Lecture 3B Flashcards

Lecture 3b. Forest Dynamics, Growth, and Yield (Part 2)

1
Q

Why is Growth and Yield Modeling Important?

A

Helps predict future forest conditions, supports decision-making in forest management and conservation, and improves sustainable timber production and resource allocation.

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2
Q

What is Growth?

A

The increase in tree volume, biomass, or height over time.

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3
Q

The increase in tree volume, biomass, or height over time.

A

Growth

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4
Q

What is Yield?

A

The total volume of timber or biomass that can be harvested at a specific time.

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5
Q

The total volume of timber or biomass that can be harvested at a specific time.

A

Yield

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6
Q

What is Site Index?

A

A measure of site productivity based on the height of dominant trees at a reference age (e.g., 50 years for some species).

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7
Q

A measure of site productivity based on the height of dominant trees at a reference age (e.g., 50 years for some species).

A

Site Index

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8
Q

What is Annual Growth Rate?

A

Growth measured over a specific period, used to estimate long-term forest productivity.

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9
Q

Growth measured over a specific period, used to estimate long-term forest productivity.

A

Annual Growth Rate

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10
Q

What is Basal Area?

A

The cross-sectional area of all trees in a forest stand, used in growth calculations.

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11
Q

The cross-sectional area of all trees in a forest stand, used in growth calculations.

A

Basal Area

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12
Q

What is Increment?

A

The increase in tree size over time, categorized as current, mean, or periodic.

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13
Q

The increase in tree size over time, categorized as current, mean, or periodic.

A

Increment

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14
Q

Growth and Yield Models (5)

A

Annual Growth Rate
Deterministic Models
Stochastic Models
Empirical Models
Process-Based Models

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15
Q

What is the Annual Growth Rate formula?

A

Growth divided by the number of years in the period.

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16
Q

Growth divided by the number of years in the period.

A

Annual Growth Rate formula

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17
Q

What are Deterministic Models?

A

Models that predict fixed outcomes based on given input variables and assume no randomness, useful for controlled conditions.

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18
Q

Models that predict fixed outcomes based on given input variables and assume no randomness, useful for controlled conditions.

A

Deterministic Models

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19
Q

What are Stochastic Models?

A

Models that incorporate random variation to account for natural unpredictability and provide different possible outcomes with associated probabilities.

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20
Q

Models that incorporate random variation to account for natural unpredictability and provide different possible outcomes with associated probabilities.

A

Stochastic Models

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21
Q

What are Empirical Models?

A

Models based on observed data rather than theoretical assumptions, used for practical yield forecasting.

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22
Q

Models based on observed data rather than theoretical assumptions, used for practical yield forecasting.

A

Empirical Models

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23
Q

What are Process-Based Models?

A

Models that simulate physiological processes like photosynthesis and carbon allocation and require extensive field data and computational resources.

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24
Q

Models that simulate physiological processes like photosynthesis and carbon allocation and require extensive field data and computational resources.

A

Process-Based Models

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25
Q

What is the Estimation process in model development?

A

Determining statistical coefficients to describe growth and yield relationships.

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26
Q

Determining statistical coefficients to describe growth and yield relationships.

A

Estimation process in model development

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27
Q

What is Evaluation in model development?

A

Assessing model performance using accuracy tests and validation techniques.

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28
Q

Assessing model performance using accuracy tests and validation techniques.

A

Evaluation in model development

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29
Q

What is Verification in model development?

A

Ensures the model functions logically without errors.

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30
Q

Ensures the model functions logically without errors.

A

Verification in model development

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31
Q

What is Validation in model development?

A

Tests the model against real-world data to confirm accuracy.

32
Q

Tests the model against real-world data to confirm accuracy.

A

Validation in model development

33
Q

What is Sensitivity Analysis?

A

Examining how changes in input variables affect model predictions.

34
Q

Examining how changes in input variables affect model predictions.

A

Sensitivity Analysis

35
Q

What is Uncertainty Assessment?

A

Recognizing limitations in data collection and assumptions.

36
Q

Recognizing limitations in data collection and assumptions.

A

Uncertainty Assessment

37
Q

What is Simulation?

A

Uses computer models to estimate forest stand development over time and can test different scenarios for management planning.

38
Q

Uses computer models to estimate forest stand development over time and can test different scenarios for management planning.

A

Simulation

39
Q

What is Calibration

A

Adjusts the model to match specific local conditions (e.g., climate, soil type) and involves refining parameters to improve predictive accuracy.

40
Q

Adjusts the model to match specific local conditions (e.g., climate, soil type) and involves refining parameters to improve predictive accuracy.

A

Calibration

41
Q

What is Monitoring?

A

Continuous evaluation to refine predictions and correct errors, requiring long-term data collection for model updates.

42
Q

Continuous evaluation to refine predictions and correct errors, requiring long-term data collection for model updates.

A

Monitoring

43
Q

What is the relationship between model complexity and scale?

A

The complexity of forest processes and structures increases with temporal and spatial levels of integration: Tree → Stand → Landscape.

44
Q

The complexity of forest processes and structures increases with temporal and spatial levels of integration: Tree → Stand → Landscape.

A

relationship between model complexity and scale

45
Q

What are Black-Box Models?

A

Models that show statistical correlations between inputs & outputs without explaining underlying processes.

46
Q

Models that show statistical correlations between inputs & outputs without explaining underlying processes.

A

Black-Box Models

47
Q

What are White-Box Models?

A

Models that consider physiological and ecological processes, providing mechanistic insights.

48
Q

Models that consider physiological and ecological processes, providing mechanistic insights.

A

White-Box Models

49
Q

What are Whole Stand Models?

A

Models that estimate forest growth and yield at the stand level without differentiating between individual trees.

50
Q

Models that estimate forest growth and yield at the stand level without differentiating between individual trees.

A

Whole Stand Models

51
Q

Type of Whole Stand Models

A

Density-free models
Variable-density models
Diameter distribution models

52
Q

What are Density-free models?

A

Models that assume maximum or average stand density and are the basis for traditional yield tables.

53
Q

Models that assume maximum or average stand density and are the basis for traditional yield tables.

A

Density-free models

54
Q

What are Variable-density models?

A

Models that use stand density as an explicit factor.

55
Q

Models that use stand density as an explicit factor.

A

Variable-density models

56
Q

What are Diameter distribution models?

A

Models that refine whole stand models by incorporating diameter class structure (tree size, height, volume).

57
Q

Models that refine whole stand models by incorporating diameter class structure (tree size, height, volume).

A

Diameter distribution models

58
Q

What are Diameter Class Models?

A

Models that simulate growth for each diameter class separately, summing volumes to obtain total stand volume.

59
Q

Models that simulate growth for each diameter class separately, summing volumes to obtain total stand volume.

A

Diameter Class Models

60
Q

What is the common approach for short-term predictions in Diameter Class Models

A

Stand table projection

61
Q

Stand table projection

A

What is the common approach for short-term predictions in Diameter Class Models

62
Q

Mortality, harvest, and ingrowth

A

must be computed separately in Diameter Class Models

62
Q

What must be computed separately in Diameter Class Models

A

Mortality, harvest, and ingrowth

63
Q

What are the assumptions of Diameter Class Models?

A
  • All tree diameters in a diameter class equal the midpoint diameter for that class
  • Trees of a diameter class all grow at the same average rate
64
Q

What is the Movement ratio in Diameter Class Models?

A

Estimates the proportion of trees shifting to a higher DBH class.

65
Q

What is the Movement ratio in Diameter Class Models?

A

Estimates the proportion of trees shifting to a higher DBH class.

66
Q

Estimates the proportion of trees shifting to a higher DBH class.

A

Movement ratio in Diameter Class Models

67
Q

What are the applications of Regression Models in Forest Management?

A
  • Weight Scaling for Sawlogs (predicting sawlog volume based on truckload weight).
  • Stem Taper Analysis (estimating volume distribution within a tree).
  • Merchantable Volume Predictions (estimating usable wood volume for different top diameters).
  • Biomass Prediction (tree weight estimations using DBH and height).
  • Yield Estimation for Uneven-Aged Stands (predicting long-term forest productivity).
68
Q

What is Site Index?

A

A measure of site quality based on dominant tree height at a reference/index age (e.g., 50-100 years), commonly used for even-aged stands.

69
Q

A measure of site quality based on dominant tree height at a reference/index age (e.g., 50-100 years), commonly used for even-aged stands.

A

Site Index

70
Q

What do Site index curves help predict?

A

Future stand height development.

71
Q

Future stand height development.

A

Site index curves help predict

72
Q

What are the types of site index curves?

A

Anamorphic curves
Polymorphic curves

73
Q

Anamorphic curves

A

same shape, different intercepts

74
Q

same shape, different intercepts

A

Anamorphic curves

75
Q

different shapes and intercepts

A

Polymorphic curves

76
Q

Polymorphic curves

A

different shapes and intercepts