Forest Mensuration E-Book

John A. Kershaw

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1236

Veröffentlichungsjahr: 2016

Beschreibung

Forest mensuration – the science of measurement applied to forest vegetation and forest products – holds value for basic ecology as well as sustainable forest management.  As demands on the world’s forests have grown, scientists and professionals are increasingly called on to quantify forest composition, structure, and the goods and services forests provide.  Grounded in geometry, sampling theory, and ecology as well as practical field experience, forest mensuration offers opportunities for creative problem solving and critical thinking. 

This fifth edition of the classic volume, Forest Mensuration, includes coverage of traditional and emerging topics, with attention to SI and Imperial units throughout. The book has been reorganised from the fourth edition to better integrate non-timber and ecological aspects of forest mensuration at the tree, stand, forest, and landscape scales throughout.  The new edition includes new chapters that specifically address the integration of remotely sensed data in the forest inventory process, and inventory methods for dead and downed wood. One unifying theme, not only for traditional forestry but for the non-timber inventory and for remote sensing, is the use of covariates to make sampling more efficient and spatially explicit.  This is introduced in the introductory chapter on statistics and the chapter on sampling designs has been restructured to highlight this approach and lay the foundation for further learning. New examples will be developed throughout the textbook with an emphasis on current issues and international practice.

Students in applied forestry programs will find ample coverage of forest products and timber inventory, while expanded material on biodiversity, biomass and carbon inventory, downed dead wood, and the growing role of remote sensing in forest assessment will be valuable to a broader audience in applied ecology.

Leseprobe

Table of Contents

COVER

TITLE PAGE

PREFACE

1 INTRODUCTION

1.1. ROLE OF FOREST MENSURATION IN FOREST MANAGEMENT

1.2. FOREST MENSURATION AS A TOOL FOR MONITORING FORESTS

1.3. RELEVANCE OF FOREST MENSURATION FOR ECOLOGY AND NONTIMBER RESOURCES

1.4. DESIGN AND PLANNING OF INVENTORIES

2 PRINCIPLES OF MEASUREMENT

2.1. SCALES OF MEASUREMENT

2.2. UNITS OF MEASUREMENT

2.3. SYSTEMS OF MEASUREMENT

2.4. VARIABLES

2.5. PRECISION, ACCURACY, AND BIAS

2.6. SIGNIFICANT DIGITS AND ROUNDING OFF

2.7. DATA SUMMARY AND PRESENTATION

2.8. FUNDAMENTAL MEASUREMENTS

3 BASIC STATISTICAL CONCEPTS

3.1. DESCRIPTIVE STATISTICS

3.2. FREQUENCY DISTRIBUTIONS

3.3. MEASURES OF CENTRAL TENDENCY

3.4. MEASURES OF DISPERSION

3.5. SAMPLING ERROR

3.6. SAMPLE SIZE DETERMINATION

3.7. INFLUENCE OF SCALAR TRANSFORMATIONS AND THE ESTIMATION OF TOTALS

3.8. CORRELATION AND REGRESSION ESTIMATION

3.9. USE OF COVARIATES TO IMPROVE ESTIMATION

4 LAND AREA DETERMINATION IN FOREST MENSURATION

4.1. LAND DISTANCE AND AREA UNITS

4.2. MEASURING DISTANCES

4.3. MEASURING AREA IN THE FIELD

4.4. MEASURING AREA USING MAPS AND PHOTOS

4.5. DETERMINATION OF PHOTO SCALE

4.6. DETERMINATION OF DIRECTION USING A COMPASS

4.7. THE U.S. PUBLIC LAND SURVEYS

4.8. GLOBAL POSITIONING SYSTEMS

4.9. GEOGRAPHIC INFORMATION SYSTEMS

5 INDIVIDUAL TREE PARAMETERS

5.1. AGE

5.2. TREE DIAMETERS AND CROSS‐SECTIONAL AREAS

5.3. HEIGHT

5.4. FORM

5.5. CROWN PARAMETERS

5.6. REGRESSION AND ALLOMETRIC APPROACHES

6 DETERMINATION OF TREE VOLUME, WEIGHT, AND BIOMASS

6.1. MEASUREMENT OF INDIVIDUAL TREES

6.2. ALLOMETRIC EQUATIONS FOR VOLUME, WEIGHT, AND BIOMASS

6.3. TABULAR ESTIMATION

6.4. VOLUME AND BIOMASS DISTRIBUTION IN TREES

6.5. OTHER METHODS OF ESTIMATING TREE CONTENT

6.6. APPLICATIONS TO SEEDLINGS AND UNDERSTORY VEGETATION

6.7. APPLICATIONS TO SNAGS AND DOWN WOODY MATERIAL

7 MEASUREMENT OF PRIMARY FOREST PRODUCTS

7.1. UNITS OF MEASUREMENT OF FOREST PRODUCTS

7.2. LOG RULES

7.3. BOARD FOOT LOG RULES

7.4. LOG SCALING

7.5. SCALING STACKED VOLUME

7.6. VOLUME UNIT CONVERSION

7.7. SCALING BY WEIGHT

8 STAND PARAMETERS

8.1. AGE

8.2. SPECIES COMPOSITION

8.3. DIAMETER

8.4. HEIGHT

8.5. VOLUME, WEIGHT, AND BIOMASS

8.6. CROWN AND CANOPY MEASUREMENTS

8.7. UNDERSTORY AND REGENERATION

8.8. SITE QUALITY

8.9. DENSITY AND STOCKING

9 SAMPLING UNITS FOR ESTIMATING PARAMETERS

9.1. THE FACTOR CONCEPT

9.2. FIXED‐AREA PLOTS

9.3. SAMPLING TREES WITH VARIABLE PROBABILITY

9.4. OTHER EXAMPLES OF VARIABLE PROBABILITY SAMPLING

9.5. DISTANCE‐BASED SAMPLING UNITS

9.6. SELECTING APPROPRIATE SAMPLING UNITS

10 SAMPLING DESIGNS IN FOREST INVENTORIES

10.1. BASIC CONSIDERATIONS

10.2. SIMPLE RANDOM SAMPLING (SRS)

10.3. SYSTEMATIC SAMPLING (SYS)

10.4. SELECTIVE OR OPPORTUNISTIC SAMPLING

10.5. STRATIFIED SAMPLING (STS)

10.6. CLUSTER SAMPLING

10.7. MULTISTAGE SAMPLING

10.8. SAMPLING WITH COVARIATES

10.9. LIST SAMPLING

10.10. 3P SAMPLING

11 INVENTORY OF STANDING TREES USING SAMPLING WITH VARYING PROBABILITY

11.1. HORIZONTAL POINT SAMPLING (HPS)

11.2. SUBSAMPLING IN HPS

11.3. OTHER VARIABLE PROBABILITY SAMPLING TECHNIQUES

12 INVENTORY OF DOWNED DEAD MATERIAL USING SAMPLING WITH VARYING PROBABILITY

12.1. FIXED‐AREA PLOTS

12.2. LINE INTERSECT SAMPLING

12.3. ANGLE GAUGE METHODS

12.4. PERPENDICULAR DISTANCE SAMPLING (PDS)

12.5. OTHER METHODS

12.6. DESIGN CONSIDERATIONS AND SELECTION OF METHODS

13 INTEGRATING REMOTE SENSING IN FOREST INVENTORY

13.1. TYPES OF REMOTELY SENSED DATA

13.2. REMOTE SENSING FOR STRATIFICATION

13.3. INDIVIDUAL TREE MEASUREMENTS

13.4. REMOTE SENSING FOR COVARIATES

14 MEASUREMENT OF TREE AND STAND GROWTH

14.1. INDIVIDUAL TREE GROWTH

14.2. DIRECT MEASUREMENT OF TREE GROWTH

14.3. RECONSTRUCTING TREE GROWTH

14.4. STAND AND FOREST GROWTH

14.5. MEASUREMENT OF STAND AND FOREST GROWTH AND YIELD

14.6. CONSIDERATIONS FOR THE DESIGN AND MAINTENANCE OF PERMANENT SAMPLE PLOT SYSTEMS

14.7. GROWTH AND YIELD MODELS

APPENDIX

REFERENCES

INDEX

END USER LICENSE AGREEMENT

List of Tables

Chapter 02

TABLE 2.1. Classification of Scales of Measurement

TABLE 2.2. Examples of SI Derived Units

TABLE 2.3. Supplemental Units Recognized by SI

TABLE 2.4. Prefixed and Symbols Used in SI

Chapter 03

TABLE 3.1. Effects of Scalar Transformations of the Form

Y

 = 

mX

on Parameter Estimates

TABLE 3.2. Parameter estimates, standard errors,

t

statistics, and associated

p

‐values for the simple linear regression, height = 

b

0

 + 

b

1·diameter, based on the data shown in Fig. 3.7

TABLE 3.3. Model formulation, parameter estimates, and goodness‐of‐fit criteria for various models applied to the height–diameter data shown in Fig. 3.7 (estimated lines are shown in Fig. 3.8)

TABLE 3.4. Data on root weight and crown cover of black cohosh, drawn from Chamberlain et al. (2013)

Chapter 05

TABLE 5.1. Effects of Eccentricity on Cross‐Sectional Area Estimation

TABLE 5.2. Average Upper‐Log Taper Inside Bark (inches) in 16‐ft Logs

TABLE 5.3. Taper Percentages (Percent of dbh at Height on Tree) According to dbh Class, Height, Location of Tree and Species

TABLE 5.4. Diameter at Breast Height and Dry Foliage Biomass for

Poecilanthe effusa

Trees in the Eastern Amazon

Chapter 06

TABLE 6.1. Equations to Compute Cubic Volume of Important Solids

TABLE 6.2. Sample Stem Measurements for Determination of Volume Using Newton’s, Smalian’s, and Huber’s Methods

TABLE 6.3. Diameter and Bark Measurements of 20 White Oak Trees

TABLE 6.4. Nutrient Concentrations (%) by Species and Tree Component

TABLE 6.5. Carbon Content of Selected Hardwood and Conifer Species by Biome (adapted from Thomas and Martin, 2012): Comparison of IPCC (2006) and Review of Published Values

TABLE 6.6. Gross Volume

a

in Cubic Feet (Excluding Bark) in 8‐ft Height Intervals for Hardwood Species in the Central States

TABLE 6.7. Local Volume Table (Total Volume, m

3

) for Noonan Forest in Central New Brunswick, Canada

TABLE 6.8. Weight in Pounds of Green Wood in Merchantable Stem of Red Pine to a 4‐in. Top

TABLE 6.9. Average Distribution of Tree Volumes by Logs According to Log Position

TABLE 6.10. Tree Stem Volume Structure According to Cylindrical Form Factors

TABLE 6.11. Proportions of Total Stem Volume for Specified Ratios of Upper Height:Total Height (

h

u

/

H

) and Upper Diameter:dbh (

d

u

/

D

)

TABLE 6.12. Example of Proportional Weights of a Tree in Eight Components

TABLE 6.13. Height Accumulation Coefficients,

A

,

B

, and

C

, to Compute Cubic‐Foot Volume by 2‐in. Taper Steps, 4‐ft Unit Heights and Various Mean dib/dob Ratios

TABLE 6.14. Sample Tree Data for Computation of Volume by Height Accumulation by 2‐in. Taper Steps and 4‐ft Unit Heights

TABLE 6.15. Decay Class System for Standing Dead Trees

TABLE 6.16. Decay Class System For Downed Dead Wood

Chapter 07

TABLE 7.1. Comparison of Volumes of 16‐Foot Logs from Different Log Rules

TABLE 7.2. Example of a Standard Volume Table, Using Board Foot Volume, International 1/4‐in. Rule, for Red Oak (

Quercus rubra

) in Pennsylvania

TABLE 7.3. Example of a Local Volume Table for Yellow Poplar (

Liriodendron tulipifera

) in Stark County, Ohio, Using International Rule (1/4‐in. Kerf)—Merchantable Stem to a Variable Top Diameter

TABLE 7.4. Specimen of a Comprehensive Tree Volume Tarif Table

TABLE 7.5. Board Foot–Cubic Foot Ratios for 16‐ft Logs by Taper Rate and Scaling Diameter

TABLE 7.6. Sample Weights and Moisture Contents of Some Species Used for Pulpwood (All Weights Are per Cord)

TABLE 7.7. Board Foot Lumber Yields from Loblolly and Shortleaf Pine

Chapter 08

TABLE 8.1. Species Composition for a Mixed Species Stand in New Brunswick, Canada

TABLE 8.2. Number of Trees per Hectare by Species and 2‐cm dbh Classes for a Mixed Species Stand in New Brunswick, Canada

TABLE 8.3. Methods

a

for Sampling Understory Vegetation

Chapter 09

TABLE 9.1. Dimensions of Commonly Used Fixed‐Area Plots

TABLE 9.2. A Combined Stand and Stock Table for a Northern Hardwood Stand Located in Central New Brunswick

TABLE 9.3. Diameter : Plot Radius Ratios, Gauge Constants, and Angles for Common Imperial and SI BAFs

TABLE 9.4. A Combined Stand and Stock Table for a Northern Hardwood Stand Located in Central New Brunswick

Chapter 10

TABLE 10.1. Summary Statistics and Total Estimates for the Simple Random Sample of the 25 ha Forest Shown in Figure 10.1b.

TABLE 10.2. Summary Statistics and Total Estimates for the Systematic Sample of the 25 ha Forest Shown in Figure 10.4

c

Using the Variance and Standard Error Formulas for Simple Random Sampling

TABLE 10.3. Summary Statistics and Total Estimates for the Stratified Random Sample of the 25 ha Forest Shown in Figure 10.9 Using the Variance and Standard Error Formulas for Simple Random Sampling

TABLE 10.4. Comparison of Population Values (per ha) to Sample Estimates from Simple Random Sampling, Systematic Sampling, and Stratified Sampling

TABLE 10.5. Cluster Summaries for Volume/ha for Systematic Sampling with Multiple Random Starts

TABLE 10.6. Number of Stands, Total Area, and Range of Stand Sizes by Stand Type for the 1500 ha Noonan Forest in New Brunswick, Canada

TABLE 10.7. Strata Estimates and the Component Calculations for a Stratified Two‐Stage Sample of Noonan Forest

TABLE 10.8. Basal Area (m

2

/ha) and Total Volume (m

3

/ha) for Thirty 100 m

2

Plots Randomly Sampled from the Forest Shown in Plate 1

TABLE 10.9. List of Compartments and Individual and Cumulative Areas for Use in List Sampling with Varying Probabilities, and the Total Board Foot Volume in Each Compartment

TABLE 10.10. Random Integers, Selected Compartment, and Associated Area and Board Foot Volume for List Sample

Chapter 11

TABLE 11.1. An Example Big BAF Tally from a Mixed Intolerant Hardwood–Spruce–Fir Stand in central New Brunswick, Canada

TABLE 11.2. Count and Sum of Predicted Heights (Hp, ft) by Plot for the Thirty 10 F BAF Sample Points Collected in a Mixed Oak–Hickory–Maple Forest in Central Indiana

Chapter 12

TABLE 12.1. Example Calculation of Volume Per Hectare for an 0.10‐ha Circular Plot Under Three Different Protocols

TABLE 12.2. Useful Constants and Relationships for Gauges to Be Used in Transect Relascope Sampling and Point Relascope Sampling

TABLE 12.3. Limiting Distances (

R

i

) for PDS with Probability Proportional to Volume, Using VF = 200 ft

2

/acre (

k

A

 = 0.009183 ft)

TABLE 12.4. Limiting Distances (

R

i

) for Distance‐Limited PDS with Probability Proportional to Volume, Using VF = 200 ft

2

/acre (

k

A

 = 0.009183 ft) and an

R

max

of 66′

TABLE 12.5. Limiting Distances, Base Factors, and Derived Factors for Other Downed Wood Attributes, When PDS Is Implemented Using Probability Proportional to Length, to Surface Area, and to Volume

Chapter 13

TABLE 13.1. Spectral Bands of the LANDSAT 7 and LANDSAT 8 Sensor Systems

TABLE 13.2. Stand Typing System for Aerial Photographs

Chapter 14

TABLE 14.1. Measurements for Stem Analysis of a 39‐Year‐Old Western Hemlock

TABLE 14.2. Height Summary for Stem Analysis of a 39‐Year‐Old Western Hemlock

TABLE 14.3. Determination of Diameter Increment from Increment Cores

TABLE 14.4. Growth Data From a 1/5‐acre Fixed‐Area Permanent Sample Plot—Growth Period: 10 Years

TABLE 14.5. An Example Permanent Horizontal Point Sample and Two Measurement Periods (BAF = 20F (ft

2

/acre/tree tallied), 5 Years Between Measurements, dbh Measured in Inches, Height in Feet, and Volume in Cubic Feet)

TABLE 14.6. The Subtraction Method for Calculating Growth on Variable Probability Sample Points

TABLE 14.7. The Fixed‐Inclusion‐Zone Method for Calculating Growth on Variable Probability Sample Points

TABLE 14.8. Growth Estimates Derived From Critical Height Estimates

TABLE 14.9. Calculation of 10‐Year Predicted Volume Growth per Acre, Assuming that All Trees in Each Diameter Class Are Located at the Class Midpoint, and that All Trees Grow at the Average Rate

TABLE 14.10. Calculations of 10‐Year Predicted Volume Growth per Acre, Assuming Trees in Each Diameter Class Are Evenly Distributed through the Class, and Each Tree Grows at the Average Rate

TABLE 14.11. Determination of Tree Movement Ratios From Raw Data for 8‐in. dbh Class

TABLE 14.12. Example of a Normal Yield Table

TABLE 14.13. Variable Density Yield Table for Loblolly Pine

Appendix

TABLE A.1. Some Conversion Factors for Common Units of Measure

TABLE A.2. Areas of Some Plane Figures

TABLE A.3. Volume and Surface Areas of Some Solids

TABLE A.4. Critical Values of Student’s

t

‐Distribution

TABLE A.5.A. Plot Radius (ft) and Tree Factor (trees/acre) for Horizontal Point Sampling in Imperial Units by Basal Area Factor (BAF, ft

2

/acre) and dbh (in.)

TABLE A.5.B. Plot Radius (m) and Tree Factor (trees/ha) for Horizontal Point Sampling in SI Units by Basal Area Factor (BAF, m

2

h

a

−1

) and dbh (cm)

TABLE A.6.A. Equations for Obtaining Common Factors and Constants for Various Forms of PPS Sampling in Imperial Units

TABLE A.6.B. Equations for Obtaining Common Factors and Constants for Various Forms of PPS Sampling in SI Units

TABLE A.7. Plot Summary Data for the 100 m

2

Circular Plot Simple Random Sample Shown in Figure 10.1 (Values Are Per Plot)

TABLE A.8. Plot Summary Data for the 100 m

2

Circular Plot Systematic Sample Shown in Figure 10.4

c

(Values Are Per Plot)

TABLE A.9. Plot Summary Data for the 100 m

2

Circular Plot Stratified Random Sample Shown in Figure 10.9 (Values Are Per Plot)

TABLE A.10. Plot Summary Data for the 100 m

2

Circular Plot Systematic Sample with Multiple Starts Shown in Figure 10.12 (Values Are Per Plot)

TABLE A.11. Plot‐Level Summaries for the 100 m

2

Circular Plot Two‐Stage Sample of Noonan Research Forest (Values Are Per Plot)

TABLE A.12. Measured Tree Data for Point‐3P Sample (Section 11.2.2)

List of Illustrations

Chapter 02

FIG. 2.1. Precision, bias, and accuracy of a target shooter. The target’s bull’s eye is analogous to the unknown true population parameter, and the holes represent parameter estimates based on different samples. The goal is accuracy, which is the precise, unbiased target .

Chapter 03

FIG. 3.1. A 25‐hectare example forest divided into one hundred 50 m by 50 m plots. The numbers in each plot represent the stems/hectare for that plot.

FIG. 3.2. Distribution of trees/ha for the 100 sample plots from the example forest shown in Fig. 3.1: (

a

) histogram and (

b

) frequency polygon and frequency curve.

FIG. 3.3. The normal distribution: (

a

) influence of changes in the value of the mean,

μ

, and (

b

) influence of changes in the value of the standard deviation,

σ

.

FIG. 3.4. Ranges of possible sample means versus sample size for the 25 ha forested area shown in Fig. 3.1.

FIG. 3.5. Distribution of sample means based on a sample of size 20 from the example forest shown in Fig. 3.1. (only 250,000 of the 5.36 × 10

20

possible samples are shown.)

FIG. 3.6. Examples of the relationship between two variables under different levels of correlation: (

a

)

r

 = −1.00, (

b

)

r

 = −0.75, (

c

)

r

 = 050, (

d

)

r

 = −0.25, (

e

)

r

 = 0.00, (

f

)

r

 = 0.25, (

g

)

r

 = 0.50, (

h

)

r

 = 0.75, and (

i

)

r

 = 1.00.

FIG. 3.7. Graph of total height versus diameter for 349 western hemlock trees in western Washington, USA.

FIG. 3.8. Western hemlock height–diameter data with simple linear regression line.

FIG. 3.9. Residual diagnostic graphs for assessing linear regression assumptions: (

a

) histogram of residuals, (

b

) normal quantile plot of residuals, (

c

) residuals versus independent variables, and (

d

) residuals versus predicted dependent variable. (The dashed lines in c and d are smoothed local regression trend lines to highlight patterns.)

FIG. 3.10. Second‐phase sample from the black cohosh data of Chamberlain et al. (2013), along with the corresponding ratio and regression relationships fitted to the data.

Chapter 04

FIG. 4.1. Applying slope corrections.

FIG. 4.2. Coordinates for vertices of a polygon.

FIG. 4.3. Area by dot grid.

FIG. 4.4. Area measurement with planimeter.

FIG. 4.5. Diagram illustrating how ground objects are imaged in the positive plane for vertical photographs. (

a

,

b

,

c

, are photo images of ground points

A

,

B

,

C

. Thus,

ab

is photo distance and

AB

is ground distance.

H

 = height of lens above mean sea level;

h

 = height of terrain above mean sea level; (

H

 − 

h

) = height of lens above the ground (i.e., flying height above ground).

FIG. 4.6. World map showing magnetic declinations for January 1, 2010. Contours of declination are in 5° intervals. Declination data were calculated using the National Geographic Data Center’s Geomagnetic Calculator (NOAA 2013).

FIG. 4.7. United States Public Land Survey: (

a

) standard parallels and guide meridians, (

b

) subdivisions of a township, and (

c

) subdivisions of sections.

FIG. 4.8. GPS basics: (

a

) components of the system, (

b

) triangulation, and (

c

) triangulation via satellite signal.

FIG. 4.9. Data layers in GIS.

Chapter 05

FIG. 5.1. Determining age of trees: (

a

) whorl counts and (

b

) annual ring counts.

FIG. 5.2. Increment tools: (

a

) increment borer, (

b

) increment hammer, (

c

) bark gauge, and (

d

) extracted increment core.

FIG. 5.3. Standard points for measurement of dbh: (

a

) level ground, (

b

) sloped ground, (

c

) uneven ground, (

d

) leaning tree, (

e

) crook at breast height, (

f

) defect at breast height, (

g

) fork at breast height—1 tree, (

h

) Fork below breast height—2 trees, and (

i

) buttressed tree. Note bh = breast height (4.5 ft in the United States, and usually 1.3 m in countries using SI units).

FIG. 5.4. Diameter measuring instruments: (

a

) beam calipers, (

b

) Mantax computer calipers, (

c

) tree calipers, and (

d

) diameter tape.

FIG. 5.5. The Biltmore stick.

FIG. 5.6. Bitterlich’s sector fork (Visiermesswinkel or Sektorluppe).

T

is the tangential line of sight,

D

is the diameter read from the scale (note: this instrument was recently renamed the Treemeter).

FIG. 5.7. (

a

) The Spiegel relaskop and (

b

) the principle of the optical fork.

FIG. 5.8. Electronic dendrometers: (

a

) the Criterion RD 1000 and (

b

) the LaserAce 1000 Rangefinder.

FIG. 5.9. Surface area of a tree stem of paraboloid form.

FIG. 5.10. Tree height and stem length classification: (

a

) excurrent form and (

b

) deliquescent form.

h

, total height;

h

b

, bole height;

h

m

, merchantable height;

h

s

, stump height;

l

c

, crown length;

l

m

, merchantable length;

l

d

, defective length. Shading denotes defective portion.

FIG. 5.11. The Christen hypsometer.

FIG. 5.12. Merritt hypsometer.

FIG. 5.13. Measuring tree height with hypsometers based on tangents of angles: (

a

) measurement on level ground and (

b

) measurement on sloped ground where the tree is above eye level.

FIG. 5.14. Examples of hypsometers based on trigonometric principles: (

a

) Abney level, (

b

) Suunto clinometer, (

c

) Haga altimeter, and (

d

) Blume–Leiss altimeter.

FIG. 5.15. Examples of electronic hypsometers: (

a

) Leiss BL 7, (

b

) TruPulse laser hypsometer, (

c

) Opti‐Logic Insight laser hypsometer, and (

d

) Haglöf Vertex IV ultrasonic hypsometer.

FIG. 5.16. Errors in tree height measurement: (

a

) correctly identify the top of the tree and (

b

) estimate error of leaning trees.

FIG. 5.17. Errors associated with height measurement: (

a

) effect of a 2° angle measurement and (

b

) effect of a 1 m horizontal distance measurement. Contour values are in meters error of tree height

FIG. 5.18. Crown projection measurement: (

a

) crown projection area, (

b

) right‐angle prism densitometer, and (

c

) spherical crown densitometer.

FIG. 5.19. Height–diameter equations fitted to 349 western hemlock trees: (

a

) eq. (5.20), (

b

) eqs. (5.21) and (5.22), (

c

) eqs. (5.23) and (5.24), and (

d

) eqs. (5.25) and (5.26).

FIG. 5.20. Allometric equations for foliage biomass fitted to data from 10 understory trees in a secondary Amazonian rainforest: (

a

) natural log‐transformed space and (

b

) normal measurement space.

Chapter 06

FIG. 6.1. Cumulative component ratios (% above‐ground biomass) by tree component for: (

a

) conifer species and (

b

) hardwood species.

FIG. 6.2. Solids of revolution descriptive of tree form.

FIG. 6.3. Geometric forms assumed by portions of a tree stem.

FIG. 6.4. Stem volume calculations: (

a

) log measurements, (

b

) Newton’s methods, (

c

) Smalian’s method, and (

d

) Huber’s method.

FIG. 6.5. Graphical estimation of the volume of a section of a tree.

FIG. 6.6. Relationship between corresponding diameters inside and outside bark of white oak (see Table 6.3).

FIG. 6.7. Measurements for determination of volume by height accumulation.

Chapter 07

FIG. 7.1. Photographic method of determine solid wood content in stacked wood.

FIG. 7.2. Cumulative weekly weight loss due to moisture loss for 21 red oak sawlogs, and the associated relative humidity record.

Chapter 08

FIG. 8.1. Age distribution at root collar and breast height for a mixed oak–maple–birch–hemlock stand in New England.

FIG. 8.2. Examples of stand profiles and diameter distributions for even‐aged oak stands at three different ages: (

a

) 20‐year‐old stand, (

b

) 60‐year‐old stand, and (

c

) 100‐year‐old stand (stand data for site index 80, Schnur, 1937).

FIG. 8.3. Diameter distribution for an uneven‐aged stand of oak–mixed hardwoods in Missouri: (

a

) stand profile and (

b

) diameter distribution.

FIG. 8.4. Natural log of number of trees per hectare by diameter class for the forest depicted in Figure 8.3. (Note: diameters greater than 42.5 cm were excluded only for illustration purposes.)

FIG. 8.5. Observed number of trees and fitted Weibull distribution: (

a

) 20‐year‐old stand, (

b

) 60‐year‐old stand, (

c

) 100‐year‐old stand, and (

d

) uneven‐aged stand.

FIG. 8.6. Crown class and canopy strata: (

a

) single stratum canopy and (

b

) multistrata canopy (

D

, dominant;

C

, codominant;

I

, intermediate; and

S

, suppressed).

FIG. 8.7. Frame quadrat method for estimating vegetation cover: (

a

) frame quadrat and (

b

) photographic “frame” quadrat.

FIG. 8.8. Point intercept method for estimating cover: (

a

) point frame intersections (

f

, first hit on forb;

g

, first hit on grass; and

s

, first hit on soil) and (

b

) photographic point intercept method.

FIG. 8.9. Transect techniques for cover estimation: (

a

) line intercept technique and (

b

) point transect method.

FIG. 8.10. Sampling cover with probability proportional to size: (

a

) angle gauge, (

b

) counting cover samples, and (

c

) cover point sample.

FIG. 8.11. Stocked quadrat method: (

a

) the land area divided into quadrats such that each quadrat contains one tree, (

b

) a circular stocked quadrat plot with area equal to spacing

2

(i.e.,

), (

c

) zone of no stocking when

, (

d

) a circular stocked quadrat plot with radius equal to half the diagonal spacing, and (

e

) sample geometry associated with (

d

).

FIG. 8.12. Site index curves for natural stands of white pine in the southern Appalachians.

FIG. 8.13. Site index for uneven‐aged stands of red spruce.

FIG. 8.14. Stand density index (SDI) chart for white pine in southeastern New Hampshire. Equation is

. Based on 53 sample plots.

FIG. 8.15. Examples of distance‐dependent point density measures: (

a

) area overlap concept and (

b

) area potentially available.

FIG. 8.16. Basal area stocking guide for upland hardwood stands. The area between curves A and B indicates the range of stocking where the trees can fully utilize the growing space. Curve C shows the lower limit of stocking necessary to reach the B level in 10 years on average sites.

FIG. 8.17. Douglas‐fir stand density management diagram.

Chapter 09

FIG. 9.1. The factor concept. Each tree on a 0.2‐acre plot represents 5 trees per acre.

FIG. 9.2. Selection of trees in fixed‐area plot sampling: (

a

) plot‐centered approach and (

b

) tree‐centered approach.

FIG. 9.3. Methods of boundary slopover correction from a plot‐centered perspective: (

a

) plot shifting; (

b

) plot enlargement; (

c

) plot area adjustment; (

d

) half plot; (

e

) mirage method; (

f

 ) walkthrough method.

FIG. 9.4. Selection of trees in horizontal point sampling: (

a

) point‐centered approach and (

b

) tree‐centered approach.

FIG. 9.5. Gauge constant

k

where

D

is tree diameter in inches and

R

is plot radius in feet.

FIG. 9.6. Tree size and plot radius.

I

, designates “in” trees;

B

, border trees; and

O

, out trees.

FIG. 9.7. Other forms of PPS sampling: (

a

) vertical point sampling, (

b

) horizontal line sampling, and (

c

) vertical line sampling.

FIG. 9.8. Nearest‐neighbor sampling.

Chapter 10

FIG. 10.1. Simple random sampling: (

a

) random sampling from a grid of 10 m by 10 m square plots, (

b

) random sampling by generating random plot center coordinates, and (

c

) random sampling using 10 m by 500 m strips.

FIG. 10.2. Influence of plot size on resulting standard error estimates: (

a

) uncorrected (infinite population) standard error by plot size and sample size, (

b

) corrected (finite population) standard error by plot size and sample size, (

c

) uncorrected (infinite population) standard error by plot size and sample intensity, and (

d

) corrected (finite population) standard error by plot size and sample intensity.

FIG. 10.3. Influence of plot size on: (

a

) mean density (trees/ha) estimates, (

b

) mean basal area (m

2

/ha) estimates, (

c

) mean total volume (m

3

/ha) estimates, (

d

) standard error of mean density, (

e

) standard error of basal area, and ( 

f

 ) standard error of volume.

FIG. 10.4. Systematic sampling layout: (

a

) random start within the forest, (

b

) random start from selected corner, and (

c

) random start from selected corner with offset grid.

FIG. 10.5. Systematic sampling layout with a randomly rotated selection grid.

FIG 10.6. Forest area divided into strips for systematic strip inventory. There are 10 clusters of 5 strips per cluster.

FIG. 10.7. Timed meander sampling: (

a

) schematic illustration of timed meander path through a stand and (

b

) species–effort curve for Consumers Power Company field unit 1, Ottawa, MI.

FIG. 10.8. Stratification of the forest shown in Plate 1 into 3 strata based on species composition.

FIG. 10.9. Stratified random sample layout using proportional allocation and an overall 6% sample intensity.

FIG. 10.10. Comparison of distribution of means arising from: (

a

) 500 simple random samples, (

b

) 20 systematic samples, and (

c

) 500 stratified random samples. The number of sampling units (sample size) was 150 for each simulated sample.

FIG. 10.11. Typical cluster configurations.

FIG. 10.12. Systematic sample with multiple random starts. Each systematic sample represents a cluster. The different plot color patterns represent the six different clusters.

Chapter 11

FIG. 11.1. Projecting a horizontal angle (

a

) by prolonging two lines of sight and (

b

) by deviating the light rays through a fixed angle.

FIG. 11.2. Common angle gauges: (

a

) angle gauge and (

b

) metric relascope.

FIG. 11.3. Representation of image deflection using a prism: (

a

) deflection through the cross section of prism and trees and (

b

) deflection as seen by the observer when using a round prism.

FIG. 11.4. The geometry of prism calibration. Target of width

w

located at distance

B

, which will provide the “completely offset” picture. Dotted lines pertain to a hypothetical borderline tree located properly in the generated gauge angle

θ

.

FIG. 11.5. Gauge constant for photographic horizontal point sampling.

FIG. 11.6. Influence of basal area factor on mean basal area (m

2

) per hectare. Based on 700 sample points collected in central New Brunswick, Canada.

FIG. 11.7. The effect of prism rotation on the amount of horizontal deflection: (

a

) prism in unrotated position on a borderline tree; (

b

) prism rotated in the vertical plane perpendicular to the line of sight—reduced horizontal deflection; (

c

) prism tipped in the vertical plane parallel to the line of sight—increased horizontal deflection; and (

d

) prism swung in the horizontal plane—increased horizontal deflection.

FIG. 11.8. Horizontal line sampling: (

a

) inclusion zone for a single tree, (

b

) inclusion zones for several trees along the transect, and (

c

) inclusion zone for modified HLS.

FIG. 11.9. Critical height sampling.

Chapter 12

FIG. 12.1. A piece of downed wood and its inclusion zone when a circular plot has been used with the large‐end protocol (solid line) and with the sausage protocol (dashed line).

FIG. 12.2. An LIS sample point, its sample line, and several pieces of downed wood (

a

). The line crosses pieces

A

and

D

so those pieces are tallied. The corresponding inclusion zones are shown in (

b

).

FIG. 12.3. A piece of downed wood and its corresponding TRS blobs (inclusion zones), equivalent to PRS inclusion zones, for three different gauge angles.

FIG. 12.4. A TRS sample point, its sample line, and several pieces of downed wood (

a

). Pieces

A

,

C

, and

D

appear wider than the gauge from at least one point on the line, so those pieces are tallied. Piece

E

appears wider, but its center is past the end of the line, so it is not tallied. The corresponding inclusion zones are shown in (

b

).

FIG. 12.5. A PRS sample point and several pieces of downed wood along with their inclusion zones. The sample point falls within the inclusion zones for pieces

D

and

E

, so those pieces are tallied.

FIG. 12.6 A PDS sample point, and several pieces of downed wood (

a

). The line of sight from the sample point to the perpendicular point on each piece is indicated by a dashed line; pieces

B

and

C

have no perpendicular point. The corresponding inclusion zones are shown in (

b

); the sample point falls within the zones for pieces

A

,

D

, and

E

, so those pieces are tallied.

FIG. 12.7. Close‐up of the inclusion zone for a piece of downed wood using PDS with probability proportional to volume.

Chapter 13

FIG. 13.1. An analog aerial photograph of a portion of the Allegheny National Forest in Pennsylvania, taken in September 1958 at a nominal 1 : 20,000 scale with a camera using 9‐in.

2

panchromatic film. Forest roads, different silvicultural treatments, and alternative land uses such as agriculture are clearly discernable. At high magnification, the size and shape of individual tree crowns is measurable, and using an adjacent, overlapping photo, tree heights can be measured using stereophotogrammetry.

FIG. 13.2. A vertical slice through a northern hardwood–spruce–fir stand in New Hampshire, and a corresponding idealized airborne LiDAR waveform.

FIG. 13.3. A 1‐m resolution digital elevation model for a portion of the White Mountain National Forest in New Hampshire generated from airborne LiDAR data. In addition to landforms, cultural features (including stone walls, disused roads, and an open well) can be discerned.

FIG. 13.4. A terrestrial LiDAR unit collecting a dense point cloud within a tropical rainforest.

FIG. 13.5. The RADAR portion of the electromagnetic spectrum.

Chapter 14

FIG. 14.1. The curve of (

a

) cumulative growth, (

b

) growth rate, and (

c

) growth acceleration (

W

 = size,

t

 = age).

FIG. 14.2. Height growth curves: (

a

) the cumulative height growth curve and (

b

) the curves of periodic annual height growth (PAI) and mean annual height growth (MAI); PAI and MAI curves derived from cumulative height growth curve.

FIG. 14.3. Diameter growth curves: (

a

) absolute annual growth and (

b

) percent annual growth.

FIG. 14.4. Root growth measurements: (

a

) soil coring, (

b

) minirhizotron tube and camera, and (

c

) sequence of minirhizotron images.

FIG. 14.5. Stem analysis for a 39‐year‐old western hemlock; taper curves at 5‐year intervals.

FIG. 14.6. Schematic representation of the changes in stand structure of an even‐aged stand due to growth over a 10‐year period (Beers, 1962).

FIG. 14.7. Geometric representation of growth on permanent variable probability point samples: (

a

) using the subtraction method (circles depict increases in volume:basal area ratio not inclusion zone) and (

b

) critical height sampling for growth.

Guide

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FOREST MENSURATION

Fifth Edition

JOHN A. KERSHAW, JR.MARK J. DUCEYTHOMAS W. BEERSBERTRAM HUSCH

This edition first published 2017 © 1972, 1983, 1993, 2003, 2017 by John Wiley & Sons, Ltd

Registered OfficeJohn Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK

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Limit of Liability/Disclaimer of Warranty: While the publisher and author(s) have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. It is sold on the understanding that the publisher is not engaged in rendering professional services and neither the publisher nor the author shall be liable for damages arising herefrom. If professional advice or other expert assistance is required, the services of a competent professional should be sought.

Library of Congress Cataloging‐in‐Publication Data

Names: Kershaw, John A., Jr., 1962– author. | Ducey, Mark J., author. | Beers, Thomas W., author. | Husch, Bertram, 1923– author.Title: Forest mensuration / by John A Kershaw, Jr., Mark J Ducey, Thomas W Beers, Dr. Bertram Husch.Description: Fifth edition. | Chichester, UK ; Hoboken, NJ : John Wiley & Sons, 2017. | Includes bibliographical references and index.Identifiers: LCCN 2016036142| ISBN 9781118902035 (cloth) | ISBN 9781118902004 (epub)Subjects: LCSH: Forests and forestry–Measurement. | Forest surveys.Classification: LCC SD555 .K47 2017 | DDC 634.9–dc23LC record available at https://lccn.loc.gov/2016036142

A catalogue record for this book is available from the British Library.

Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books.

PREFACE

It was not without some trepidation that the two lead authors undertook the revision of this text. The 12 years since the last revision has seen many substantial changes to the field of forest mensuration and has marked a passing of a generation of mensurationists as reflected in the change of authorship of this text. The authors attempted to reflect this change while preserving the classic coverage that this text has been known for.

We first want to mark the passing of Dr. Bertram Husch, who served as lead author on this text through four editions and over 40 years. The first edition of this text, released in 1963, marked the first comprehensive coverage of the field of forest mensuration from a statistical perspective. We also acknowledge the passing of several other great mensurationists since the publication of the last edition, many of whom are cited throughout this text: Dr. Walter Bitterlich, Dr. Lew Grosenbaugh, Dr. Al Stage, Dr. Benno Hesske, Dr. George Furnival, Dr. Boris Zeide, Dr. Paul Van Deusen, and Mr. Bill Carr. They dedicated their lives to forest mensuration, advanced our knowledge of its principles and applications, and contributed much to the education of the current generation of mensurationists.

Forest mensuration is a living science, and one that continues to advance and grow as we build our understanding and as society’s needs and expectations of forests change. We have attempted to reflect those changes in this new edition. The fourth edition saw a major reorganization of the materials and the introduction of nontimber vegetation measurement and carbon estimation. This edition builds upon those changes. We moved the planning of a forest inventory from late in the book upfront to Chapter 1 and place a greater emphasis on estimation throughout. The coverage of nontimber vegetation and carbon accounting has expanded to reflect the current emphasis on these factors in forest management and forest inventory. These factors are integrated throughout the text rather than covered in separate chapters. We have developed an expanded chapter on sampling and estimating dead and down woody debris (Chapter 12) and added a new chapter on the use of remote sensing in forest inventory (Chapter 13). We have developed worked examples for all of the sample designs and have provided the base data so that instructors and students can work through examples in their classes. We have maintained the use of both Imperial units and SI units throughout the text. One criticism of the fourth edition was the deletion of the tables of variable probability sampling factors that appeared in the third edition; we have included these tables in this edition and appreciated the feedback from our colleagues.

The authors acknowledge the help of many friends and colleagues during the preparation of this revision. Drs. David Larsen, Peter Marshall, Robert Froese, Tzeng Yih Lam, Kim Iles, and Andrew Robinson commented on the structure of the book early during its development, and Tom Lynch provided especially valuable comments on a late draft. Dr. Jeffrey Gove commented on many of the datasets and examples used throughout the book. Dr. Jim Chamberlain provided data on black cohosh used in Chapter 3. R. Andy Colter provided LiDAR data from the USDA Forest Service, and Jamie Perkins assisted with the optical imagery in Chapter 13. Dr. Joel Hartter, Russell Congalton, Forrest Stevens, and Michael Palace provided outstanding insights on the role of remote sensing through the Communities and Forests in Oregon (CAFOR) project. Tzeng Lam and Andrew Robinson provided critical feedback on several chapters. Laird van Damme provided feedback on the structure of the book from a practitioner’s perspective. Ethan Belair carefully proofread the final versions of the chapters, and Julia Smith and Madison Poe helped with page proofs. Finally, we want to thank our families for their patience, encouragement, tolerance, and occasional distractions as this project progressed and protracted far longer than we led them to believe at the start.

1INTRODUCTION

In the first widely available book on Forest Mensuration in North America, Henry S. Graves (1906) wrote: “Forest mensuration deals with the determination of the volume of logs, trees, and stands, and with the study of increment and yield.” The Dictionary of Forestry (Helms, 1998) states that “Forest mensuration is the determination of dimensions, form, weight, growth, volume, and age of trees, individually or collectively, and of the dimensions of their products.” This definition is essentially a paraphrase of the 1906 definition given by Henry S. Graves. Although some foresters feel this definition is still adequate, this text considers that mensuration should embrace new measurement problems that have arisen or have been recognized as the horizons of forestry have expanded.

If we accept the challenge of a broader scope, we must ask: “To what degree should mensuration be concerned with measurement problems of wildlife management, recreation, watershed management, and the other aspects of multiple–use forestry?” One might argue that it is unrealistic to imagine that forest mensuration can take as its domain such a diverse group of subjects. The objection becomes irrelevant if we recognize forest mensuration, not as a collection of specific techniques, but as a subject of study that provides principles applicable to a wide range of measurement problems. We view the measurement and quantification of all aspects of forest vegetation as within the domain of forest mensuration. Moreover, many ideas, approaches, and techniques have been developed within the context of traditional forest mensuration that have broad applicability for forest ecology, wildlife habitat, recreation, and watershed management. This book, in addition to a treatment of the traditional product‐oriented measurement problems of forestry, will also provide a unified foundation of principles for solving measurement problems in other aspects of forestry.

During the latter half of the twentieth century, the application of statistical theory and the use of computers, electronics, and lasers wrought a revolution in the solution of forest measurement problems. Consequently, mensurationists must have a degree of competence in their use as well as in basic mathematics and statistics. Knowledge of calculus is also desirable. In addition, familiarity with systems analysis and operational research, approaches to problem solving that depend on model building and techniques that include simulation and mathematical programming, will also be valuable, especially in advanced and more sophisticated treatments of forest mensurational problems. We do not presume that all readers of this text will have such a deep and broad background, and have tried to present forest mensuration in a way that is accessible to new students but provides a comprehensive overview of the possibilities of the field.

1.1. ROLE OF FOREST MENSURATION IN FOREST MANAGEMENT

Forest mensuration is one of the cornerstones in the foundation of forestry. Forestry in the broadest sense is a management activity involving forest land, the plants and animals on the land, and humans as they use the land. Much of the forest land in North America and in other parts of the world is under active forest management. In many jurisdictions, foresters are required to complete detailed long‐term forest management plans, especially on public lands. These plans require foresters to make detailed predictions about the growth and yield of forest resources, and how harvesting and other forest management activities influence the flow of timber and other resources. Based on the outputs from these models, forest managers make decisions about where, when, how, and how much forest land should be treated. Elsewhere, management planning may reflect shorter time horizons, but the decisions are no less critical. Good forest management decisions require good tools to analyze the impacts of management activities on the quantities and flows of the various forest resources and on the state of the forest itself. These tools require good models and, ultimately, these models require good data. The acquisition of this data is the subject of this book.

Foresters are faced with many decisions in the management of a forest. The following questions are examples of the problems that must be solved for a particular forest:

What silvicultural treatment will result in best regeneration and growth?

What species is most suitable for reforestation?

Is there sufficient timber to supply a forest industry and for an economical harvesting operation?

What is the value of the timber and land?

What is the recreational potential?

What is the wildlife potential?

What is the status of biodiversity on the area?

What is the status of the forest as a carbon sink?

A forester needs information to answer these and countless other questions and to make intelligent decisions or recommendations to a client. This information often is needed in quantifiable terms. In most situations, the axiom holds, “You can’t efficiently make, manage, or study anything you don’t locate and measure.” At the same time, resources for measurement are usually limited, so information must be acquired efficiently. In this sense, forest mensuration is the application of measurement principles to obtain quantifiable information for forest management decision‐making.

To summarize, forest mensuration is concerned with obtaining information about forest resources and conditions. The ultimate objective of forest mensuration is to provide quantitative information about the forest and its resources that will allow making reasonable decisions on its destiny, use, and management.

1.2. FOREST MENSURATION AS A TOOL FOR MONITORING FORESTS

To many, a forest, if not affected by cutting, fire, or some other calamity, is a stable, unchanging entity. Actually, a forest is a dynamic system that is continuously changing. Although this may not be evident over a short term, such as a few years, change is always present: some trees increase their dimensions, others die, and new trees germinate and enter the forest. Consequently, the information obtained about the status of a forest area at a given time is only valid for a length of time that depends on the vegetation itself, and on environmental and external pressures affecting the forest. This means that the mensurational information regarding the forest must be updated periodically by monitoring procedures so that the appropriate management and policy decisions may be taken.

Throughout the twentieth century, the demand on forest resources increased worldwide (Westoby, 1987). In the opening decades of the twenty‐first century, this increase is expected to continue. During the last 40 years, not only have the demands for timber increased, foresters also have been required to manage for other resources including wildlife habitat, water quality, recreational opportunities, and biodiversity. An increase in the public awareness of the influence of human activities on the environment has resulted in the development of a number of forest certification procedures to ensure that forest management activities are sustainable both economically and environmentally. These procedures require forest managers to document and monitor the impacts of forest management activities on a wide range of forest resources, not just timber.

Monitoring must consider changes in composition, structure, size and health of forests (Max et al., 1996). To be effective, monitoring must be comprehensive. In these situations, foresters must increase the scope of their inventories, and the models they use, to include information on multiple aspects of forest structure, not just timber‐producing trees. To be cost effective, forest managers will be required to design and implement new sampling strategies and measurement procedures to meet the demand for increased information.

In the early 1900s, as professional forestry was beginning in North America, the need to use the quantitative tools forest mensuration and forest inventory offered to monitor forest resources was quickly recognized (Bates and Zon, 1922). Zon (1910), in one of the first attempts to assess global forest resources, recognized the need for systematic monitoring of forest resources on both a national and global basis. The Scandinavian countries (Norway in 1919, Finland in 1920, and Sweden in 1923) were the first countries to implement systematic national forest inventories (NFIs) based on modern statistical principles (Tokola, 2006; Tomppo, 2006). The United Kingdom began NFIs in 1924 and the United States in 1930. The Food and Agricultural Organization of the United Nations began compiling global assessments of forest resources in 1947. Many other European and Asian countries began NFIs in the 1960s and 1970s. In the late 1980s and early 1990s, many countries, including the United States and Sweden, redesigned their NFIs, adopting a systematic‐grid‐based sample design and consistent plot designs and remeasurement intervals. A number of countries around the world have redesigned or adopted similar protocols for their NFIs. Although most NFIs began with a focus on timber and related resources, nearly all are actively broadening to address the full range of economic, conservation, and environmental challenges faced by regional and national policymakers. Most countries have also integrated remote sensing, aerial photography, and/or LiDAR data into the analyses of ground‐based data.

While the various NFIs differ by the specific measurements made, plot sizes and layouts, grid intensities, and remeasurement intervals, the newer designs make it easier to compare estimates across countries (Liknes et al., 2013). The information collected has many important uses including

helping policymakers at national and regional levels to formulate good forest policy, and to assess the sustainability of current and past policy;

enabling land managers to devise better management plans and to assess effects of current and past management practices on the land;

serving as a starting point for scientific investigations in a variety of areas that involve changes in forest ecosystems over time;

formulating business plans that will be both economically and ecologically sustainable over time;

keeping the public informed about the health and sustainability of a nation's forests; and

providing consistent and reliable reporting statistics to demonstrate compliance with various treaty obligations including conservation and biodiversity commitments and carbon offset accounting.

Most countries provide online access to their NFI data and extensive online report generating capabilities.

1.3. RELEVANCE OF FOREST MENSURATION FOR ECOLOGY AND NONTIMBER RESOURCES

Ecologists, conservation biologists, and wildlife managers, like forest managers, require quantitative information to make informed decisions. Sometimes, these decisions are about choices of management actions, and sometimes about choices between competing scientific hypotheses; but either type of choice depends on high‐quality data that are collected and analyzed in a cost‐effective way. While the emphasis of this text is the measurement, sampling, and estimation of the tree component of the forest vegetation, the basic principles of mensuration can be applied to a range of forest structures and attributes, and we have included some coverage of measurements for these characteristics. Estimation of species diversity, abundance, biomass, and carbon content utilize similar techniques for measurement and sampling, whether the focus is on trees, shrubs, herbs, or grasses. Of course, the types of measurements made, the tools used, the size, shape, and type of sample plots, and the sample intensity will vary depending upon the size of individuals, the spatial heterogeneity, and the estimates required (Bonham, 2013).

Ecological studies require measurements that are effective, accurate, and precise (Ford, 2000). Many of the stand structure parameters useful to foresters for making management decisions (e.g., density, basal area, volume, biomass, and crown cover) are useful parameters for estimating nontimber resources and ecological indices. Wildlife managers often rely on measures of stand structure to assess habitat quality and suitability. The need for accurate measurements and robust sample designs are just as important for these applications as they are for timber management.