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         Geoplanning 
Vol 5, No. 1, 2018, 35-42                                                                                                                                                          Journal of Geomatics and Planning 

                                                                                                 E-ISSN: 2355-6544 
http://ejournal.undip.ac.id/index.php/geoplanning 

doi: 10.14710/geoplanning.5.1.35-42 

ESTIMATING MANGROVE FOREST DENSITY USING GAP FRACTION METHOD AND 
VEGETATION TRANSFORMATION INDICES APPROACH  

N. Khakhima, A. C. P. Putrab, T. U. Widhaningtyasb 

a Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia  
b Remote Sensing Department, Earthline, Jakarta, Indonesia  

 

Abstract: Mangrove forest represented a coastal ecosystem in Indonesia. Theoretical 

validation and in-field measurement by calculating the number of trees and the density 
data that was validated through remote sensing would not be appropriate because the 
remote sensing recorded canopy density and not tree stands. New method canopy 
photography or gap fraction method was the technique to predict sun radiation using 
the photograph taken upward through extremely wide lens and classification object 
image. The objectives of the study were (1) to examine the acuracy of the estimation of 
the mangrove forest density using vegetation index transformation, and (2) to map the 
mangrove forest condition. The location of the study was Alas Purwo Resort Grajagan 
National Park area. The material of the study was Landsat-8 OLI image recorded on 
January 19th, 2016 using SAVI vegetation index transformation method. Gap fraction 
filed measurement method was a new method in Indonesia. The results of the study 
showed that the regression of the SAVI index between index transformation value and 
in-field condition (R2) was 0.566, the forest density estimation resulting from the SAVI 
index transformation had the RMSE of 2.334178 and the density of the mangrove forest 
in Grajagan Bay of the Alas Purwo National Park included low density of 0-12.5% (30.42 
ha), medium density of 12.6-25% (116.55 ha), and high density of 25.1-37.6% (463.68 
ha).  
. 

 Copyright © 2018 GJGP-UNDIP  

This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license. 

Khakhim, N., Putra, A, C, P., & Widhaningtyas, T, U. (2018). Estimating mangrove forest density using gap fraction method and vegetation 

transformation indices approach. Geoplanning: Journal of Geomatics and Planning, 5(1), 35-42. doi: 10.14710/geoplanning.5.1.35-42 

1. INTRODUCTION 

Indonesia had a very wide coastal area with 95.181 km coastal line. Mangrove forest represented costal 
ecosystem and formed because of the presence of wave protection, fresh water input, sedimentation, tidal 
water flow, and warm temperature (Walsh, 1974). The mangrove forest in Grajagan Bay represented the 
most natural mangrove forest in Java Island (Sudarmadji, 2011). A part of the mangrove forest belonged to 
Alas Purwo National Park or to Grajagan Resort. According to the decree of the Minister of Forestry No. 283 
KPTS-II/1992 Alas Purwo National Park was a conservation area. 

Remote sensing was considered to be more effective in mapping mangrove condition than a conventional 
or terrestrial method. According to (Fawzi, 2015)  the remote sensing of the mangrove forest was able to 
provide detailed physical aspects of the forest such as species, zoning, change in land use arrangement, and 
mangrove physical mapping. According to (Lee & Yeh, 2009) mangrove had unique spectral reflection 
representing the combination of ground, water and vegetation because the mangrove grew in coastal area. 
Additionally, the basic reflection of mangrove canopy also influenced the spectral reflection of the 
mangrove (Kuenzer, Bluemel, Gebhardt, Quoc, & Dech, 2011). Forest density could be estimated in the 
remote sensing using vegetation index transformation approach. The vegetation index transformation was 
a mathematical model developed to estimate forest density (Danoedoro, 2012). The objectives of the study 
were to examine the accuracy of the estimation of mangrove forest density using remote sensing with 

OPEN ACCESS 

Article Info: 
Received: 14 Dec 2016 
in revised form: 31 May 2017 
Accepted: 3 November 2017 
Available Online:  30 April 2018 
 
Keywords:  
Mapping, Estimate, Remote 
Sensing, Mangrove Forest, Gap 
Fraction, Landsat-8 OLI 
 
Corresponding Author: 
Nurul Khakhim 

Faculty of Geography, Universitas 

Gadjah Mada, Yogyakarta 

Email: nurulk@ugm.ac.id  

  

https://doi.org/10.14710/geoplanning.5.1.35-42
https://doi.org/10.14710/geoplanning.5.1.35-42
mailto:nurulk@ugm.ac.id


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36 | 
 

vegetation index transformation approach and to map the mangrove forest density in Grajagan Bay of Alas 
Purwo National Park. 

Theoretical validation and in-field measurement by calculating the number of trees and the density data 
that was validated through remote sensing would not be appropriate because the remote sensing recorded 
canopy density and not tree stands. Therefore, a new method of gap fraction or hemispherical photography 
that was well-known as fisheye photography or canopy photography was the technique to predict sun 
radiation using the photograph taken upward through extremely wide lens (Rich, 1990). The resulting 
perspective was usually close to 180 degree that covered all sky directions. The resulting photographs 
recorded sky obstruction geometry by vegetation canopy or other close features. The geometry could 
measure objects exactly and used to calculate the sun radiation transmitted through the vegetation canopy 
and also to predict the canopy structure aspects such as LAI (leaf area index). The measurement of the 
canopy width using the gap fraction was based on the calculation of observable sky proportion as the 
function of sky direction with the canopy fraction recorded by the photographs. Scientists began to develop 
threshold method to separate canopy types through hemispherical photographs. The factor that influenced 
the classification of objects was the exposure of the taken photographs (Jonckheere, Nackaerts, Muys, & 
Coppin, 2005). The classification of the photographs involved picture pixels representing both observable 
and non-observable sky directions with the intensity value of the pixels above the classified threshold as the 
intensity value of the pixels and observed below the threshold classified as non-observable. Recently, 
advancement has been made in developing automatic threshold algorithm. However, there were still many 
things to do till the technique was fully applicable (Inoue, Yamamoto, Mizoue, & Kawahara, 2002). 

The gap fraction of the hemispherical photography was useful in making the measurement of the canopy 
parameter easier through remote sensing, such as LAI (leaf area index) to find out the productivity of a 
plant calculated on the basis of the width of its leaves for photosynthesis per land surface unit (watson, 
1947) (Jonckheere et al., 2004).  The canopy width played an important role in identifying the information 
through the remote sensing. In the gap fraction method some assumed that leaf element was uneven so 
that there was significant difference between width leaf and needle-shape leaf (Smith, 1991) (Bolstad & 
Gower, 1990).  

2. DATA AND METHODS 

2.1. Study Area 

The location of the study was Grajagan Bay of Alas Purwo National Park. It was geographically situated at 
114o13’20,203” east longitude, 114o20’45,979” east longitude and 8o35’52,79” south latitude and 
8o37’28,697 south latitude as indicated in the map (Figure 1). Dominant water movement taking place in 
estuary was sea water that salinity would influence the zoning of the location. Therefore, the results of in-
field observation of the zoning of the mangrove forest in Grajagan Bay could be classified into 2, south zone 
and north zone on the basis of the boundary of the sea water of the estuary. Based on the in-field 
observation there were 15 species of the mangrove of the Grajagan Bay of Alas Purwo National Park, but 
according to Wetland International there were 27 species spreading in the National Park and Perum 
Perhutani.  

2.2. Image Processing 

The study used the data of Landsat-8 OLI with 2 sensors carried by Landsat of 8th generation, which were 

OLI and Thermal. However, it used the OLI sensor. The Landsat-9 OLI consisted of 9 bands and according to 

(Irons, 2015) with the following details: coastal band (0.43-0.45 m), blue band (0.450-0.51 m), green 

band (0.53-0.59 m), red band (0.64-0.67 m), near infrared band (0.85-0.88 m), SWIR1 band (1.57-1.65 

m), SWIR2 band (2.11-2.29 m)), panchromatic band (0.50-0.68 m)), and cirrus band (1.36-1.38 m) with 

the resolution of 30 meters for multispectral imaging and 15 meters for panchromatic imaging. The reason 

for the use of the Landsat-8 OLI image was that it had complete band with minimum cloud coverage of 

11.6% on ground and when the cloud coverage on the ground was >30%, the cloud would cover targeted 

object and the imaging result was useless. And then, radiometric and atmospheric corrections were made 

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to obtain biophysical parameters (Jensen & Lulla, 1987). The atmospheric correction was made using 

histogram adjustment method. The histogram adjustment method was easy and simple to use by discarding 

the offset value of the image (Danoedoro, 2012). The image was processed using software ENVI 4.8.  

 
 
 
 
 
 
 
 
 
 
 
 
 

 
Figure 1. The location of the study in Grajagan Bay of Alas Purwo National Park. 

2.3. Field Measurement 

The activity of the field measurement was conducted in April 2016 in Alas Purwo National Park area. The 

measurement was made using DSLR camera Nikon D5100 with 18-55 mm lens and additional fisheye 

converter lens to change the resulting image into fisheye image (Figure 2a). The portraying was conducted 

using the fisheye lens because it was the easiest and more appropriate lens for processing the resulting 

canopy density image with the software can eye. 

The data of the canopy density was processed using the software can eye because it was a new software 

and gave more appropriate results with the in-field condition. It was necessary in the in-field portraying to 

consider the mean height that was breast height on squat position (Figure 2b). 

 

 
  

 

 

 

 

(a)                                                                                (b)  
Figure 2.  (a) Field measurement. (b) The results of vertical fisheye portraying and Figure (b). The in-field 

portraying process. 

2.4. Field Measurement 

Vegetation transformation index used in the study was index SAVI (soil adjusted vegetation index). The SAVI 

vegetation index was the best one considering the factor of land influence (L). According to (Jensen & Lulla, 

1987), (Danoedoro, 2012) the SAVI vegetation index was able to reduce land background because of the 

presence of the factor of land spectral noise during atmosphere correction. The formula of the SAVI (Eq.1) 

vegetation index is: 

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The factor of land influence (L) used in the study was 0.5 because it was the result of the field identification 

of the mangrove forest, which was moderately dense. Additionally, it was the most common practice to use 

the value because it could accommodate both low and high vegetation areas (Champagne in Ilham, 2012). 

The process was carried out using the SAVI vegetation index in which NIR was near infrared band and RED 

was red band in the Landsat-8 image resulting from the atmosphere correction processing. The results of 

the processing of the SAVI vegetation index transformation were statistically analyzed using regression and 

correlation methods to find out the correlation between the results of the estimation of the image and the 

in-field condition. The regression analysis resulted in the equation Y = ax+b to develop forest density 

estimation model.  

3. RESULTS AND DISCUSSION 

In remote sensing the recorded forest density was the canopy density so that it was more appropriate and 

the results were more accurate. If the measurement was conducted using canopy approach, the remote 

sensing recorded the canopy density and not the density of tree stands so that a new method of gap 

fraction or hemispherical photography was developed. The gap fraction method was new method and has 

not been widely used in Indonesia. The measurement using this method was very simple because it used a 

camera and fisheye lens and the photographs were taken upward or downward depending on the objective 

of the photography. The results of the upward photographs might be seen in (Figure 2a). The study used 

the upward photography to avoid noises of other objects in the resulting photographs. The average in-field 

canopy density was 21.6% or 0.216 of 46 distributed sample points. The resulting canopy density was 

processed using can eye software accessible in http://www6.paca.inra.fr/can-eye (Weiss, 2002). The 

process was carried out by tracing the resulting photographs taken using fisheye lens that tended to be 

convex into low watermark canopy density as reference so that the results of the classification of the 

canopy and non-canopy density would be very different as illustrated in (Figures 3a and 3b).  

 

 

 

 

 

     (a)            (b) 

Figure 3. (a) The scheme of the processing of the forest density. (b) The results of the classification of the 

canopy and non-canopy density and Figure (b). The results of the processing of forest density. 

According to (Jonckheere et al., 2005), the classifying process was based on the threshold given by the 
processing software. The classification would give canopy density through the gap between sky and canopy 
object. The classification would influence the width of the coverage area of the lens, which was increasingly 
wider and close to 180o and the results would be better because the influencing factors of the density were 
the angle of sun and the ability of the camera in catching objects such as over exposure or under exposure. 
The photographs were usually made using auto method that the classification of the objects was based on 
the incoming light into the camera considered to be the same and there was not any under exposure or 
overexposure. The more the under exposure influenced the density, the higher the density would be, while 
the more the over exposure influenced the density, the smaller the classified objects would be. 

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The resulting density of the in-field measurement would be used as observation data that would 

subsequently be used in statistical test using regression to find out the correlation between the in-field 

density and the estimated data resulting from the image processing using vegetation index transformation, 

which was SAVI because the SAVI was according to (Putra & Khakhim, 2016) was appropriate method in 

mangrove forest density mapping with good accuracy and appropriate with the in-field condition. The 

accuracy of the SAVI reached 80%. It was consistent with other study by (Murti & others, 2013) in which a 

representation was made among RVI, NDVI and SAVI and the results were indicative of the canopy density 

with the accuracy above 80%. 

The results of the regression (R2) of the observation results of the image processing showed that the 

regression (R2) was 0.566 and the equation Y= 107.96x + 5.2859 as illustrated in (Figure 4). Consequently, 

the equation Y=107.96x + 5.2859 was applicable in spatially mapping the canopy density. The regression 

value above would result in canopy density estimation model in mangrove forest that was based on the 

estimation results of the regression equation between the in-field measurement sample and the condition 

of the remote sensing.  Actually, a linear regression might be carried out only by observing how appropriate 

the estimation results of the long distance image and the in-field actual condition. Therefore, the regression 

Y= ax+b would give the regression value (R2) that enables us to find out the correlation. The closer was the 

correlation (R), the more appropriate it would be with the in-field condition. Thus, the resulting correlation 

of the regression model was close and able to estimate the mangrove forest density using the vegetation 

index transformation SAVI. There were 30 samples used in the regression to develop the forest density 

estimation model. 

Figure 4. The regression of the results of the in-field density measurement and the results of the 
vegetation index transformation processing. 

The results of the regression equation would be returned to the long distance image in which the x value 

would be substituted by the result of the vegetation index transformation processing SAVI and it would give 

the estimated forest density. The estimated forest density might be observed in (Figure 5). The resulting 

mangrove forest density was classified in 3: low, medium and high. Based on the density there were low 

density forest (0-12.5%), medium density forest (12.6-25%), and high density forest (25.1-37.6%) in (Table 

1). 

Table 1. The Result Table of Density Mangrove 

Density Class Range Class (%) Extensive (ha) Pixels 

Low 0-12,5 30,42 338 

Medium 12,6-25 116,55 1295 

High 25,1-37,6 463,68 5152 

Total 610,65 6785 

 

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It was necessary to find out the accuracy of the resulting forest density in order to find the error of the 

forest density estimation. The accuracy was measured using RMSE (roots mean square error) method 

commonly used in the remote sensing. According to (Kamal, Hartono, Wicaksono, Adi, & Arjasakusuma, 

2016), the method was used to predict the visual error between the data resulting from the observation 

and the estimation data. The bigger was the RMSE, the lower the resulting accuracy would be. The number 

of the samples used in the accuracy test was 14 samples. There supposed to be 15 samples ideally to find 

out the accuracy of the samples. However, 14 samples were used because there in-field sampling failed to 

comlete the number. However, 14 samples were still considered to give accuracy in the measurement. The 

location of the sample drawing for the accuracy test must be widely separated to the location of the 

samples for the estimation model for more ideal accuracy and did not have any significant impact on the 

model, if the estimated model sample and the accuracy were close, the resulting accuracy would be better 

and hence it was less representative. 

The results of the calculation of the accuracy showed that the RMSE was 2.334178. According to 

(Makridakis et al., 1982) the low RMSE value indicated that the resulting value variation of the estimation 

model was close to its observation variation. The higher was the regression value of the in-field observation 

results and the estimated results were high, the better the resulting RMSI would be. Good RMSE was the 

one with low value. The lower was the RMSI value, the better it was. Based on (Putra & Khakhim, 2016), 

among the RMSE SAVI value and other vegetation index transformation methods the best was the SAVI 

with the lowest RMSE value. Therefore, the RMSE value of 2.334178 was low enough for a model to be 

accurate. The lower was the RMSE value, the better it would be. In other words, the closer was it to 0.00, 

the better it would be (Kamal et al., 2016). The 1:1 line in (Figure 5) was helpful in understanding the 

estimation results of the forest density whether it was underestimate or overestimate. It was a red and 

diagonally dividing line indicative of overestimation or underestimation. If it was underestimated, the 

sample distribution would be concentrated in the in-field observation and vice versa. The results of the 

study showed that the forest density was underestimated and the in-field density was >25% in which there 

were some samples concentrated in and heading to the underestimated density. It was influenced by the 

tidal condition of the forest. 

 

Figure 5. The results of the calculation of the RMSE (root means square error) of the vegetation 
index transformation SAVI of mangrove forest. 

 
 
 

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4. CONCLUSION 

If the forest density was measured using remote sensing, the object for the measurement was the canopy 
that was measured through spectral reflection so that it was the forest canopy density that was measured 
in the measurement. The forest canopy density was measured using gap fraction method or hemispherical 
photography method. The method was more appropriate in identifying the forest density, especially the 
canopy density because it used the covering width of hemispherical lens. The samples of the study were 
measured using the vegetation index transformation SAVI that could give in-field mean canopy density of 
21.6%, while the estimated density was 25% and the resulting RMSE was 2.334178 and categorized into 
good enough.  

5. ACKNOWLEDGMENTS 

We would like to thank Mr. Muhammad Kamal S.Si., M.GlS., Ph.D who knew the new gap fraction 

method, the Office of Alas Purwo National Park for the opportunity to conduct a research, and Mr. 

Supangat, Mr. Tri, Mr. Marzuki, Mr. Haryanto, and Toro who have helped us in drawing samples in field. 

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