3.3 Distance-Independent Versus Distance-Dependent Competition Indices
228(1)
References
228(5)
10 Modeling Forest Stand Development
233(12)
10.1 Need for Stand Models
233(1)
10.2 Approaches to Modeling Forest Stands
234(3)
10.3 Prediction, Parsimony and Noise
237(1)
10.4 Level for Modeling Forest Stands
238(1)
10.5 Field Data for Growth and Yield Modeling
239(3)
10.6 Looking Ahead
242(3)
References
243(2)
11 Whole-Stand Models for Even-Aged Stands
245(16)
11.1 Background
245(1)
11.2 Growth and Yield Relationships
246(1)
11.3 Variable-Density Growth and Yield Equations
247(2)
11.3.1 Schumacher-Type Equations
247(1)
11.3.2 Chapman-Richards Equations
248(1)
11.4 Compatible Growth and Yield Equations
249(4)
11.4.1 Analytic Compatibility
249(1)
11.4.2 Ensuring Numeric Consistency
250(3)
11.5 Growth Models Based on Annual Increments
253(1)
11.6 Simultaneous Systems of Growth and Yield Equations
253(2)
11.7 Mixed-Effects Models for Growth and Yield Prediction
255(1)
11.8 State Space Models
256(5)
References
258(3)
12 Diameter-Distribution Models for Even-Aged Stands
261(38)
12.1 Estimating Yields by Size Class Using a Distribution Function Approach
261(19)
12.1.1 Selecting a Distribution Function
262(2)
12.1.2 Characterizing Diameter Distributions Using Parameter Prediction
264(2)
12.1.3 Characterizing Diameter Distributions Using Parameter Recovery
266(7)
12.1.4 Evaluations of Alternative Distributions and Parameter Estimation Methods
273(4)
12.1.5 Characterizing Diameter Distributions of Mixed-Species Stands
277(1)
12.1.6 Bivariate Approach
278(2)
12.2 Modeling Height-Diameter Relationships
280(2)
12.3 Predicting Unit-Area Tree Survival
282(4)
12.4 Alternatives to the Distribution Function Approach
286(13)
12.4.1 Percentile-Based Distributions
286(3)
12.4.2 Ratio Approach
289(2)
12.4.3 Functional Regression Tree Method
291(1)
References
292(7)
13 Size-Class Models for Even-Aged Stands
299(12)
13.1 Defining Size Classes
299(1)
13.2 Stand-Table Projection
299(8)
13.2.1 Stand-Table Projection Based on Change in Relative Basal Area
300(1)
13.2.2 A Distribution-Independent Approach to Stand Table Projection
301(1)
13.2.3 Stand Table Projection Algorithms that Incorporate a Diameter Growth Function
302(5)
13.3 Percentile-Based Models
307(1)
13.4 Related Approaches
308(3)
References
308(3)
14 Individual-Tree Models for Even-Aged Stands
311(28)
14.1 Approach
311(1)
14.2 Types of Individual-Tree Models
311(1)
14.3 Growth Functions
312(1)
14.4 Distance-Dependent Models
312(5)
14.4.1 Example Model Structure for Pine Plantations
313(2)
14.4.2 A Model for Complex Stand Structures
315(2)
14.5 Generating Spatial Patterns
317(2)
14.6 Controlling Plot Edge Bias
319(1)
14.7 Distance-Independent Models
320(5)
14.7.1 Example Model for Pure, Even-Aged Stands
321(3)
14.7.2 A Distance-Independent Modeling Platform for Complex Stand Structures
324(1)
14.8 Annualized Growth Predictions from Periodic Measurements
325(1)
14.9 Simultaneous Estimation of Model Component Equations
326(2)
14.10 Incorporating Stochastic Components
328(1)
14.11 Relating Predictions from Whole-Stand
And Individual-Tree Models
329(1)
14.12 Comparisons of Growth and Yield Models with Varying Levels of Resolution
330(2)
14.13 Developing Growth and Yield Models with Consistency at Varying Levels of Resolution
332(7)
References
333(6)
15 Growth and Yield Models for Uneven-Aged Stands
339(24)
15.1 Special Considerations for Modeling Uneven-Aged Stands
339(1)
15.2 Whole-Stand Models
340(3)
15.2.1 Equations Based on Elapsed Time
340(1)
15.2.2 Whole-Stand Models with Stand-Table Information
341(2)
15.3 Diameter Distribution Approach
343(3)
15.4 Size-Class Models
346(11)
15.4.1 Stand-Table Projection Equations
346(1)
15.4.2 Matrix Model Approach
347(10)
15.5 Individual-Tree Models
357(6)
15.5.1 A Distance-Dependent Approach
357(1)
15.5.2 A Distance-Independent Model
358(2)
References
360(3)
16 Modeling Response to Silvicultural Treatments
363(42)
16.1 Need to Model Response to Silvicultural Treatments
363(1)
16.2 Modeling Response of Juvenile Stands
364(2)
16.3 Frameworks for Modeling Stand Level Response
366(2)
16.3.1 Response Functions
366(2)
16.3.2 Distributing Stand Growth Response to Individual Trees
368(1)
16.4 Modeling Response to Selected Silvicultural Treatments
368(37)
16.4.1 Thinning
369(10)
16.4.2 Vegetation Control
379(6)
16.4.3 Fertilizer Applications
385(8)
16.4.4 Genetic Improvement
393(5)
References
398(7)
17 Modeling Wood Characteristics
405(24)
17.1 Need for Information on Wood Characteristics
405(1)
17.2 Juvenile Wood
406(4)
17.2.1 Characteristics of Juvenile Wood
406(1)
17.2.2 Estimating Juvenile-Mature Wood Demarcation
407(3)
17.3 Importance of Specific Gravity
410(3)
17.3.1 Models for Estimating Wood Specific Gravity
410(2)
17.3.2 Impacts of Silviculture and Site on Specific Gravity
412(1)
17.3.3 Relating Specific Gravity to Pulp Yields and Mechanical Properties
412(1)
17.4 Modeling Ring Widths
413(2)
17.5 Modeling Branches and Knots
415(5)
17.5.1 Number, Size and Location of Branches
415(4)
17.5.2 Models of Knots
419(1)
17.6 Incorporating Wood Quality Information into Growth and Yield Models
420(2)
17.7 Linking Growth and Yield Models with Sawing Simulators
422(7)
References
423(6)
18 Model Implementation and Evaluation
429(18)
18.1 Model Implementation in Forest Simulators
429(4)
18.1.1 Input/Output
430(2)
18.1.2 Visualization
432(1)
18.2 Model Evaluation
433(11)
18.2.1 Theoretical Aspects of Model Building
435(1)
18.2.2 Logic of Model Structure and Biological Aspects
436(1)
18.2.3 Characterization of Model Errors
437(5)
18.2.4 Data for Model Validation
442(2)
18.3 Applying Growth and Yield Models
444(3)
References
445(2)
Index 447
Dr. Harold Burkhart, University Distinguished Professor Department of Forest Resources and Environmental Conservation, College of Natural Resources and Environment, Virginia Tech University, Blacksburg, VA 24061, USA Interest Areas: Modeling forest stand dynamics, growth and yield; applying quantitative analysis techniques to forestry problems. B.S., Oklahoma State University (1965) M.S., University of Georgia (1967) Ph.D., University of Georgia (1969) Current research project Cooperative Research Program in Growth and Yield of Managed Stands of Loblolly Pine (Twelve industrial forestry firms plus Virginia Department of Forest Resources and Environmental Conservation and USDA Forest Service, 1979-present): The objective of this Cooperative is to develop loblolly pine tree growth and stand development models sufficiently flexible to account for the effects of intensive cultural practices, with output sufficiently detailed to allow for analyses of a full range of utilization options. The Cooperative maintains three large field studies: (1) a set of designed spacing trials, (2) a region-wide set of growth plots in intensively managed plantations, and (3) two pruning experiments and is part of the NSF Center for Advanced Forestry Systems.