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Research Note RMRS-RN-52. 2013

Rocky Mountain Research Station

Research Note

United States Department of Agriculture

Forest Service

Rocky Mountain Research Station

Research Note RMRS-RN-52

June 2013

Comparison of Kalman Filters in Combining Panel Data 

From the Annual Inventory System of the South Korea 

National Forest Inventory

Tzeng Yih Lam, Raymond L. Czaplewski, Jong Su Yim, 

Kyeong Hak Lee, Sung Ho Kim, Rae Hyun Kim

Citation:

Tzeng Yih Lam, Raymond L. Czaplewski, Jong Su Yim, Kyeong 

Hak Lee, Sung Ho Kim, and Rae Hyun Kim. 2013. Comparison 

of Kalman filters in combining panel data from the annual 

inventory system of the South Korea National Forest Inventory.

Res. Note RMRS-RN-52. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station. 

15 p.

Authors:

Tzeng Yih Lam (corresponding author)a, Division of Forest 

Management, Korea Forest Research Institute, Seoul, South Korea.

Raymond L. Czaplewski, Forest Inventory and Analysis Program, 

Rocky Mountain Research Station, Fort Collins, Colorado.

Jong Su Yim, Kyeong Hak Lee, Sung Ho Kim, Rae Hyun Kim,

Division of Forest Management, Korea Forest Research Institute, 

Seoul, South Korea.

aPresent Address: 18, Jalan Sibu 12, Taman Wahyu, Jalan Ipoh, 

68100 Kuala Lumpur, Malaysia.

Introduction

National Forest Inventories (NFIs) serve a primary purpose of providing crucial information for formulating national forest policy, environmental planning and reporting to 

international processes (Tomppo and others 2010). Pressure for timely and reliable forestry 

statistics urges countries to put a NFI in place or to consider alternative designs. Some 

countries, for example, USA and Finland, moved from a periodic inventory design to an 

annual inventory design (Lawrence and others 2010) with the Swedish NFI being one of 

the earliest examples of the transition (Matérn 1960, Ranneby and others 1987). An annual 

inventory systematically subdivides a plot network, with equal proportion, into a number 

of panels equal to the length of measurement cycle (Reams and others 2005). A measurement cycle is typically five or ten years. Panels are measured successively on an annual basis. 

Remeasurement starts in the next cycle once all panels are visited. Thus, an annual inventory is expected to meet the demand for continually updated information.

An analytical challenge with annual inventory is deriving accurate and precise estimates 

of forest statistics for a single year. An unbiased estimate of population mean is the design-based mean estimate from panel data (Van Deusen 2002). However, McRoberts and 

Hansen (1999) found that the precision of the estimate was unacceptable due to reduced 
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Research Note RMRS-RN-52. 2013

sample size from subdividing total samples into panels. The latter has motivated research 

in combining time-series of multiple panels to achieve desirable precision. There are a multitude of approaches for combining panel data including moving average and temporally 

different methods (Patterson and Reams 2005), growth models (Lessard and others 2001), 

time series models (Johnson and others 2003), and mixed estimator (Van Deusen 2002).

Recent development has seen a proliferation of imputation methods for combining panel 

data (Eskelson and others 2009). The drawback is the requirement for auxiliary information such as remotely sensed land use classification or climatic variables. With the advent of 

satellite imagery and airborne sensors, this information could be obtained easily (Czaplewski 

1999). However, not all countries—especially developing countries—with the intention of 

adopting an annual inventory system have the resources to invest in remotely sensed auxiliary 

information or human resources to process the information. Thus, alternative methods that 

do not rely on auxiliary information may be more appropriate under such circumstances.

One alternative is the Kalman filter, which is well known in many fields of study, such 

as aerospace engineering, and economics (Petris and others 2009), but the experience in 

forest inventory is limited (exception see Dixon and Howitt 1979, Czaplewski 2010). The 

Kalman filter is a recursive statistical estimator that combines a model prediction and an observation into a weighted average at each calculation step or time increment (Czaplewski and 

Thompson 2009). In the context of annual inventory, the model describes how a population 

is expected to change over time, and the observation comes from a panel estimate. Hence, the 

combination of model- and design-based estimates is expected to reduce risk from model bias 

and preserve gain in precision from the modeling process (Czaplewski and others 2012). With 

this advantage, the Kalman filter is potentially an effective method in combining panel data.

The goal of this study was to explore the Kalman filter as an alternative method for combining panel data when auxiliary information is not readily available. The study does not 

attempt to identify a universally best model that applies across any annual inventory. Instead, 

the objective was to study advantages, disadvantages and application of three specifications 

of the Kalman filter in contrast to conventional methods of moving average and weighted moving average under three populations with different characteristics of change. Each 

method assumes different rates of change in a population, and therefore, each is a separate 

hypothesis (Czaplewski and others 2012). This study could be viewed from a perspective of 

test-of-hypothesis: which model best predicts the trends in population change. Based on these 

observations, a recommendation is made on possible choice of model for a given set of panel 

data.

Materials and Methods

Data

This study used the panel data from the 5th South Korea NFI (NFI-5) as a means for making comparisons. The NFI program in South Korea was initiated in the early 1960s based on 

periodic inventory design, but was later enhanced in the fifth session to an annual inventory 

with new features such as new plot design (Kim 2009). The annual inventory had a measurement cycle of five years with the first cycle completed during years 2006 to 2010. A systematic 

grid of 4 km spacing was overlaid over the whole country, yielding about 4,000 grid points 

covered by forest. A forest was defined as having a minimum area of 0.5 ha, minimum width 

of 30 m, crown cover >10% and tree height >5 m (KFRI 2009). The grid points were systematically subdivided into five non-overlapping panels with each panel covering the whole 

country.
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Research Note RMRS-RN-52. 2013

A permanent sample plot (PSP) was installed at each grid point. A PSP consisted of four 

subplots with one at the center and three at 50 m away from the center and at azimuths of 

0o, 120o and 240o, respectively (KFRI 2009). Each subplot was a tri-areal plot consisting of 

three nested circular plots: (1) 0.08-ha plot for trees with diameter at breast height (DBH) 

≥30 cm, (2) 0.04-ha plot for trees with 6 <DBH <30 cm, and (3) 0.003-ha plot for saplings 

and shrubs (KFRI 2009). Observed variables included species, DBH, height, damage, and 

soil condition, and so on.

This study compared the following provinces in South Korea: Gyeongsangbuk-do, 

Gyeonggi-do, and Jeju-do. They were chosen for differences in geographical coverage, size, 

anthropogenic influence, and forest ecosystem. Gyeongsangbuk-do is the largest and the 

most mountainous province located in the east covering an area of 19,400 km2 and is 

dominated by Korean red pine (Pinus densiflora S. et Z.). Gyeonggi-do is located in the 

northwest and ranks fifth in size, but the province has the highest population density. Thus, 

its dominant coniferous and deciduous forests are influenced by anthropogenic factors. 

Jeju-do is a volcanic island and the smallest province in the country (1,850 km2) located 

in the south. Because of distinct parent material and humid subtropical climate, Jeju-do 

has mostly intact deciduous forest. Due to its small area, a systematic grid of 2 × 2 km 

was installed to achieve sufficient sampling intensity. The total number of subplots for 

Gyeongsangbuk-do, Gyeonggi-do and Jeju-do were 3236, 1001, and 536, respectively, of 

which 20% were measured during any single year.

For each province, the studied variable was mean annual growing stock, that is, volume 

per hectare (m3/ha). Volume of an individual tree was estimated from volume equations in 

Kwon and others (2001) and then expanded to per ha by appropriate expansion factors. For 

each subplot, total volume and volumes of coniferous and deciduous groups were calculated 

by summing expanded individual tree volume accordingly. Based on the estimated volumes 

per hectare of the two groups, the subplot was classified into three general forest types: 

coniferous, deciduous, and mixed forests.

Let ytjk be volume/ha (growing stock) for k-th subplot of j-th forest type in t-th panel, ntj

be number of subplots of j-th forest type in t-th panel, and nt be number of subplots in t-th 

panel, where j = 1,...,3 and t = 1,...,5 (year 2006 to 2010). Estimated mean and variance 

of growing stock for j-th forest type in t-th panel were

y y /n k

n = =1 tj tjk tj / tj and

var ( ) y y( ) y n / ( (n 1)) tj

2

tj = -tjk - k

n

tj tj =1 / tj ,

respectively. Due to different number of subplots between forest types, the mean and its 

variance of annual growing stock for t-th panel using methods in Yim and others (2011) 

were, respectively,

y n( /n y )

j 1

3

= = t t / j t tj (1)

var 2

2 2

( ) ( )y

n

n

var y

n

n

n

y y

j 1

3

= + -

= t

t

tj

tj t

tj

t

tj t / > ^ h H (2)

where, 3

n n t t = j j = 1 / .
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Research Note RMRS-RN-52. 2013

The variance estimator assumed each subplot was an independent Primary Sampling 

Unit, following the procedure in Yim and others (2011). This estimator simplified the calculation of mean annual growing stock in this study. Regardless of its statistical properties, 

this variance estimator was sufficient for comparisons of relative performances among methods and test-of-hypothesis on population. It was not the intention of this study to draw any 

significant inferences for decision making. Lastly, this estimator reflects the current practice 

in South Korea while development of variance estimators is in progress. Lastly, yt (Eq. 1) 

and var yt ^ h (Eq. 2) were denoted as single panel estimates at t-th panel (SINGLE), and 

were inputs to the methods described below.

Moving Average and Weighted Moving Average

The moving average (MA) is a basic method for combining panel data to increase precision of current estimate (Roesch and Reams 1999). Due to its simplicity, the method is 

adopted by several countries implementing annual inventory systems, for example, Sweden 

NFI program (Axelsson and others 2010). However, MA has often been criticized for lag 

bias (Johnson and others 2003). Nonetheless, it served as a benchmark comparison in this 

study. Roesch and Reams (1999) argued that MA might have the tendency to mask temporal trends, and they proposed weighted moving average (WMA) whereby more recent 

panels would receive heavier weights. On this basis, a priori weights were assigned according 

to Table 1. The rules behind the weight assignment were: (1) the most recent panel received 

half of the total weight, and (2) weights diminished at a rate of 50% successively. The rules 

were relaxed for some cases to meet the constraint that total weight summed to one.

First, let yWMA t, be the WMA estimate of mean annual growing stock for t-th panel,

y w y

T

t

1

= = WMA t, / T T (3)

where wT was the weight assigned to corresponding T-th single panel estimate of mean 

annual growing stock, and the estimated variance of yWMA t, was,

var 2

y w var y

T

t

1

= = WMA t, T T ^ ^ h h / (4)

The MA was a special case of WMA, where for T-th panel, wT = 1/t. Finally, let y MA,t

and var y MA,t ^ h be the MA estimate of mean annual growing stock for t-th panel and its 

estimated variance, respectively.

Table 1. The a priori weights for weighted moving average (WMA).

t-th Panel

Weights (wT)

w1 w2 w3 w4 w5

1 1.00 – – – –

2 0.40 0.60 – – –

3 0.10 0.30 0.60 – –

4 0.10 0.15 0.25 0.50 –

5 0.03 0.07 0.15 0.25 0.50
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Research Note RMRS-RN-52. 2013

Kalman Filters

The Kalman filter resembles a recursive predictor-corrector algorithm for solving a numerical problem, which is analogous to the method of recursive estimation (Pollock 2002, 

Welch and Bishop 2006). It consists of two sets of equations—prediction equations and 

correction equations. The prediction equations predict the mean annual growing stock and 

its error covariance for the time step based on a model of changes in the variable during 

a single year. The best estimate of mean annual growing stock during the preceding year 

serves as initial conditions in the model. When a panel estimate of mean annual growing 

stock and its error covariance are obtained at the next time step, the correction equations are 

a weighted average of the observed and predicted estimates and error covariances. The more 

precise estimate receives more weight, and the less precise estimate receives proportionally less weight. The correction equations are a generalization of the composite estimator 

(Gregoire and Walters 1988). Thus, the final estimates consist of a weighted average estimate and error covariance for the variable of interest. This procedure is repeated for any 

number of time steps. For additional information, refer to Maybeck (1979) and Czaplewski 

and Thompson (2013).

Three Gaussian linear state-space models were studied, that is, random walk plus noise 

model (WALK), linear growth plus noise model (LINEAR), and exponential growth plus 

noise model (EXP). A state-space model is a mathematical model representing the underlying stochastic process of a system (Czaplewski 2010). A Gaussian linear state-space model 

is a special case such that the model is a linear function of variables and is built on the 

normality assumption (Petris and others 2009). Under this assumption, the Kalman filter 

is a maximum likelihood estimator (Maybeck 1979). The actual computation of a statespace model is complex, but application of the Kalman filter coupled with the normality 

assumption simplifies the computation significantly (Petris and others 2009). The WALK, 

LINEAR and EXP were laid out in the state-space model representation.

The state-space model for WALK was (Petris and others 2009),

2

2

, ,

y , ,

w w N

v v N

0

0

+

+

n n v

n v

= +

= +

,

,

t t t w t

t t t t v t

t - 1

^

^

h

h (5)

where μt and μt–1 were state variables for t- and (t–1)-th panels, vt and wt were normally distributed random errors and assumed uncorrelated, and the estimator of 2

vv t, was var( ) yt .

The second equation of WALK (Eq. 5) specified the stochastic model that the change in 

mean annual growing stock was a random process with mean zero over panels (Petris and 

others 2009). The ratio between the two variances 2 2

v v/ w t, , v t was called signal-to-noise 

ratio (SNRWALK), which influenced the performance of WALK (Petris and others 2009). 

Increasing SNRWALK would weigh the observation heavier in the correction equations of 

the Kalman filter (Johnson and others 2003). There is no strict rule of assigning a value 

to SNRWALK; so, it was assumed 0.1 for this study implying that 2

vw t, was 10% of 2vv t, .

The state-space model for LINEAR, elaborated from WALK, was (Petris and others 2009),

2

2

2

, ,

, ,

y , ,

w w N

w w N

v v N

0

0

0

+

+

+

n n b v

b b v

n v

= + +

= +

= +

, , ,

, , ,

,

t t t t w t

t t t t w t

t t t t v t

1 1 1 1

1 2 2 2

-

-

t - 1

^

`

`

h

j

j

(6)

where the random errors vt, w1,t and w2,t were assumed normally distributed and uncorrelated.

The parameter bt was interpreted as growth rate (Petris and others 2009). Hence, 

the stochastic model of LINEAR (last two equations in Eq. 6) implied that current panel 

w1,t

w2,t
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Research Note RMRS-RN-52. 2013

population mean annual growing stock nt depended on the previous panel mean annual growing stock nt - 1 and a rate of change. The latter might change over panels if 

2 vw 2 0 2,t . For LINEAR, there were two signal-to-noise ratios, that is, 2 2

v v/ w v t, 1,t

(SNRLINEAR1) and 2 2

v v/ w v t, 2,t (SNRLINEAR2) (Johnson and others 2003). Both ratios 

behaved in similar fashion to SNRWALK. For this study, SNRLINEAR1 and SNRLINEAR2

were assumed 0.1 and 0.01, respectively. We assumed a small ratio for SNRLINEAR2 because 

the data for this study had only five panels and might not be long term enough to detect 

obvious change in the growth rate bt .

The state-space model for EXP (Czaplewski and Thompson 2013) was specified as,

2

2 , ,

y , ,

w w N

v v N

0 CV

0

+ :

+

n {n

n v

= + n

= + ,

t t t t

t t t t v t

- - 1 1 t

^

^

h

6 @ h (7)

where { was exponential growth factor and CV was coefficient of variation.

The EXP (where { ≠ 1) resembled WALK (where { = 1) with two exceptions. The parameter { implied that mean annual growing stock at t-th panel increased by a factor of {

from that of (t–1)-th panel. Because of the reforestation effort in the 1950s, forest site condition has recuperated and growing stock has increased recently (Lee 2010). Therefore, the 

assumption of exponential growth was consistent with the past history and anthropogenic 

influence on the country forests. The { was estimated from the published growing stock 

statistics from 1981 to 2005 (Korea Forest Service 2010). It was the average of the first difference of the growing stock series; thus, the average { values were 1.058, 1.077, and 1.039 

for Gyeongsangbuk-do, Gyeonggi-do and Jeju-do, respectively. Lastly, the variance of the 

stochastic model implied that underlying stochastic process was assumed heteroscedastic, 

which is common in many natural systems. For practicality, CV was assumed 0.01.

The results of fitting WALK, LINEAR, and EXP were filtered mean annual growing 

stock for each method y y , and y WALK, , t LINEAR t EXP t, ^ h and its associated estimated variance var (y y ), var ( and ( var y )) WALK, , t LINEAR t EXP t, ^ h for t-th panel.

Bootstrap Method

Bootstrap method was used to compare performances of MA, WMA, WALK, LINEAR 

and EXP relative to SINGLE for each province. The procedure in the following was described for a province, but was repeated for all three provinces. At t-th panel, bootstrapping 

was independently carried out for each of the three forest types, that is, coniferous, deciduous and mixed forests. In other words, a sample of ntj subplots was randomly selected with 

replacement for j-th forest type in the t-th panel. On average, ntj was approximately 216, 

67 and 36 subplots for a forest type in one panel for Gyeongsangbuk-do, Gyeonggi-do, and 

Jeju-do, respectively. Then, Eq. 1 and Eq. 2 were applied to estimate SINGLE for the t-th 

panel. After that, the five methods (MA, WMA, WALK, LINEAR, and EXP) were fitted 

with the SINGLE estimates. The sampling and estimation steps were repeated 1000 times 

for the t-th panel resulting in 1000 estimates of SINGLE, MA, WMA, WALK, LINEAR 

and EXP. With the bootstrapped estimates, bootstrap average +

y METHOD t ^ h and bootstrap 

variance var ( ) y +

METHOD,t ^ h of mean annual growing stock for a method at the t-th panel 

were calculated as

+ 1000 + 1000 2

y y = = / ( 1000 and var y ) / y y - 999 +

, , m METHOD , , , , t m METHOD t M m ETHOD t m METHOD t METHOD,t = = 1 1 / / ^ h ,

respectively. Lastly, the above procedure was repeated for each of the five panels.
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Research Note RMRS-RN-52. 2013

The statistics for evaluating relative performances between methods were mean and 

standard deviation of errors. For a method and t-th panel, error was defined as the difference 

between m-th bootstrapped estimate of mean annual growing stock of the method

y METHOD, ,t m ^ h and the m-th bootstrapped SINGLE estimate ^ h yt m, , that 

is, e y , = - y , , , t m METHOD t m t m for m = 1,...,1000. Thus, mean (ME) and 

standard deviation (SDE) of the error were 1000 MEt t = e ,m/1000 m = 1 / and 

1000 SDE et m, ME /999 2

t t = - m = 1 / ^ h , respectively. The distribution of et,m for t-th panel 

and a method was tested for normality with the Shapiro-Wilk test (Shapiro and Wilk 

1965). The analysis was done in R open source statistical software (R Development Core 

Team 2011).

Results

The MA clearly showed symptoms of lag bias when the method was applied in combining panel data for all three provinces, with the exception of Gyeonggi-do from year 2006 

to 2009. The MA estimates in general could not trace the trends of SINGLE estimates 

(Fig. 1). The lag bias apparently produced underestimation for Gyeongsangbuk-do on average (Fig. 1A) and for the majority of the bootstrapped estimates (Fig. 2A). On the other 

hand, overestimation was observed for Jeju-do (Fig. 1C and Fig. 2K). For Gyeonggi-do, 

MA agreed well with SINGLE estimates for the first four years as indicated by average 

zero error (Fig. 2F), but the symptoms of lag bias were strongly evident in 2010 (Fig. 1B). 

The MA was among the five methods that had the highest increase in estimated precision 

Fig. 1. Bootstrap average of mean growing stock ( METHOD t, y

 ) from year 2006 to 2010 for the six methods and 

three provinces: (A) Gyeongsangbuk-do, (B) Gyeonggi-do, and (C) Jeju-do. The SINGLE is depicted by solid 

gray line with ○ with the error bars indicating one bootstrap standard deviation. The five methods are: MA 

(solid line with •), WMA (solid line with ■), WALK (dotted line with ○), LINEAR (dotted line with □) and EXP 

(dotted line with ◊).
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Research Note RMRS-RN-52. 2013

(Eq. 4) after combining panel data. The estimated relative standard errors in 2010 were 

52–58% of those in 2006 for the three provinces (Table 2). However, the bootstrap showed 

that MA had the largest variation in error among the five methods for all provinces (SDE in 

Table 3). The tests supported normally distributed errors (p-values >0.14, Table 3).

The WMA on average showed less evidence of lag bias compared to MA (Fig. 1), that is, 

estimates of WMA captured the trends of SINGLE better. The apparent underestimation 

for Gyeongsangbuk-do (Fig. 2B) and Gyeonggi-do in 2010 (Fig. 2G) and the overestimation for Jeju-do (Fig. 2L) were less. However, the reduction in estimated standard error 

(Eq. 4) from combining panel data was less than the MA. The estimated relative standard 

errors were only reduced by 40–50% in 2010 (Table 2). The reduction in estimated variance of WMA compared to MA was also indicated by smaller SDE (Table 3). Similarly with 

MA, normal distribution for the errors was supported by the test of normality (p-values 

>0.08, Table 3).

The performance of WALK was almost identical to MA in terms of trends in the estimates and errors (Fig. 1 and Fig. 2). However, there were some differences. The reduction 

in estimated relative standard errors from combining panel data was not as much as MA, 

i.e., only 42–52% reductions in 2010 (Table 2). In some cases, the SDE of WALK was 

lower than that of MA (Table 3). However, the most noticeable difference between the two 

methods was that the normality test did not support the hypothesis of normally distributed 

errors especially for Jeju-do (p-values <0.03, Table 3).

LINEAR was the method that best traced the SINGLE estimates. The average 

LINEAR estimates were generally similar to SINGLE estimates and were within one 

standard deviation of latter (Fig. 1). Furthermore, the trends in error over the years did 

not show any excessive over- and underestimation with the exception of Gyeonggi-do in 

2010 (Fig. 2D, I and N). LINEAR has achieved the least increase in estimated precision 

Fig. 2. The trends of error (et,m) from year 2006 to 2010 for the five methods and three provinces. The gray lines 

depict 1000 bootstrapped error with the horizontal solid black lines depicting zero error and the dotted black 

lines depicting mean error.
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Research Note RMRS-RN-52. 2013

Table 2. Average estimated relative standard error (%) of the three provinces for the five panels and the six methods. 

Relative standard error is estimated as 100 # var (y y METHOD ) / , M t t ETHOD, and is then averaged from 1000 

bootstrapped estimates.

Estimation Methods 2006 2007 2008 2009 2010

Gyeongsangbuk-do

SINGLE 2.04 2.03 1.79 1.90 2.11

MA 2.04 1.44 1.12 0.97 0.88

WMA 2.04 1.47 1.27 1.12 1.22

WALK 2.04 1.47 1.20 1.10 1.12

LINEAR 2.04 2.03 1.67 1.56 1.57

EXP 2.04 1.50 1.26 1.21 1.24

Gyeonggi-do

SINGLE 3.42 4.05 3.54 4.04 3.29

MA 3.42 2.64 2.12 1.88 1.63

WMA 3.42 2.76 2.49 2.30 2.09

WALK 3.42 2.70 2.26 2.18 2.00

LINEAR 3.42 4.05 3.33 3.29 2.76

EXP 3.42 2.67 2.21 2.06 1.86

Jeju-do

SINGLE 6.27 5.42 6.31 5.58 5.41

MA 6.27 4.15 3.48 2.97 2.62

WMA 6.27 4.12 4.13 3.34 3.15

WALK 6.27 4.15 3.70 3.28 3.01

LINEAR 6.27 5.42 5.64 4.87 4.41

EXP 6.27 4.11 3.50 3.04 2.75

Table 3. The SDE and p-value from the Shapiro-Wilk test of normality in parenthesis of the three provinces for the 

five panels and the six methods. Significant p-value (α = 0.05) is in bold and italic.

Estimation Methods 2006 2007 2008 2009 2010

Gyeongsangbuk-do

MA 0.00 (–) 1.73 (0.67) 2.05 (0.51) 2.10 (0.14) 2.61 (0.71)

WMA 0.00 (–) 1.39 (0.67) 1.28 (0.28) 1.44 (0.15) 1.69 (0.08)

WALK 0.00 (–) 1.68 (0.54) 1.99 (0.39) 1.92 (0.06) 2.54 (0.23)

LINEAR 0.00 (–) 0.00 (–) 1.03 (0.00) 1.26 (0.96) 2.02 (0.06)

EXP 0.00 (–) 1.47 (0.64) 1.70 (0.14) 1.51 (0.06) 2.17 (0.01)

Gyeonggi-do

MA 0.00 (–) 3.22 (0.54) 3.71 (0.96) 4.02 (0.67) 4.35 (0.51)

WMA 0.00 (–) 2.58 (0.54) 2.35 (0.49) 2.72 (0.20) 2.83 (0.51)

WALK 0.00 (–) 3.23 (0.75) 3.42 (0.59) 3.77 (0.63) 4.07 (0.05)

LINEAR 0.00 (–) 0.00 (–) 1.84 (0.00) 2.69 (0.66) 3.15 (0.02)

EXP 0.00 (–) 3.03 (0.50) 3.28 (0.26) 3.61 (0.31) 3.90 (0.03)

Jeju-do

MA 0.00 (–) 6.59 (0.29) 8.09 (0.80) 7.58 (0.89) 7.33 (0.34)

WMA 0.00 (–) 5.28 (0.24) 4.93 (0.85) 5.23 (0.45) 4.73 (0.20)

WALK 0.00 (–) 5.57 (0.00) 7.51 (0.00) 6.34 (0.03) 5.64 (0.02)

LINEAR 0.00 (–) 0.00 (–) 4.17 (0.00) 4.44 (0.00) 4.01 (0.02)

EXP 0.00 (–) 5.42 (0.00) 7.49 (0.00) 6.47 (0.07) 5.77 (0.02)
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Research Note RMRS-RN-52. 2013

from combining panel data; the reduction in estimated relative standard error was merely 

20–30% in 2010 (Table 2). When comparing to other methods, LINEAR generally had 

the smallest SDE. However, the test of normality strongly suggested that errors were not 

normally distributed for many panels across the three provinces (p-values <0.02, Table 

3). However, in absence of normality, the Kalman filter remains a minimum-variance 

estimator (Maybeck 1979).

The results from EXP fitting were mixed. On average, it adequately captured the trend 

of SINGLE estimates for Gyeongsangbuk-do but not so for Gyeonggi-do and Jeju-do 

(Fig. 1). The former was supported by the trends of error for Gyeongsangbuk-do over 

the years (Fig. 2E). On the other hand, the errors were mostly positive for the other two 

provinces, especially Jeju-do (Fig. 2J and O), which is an indicator of possible estimation 

bias. The reduction in estimated relative standard error by EXP covered a wide range, 

that is, 39–56% in 2010 (Table 2). The SDE of EXP was moderate in most cases and 

was generally between those of MA and WMA (Table 3). However, there was significant 

indication of non-normally distributed errors for year 2010 for all three provinces (pvalues <0.03, Table 3).

Lastly, estimated relative standard error (Table 2) was compared to bootstrap relative 

standard deviation (Table 4) to assess the variance estimators of SINGLE, MA, WMA, 

WALK, LINEAR and EXP var (y ) METHOD,t ^ h . In general, the estimated relative standard 

error of the six methods was similar to or was higher than the corresponding bootstrap 

relative standard deviation for Gyeongsangbuk-do and Gyeonggi-do for all panels. The 

former was on average 2-11% higher than the latter. This is evidence for bias in the 

variance estimators. On the other hand, the estimated relative standard error of the six 

methods was smaller than the corresponding bootstrap relative standard deviation for 

Jeju-do with the exception of year 2006 and 2010.

Table 4. Average bootstrap relative standard deviation (%) of the three provinces for the five panels and the six 

methods. Bootstrap relative standard deviation is calculated as METHOD,t

+ 100 # var y( ) /y +

METHOD,t .

Estimation Methods 2006 2007 2008 2009 2010

Gyeongsangbuk-do

SINGLE 1.94 1.95 1.84 1.82 2.10

MA 1.94 1.36 1.10 0.95 0.88

WMA 1.94 1.39 1.30 1.09 1.22

WALK 1.94 1.36 1.13 0.97 0.91

LINEAR 1.94 1.95 1.70 1.54 1.50

EXP 1.94 1.39 1.15 1.07 1.02

Gyeonggi-do

SINGLE 3.18 3.92 3.32 3.73 3.13

MA 3.18 2.53 2.00 1.81 1.56

WMA 3.18 2.66 2.35 2.20 1.98

WALK 3.18 2.51 2.02 1.85 1.64

LINEAR 3.18 3.92 3.18 3.11 2.59

EXP 3.18 2.55 1.98 1.92 1.63

Jeju-do

SINGLE 6.15 5.42 6.67 5.44 5.35

MA 6.15 4.16 3.63 3.08 2.67

WMA 6.15 4.14 4.41 3.38 3.10

WALK 6.15 4.19 3.71 3.13 2.75

LINEAR 6.15 5.42 6.03 4.79 4.36

EXP 6.15 4.19 3.75 3.18 2.75
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Research Note RMRS-RN-52. 2013

Discussion

The MA has been criticized for lag bias especially when a population is changing rapidly over time, that is, producing biased estimate of current year’s population parameter 

(Johnson and others 2003) and underestimation of the variance. Having the largest SDE 

indicates that accuracy of the method could be very low, suggesting that the method could 

be unreliable in some instances. On the other hand, Johnson and others (2003) found that 

MA performed satisfactorily for simulated populations with small to moderate temporal 

change, which was supported in our study. Hence for a population that remains stable over 

time, MA could actually be the most suitable method. However, the demand for more 

current estimates with annual surveys is driven by concern that forest populations are not 

stable.

Incorporating weights into estimation has evidently allowed WMA to reduce lag bias, at 

least in this study. Eskelson and others (2009) found that WMA provided improved estimates in terms of bias and root mean square error (RMSE) compared to MA. Furthermore, 

a smaller SDE suggests that WMA could accommodate more variety of trends. Another 

issue is assigning appropriate weights to panels. Weights are ideally selected based on 

knowledge of trend inherent in data, but this is generally unknown (Eskelson and others 

2009). The choice of weights would affect WMA performance. Arner and others (2004) 

found that RMSE for mean volume and volume change increased with heavier weighting 

on recent year rotating panel data from eight states in eastern USA.

The WALK and LINEAR are asymptotically time series autoregressive integrated moving average (ARIMA) models, specifically ARIMA(0,1,1) and ARIMA(0,2,2), respectively 

(Petris and others 2009). The similar results, especially lag bias, between WALK and MA 

are supported by the simulation study from Johnson and others (2003). Johnson and others 

(2003) also found that LINEAR fitted their data too closely and warned that such flexibility 

might result in the method merely tracking noise rather than any underlying trend. As with 

selecting weights for WMA, selecting a value for the SNRs of WALK and LINEAR could 

be as arbitrary (Johnson and others 2003). It depends on the degree of trend smoothing 

desired. By increasing the SNR, even WALK could match the observed data closely, but 

with an increase in estimated variances. Lastly, the observed non-normality in error suggests 

that further study is needed to determine fully the properties of both models.

The EXP takes advantage of state-space model by purposely injecting a priori knowledge about underlying process into modeling. It accurately captured the general trend of 

mean annual growing stock for Gyeongsangbuk-do. Also, it is not surprising that the model 

performed poorly when the assumed process differed from the observed from year 2006 

to 2010 for Jeju-do. This might suggest that historical rate of growth for the province has 

changed in that period. However, EXP eventually adapted to the observed decreasing trend 

in mean annual growing stock of Jeju-do in spite of model-misspecification. This illustrates 

the robust nature of the Kalman filter, where prediction and observation sequentially influence the final estimate. Czaplewski and Thompson (2013) found that a simple Kalman 

filter model could produce more accurate estimate than MA for rapidly changing population with detailed residual analysis. Such analysis could detect symptoms of poor model 

or inaccurate model parameters, and should be studied in future research. Lastly, nonnormally distributed errors could affect inference of confidence interval, which is calculated 

assuming that mean is normally distributed.

This study assumed subplots to be independent and ignored the cluster structure of 

the 5th NFI inventory design, which was in-line with the current institutional practice. 
12

Research Note RMRS-RN-52. 2013

This would likely deflate standard error, which in turn, would inflate precision. In other 

words, estimated variance var y METHOD,t ^ h ^ h would be smaller than expected, and bootstrap variance var y +

METHOD,t ^ ^ hh was used as the basis for this comparison. Contrary to the 

expectation, this was not observed for Gyeongsangbuk-do, Gyeonggi-do and only partially 

for Jeju-do. This might suggest that the current variance estimator for mean annual growing 

stock (var yt ^ h, Eq. 2) is positively biased for certain populations; however, the cause for 

the bias is obscure. This highlights the immediate need for further research into variance 

estimators conforming to the inventory design of the 5th NFI, and to view this study with 

caution. A proposed future analysis is developing mean and its variance estimators for annual growing stock on the cluster level and evaluating the five methods of combining panel 

data on that level.

Any comparisons of estimated relative standard errors among the methods are confounded by bias in the variance estimators. MA and WMA are known to suffer from lag bias 

(Johnson and others 2003), which is also a source of bias in the variance estimators of both 

methods because the squared error of lag bias is ignored in the estimators. WALK, LINEAR 

and EXP are model-unbiased (Maybeck 1979), that is, they are unbiased if, and only if, the 

model predictions are unbiased. However, in the best case, only one of these different model 

specifications is model-unbiased, and it maybe that they all suffer from model-prediction 

bias. Hence, even though the estimated relative standard errors for MA were less than the 

other methods, this does not imply that MA is more precise due to its variance estimator not accounting for lag bias. Similarly for WMA, comparisons are confounded by the 

unknown magnitude of bias in the variance estimators. Comparison in precision between 

moving average methods and the Kalman filter is tricky because it is confounded by different types of bias in the variance estimators. Bias is a property of a sample-survey estimator 

over all possible samples. The true magnitude of bias in any of the methods considered here 

is unknown because we have only one sample out of very large number of possible samples. 

Therefore, caution is prudent when comparing estimated relative standard errors among the 

different methods. All of these estimated relative standard errors are likely biased, and we 

have no definitive method to determine magnitudes of any of these biases.

The choice of a method for combining a set of panel data from an annualized inventory 

should consider several criteria: (1) ability to increase precision from combining panel data, 

(2) inherent number of assumptions, (3) capturing the population underlying process, (4) 

forecasting, and (5) incorporating data from remeasured panels. All methods in this study 

produce lower estimated variance. The gain in estimated precision for the Kalman filter 

depends on model specifications and populations with EXP performing the best for two 

provinces. The MA and WMA require one assumption in model specification, that is, assigning weights to panel data, whereas the three Kalman filter methods require at least two 

assumptions in model specification. Having more assumptions does not necessarily imply a 

method to be inferior as long as they are justifiable. However, it does require more thoughtfulness and defensible arguments. The EXP is the single method that explicitly incorporates 

knowledge of the underlying process of a population into modeling. Thus, interpretation 

of the estimates between panels could be directly related to growth. However, the results of 

Gyeonggi-do and Jeju-do highlighted the need to carefully consider the underlying process. 

If the underlying process is poorly represented, the resulted model fit will be poor. In contrast, estimates from the moving average method do not carry such interpretation because 

measurements between panels are not strictly remeasurements (Czaplewski and Thompson 

2013). The ability of the Kalman filter for forecasting has been demonstrated in Petris and 

others (2009), and one could specify the state-space model to properly take in remeasurement data. Specification of the moving average methods do not offer a direct forecasting, and 
13

Research Note RMRS-RN-52. 2013

to best of our knowledge, such issue has not been addressed in the literature. Lastly, attempts 

have been made to modify MA to incorporate remeasured panels (see Van Deusen 2002).

The results did not offer a clear picture on the choice of a method for the 5th South 

Korea NFI as each method has its advantages and disadvantages. Unless a population is 

unchanged over time, MA and WMA, and their variance estimators, are known to be biased by design whenever they are used to estimate population variables at one point in 

time. WALK, LINEAR and EXP are model-unbiased, but only on the condition that the 

prediction model produces unbiased predictions, and there is little basis for asserting this 

assumption. Only circumstantial evidence is available to assess the relative degrees of bias 

among methods, and this evidence is discussed above. Nonetheless, we recommend the 

Kalman filter over the moving average. This study has demonstrated that the Kalman filter 

could be specified either as a conventional time series model or a model with hypothesized 

processes. Because of this flexibility, considerable thought is required to formulate an appropriate model specification. Furthermore, one has to contend with assumptions associated 

with the Kalman filter models, for example, values of signal-to-noise ratios (WALK and 

LINEAR) and a model representing underlying population change (EXP). It is necessary to 

understand how different ratios and accuracy in representing underlying processes impact 

model fit for different populations. Estimation of SNR might be possible through analysis of residual differences between model predictions and observed estimates (Czaplewski 

and Thompson 2009), but requires further investigation that is outside the scope of this 

study. Among the three Kalman filter models, EXP incorporates knowledge of the underlying population process, and is beneficial for interpreting results as growth. From another 

perspective, different model specifications of the Kalman filter can be viewed as test-ofhypothesis regarding characteristics of change in a population; thus, further substantiating 

our recommendation of the Kalman filter.

More broadly speaking, moving average methods could be a standard method for analyzing a country annual national forest inventory. If the methods fail to provide adequate 

model fit, it is an indication that the underlying population changes at a drastic rate beyond 

which could be captured by the methods. Thus, one could then refer to the Kalman filter 

with different model specifications representing different degrees of change as a test-ofhypothesis on the population underlying rate of change.

Acknowledgments

We sincerely thank the Korea Forest Research Institute for the funding to conduct this 

research, and the staff of the Division of Forest Resources Information and many others who have contributed to the 5th National Forest Inventory. The NFI is collaboration 

among Korea Forest Service, Korea Forest Research Institute, and Forest Inventory Center 

of National Forestry Cooperatives Federation. We thank Dr. David Turner, Loa Collins and 

three anonymous reviewers for review of the manuscript.

Reference

Arner, S.L., Westfall, J.A., and Scott, C.T. 2004. Comparison of annual inventory designs 

using Forest Inventory and Analysis Data. Forest Science 50(2): 188–203.

Axelsson, A.-L., Ståhl, G., Söderberg, U., Peterson, H., Fridman, J., and Lundström, A. 2010. 

Sweden. In: Tomppo, E., Gschwantner, T., Lawrence, M., and McRoberts, R.E. (ed.). 
14

Research Note RMRS-RN-52. 2013

National forest inventory: pathways for common reporting. Springer Science+Business 

Media, Heidelberg-Dordrecht-London-New York. p. 541–554.

Czaplewski, R.L. 1999. Multistage remote sensing: toward an annual national inventory. 

Journal of Forestry 97(12): 44–48.

Czaplewski, R.L. 2010. Complex sample survey estimation in static state-space. Gen. Tech. 

Rep. RMRS-GTR-239. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 124 p.

Czaplewski, R.L., and Thompson, M.T. 2009. Opportunities to improve monitoring of 

temporal trends with FIA panel data. In: McWilliams, W., Moisen, G., and Czaplewski, 

R.L. (ed.). Forest Inventory and Analysis (FIA) symposium 2008; October 21-23, 2008; 

Park City, UT. Proc. RMRS-P-56CD. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 58 p.

Czaplewski, R.L., Thompson, M.T., and Moisen, G.G. 2012. An efficient estimator to monitor rapidly changing forest conditions. In: Morin, R.S., and Liknes, G.C. (ed.). Moving 

from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; December 

4-6, 2012; Baltimore, MD. p. 416-420. Gen. Tech. Rep. NRS-P-105. Newtown Square, 

PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [CDROM]. 478 p.

Czaplewski, R.L., and Thompson, M.T. 2013. Model-based time-series analysis of FIA panel 

data absent re-measurements. Res. Pap. RMRS-RP-102WWW. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 13 p.

Dixon, B.L., and Howitt, R.E. 1979. Continuous forest inventory using a linear filter. Forest 

Science 25(4): 675–689.

Eskelson, B.N.I., Temesgen, H., and Barrett, T.M. 2009. Estimating current forest attributes from paneled inventory data using plot-level information: a study from the Pacific 

Northwest. Forest Science 55(1): 64–71.

Gregoire, T.G., and Walters, D.K. 1988. Composite vector estimators derived by weighting 

inversely proportional to variance. Canadian Journal of Forest Research 18(2): 282–284.

Johnson, D.S., Williams, M.S., and Czaplewski, R.L. 2003. Comparison of estimators for 

rolling samples using Forest Inventory and Analysis data. Forest Science 49(1): 50–63.

KFRI. 2009. Field manual of the 5th National Forest Inventory in Korea ver 1.3. Korea Forest 

Research Institute, Seoul, Republic of Korea. 37 p.

Kim, S.H. 2009. The National Forest Inventory program of Korea: findings and lessons. XIII 

World Forestry Congress, Buenos Aires, Argentina, 18–23 October 2009.

Korea Forest Service. 2010. Statistical yearbook of forestry. Korea Forest Service, Daejeon, 

Republic of Korea. 491 p.

Kwon, S.D., Rho, D.K., Lee, K.H., and Son, Y.M. 2001. Timber resources evaluation program for major species in Korea. KFRI Journal of Forest Science 64: 78–86.

Lawrence, M., McRoberts, R.E., Tomppo, E., Gschwantner, T., and Gabler, K. 2010. 

Comparisons of national forest inventories. In: Tomppo, E., Gschwantner, T., Lawrence, 

M., and McRoberts, R.E. (ed.). National forest inventory: pathways for common reporting. Springer Science+Business Media, Heidelberg-Dordrecht-London-New York. p. 

19–32.

Lee, D.K. 2010. Korean forests: lessons learned from stories of success and failure. Korea 

Forest Research Institute, Seoul, Republic of Korea. 74 p.

Lessard, V.C., McRoberts, R.E., and Holdaway, M.R. 2001. Diameter growth models using 

Minnesota Forest Inventory and Analysis data. Forest Science 47(3): 301–310.
15

Research Note RMRS-RN-52. 2013

Matérn, B. 1960. Spatial variation—Stochastic models and their application to some 

problems in forest surveys and other sampling investigations. Meddelanden Statens 

Skogsforskningsinstitut 49(5): 144.

Maybeck, P.S. 1979. Stochastic models, estimation, and control. Volume 1. Academic Press, 

New York. 423 p.

McRoberts, R.E., and Hansen, M.H. 1999. Annual forest inventories for the north central region of the United States. Journal of Agricultural, Biological, and Environmental Statistics 

4(4): 361–371.

Patterson, P.L., and Reams, G.A. 2005. Combining panels for forest inventory and analysis estimation. In: Bechtold, W.A., and Patterson, P.L. Gen. Tech. Rep. SRS-80. Asheville, NC: 

U.S. Department of Agriculture, Forest Service, Southern Research Station. p. 69–74.

Petris, G., Petrone, S., and Campagnoli, P. 2009. Dynamic linear models with R (Use R). 

Springer Science+Business Media, Heidelberg-Dordrecht-London-New York. 265 p.

Pollock, D.S.G. 2002. Recursive estimation in econometrics. Working Paper No. 462. 

Department of Economics at Queen Mary, University of London, London. 41 p.

R Development Core Team. 2011. R: A language and environment for statistical computing. 

R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Available 

at: http://www.R-project.org. [Cited 9 May 2011].

Ranneby, B., Cruse, T., Hägglund, B., Jonasson, H., and Swärd, J. 1987. Designing a new 

national forest survey for Sweden. Studia Forestalia Suecica 177. Swedish University of 

Agricultural Sciences, Uppsala, Sweden. 29 p.

Reams, G.A., Smith, W.D., Hansen, M.H., Bechtold, W.A., Roesch, F.A., and Moisen, 

G.G. 2005. The Forest Inventory and Analysis sampling frame. In: Bechtold, W.A., and 

Patterson, P.L. Gen. Tech. Rep. SRS-80. Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. p. 11–26.

Roesch, F.A., and Reams, G.A. 1999. Analytical alternatives for an annual inventory system. 

Journal of Forestry 97(12): 33–37.

Shapiro, S.S., and Wilk, M.B. 1965. An analysis of variance test for normality (complete 

samples). Biometrika 52(3–4): 591–611.

Tomppo, E., Schadauer, K., McRoberts, R.E., Gschwantner, T., Gabler, K., and Ståhl, G. 2010. 

Introduction. In: Tomppo, E., Gschwantner, T., Lawrence, M., and McRoberts, R.E. (ed.) 

National forest inventory: pathways for common reporting. Springer Science+Business 

Media, Heidelberg-Dordrecht-London-New York. p. 1–18.

Van Deusen, P.C. 2002. Comparison of some annual forest inventory estimators. Canadian 

Journal of Forest Research 32(11): 1992–1995.

Welch, G., and Bishop, G. 2006. An introduction to the Kalman filter. Report TR 95-041. 

Chapel Hill, NC, Department of Computer Science, University of North Carolina, USA. 

Available at: http://www.cs.unc.edu/~welch/kalman/. [Cited 9 May 2011].

Yim, J.S., Kim, Y.H., Kim, S.H., Jeong, J.H., and Shin, M.Y. 2011. Comparison of the knearest neighbor technique with geographical calibration for estimating forest growing 

stock volume. Canadian Journal of Forest Research 41(1): 73–82.
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