Spatial Model of Canopy Density in Mangrove Forest

of Percut Sei Tuan

Nurdin Sulistiyono

12*

, Khairil Amri

, Pindi Patana

, Achmad Siddik Thoha

Department of Forest Conservation, Faculty of Forestry, Universitas Sumatera Utara, Jl. Tridarma Ujung No.1 Kampus

Universitas Sumatera Utara, Medan North Sumatra 20155, Indonesia

Center of Excellence for Natural Resources-Based Technology, Mangrove and Bio-Resources Group,

Universitas Sumatera Utara, Medan North Sumatra 20155, Indonesia

Keywords: Canopy Density, Remote Sensing, GIS, Percut Sei Tuan, NDVI

Abstract: Information about canopy density is needed in many ways, for example, in estimating forest degradation and

forest quality. Utilization of vegetation index values on satellite imagery can be used to predict canopy density

distribution. This study aims to predict canopy density distribution in mangrove forests. The methodology

used is using regression analysis by connecting Normalized Difference Vegetation Index (NDVI) value with

canopy density values in the field. The NDVI value is derived from Landsat 8 satellite images, while the

canopy density percentage is obtained by using a camera. The spatial distribution of canopy density is obtained

through spatial modeling using Geographic Information System (GIS). The results showed that the NDVI

value of the linear regression model could be used to predict the density distribution of mangrove forest

canopy with r square value of 59.0% and sig value <0.005.

1 INTRODUCTION

Land cover changes in mangrove forest into other

land uses such as agriculture, mining, and settlement

are the causes of deforestation in secondary mangrove

forests in North Sumatra (Basyuni et al., 2018). in

another hand, the existence of mangrove ecosystems,

is very important for supporting survival in coastal

zones (Duke et al., 2007). Therefore, efforts are

needed for rehabilitating mangrove forests so that the

mangrove forests fungction can optimally.

Information about canopy cover is needed in

environmental rehabilitation planning activities

(Azizia et al., 2008), including in the mangrove

ecosystem. Canopy cover is defined as the proportion

of forest floor covered by a tree canopy projected

vertically (Jennings et al, 1999). Monitoring changes

in canopy cover is needed as an initial effort to

determine rehabilitation priority areas.

The utilization of vegetation indices on satellite

images such as NDVI can be used to predict the

spatial distribution of canopy density (Wachid et al.,

2017). This study aims to predict the spatial

distribution of mangrove forest canopy cover in

Percut Sei Tuan.

2 MATERIALS AND METHOD

2.1 Studi Area

The data collection process was carried out on the

mangrove forest landscape in Percut Sei Tuan

Subdistrict, Deli Serdang Regency, North Sumatra

Province. The geographical position of the mangrove

forest landscape in Percut Sei Tuan is located at

latitude 3.68

N - 3.77

N and at longitude 98.70

E -

98.83

E. The map of the research area can be seen in

Figure 1.

Sulistiyono, N., Amri, K., Patana, P. and Thoha, A.

Spatial Model of Canopy Density in Mangrove Forest of Percut Sei Tuan.

DOI: 10.5220/0008388000420045

In Proceedings of the International Conference on Natural Resources and Technology (ICONART 2019), pages 42-45

ISBN: 978-989-758-404-6

Figure 1: Site research location

2.2 Analysis of Vegetation Indices

NDVI is one of the most widely used vegetation

indices in remote sensing including estimating

canopy density. In this study NDVI values were

obtained from Landsat satellite images 8 Path / Row

129/57 recording on July 1, 2016. The NDVI formula

was calculated by equation (Rouse et al., 1973):

NDVI =

(NIR-R)

(1)

(NIR + R)

where :

NIR : Digital value in Near Infrared bands

R : Digital value in the Red band

2.3 Analysis of Canopy Density

Measurement of canopy density in the reseach

location is conducted through photographing canopy

density from the bottom at the center point of a

30x30m sized plot. Furthermore, canopy density

photos are processed using the canopy cover free

android application to get the value of the canopy

density percentage.

Estimation of canopy density distribution is done

by regressing 30 plots of value data on canopy cover

measurements in the field with NDVI values on

landsat satellite images 8. The ordinary least square

regression equation model used in this study is:

Linear:

Y = a + bx

(2)

Exponential:

y = a exp

(3)

Information :

Y : canopy density (%)

x : NDVI

a, b : constanta

The classification of canopy density criteria in this

study refers to Departemen Kehutanan (2005). Criteria

for canopy density classification can be seen in table

Table 1: Criteria for canopy density classification

Criteria

Score

Low canopy density

< 50 %

Medium canopy density

50 – 69 %

High canopy density

> 70 %

3 RESULT AND DISCUSSION

3.1 Distribution of NDVI

Based on NDVI analysis using landsat 8 satellite

imagery in 2016, mangrove forest cover has a

minimum NDVI value range of -0.259 and a

maximum NDVI value of 0.529 (Figure 2). The

greater the NDVI value indicates the closure of

vegetation cover, on the contrary the smaller the

NDVI value shows that vegetation cover is

increasingly rare (Sulistiyono et al., 2018).

Figure 2: Spatial distribution of NDVI in mangrove forest

in Percut Sei Tuan

3.2 Model Prediction of Canopy Density

The results of statistical tests that describe the

relationship between canopy density and NDVI can

be seen in table 1. The ANOVA (analysis of variance)

test results on the two regression models

tested, obtained information that the NDVI value can

be used to predict canopy density (sig ANOVA

<0.05). Based on the determination coefficient value

(R square), the best canopy density estimator model

Spatial Model of Canopy Density in Mangrove Forest of Percut Sei Tuan

is a linear model with an R square value of 59%. The

results of this study are relatively similar when

compared with the results of the Wachid et al (2017)

study which produced an r square value of 59.89% in

mangrove vegetation in Teluk Jor.

Table 1: Result of statistical models using OLS regression

Model

Regression

Sig ANOVA

R square

Linear

y = - 0.457 + 13.862 NDVI

0.000

0.590

0.768

Exponential

y = 50.881 e

11.983 NDVI

0.000

0.451

0.671

Based on the selected linear regression model, the

area and percentage of canopy density in each class

can be seen in table 2. The low canopy density class

is 606.80 ha (57.8%), while the high canopy density

class is only 2.25 ha (0.21%). This indicates that the

landscape of the mangrove forest in Percut Sei Tuan

is dominated by vegetation with a low canopy

density. This can also be an indication that the level

of disturbance to the mangrove vegetation in Percut

Sei Tuan is quite high.

Table 2: Class canopy density in mangrove forest Percut Sei

Tuan

Class of canopy density

Area

(ha)

Percentage

(%)

Low canopy density

606.80

57.80

Medium canopy density

440.86

41.99

High canopy density

2.25

0.21

Total

1,049.91

100.00

The spatial distribution of canopy density in the

mangrove forest landscape of Percut Sei Tuan can be

seen in Figure 3. The distribution of the class of low

density canopy (pink color) is distributed along the

outer land line dominated by Avicennia sp. Low

density canopy classes are also widely seen in former

pond areas.

Figure 3: Spatial distribution of canopy density class in

mangrove forest in Percut Sei Tuan

4 CONCLUSIONS

The result showed that NDVI approaches can

estimate the forest canopy cover with r

square value

59.0 %. Low canopy density (57.8%) is majority

canopy density in mangrove forest of Percut Sei

Tuan.

ACKNOWLEDGEMENTS

This study was partly supported by a TALENTA

Grant 2017 (No. 104/UN5.2.3.1/PPM/KP-

TALENTA USU/2017) from Sumatera Utara

University.

REFERENCES

Azizi, Z., Najafi, A., Sohrabi, H. 2017. Forest canopy

density estimating, using satellite images. The

International Archives of the Photogrammetry, Remote

Sensing and Spatial Information Sciences 2008; Part

B8, 37 : 1127-1130.

Basyuni, M., Sulistiyono, N. 2018. Deforestation and

reforestation analysis from land-use changes in North

Sumatran Mangroves. 1990-2015. IOP Conf. Series:

Materials Science and Engineering 309 (2018) 012018.

doi:10.1088/1757-899X/309/1/012018.

Departemen Kehutanan Direktorat Jenderal Rehabilitasi

Lahan dan Perhutanan Sosial., 2005. Pedoman

inventarisasi dan identifikasi lahan kritis mangrove.

Manual Procedure for mangrove critical land

identification.

Duke N. C., Meynecke J. O., Dittmann, S., Ellison A. M.,

Anger K., Berger U., Cannicci S., Diele K., Ewel K. C.,

Field C. D. 2007. A world without mangroves? Science,

317(5824), 41-42.

Jennings, S. B., Brown, N. D., Sheil, D. 1999. Assessing

forest canopies and understory illumination: canopy

closure, canopy cover and other measures. Forestry

72(1): 59–74.

Rouse J. W., Haas R. H., Schell J. A., Deering D. W. 1973.

Monitoring vegetation systems in the Great Plains with

ERTS. In: Fraden S.C., Marcanti E.P., Becker M.A.

(eds): 3rd ERTS-1 Symposium, NASA SP-351,

Washington, D.C., Dec 10–14, 1973: 309–317.

ICONART 2019 - International Conference on Natural Resources and Technology

Sulistyono, N., Basyuni, M., Slamet, B., 2018. Land surface

temperature distribution and development for green

open space in Medan city using imagery-based satellite

Landsat 8, Friendly City 4 ‘From Research to

Implementation For Better Sustainability’. In IOP

Conf. Series: Earth and Environmental Science 126 (1),

012128 doi:10.1088/1755-1315/126/1/012128

Wachid, M. N., Hapsara, R. P., Cahyo, R. D., Wahyu, G.

N., Syarif, A. M., Umarhadi, D. A., Fitriani, A. N.,

Ramadhanningrum, D. P., Widyatmanti, W. 2017.

Mangrove canopy density analysis using Sentinel-2A

imagery satellite data. In IOP Conf. Series: Earth and

Environmental Science 70, 012020. do i

:10.1088/1755-1315/70/1/012020

Spatial Model of Canopy Density in Mangrove Forest of Percut Sei Tuan