Estimation of Basal Area in West Oak Forests of Iran Using Remote Sensing Imagery

doi:10.4236/ijg.2012.32044

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International Journal of Geosciences, 2012, 3, 398-403

http://dx.doi.org/10.4236/ijg.2012.32044 Published Online May 2012 (http://www.SciRP.org/journal/ijg)

Estimation of Basal Area in West Oak Forests of Iran

Using Remote Sensing Imagery

Loghman Ghahramany1, Parviz Fatehi2, Hedayat Ghazanfari1

1Department of Forestry, College of Natural Resources, University of Kurdistan, Sanandaj, Iran

2Remote Sensing Laboratories, Department of Geography, University of Zurich, Zurich, Switzerland

Email: [email protected], [email protected]

Received November 20, 2011; revised January 2, 2012; accepted February 7, 2012

ABSTRACT

The objective of this study is to evaluate the capability of satellite imagery for the estimation of basal area in Northern

Zagros Forests. The data of the high resolution geometric (HRG) sensor of SPOT-5 satellite dated in July 2005 were

used. Investigation of the quality of Satellite images shows that these images have no radiometric distortion. Overlaying

of geocoded images with the digital topographic maps indicated that the images have high geometric precision. A num-

ber of 319 circular plots (0.1 ha) were established using systematic random method in the study area. All trees having

diameter at breast height (DBH) (i.e. 1.3 m above ground) greater than 5 cm were callipered in each plot. Basal area in

each plot was determined using field data. Main bands, artificial bands such as vegetation indices and principle compo-

nent analysis (PCA) were studied. Digital numbers related to each plot were extracted from original and artificial bands.

All plots were ordinated by major geographic aspects and the best fitted regression models were determined for both the

study area without consideration of aspects and with consideration of major geographic aspects by multiple regression

analysis (step wise regression). The results from regression analysis indicated that the square root of basal area without

consideration of aspects has a high correlation with band B1 (r = –0.60). The consideration of aspects resulted in corre-

lation of different indices with square root of basal area such that in northern forests, band B1 had higher correlation

coefficient (r = –0.67) among other indices. In Eastern forests, the same band showed correlation of basal area with dif-

ferent correlation coefficient (r = –0.65). In southern and western forests, the square root of basal area had higher corre-

lation (r = –0.68) with RVI. The use of the square root of basal area as a dependent variable in multivariate linear re-

gression improved the results. The assessment of model validity indicated that the proposed models are properly valid.

Keywords: Northern Zagros Forests; Basal Area; SPOT-5 Data

1. Introduction

Northern Zagros oak forests represent the most important

forest communities in natural forested landscapes in west

of Iran [1]. These forests have a great economical value

and are a source of revenue for local residents. Even

though these forests have been known as conservative

forests, the local communities have to traditionally utilize

them as a source. In this traditional forest management,

the owner of forest applies mixture styles of evenaged

coppice on tree (on trunk & crown) and unevenaged

coppice (on ground) for preparing food for domestic

animals and for wood [1]. These forests produce little

amount of woods for industry and have low standing

volume; however, tree crowns are utilized in every 3-

year period. Thus, the basal area can be an appropriate

index to determine the changes in these forests and can

provide valuable information about the effect of man-

agement activities on the forests. The estimation of this

index using field study is difficult, taking plenty of time

and needing a great deal of money. In last decades, the

science of forest biometry has markedly progressed, re-

sulting in using more sophisticated instruments in forest

inventory. This provides an opportunity for collecting

data and computing indices with less field working and

time. One of the most important instruments used in for-

est inventory is remote sensing. The integration of the

data and information collected from satellite images and

field data is the base of modern forest biometry [2].

Many studies have used satellite data to estimate forest

quantitative parameters such as density, standing volume,

basal area, and etc. in last two decades [3]. Ripple et al.

compared Digital Landsat Thematic Mapper (TM) and

Satellite SPOT High Resolution Visible (HRV) images

of coniferous forests to estimate standing volume using

correlation and regression analyses. Significant inverse

relationships were found between softwood volume and

the spectral bands from both sensors (P < 0.01). The

highest correlations were found between the logarithm of

L. GHAHRAMANY ET AL. 399

softwood volume and the near-infrared band (HRV band

3, r = –0.89; TM band 4, r = –0.83) [4]. Brockhhause &

Khorram compared Spot and Landsat-TM data to inven-

tory forest resources. Their results showed that there is a

significant correlation between band XS3 (Infra-Red)

and TM bands (2, 3, 4, 5 and 7). They found no signifi-

cant correlation between age classes and Spot bands,

while a significant correlation was found between age

classes and TM bands. The correlation coefficients be-

tween basal area and TM band7 and between age classes

and TM band5 were –0.48 and –0.62, respectively [5].

Xian et al. were investigated the capability of TM data

for the estimation of canopy density and standing volume.

Their results indicated that applying TM data can reduce

the number of samples and the inventory costs by 60% -

70% and can provide more reasonable volume and can-

opy density estimates [6]. Xu et al. evaluated the ability

of TM data for the estimation of oak forest canopy in dry

season. Their results illustrated that the band TM3 had

the highest correlation coefficient (–0.83) and the coeffi-

cient of determination (0.69) with canopy. The NDVI

index showed higher coefficients of correlation (0.84)

and determination (0.70) with canopy than SR index. The

cubic NDVI index had higher coefficients of correlation

(0.85) and determination (0.73) with canopy [7]. Suarez

et al. indicated that the SPOT4 and ETM+ have both a

high capability to estimate stand height, diameter and

basal area in afforested stands [8]. Jao et al. studied the

estimation of canopy density on an ever-green oak forest

using aerial photographs and TM satellite images and

resulted that this estimation is reasonably possible [9].

Arzani et al. used Landsat satellite data to study the pro-

duction and some vegetation characteristics in two dif-

ferent climates. They found that the VNIR1 and VNIR2

indices show higher correlations with production, canopy

percentage and foliage percentage in semi-arid areas,

while in arid areas, the NDVI index had higher correla-

tion with the vegetation characteristics [10]. Darvishsefat

et al. applied the ETM+ data imagery for Haloxylon spp.

canopy estimation and figured out the NDVI index has a

reasonable correlation (r = 0.65) with density [11]. The

objective of this study is to evaluate the capability of

satellite imagery for the estimation of basal area in Nor-

thern Zagros Forests.

2. Materials and Methods

2.1. Study Area

The study area (15,700 ha) is located in a mountainous

managed mixed forest (Northern Zagros forests) in Kur-

distan province in the west of Iran (Figure 1 and Table

1). The study area is positioned in 45˚30′N to 46˚15′N

longitude and 35˚45′E to 36˚15′E latitude. The elevation

of the study area ranges from 1200 m to 2200 m above

Figure 1. Study area location and sample plots distr i bution.

Table 1. Descriptive statistics of basal area in studied

stands.

Minimum

(m2/ha)

Maximum

(m2/ha)

Std. deviation

(m2/ha)

Mean ± std. error

(m2/ha)

Stands

1.96 42.30 ±7.78 13.96 ± 0.43

Exclusive

geographical

direction

1.96 35.84 ±8.70 15.57 ± 0.88

Northern-faced

forests (n = 96)

2.51 30.79 ±5.94 11.96 ± 0.66

Southern-faced

forests (n = 78)

2.19 42.30 ±7.86 13.64 ± 0.88

Eastern-faced

forests (n = 76)

2.39 35.30 ±7.76 14.18 ± 0.97

Western-faced

forests (n = 62)

sea level. Average annual precipitation is about 760

mm/year and average annual temperature is between 12˚C

to 18˚C [1]. The dominant tree layer mainly consists of

Quercus Libani Oliv. (more than 50%), Quercus brantii

Lindl. (30%) and Quercus infectoria Oliv. (20%) with

Crataegus spp, Pyrus spp, Acer cineracens and Pistacia

mutica as the most common admixed tree species.

2.2. Satellite Data

The data of the high resolution geometric (HRG) sensor

of SPOT5 satellite dated in July 2005 were used for pur-

poses of this study. Radiometric correction and orthorec-

tification process was done by SPOT Imaging Corpora-

tion. However, investigation of the quality of satellite

images shows that these images have no radiometric dis-

tortion. Overlaying of geocoded images with the digital

topographic maps indicated that the images have high

geometric precision.

2.3. Field Data and Regression Analysis

A number of 319 circular plots (0.1 ha) were established

using systematic random method in the study area. All

L. GHAHRAMANY ET AL.

400

trees having diameter at breast height (DBH) (i.e. 1.3 m

above ground) greater than 5 cm were callipered in each

plot. Basal area in each plot was determined using field

data. Main bands, artificial bands such as vegetation in-

dices and principle component analysis (PCA) were stu-

died. Digital numbers related to each plot were extracted

from original and artificial bands. Correlation and re-

gression analysis were performed to study the statistical

relationships between standing basal area and digital

numbers of satellite data. All plots were ordinated by

major geographic aspects and the best fitted regression

models were determined for both study areas without

consideration of aspects and consideration of major geo-

graphic aspects by multiple regression analysis (step wise

regression). The use of square root of basal area as a de-

pendent variable in multivariate linear regression im-

proved the results. A number of 32 sample plots from the

319 measured plots were randomly selected and served

as control plots for verification of derived models. These

control plots were not used for correlation and regression

analysis.

3. Results

3.1. Suggested Model for Northern-Faced

Forests

In the northern-faced forests, the root of basal area

[SQRT(BA)] had highest coefficient of correlation with

band B1 (r = 0.67) among the other indices (Table 7). In

the model for the northern-faced forests, the band B1 is a

predictive variable. According to the ANOVA of regres-

sion, the hypothesis of “no linear relationship” was re-

jected with significant level of 99% (F = 69.88, P < 0.01).

The hypotheses of “The slope of the regression model =

0” and “The intercept of the regression model = 0” were

rejected at the significant level of 99% (Table 2). The

Kolmogrov-Smirnov test showed that the distribution of

residuals was normal (P = 0.66, K-S Z = 0.732). Nine out

of 10 control samples were accepted in validity test of

the model.

3.2. Suggested Model for Southern-Faced Forests

The root of basal area [SQRT(BA)] showed the highest

coefficient of correlation with RVI index (r = 0.68)

among the other indices (Table 7) in the southern-faced

forests. The RVI and B3 are predictive variables for the

model of these forests. Multiple correlation coefficient

between SQRT(BA) and predictive variables is 72%. The

ANOVA of regression model revealed that hypothesis of

“no linear relationship” is rejected with significant level

of 99% (F = 37.71, P < 0.01). The hypotheses of “The

slope of the regression model = 0” and “The intercept of

the regression model = 0” are laso rejected at the signifi-

cant level of 99% (Table 3). The distribution of residuals

Table 2. Regression coefficients and summery model for su-

ggested model for northern forests.

Beta P t SE CoefficientsModel

- <0.01 12.825 0.270 3.467 b0

–0.670 <0.01 –8.359 0.002 0.018 b1

SQRT(BA) = b0 + b1B1

N = 88 R2Adj.= 0.44 MSE = 0.073 F = 69.88 P < 0.01

Bias = –0.017 m2/ha Bias = –4.62%

Table 3. Regression coefficients and summery model for

suggested model for southern Forest.

Beta P t SE CoefficientsModel

- <0.01 7.705 0.513 3.955 b0

–0.381 <0.01 –2.930 0.002 –0.007 b3

–0.973 <0.01 –7.481 0.265 –1.980 RVI

SQRT(BA) = b0 + b1B3 + b2RVI

n= 72 R2Adj =0.51 MSE = 0.037 F = 37.71 P < 0.01

Bias = –0.035 m2/ha Bias = –2.94%

was normal (P = 0.67, K-S Z = 0.76) based on the Kol-

mogrov-Smirnov test. The whole control samples were

accepted in validity test of the model.

3.3. Suggested Model for Eastern-Faced Forests

Band B1 showed the highest coefficient of correlation (r

= –0.66) with the root of basal area [SQRT(BA)] in the

eastern-faced forests (Table 7) as the northern-faced for-

ests; however, the PCA1 and B1 are predictive vari-

ables in their model. Multiple correlation coefficient be-

tween SQRT(BA) and predictive variables are 66%.

Based on the ANOVA of regression, all hypotheses are

rejected at the significant level of 99% (Table 4). The Col-

mogrov-Smirnov test showed that the residuals are nor-

mally distributed (P = 0.73, K-S Z = 0.76). The whole

control samples were accepted in validity test of the model.

3.4. Suggested Model for Western-Faced Forests

The western-faced forests behaved similar to southern-

faced forests as they showed the highest coefficient of

correlation between the root of basal area [SQRT(BA)]

and the RVI index (r = –0.68), shown in Table 7; while

three predictive variables including RVI, PCA2 and

PCA3 are selected for their model. Multiple correlation

coefficient between SQRT(BA) and predictive variables

are 80%. All hypotheses were rejected as the other for-

ests at the significant level of 99% (Ta ble 5). The test of

normality by the Kolmogrov-Smirnov showed the nor-

mal distribution of the residuals (P = 0.72, K-S Z = 0.69).

The whole control samples were accepted in validity test

of the model.

L. GHAHRAMANY ET AL.

401

Table 4. Regression coefficients and summery model for suggested model for eastern-faced forest.

Beta P t SE Coefficients Model

- <0.05 10.256 0.317 3.252 b0

–1.138 <0.05 –4.850 0.006 –0.027 b1

0.518 <0.05 2.206 0.004 0.008 PCA1

SQRT(BA) = b0 + b1B1 + b2 × PCA1

n = 62 R2Adj. = 0.46 MSE = 0.058 F = 27.83 P < 0.01 Bias = –0.056 m2/ha Bias = –4.3%

Table 5. Regression coefficients and summery model for suggested model for western forests.

Beta P t SE Coefficients Model

- <0.05 8.165 0.925 7.551 b0

–1.772 <0.05 –6.817 0.684 –4.662 RVI

–1.253 <0.05 –4.767 0.008 –0.038 PCA2

–0.222 <0.05 –2.405 0.014 –0.034 PCA3

SQRT(BA) = b0 + b1 RVI + b2PCA2 + b3PCA3

n = 58 R2Adj. = 0.62 MSE = 0.041 F = 31.60 P < 0.01 Bias = –0.038 m2/ha Bias = –2.7%

3.5. Suggested Model for Forests (Exclusive

Geographical Directions)

In the studied forests (exclusive geographical directions),

the root of basal area [SQRT(BA)] had the highest coef-

ficient of correlation with with band B1 (r = 0.60) among

the other indices (Table 7). In the model for the western

-faced forests, the RVI, PCA2 and PCA3 are predictive

variables. Multiple correlation coefficient between SQRT-

(BA) and predictive variables are 80%. According to the

ANOVA of regression, the hypothesis of “no linear rela-

tionship” was rejected with significant level of 99% (F =

111.19, P < 0.01). The hypotheses of “The slope of the

regression model = 0” and “The intercept of the regres-

sion model = 0” were rejected at the significant level of

99% (Table 6). The Colmogrov-Smirnov test showed

that the distribution of residuals was normal (P = 0.54,

K-S Z = 0.81). The whole control samples were accepted

in validity test of the model.

4. Conclusion

The analysis of the table of correlation matrix between

dependent variables (Digital numbers related to each plot

extracted from original and artificial bands) and the root

of basal area [SQRT(BA)] showed that in the studied

forests (exclusive geographical directions), the root of

basal area had the highest coefficient of correlation with

band B1 (r = –0.60) among the other indices (Table 7).

In the northern, eastern, southern and western-faced for-

ests the root of basal area [SQRT(BA)] had the highest

coefficient of correlation with band B1 (r = –0.67), band

B1 (r = –0.65), RVI index (r = –0.68) and RVI index (r =

–0.68), respectively. It is generally expected to have a

high relationship between band B3 (near-Infrared band)

and vegetation cover [4,5,7,9,12]; however, it was, in this

study, observed that the high relationship was established

between the basal area and band B1 (in the northern for-

ests, eastern forests and exclusive geographical direc-

tions). Similar observations have been made by Suarez et

al. [8]. The reason for this could be related to this fact

that northern and eastern forests generally have a higher

density and lower soil reflections; thus, the main bands

can provide better results. Naseri suggested using band

B1 for such studies [13]. The use of these indices has

been, on the one hand, confirmed by the higher relation-

ship of vegetation characteristics on western and south-

ern forests [10,11,13,14] and on the other hand, showed

that the vegetation characteristics have lower sensitivity

relative to sparser cover (due to the existence of lower

soil moisture and sparser vegetal cover on western and

southern forests). Hosseini and Moradi have obtained di-

fferent results [15,16]. The negative coefficient of corre-

lation indicates that the rate of reflectance in samples de-

creases with an increase in basal area. The same results

were found by Khorrami et al. [3], Azizi et al. [17] and

Ripple et al. [4] in the study of volume estimation. How-

ever, Mohammadi et al. [18] have found positive rela-

tionship in the estimation of number per ha. The regres-

sion analyses showed that the classification of samples

based on geographical aspects and determination of a

separate model for each class has increased the correla-

tion coefficient and the modified coefficient of determi-

nation up to 18% and 7% on western and southern as-

pects, respectively. This increase is about 2% on eastern

aspects and stayed without any change on northern as-

pects while the coefficient of correlation increased about

1%. Khorrami et al. carried out their research in a pure

beech stand stand only on northern aspects in order to

L. GHAHRAMANY ET AL.

402

Table 6. Regression coefficients and summery model for suggested model for studied forests (exclusive geographical direc-

tions).

Beta P t SE Coefficients Model

- <0.01 22.406 0.141 3.153 b0

−0.426 <0.01 −7.285 0.001 −0.009 B1

−1.920 <0.01 −6.389 0.124 −0.794 RVI

SQRT(BA) = b0 + b1B1 + b2RVI

n = 287 R2Adj. = 0.44 MSE = 0.062 F = 111.19 P < 0.01 Bias = −0.061 m2/ha Bias = −4.6%

Table 7. Pearson’s correlation coefficient between standing volume of sample plots and corresponded spectral values (DNs) in

original bands.

Northern-faced forests (n = 88)

PCA3PCA2PCA1 TVI SAVINRVIRVI NDVIIPVI DVI AVI B3 B2 B1

Main bands,

artificial

bands

Dependent

Variable

**0.39-

**0.6

**0.57-

**0.51

**0.49

**0.49-

**0.53-

**0.49

**0.46

**0.46 0.05-

**0.61-

**0.67- SQRT(BA)

Southern-faced forests (n = 72)

*0.27-

**0.61*0.24-

**0.68

**0.67

**0.67-

**0.68-

**0.67

**0.37

**0.40-

**0.56- SQRT(BA)

Eastern-faced forests (n = 69)

**0.36-

**0.40

**0.53-

**0.54

**0.54-

**0.55-

**0.54-

**0.54

**0.53-

**0.53- 0.05-

**0.59-

**0.66- SQRT(BA)

Western-faced forests (n = 58)

**0.43-

**0.53

**0.46-

**0.38

**0.67

**0.67-

**0.68-

**0.67

**0.66

**0.66 0.05

**0.55-

**0.65- SQRT(BA)

Studied Forests (exclusive geographical directions) (n = 287)

**0.38-

**0.44

**0.44-

**0.57

**0.55

**0.55-

**0.58-

**0.55

*0.55

**0.53

**0.53 0.06

**0.52-

**0.60- SQRT(BA)

**Significant at 99% confidence level; *Significant at 95% confidence level; ns: No significant.

eliminate the effect of geographical aspects [3]. In this

research, applying of regression analysis led to better

results and Naseri [13], Hosseini [15], and Xu et al. [7]

confirmed these findings. The use of mathematical trans-

formations such as logarithmic function, power func-

tion, root function, and reciprocal function on de pen-

dent variable (basal area) and their relationships with

independent variable resulted in an increase in correla-

tion coefficient such that the maximum coefficient was

obtained between the root of basal area and the inde-

pendent variable. Many previous studies have obtained

better results when using logarithmic functions [3,4,7,17].

In Iran, many studies on quantitative characteristics of

forest stands have conducted in northern forests with a

high density of deciduous trees. In this research, the co-

efficient of correlation was determined 66% (exclusive

geographical directions) and it was reached 80% when

considering the geographical aspects. The low canopy

density of Zagros forests and the similarity of the results

with other studies can be a good indication of relatively

reasonable capability of SPOT data for the study area

[3,17].

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