Monitoring Land-Use and Land-Cover Change in Setiu Wetland, Terengganu, Malaysia Using Remote Sensing and GIS
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International Proceedings of Chemical, Biological and Environmental Engineering, V0l. 102 (2017) DOI: 10.7763/IPCBEE. 2017. V102. 2 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: [email protected]. 10 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 (%) Multi spectral 2.44m QuickBird 2 18th October, 2002 9.9 Panchromatic 0.61m Multi spectral 1.84m GeaoEye 1 7th May, 2012 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. 11 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