Monitoring Land-use and Land-cover Change in Setiu Wetland,

Terengganu, Malaysia Using Remote Sensing and GIS

Kalsom Mat Saleh and Aidy @ Mohamed Shawal M. Muslim

Institute of Oceanography and Environment, University Malaysia Terengganu, 21030, Kuala Terengganu,

Terengganu, Malaysia.

Abstract. This study has integrated the advantages of unsupervised and supervised classification technique

for the purpose of mapping the land use/cover of Setiu Wetland as well as to discover the changes occurred

throughout the years. Two high-resolution multispectral images from QuickBird and GeoEye images were

adopted to map the land use/cover map of the study area from the year of 2002 and 2012. All classified

images were found to have good overall accuracy result which ranging from 82.58% to 93.21%. The spectral

confusion was found the reason to sand/ sand bar and muddy sand classes, hence, explaining the low

accuracy often occurred to both classes of both years. The change analysis resulting with very dynamic

changes occur to vegetation classes due to regrowth factors after land clearing. Meanwhile, the changes in

term of loss and gain between mangrove and heath vegetation was found related to spectral confusion caused

by the high heterogeneity of vegetation stand in the intermediate zone.

Keywords: QuickBird, GeoEye, hybrid classification, change analysis

1. Introduction

Setiu Wetland is the second largest mangrove area in Terengganu. Though small and fragmented, it still

provides a vast array of services to the coastal communities [1]. For instance, the intact mangrove forests

along the coastal area have reduced the impact of 2004 Indian Ocean earthquake and tsunami upon the

coastal communities as compared to other areas with no natural protection from the sea [2], [3]. Other than

acting as a buffer zone to moderate the impact of the natural catastrophe, coastal wetland also responsible for

improving the water quality and hydrology [4] and providing a high habitat quality for various flora and

fauna species [5].

However, its great importance and sensitivity was degraded due to rapid development which highly

concentrated along the coastal zones [1]. [2], [6] describe that most of the coastal forest and wetlands

particularly in the Asian countries were mainly threatened by aquaculture industries and tourism.

Clearly, a development of dynamic management of this particular area is requiring close monitoring of

the status of this important ecosystem. This study has taken the advantage of remote sensing and GIS in

assessing and evaluating the trend of land use and land cover (LCLU) changes.

The produced maps and changes analysis may provide the authorities, planners and resource managers

with useful information in developing proper management strategies [7]. Meanwhile, the ecologist,

environmental managers, and conservationist also can benefit from this study the information needed in

understanding the causes and impact of habitat deterioration [8] and further become the baseline

measurement in establishing the boundaries for protection zones in the coastal wetland areas.

2. Materials and Method

Corresponding author.

E-mail address: kalsom1985@gmail.com.

International Proceedings of Chemical, Biological and Environmental Engineering, V0l. 102 (2017)

DOI: 10.7763/IPCBEE. 2017. V102. 2

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2.1. Study area

The selected study areas were located in the District of Setiu, Terengganu between 102º 44 E to 102º

46 E and 5º 37.5 N to 5º 40 N. Both study areas are located along the Eastern Coast of Peninsular Malaysia.

The total area covered in this study is approximately 24.36km², on which covers 1.87% of the Setiu District.

The annual rainfalls fluctuated from 2990mm during dry season to 4003mm during monsoon season every

year.

The Setiu wetland located in Setiu-Chalok-Bari-Merang River Basin of Terengganu [9] and it is

connected to the South China Sea through Kuala Setiu Baharu estuary [10], [11]. It comprises various types

of ecosystem such as estuaries, mangroves, wetlands and lagoons [9].

The aquaculture activities such as shrimp farming, pond culture and brackish water cage culture were

defined as the main economic activities conducted along the coastal zone such as Kampung Saujana,

Kampung Fikri and Penarik [12]. Meanwhile, the upstream activities which takes place along the Sungai

Setiu and Sungai Ular were primarily limited to agricultural activities on which mainly dominated by oil

palm plantation [13].

2.2. Data Sets

The data used in this study can be divided into two types; satellite data and ancillary data. The satellite

data consisted of high resolution multi-spectral images acquired from different satellite sensor. A detail

description of satellite data acquired for this study is given in Table 1.

Table 1: Satellite data specification

Data Acquisition date Band /Color Resolution Cloud coverage (%)

QuickBird 2 18th

October, 2002 Multi spectral 2.44m

9.9 Panchromatic 0.61m

GeaoEye 1 7th

May, 2012 Multi spectral 1.84m

24 Panchromatic 0.46m

The most common ancillary data such as ground truth data, topographic maps, and Google Earth images

[14] were used to aid the process of geometric correction, image classification, and for accuracy assessment

of the classified results [15], [16]. The ground truth reference data were collected using Differential Global

Positioning System (DGPS) thrice throughout the study period. Two topographic maps involved in this study

are Kampung Buluh (Sheet 4166) and Kampung Merang (Sheet 4266) published by Directorate of National

Mapping Malaysia. Finally, a series of images available online from Google Earth on the other hand were

also used at the time mainly during the supervised classification and accuracy assessment.

2.3. Image Pre-processing

Firstly, all satellite imagery data were geometrically corrected to the Universal Transverse Mercator

(UTM) 48N ground coordinate grid using both digitized topographic maps and 25 ground control points

(GCP) collected in situ to register the 2002 image. The 2012 image was then co-registered using the former

corrected image. Root mean squared error (RMSE) of less than 0.4 pixel root were obtained using the nearest

neighbor resampling method and it were considered acceptable according to [17] who reported that the

RMSE should not exceed 0.5 pixel for any two dates. Through this method which available in the

RESAMPLE module provided by the IDRISI software package, all satellite data were resampled to

standardize the pixel resolution to 2.4 m.

Both corrected images were then atmospherically corrected using the Cos(t) Model technique, a model

developed by [18] to gain the apparent radiance of the ground targets [15], [19]. Atmospheric correction was

applied on both images in order to obtain more accurate change detection results [20], [21].

The linear with saturation stretch was applied to increase the visual interpretability of the satellite images.

This procedure is important in differentiating features by increasing the apparent distinction between objects

in the scene elements [22] without altering the underlying value [23]. Throughout the process, all images

were stretched linearly with 2.5-5% saturation and were proven well in improving the visual displays.

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2.4. Image Classification

A total of 13 classes were identified and considered appropriate to describe the LCLU of study area. The

selected classes were determined based on Level II Anderson scheme of LCLU classification [24] with little

modification following the local condition.

Firstly, the unsupervised ISOCLUST clustering was applied to classify all images. Each satellite data

produced 40 spectral clusters. The produced clusters were then manually assigned with the identified LCLU

classes through the labelling process. Through the first stage of classification process, only the homogenous

clusters that best corresponded with the specific LCLU classes were labelled, meanwhile, the rest of the

clusters which exhibit mixed pixels characteristics were clipped out and manually corrected through the

supervised technique.

The manual labelling for the mixed pixels clusters is a time consuming process since it requires cross

referencing with other secondary data and ground truth data for accurate identification [25]. However, this

step is crucial in refining the unidentified clusters with mixed pixels characteristics. The separated

unidentified clusters were then digitized to gain the training sites. Training sites are created to teach the

classifier and to determine the decision boundary of each feature [22]. The signature components of each

training site were then evaluated to ensure there was no overlapping between spectral classes. All

unidentified clusters were classified using the maximum likelihood classifier. The resulted categorical maps

were then assessed for its accuracy and finally overlay with the maps produced through ISOCLUST method

to complete the LCLU maps.

2.5. Accuracy Assessment

The accuracy assessments of all classified maps were evaluated by using the stratified random sampling

scheme. To produce an error matrix with reliable accuracy result, a minimum of 50 samples for each LCLU

category was recommended by [26]. Therefore, for this evaluation, a total of 650 samples were selected for

each LCLU map at the same locations. All samples were distributed throughout the classified maps

according to the scheme and the classified maps were compared to ground truth data information on pixel by

pixel basis wherever possible [21]. However, there were times that samples were not able to be collected due

to some difficulties such as historical data that are not available at the time this study conducted or there

were sample points that located in a remote area that are not accessible. Therefore, a common practice that

adopting other collateral data [25] such as topographic maps and Google Earth maps were treated as

reference data in order to aid us during the interpretation process.

3. Results and Discussions

Four classified LCLU maps of study area were produced through the hybrid classification method. All

classified maps are as shown in Fig. 1. The final classified maps were satisfactorily accepted for further

change analysis based on their accuracy evaluation.

A B

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C D

Fig. 1: A and B are the classified LCLU maps for Southern Setiu Wetland for the year of 2002 and 2012 respectively

while C and D are the classified maps for Northern Setiu Wetland for the year of 2002 and 2012 respectively.

3.1. Accuracy Evaluation

The overall accuracy, producer’s and user’s accuracies and kappa index were calculated from the error

matrices. Table 2 summarizing the four error matrices produced from the LCLU maps. Based on the overall

accuracy of the error matrices, the hybrid method clearly produced satisfied results. The classified maps of

2002 having a slightly less accuracy rate as compared to 2012 maps. This is due to the inadequate collateral

data available on the particular year to aid us during the classification process. The sand/ sand bar and muddy

sand category of the Northern Setiu having the least accuracy result amongst others. The spectral confusion

of both classes were found higher in the area along the Setiu River where sand bar that covered with brackish

water having spectral values slightly the same as with the muddy sand. The rest of the classes showing

satisfied accuracy result.

Table 2. Accuracy statistics for the classification result of Northern and Southern wetland map.

Category

Northern Southern

2002 2012 2002 2012

PA UA PA UA PA UA PA UA

AP/WC 87.50 77.78 66.67 100.00 80.00 72.73 100.00 100.00

I/C 75.00 75.00 83.33 100.00 90.00 81.82 100.00 50.00

BUA 84.62 84.62 72.73 75.00 70.00 90.91 66.67 100.00

TRAN 75.00 100.00 66.67 50.00 75.00 85.71 100.00 100.00

SALT 100.00 83.33 100.00 100.00 100.00 86.67 100.00 100.00

SS 85.71 100.00 100.00 88.89 75.00 100.00 100.00 100.00

BW 90.91 95.24 93.75 100.00 76.92 83.33 100.00 100.00

S/SB 66.67 66.67 100.00 93.33 80.00 100.00 50.00 100.00

MS 71.43 62.50 100.00 50.00 100.00 75.00 100.00 71.43

MANG 91.67 89.19 93.75 89.29 86.96 80.00 86.21 100.00

HF 75.00 91.30 86.21 92.59 73.91 80.95 94.74 90.00

GL 100.00 77.78 82.35 93.33 75.00 81.82 100.00 100.00

OP/C 87.50 70.00 80.00 57.14 87.50 77.78 100.00 75.00

Overall accuracy

(%) 85.16 89.26 82.58 93.24

Kappa coefficient 0.8304 0.8785 0.8082 0.9224

3.2. LCLU Change Analysis

Fig. 2. demonstrating the overall result of change analysis upon the LCLU maps of Setiu wetland. The

produced maps of aquaculture pond/water canal class of 2002 maps are unique. Though the actual areas of

the class are on inland area, the map revealed the other area of Setiu River particularly in the proximity of

aquaculture ponds along the river also mapped as aquaculture pond/water canal class. This is due to the 13

release of waste from the inland aquaculture activity into the nearby waterways, on which in this case also

affecting the spectral value of this particular water body to resemble the actual aquaculture pond/water canal.

However, the same areas on 2012 maps do not reveal the same pattern of spectral reflectance. This area was

affected by the aftermath of northeast monsoon season where increase in water volume and flow from the

upper stream have decreasing the concentration of aquaculture waste into the river. Therefore, both situations

resulted in the reduction of aquaculture pond/water canal area on the Northern and Southern Setiu wetland

from 2002 to 2012.

Fig. 2: Gains and losses area by category experienced by the A. Northern SetiuWetland and B. Southern Setiu Wetland

from 2002 to 2012.

In the Northern Setiu, mangrove area decline by 0.15km² mainly to heath forest class. However, this

change may be to some extent attributable to mixed pixels that lead to spectral confusion between these two

classes. Meanwhile, 0.04km² of its area have been converted to build up area, mainly as infrastructure or

buildings related to aquaculture ponds. This transition highly concentrated in Kampung Fikri and Kampung

Kubang Resing. 0.02km² of mangrove area degraded into sandy area while another 0.02km² were converted

to aquaculture pond/ water canal. Both transition mainly concentrated along the Setiu River.

The trend of changes for oil palm/coconut class is totally opposite for both study sites. While the

Northern Setiu experiencing reduction in oil palm/coconut area, the Southern Setiu otherwise indicating an

increasing trend. The increasing trend of oil palm plantation of the southern part of Setiu wetland was found

in line with government planning as mention in Structure Plan (2005-2020) of Terengganu which stating

that this particular area has been specifically assigned to be developed as part of agricultural area, mainly

focusing on oil palm plantation.

4. Conclusion

From the result, we can conclude that the changes of each class are generally dynamic. Throughout the

year, the area involving agricultural activities mainly subjected to replantation phase, hence explaining the

unstable changes of vegetation classes. The inexperienced factor also one of the causes that affecting the

quality of classified image, particularly the 2002 images. With more collateral data, it is expected that a

better classification process can be done, hence able to produce a better result of this particular year.

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