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         Geoplanning 
Vol 3, No. 2, 2016, 137-146                                                                                                                                                        Journal of Geomatics and Planning 

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

doi: 10.14710/geoplanning.3.2.137-146 

TEMPORAL VEGETATION DYNAMICS IN PEAT SWAMP AREA USING MODIS  
TIME-SERIES IMAGERY: A MONITORING APPROACH OF HIGH-SENSITIVE 
ECOSYSTEM IN REGIONAL SCALE 

Y. Setiawan a, H. Pawitan b, L. B. Prasetyo c, M. Parlindungan b, P. A. Permatasari a 

a Center for Environmental Research, Bogor Agricultural University, Kampus IPB Darmaga, Bogor 16680, Indonesia 
b Department of Geophysics and Meteorology, Bogor Agricultural University, Kampus IPB Darmaga, Bogor 16680, Indonesia 
c Department of Forest Conservation and Ecotourism, Bogor Agricultural University, Kampus IPB Darmaga, Bogor 16680, Indonesia 

Abstract: Peat swamp area is an essential ecosystem due to high vulnerability of 
functions and services. As the change of forest cover in peat swamp area has increased 
considerably, many studies on peat swamp have focused on forest conversion or forest 
degradation. Meanwhile, in the context of changes in the forestlands are the sum of 
several processes such as deforestation, reforestation/afforestation, regeneration of 
previously deforested areas, and the changing spatial location of the forest boundary. 
Remote sensing technology seems to be a powerful tool to provide information 
required following that concerns. A comparison imagery taken at the different dates 
over the same locations for assessing those changes tends to be limited by the 
vegetation phenology and land-management practices. Consequently, the 
simultaneous analysis seems to be a way to deal with the issues above, as a means for 
better understanding of the dynamics changes in peat swamp area. In this study, we 
examined the feasibility of using MODIS images during the last 14 years for detecting 
and monitoring the changes in peat swamp area. We identified several significant 
patterns that have been assigned as the specific peat swamp ecosystem. The results 
indicate that a different type of ecosystem and its response to the environmental 
changes can be portrayed well by the significant patterns. In understanding the 
complex situations of each pattern, several vegetation dynamics patterns were 
characterized by physical land characteristics, such as peat depth, land use, concessions 
and others. Characterizing the pathways of dynamics change in peat swamp area will 
allow further identification for the range of proximate and underlying factors of the 
forest cover change that can help to develop useful policy interventions in peatland 
management 
  

Copyright © 2016 GJGP-UNDIP  
This open access article is distributed under a  

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

How to cite (APA 6th Style): 
Setiawan, Y., et al. (2016). Temporal vegetation dynamics in peat swamp area using MODIS time series imagery: A monitoring approach of high-
sensitive ecosystem in regional scale. Geoplanning: Journal of Geomatics and Planning, 3(2), 137-146. doi:10.14710/geoplanning.3.2.137-146 

 

1. INTRODUCTION 

Information on ecosystem characteristics and its changes in a highly vulnerable land, such as peatlands, 
is the key to many aspects of global environmental change, environment adaptation and mitigation studies. 
Peatland ecosystems are vulnerable to changes in quantity and quality of their water supply, and it is 
expected that climate change will have a pronounced effect on peatlands through alterations in 
hydrological regimes with climate variability (Erwin, 2009). 

The importance of peatland in the tropical environment has been studied by many researchers, such as 
the estimation of CO2 emission released by forest fire (Page et al., 2011), the variation in its ecological 
function (Achard, 2002), the loss of biodiversity through land conversion (Myers et al., 2000) and the 
sustainable management of peatland (Hooijer et al., 2006). Moreover, as explained by Wösten et al. (2008) 
that inundated condition of peatland is recommended as a regional ecosystem conservation program and 

Article Info: 
Received: 11 August 2016 
in revised form: 22 September 2016 
Accepted: 29 September 2016 
Available Online: 31 October 2016 
 

Keywords:  
Temporal vegetation dynamics, peat 
swamp area, MODIS 
 

Corresponding Author: 
Yudi Setiawan 
Bogor Agricultural University, 
Kampus IPB Darmaga, Bogor 16680, 
Indonesia 
Email: setiawan.yudi@apps.ipb.ac.id  

OPEN ACCESS 

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http://dx.doi.org/10.14710/geoplanning.3.2.137-146
mailto:setiawan.yudi@apps.ipb.ac.id


 
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to reduce CO2 emissions from the lands. As the importance role of peatlands and to prevent fires in 
peatland areas, the Indonesian government has a commitment to restore about two million hectares of 
peatland areas by 2020 due to the establishment of a peatland restoration agency (Wardhana, 2016). 

In more complex tropical peatland areas in Indonesia, the peatland characteristics and its change are 
the results of many, non-linear, interactions between socio-economic and cultural conditions, biophysical 
constraints and land use history (Cole et al., 2015). The biophysical processes of peatlands ecosystem can 
be represented as interactions between different land use/cover types, biophysical conditions and spatial 
elements of the landscape (Evans & Moran, 2002). 

This study examines the feasibility of using time series satellite datasets to recognize the changes 
occurred in the peat swamp ecosystem that would be possible to consider the peatland characteristics. A 
monitoring approach to recognizing the peatland characteristics and its dynamic changes on a regional 
scale is due to simultaneous analysis of land surface attributes from long-term data sets and seasonal 
variation. Monitoring of land surface continuously can provide information on the ecosystem 
characteristics, including the actual subtle nature of inter-annual change (Setiawan & Yoshino, 2014). This 
approach will provide information how the change occurred accurately as well as how big are these 
affected areas. Moreover, based on this information, we can choose the priorities for restoration and 
further research. 

 

2. DATA AND METHODS 

2.1. Study Site 

The study site is the main island of Sumatra with an area of 473,481 km2. The island is administratively 
divided into 8 provinces, namely Aceh, North Sumatra, West Sumatra, Riau, Jambi, Bengkulu, South 
Sumatra and Lampung (Figure 1). Sumatra has a mean annual rainfall mostly less than 3,000 mm, below 
1,500 mm on the northeast coast, and below 2,000 mm in several intermontane basins from Aceh to 
Bengkulu. Over 4,000 mm fall on the off shore Mentawai Islands, on the Sibolga and Padang coasts, and 
around Mount Malintang and Mount Pantaicermin. On the mountain slopes of Bukit Amas and of northern 
Bengkulu mean annual rainfall exceeds 5,000 mm. The Eastern Islands receive 2,000 to 3,000 mm/year 
(Land Resources Department/Bina Program, 1990). 

 

Figure 1. Study site (Authors, 2016) 

 
 

2.2. Satellite Images 

We used Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index dataset derived 
from U.S. Geological Survey - Land Processes Distributed Active Archive Center (USGS LP DAAC, 2009). The 
composited 16-day product of MODIS vegetation index which is embedded in MOD13Q product contains 

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various vegetation Indexes (VI), one of them is Enhanced Vegetation Index (EVI). The EVI is developed by 
The MODIS LAND Discipline Group (MODLAND) using the equation in Huete et al. (1999). We used the 
MODIS datasets acquired from January 2001 to December 2014. The datasets captured 322 time series 
data with an interval of 16 days. Such kind of the high temporal datasets are required to determine the 
vegetation dynamics change of peat swamp areas. 

 
2.3. Maps Datasets 

We used a map of land cover that provided by the Ministry of Forestry (2013) to describe the type of 
land cover in the Sumatran peatlands. This land cover map was produced based on visual interpretation of 
Landsat 5 TM/7 ETM+. The Ministry of Forestry classified the type of land cover into 23 types as follows: (1) 
Primary dry land forest (Hp); (2) Secondary dry land forest (Hs); (3) Primary swamp forest (Hrp); (4) 
Secondary swamp forest (Hrs); (5) Primary mangrove forest (Hmp); (6) Secondary mangrove forest (Hms); 
(7). Bush/slash (B); (8) Swamp (Rw); (9) Swamp bush (Br); (10) Savannah (S), (11) Industrial forest plantation 
(Ht), (12). Plantation (Pk), (13). Dry land agriculture (Pt); (14) Mixed dry land agriculture (Pc); (15) Rice field 
(Sw); (16) Fishpond (Tm); (17) Bare land (T); (18) Transmigration (Tr); (19) Mining (Tb); (20) Airport area 
(Bdr); (21) Built-up area/Housing (Pm); (22) Water body (A); and (23) Cloud cover.  

The land cover map provided by the Ministry of Forestry is concerned primarily with natural 
vegetation categories. Consequently, in the forest class, delineated polygons could be labeled with two 
codes, representing categories un-disturbed by human activities (primary type) and those disturbed 
(secondary type). 

Moreover, some of thematic maps were used to characterize the complex situation of peat swamp 
ecosystem; such as: map of peat depth distribution, logging concession, wood-timber plantation concession 
(industrial forest plantation) and oil palm plantation. Those thematic maps were provided by the Global 
Forest Watch (2002). The peat depth was classified into 6 classes: (1) less than 50 cm; (2) 50–100 cm; (3) 
100-200 cm; (4) 200-300 cm; (5) 300-400 cm; and (6) more than 400 cm. In this study, concession areas for 
oil palm and other timber plantations as well as conservation areas were also used to seek out any 
relationship with dynamic changes in the peatlands. 

 
2.4. Image Processing 

Although MODIS VI (MOD13Q1) data have some advantages in providing basic information related to 
vegetation pattern change, time-series of these data inevitably contain disturbances caused by atmospheric 
variability and aerosol scattering (Xiao et al., 2003). Therefore, noise reduction (de-noising) or fitting a 
model to observe data is necessary before vegetation dynamics pattern can be determined. In this work, 
we used the MODIS EVI datasets filtered by an average moving window over 3 months (almost equal to 7 
time series) data in order to smooth and reduce the residual noise of these images (Figure 2).  

 
Figure 2. Average moving window over 7 time series of EVI data  

- an example for 2002 datasets (Authors, 2016) 

 

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2.5. Pattern Change Detection 

In this study, we recognized the change of vegetation dynamics using a distance of average EVI values 
for two successive three months (January-March  April-June  July-September  Oktober-December). 
All pixels in the consecutive study years during periods from 2001 to 2014 were computed by a function 
shown in the equation below (Bouman, 2009): 

 

                               [1] 
 

where k and l are two successive 3 months, and dk,l is the distance between EVI values of the two successive 
patterns of k and l three months data, Nk,Nl is the number of observations in k and l three months data, 

Nnew is the number of observation of the two pattern of k and l three months data (Nnew = Nk + Nl) and k, l 

is the mean of EVI values in k and l three months data, new is the mean of EVI values of the two pattern of k 
and l three months with following equation: 

 
                                                                                                                                [2] 

 
 
Figure 3 illustrates the change detection based on vegetation pattern change thorugh a change of the 

distance of index value in two successive time series data. A complete image processing analysis for this 
change detection method was applied in the previous work (Setiawan & Yoshino, 2014).  

 
 

Figure 3. Illustration of the forest cover change detection system  

example change pattern at period July – September 2005  (Setiawan & Yoshino, 2014) 

 
 

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Peatlands distribution 

(GWF, 2002) 

2.6. Change Pattern Thresholds 

We assumed that the difference in distance for those four periods in every year shown an 
approximately normal distribution. Therefore, we identified the pixels that had the greatest change in 
distance of EVI for each period. The three change thresholds (TH) of 2.0, 3.0, and 4.0 were selected 
corresponding to a range of z-value probabilities which was the limit to define a change or no-change in the 
temporal pattern. Based on the previous study (Setiawan et al., 2013; Setiawan et al., 2014), these value 
ranges were selected because they produced appropriate estimates of annual change values based on a 
previous change rate for the Java area. Some pixels of the change and no-change events were randomly 
selected to determine the threshold of change pattern probability. 

 
2.7. Accuracy Assessment 

In this study, accuracy assessment is performed using a finer spatial resolution Landsat TM and ETM+ 
as well as ground checking at some selected areas. The hotspot datasets derived from NOAA 8 were 
collected in the period of 2000 – 2015 from the ASEAN Specialised Meteorological Centre (ASMC) 
(http://asmc.asean.org). The ASMC was established in January 1993 as a regional collaboration programme 
among the National Meteorological Services (NMSs) of ASEAN member countries. 

 

3. RESULTS AND DISCUSSION 

Characterizing the time series vegetation dynamics will provide information about the temporal 
vegetation pattern in the peatlands. The distribution map of the change patterns of peat swamp ecosystem 
in Sumatra will be also provided. 

 
Figure 4. Temporal vegetation change in peatland areas detected  

by two successive 3 months (Analysis, 2016) 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 4 shows the distribution of vegetation pattern change in the peatland areas which was detected 

by the approach. Detail changed area in Jambi and Riau is given in Figure 5. Some significant causes of the 
change pattern in the peatlands can be identified systematically, either caused by natural forest fire or 

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Figure 6. Significant change patterns of EVI on 

selected sites as forest change detected by TH-1 

(Analysis, 2016) 

Figure 7. Dynamics change patterns of EVI on 

selected sites as forest change detected by TH-3 

(Analysis, 2016) 

human activities factor. Based on the identification of the pattern change, we found many significant 
patterns that indicated a change pattern in the peatland areas.  

 
Figure 5. Detail change distribution in Riau (left) and Jambi (right) detected detected by the change of 

temporal pattern two successive 3 months (Analysis, 2016) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The EVI pattern was used to measure reliable spatial and temporal characteristics of vegetation 

dynamics of land cover, as a means for better understanding of land characterization (Setiawan et al., 2013; 
Verburg et al., 2009). The previous study (Setiawan et al., 2016) explained that some typical EVI patterns of 
the Sumatran peat swamp indicates different type of ecosystem and/or different response of ecosystems 
to the changing environment.  

 
  
 
 

 
 
 
 
 
 
 
 
 
 
 
Monitoring temporal vegetation patterns allowed the change in peatlands to be detected including 

some properties of that change such as the location, area, time and mechanism of the change. For instance, 
Figure 6 shows several patterns of forest cover change detected in peatland areas; (1) forest conversion 
into barren land through burning (at the end of March, 2014), (2) forest area was converted into plantation 
with was firstly converted to barren land. Meanwhile, Figure 7 shows several patterns of forest cover 
changes affected by anthropogenic activities (timber forest management), where forest changed to open 
land because of the threes harvesting and the later replanting process. Regarding to the change pattern of 

Changed on May 2014 Changed on December 2013 

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EVI, the selection of thresholds (TH-1 and TH-3) was significantly affected to the accuracy of change 
patterns detection system.  

We compared the result with the hotspot occurrences with up to 90 percent of confidence, there was a 
relationship between the pattern changes in peatlands and the land fires. The highest number of hotspots 
was happened in 2006. However, the majority of hotspots was relatively constant every year during the dry 
season (July - September). It seems that the pattern changes was mainly occured in dry season when it was 
high in hotspot occurrences. This could be the case that fire is still being used both small-holders and large 
holders as the only tool for land clearing (Stolle et al., 2003). 

Moreover, the majority of pattern changes occurred in production forest zone, where forest and palm 
oil industrial estates established. In addition, the amount of changes was relatively constant every year, 
means that there have been activities in the production forest areas related to clearing for new plantation. 
However, based on limited ground checking activities, the changes of vegetation index did not wholly 
related to forest clearing; some of them were cleared from old and non-productive plantations to new 
plantations especially in oil palm companies. Meanwhile, it also happened in the several big timber 
companies, the vegetation index were drop when they harvested the trees and planted new ones after 
that. Compared to hotspot occurrences with up to 90 percent of confidence level, it was very unlikely that 
these big companies used fires for replanting their new plantations. It could be the case that logging 
companies control fire during their exploitation of concessions, logged-over forest and forest allocated to 
production. 

Conservation areas (nature preserves, protected forest, national parks, and protected peat forest) have 
also experienced changes. This is due to several conservation areas such as Berbak National Park are 
located in peatland area which is vulnerable to forest fires especially in the border with other non-
protected areas. Miettinen et al. (2012) studied peatland conversion and degradation processes in Jambi 
between 1970 and 2009. They found that nearly-pristine forest cover have declined from 95 to 73 percent 
inside the Berbak National Park and outside the National Park from 86 to 25 percent. Outside the protected 

area, 66 percent of former nearly‐pristine forests turned into degraded forests or unmanaged deforested 

areas. Large‐scale oil palm plantations accounted for 21 percent of the formerly nearly‐pristine areas 

and small‐holder agriculture for 8 percent.  

The production forest which is mainly occupied by logging companies, oil palm plantation, and industrial 
timber concessions is the most dynamic land use in Sumatra. The peatland ecosystem in this area is 
constantly changing either by opening new cultivation area or from re-planting activities by the owners. 
However, based on this study which looks the changing based on vegetation activities, it is not clear that 
the land cover and land use changes from one type to another or within the same type but different age of 
vegetation. It needs more study by combining different satellite imageries with different resolutions in 
combination with more ground checking activities. 

The performance of our change detection approach was evaluated by 100 reference points, and the 
result revealed an overall accuracy of 80.10%. Comparison of the accuracy in change detection among 
many land use types in peatlands reveals a variation. Plantation area had the greatest overall accuracy 
(94.02%), followed by agricultural land (86.62%), and natural forest (81.97%). In the natural forest, 44.61% 
of the errors is due to omission errors, meaning that the change area in those classes was assigned 
incorrectly. The result also indicates that the change in plantations could be detected more accurately 
relative to other classes.  

Overlaid the deforestation detected area of MODIS with the two different dates of LANDSAT images in 
selected locations in Sumatra are presented below. By the Figure 8, the results indicate that the approach 
developed in this study offer great promise to detect deforestation events quickly (monthly) at large scale. 

Furthermore, through these results, the understanding of environmental changes will be improved 
including carbon storage change and sequestration by terrestrial plants. Characterization of typical peat 
ecosystem based on temporal pattern analysis would provide useful information regarding the change 
dynamics in the peatland ecosystems; consequently it should be possible to provide information of the 
inter-annual ecosystem change in Sumatran peatlands. 

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Figure 8. Comparison between the result of our approach with the two different dates of 

Landsat images in selected locations in Sumatran peatlands (Analysis, 2016) 

                       

 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

4. CONCLUSION 

Due to characterizing the long-term vegetation dynamics, the main types of change in peatlands were 
recognized. The first is the change in forest lands, either by forest fire or human factors which forest is 
converted into oil palm plantation as well as an open area. The second is the temporary change in oil palm 
plantation (replanting) as well as timber forest plantation (harvesting and replanting). The third is 
agricultural development, including some trajectories such as non-agricultural lands converted into 
intensive agricultural lands. Each change category has specific change mechanisms or processes, which 
then reveals the specific spatial model for each type.  

Additionally, the results indicate that on a regional scale, many of the change patterns are affected by 
temporary changes in land cover. A change in the temporal vegetation pattern is detected as a land use 
change even if that land use type had not really changed. It is highly inter-related with climate variability, 

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for example, the ENSO (El Nino Southern Oscillation) that has significantly affected the water availability 
due to the low-intensity rainfall. Also, some agricultural lands became barren in those periods because 
there was insufficient water for plant growth. Distinguishing the actual type of change from temporary 
change by land cover dynamics provide sufficient, significant and useful information to understand the 
pattern of dynamic pattern change of peatlands in Sumatra and their future roles. 

Regional shifts in temporal vegetation dynamics, including the actual changes of land use and temporary 
changes of land cover in peat swamp areas, have numerous consequences relevant to the environment as 
well as changes in carbon and nitrogen storage, land degradation and loss of biodiversity. An understanding 
of temporal vegetation dynamics to explain the mechanisms and pathways of vegetation change pattern is 
important because of its relationship to ecosystem characteristics and socio-economic attributes of the 
peatland, and it will be discussed separately in further studies. 

5. ACKNOWLEDGMENTS 

We would like to thank the Center for Environmental Research, Bogor Agricultural University (PPLH-IPB) 
for giving us opportunity to get the research funding. This research was funded by the Indonesian 
Directorate General of Research and Higher Education (RISTEKDIKTI) for the fiscal year of 2016. 

 
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