1. Introduction
Mangrove ecosystems are found in many sub-tropical and tropical areas of the world including Malaysia (
Figure 1) and they are growing along sheltered coastlines such as river estuaries or tidal marshes [
1]. The various goods and services provided by these forests make them one of the valuable ecosystems in the world. Although mangroves constitute less than 0.4% of the world’s forests [
2], they play an important role in providing habitats for thousands of marine and pelagic species, and serving the local communities with food, medicine, fuel and building materials. They also become important in mitigating the impact of climate change by sequestering CO
2 (the main greenhouse gas, apart from water vapor) from the atmosphere as they are one of the most carbon-rich forests in the tropics [
3,
4,
5]. They also protect the coastal areas from tidal waves, tsunamis and cyclones [
6].
Figure 1.
Mangrove forests distribution in Malaysia (Upper left: 7°22′46″N, 98°55′48″E and Lower right: 0°51′10″N, 119°16′00″E) [
7].
Figure 1.
Mangrove forests distribution in Malaysia (Upper left: 7°22′46″N, 98°55′48″E and Lower right: 0°51′10″N, 119°16′00″E) [
7].
Despite their significance in providing ecological and economic services, mangroves are being lost at the rate of about 1% per year globally [
8]. The rate of loss is highest in developing countries and in Malaysia the rate is estimated to be about 1% or 1282 ha·year
−1 since 1990 [
9]. Mangroves are cleared for coastal development, aquaculture, timber and fuel production [
10]. Similar to the urbanization at global level, the southern coast of Johor-Iskandar Malaysia (IM) (
Figure 2a) is undergoing the highest economic growth rate in the country. The fast pace urbanization threatens the survival of mangrove forests. In fact, Johor experienced the third largest mangrove loss after Selangor and Pahang states in Malaysia [
11]. Mangrove forests in IM are continuously being cleared for constructing housing and industrial buildings, ports, power plants, oil storage, and a coastal way via massive reclamation works and also being transformed into urban water fronts.
Continuous loss of mangroves in this region will have a negative impact on environmental stability and on aquatic organisms and the biodiversity of the flora and fauna. Thus, an effective monitoring of mangrove forest is urgently required to prevent further loss of mangroves in Johor. Ground surveying methods and field observations are traditionally used for mapping mangrove areas. Although this can provide good mapping accuracy (cm to m), it is rather time consuming, laborious and costly; moreover, this method is not practical in a harsh mangrove environment that is temporarily inundated and hard to access [
11,
12]. Tidal change in mangrove areas makes the area change assessment more difficult by the inventory method. In past decades, multi temporal aerial photographs with high spatial resolution (<1 m) provided a local to sub-regional scale mapping and monitoring of mangrove ecosystems [
13,
14,
15]. However, the potential for obtaining good images depends on flight and local weather conditions. Alternatively, remote sensing technology that delivers satellite images covering large-spatial scale, on a continuous basis (long-term) and at reduced cost can provide up to date information on mangrove forests, their spatio-temporal changes and the mangrove trees’ health conditions. This information will provide economists, ecologists, and natural resources managers in Malaysia with valuable information to improve management strategies for mangrove ecosystems.
Remote sensing data and methods have been applied widely for mapping mangrove ecosystem distribution, species differentiation, health status, and changes of mangrove populations [
1,
11,
16]. Satellite data with fine to medium spatial resolution such as Ikonos, Quickbird, and Landsat Thematic Mapper can provide adequate spatial details for mapping mangroves areas [
16]. Meanwhile, hyperspectral images are useful in discriminating various mangrove species [
17]. In Malaysia mangrove ecosystems have been studied using various remote sensing data for mangrove detection/areal delineation [
18,
19,
20,
21], mangrove change detection [
22,
23,
24,
25,
26,
27,
28,
29], mangrove species classification [
21,
30,
31], and biomass of mangrove forest [
5]. Change detection of mangrove areas using satellite data has been conducted in Malaysia at a local scale. However a detailed analysis of the Iskandar Malaysia region, using consistent data sources and methodology and suitable spatial and temporal scales, was not available. Thus, the overall goal of this study was to evaluate satellite imagery as a tool for monitoring changes in mangrove forests in Iskandar Malaysia and the secondary objective was to evaluate training sample size on classification accuracy. Both Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM) techniques were employed to classify mangrove and other land cover types in IM using time series Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and Operational Land Imager (OLI) data. We then detected the changes in the land cover over a period of 25 years (1989–2014). Such studies are important for the development of a regional action plan in conserving mangrove resources in Malaysia.
Figure 2.
(a) The Iskandar Malaysia (IM) region in Johor State of Peninsular Malaysia shown by a Landsat image (the five flagship zones are marked as A–E); and (b) the three Ramsar sites in IM (source: Comprehensive Development Plan ii—unpublished).
Figure 2.
(a) The Iskandar Malaysia (IM) region in Johor State of Peninsular Malaysia shown by a Landsat image (the five flagship zones are marked as A–E); and (b) the three Ramsar sites in IM (source: Comprehensive Development Plan ii—unpublished).
2. Study Area
The total global coverage of mangrove forests is 15.62 Mha and of this 3.7% is found in Malaysia. Mangroves are established mostly along the west coast of Peninsular Malaysia and in the states of Sabah and Sarawak in Malaysian Borneo (
Figure 1). Mangroves in Peninsular Malaysia constitute about 17% of the total mangroves of Malaysia (0.58 Mha) and the rest are found in Eastern Malaysia in Sabah (58.6%) and Sarawak (24.4%). The main mangrove tree species found in Malaysia are from the
Rhizophoraceae family. However, there are at least a total of 70 mangroves species from 28 families that are found in this country [
7]. Mangroves in Malaysia provide various ecological, economic and social benefits to the people and country [
12].
This study focuses on Iskandar Malaysia (IM), the fastest growing national special economic region located in southern Johor, Malaysia (
Figure 2a). It was established in 2006 to bring in more focused economic and infrastructure investments and the region is administered by the Iskandar Regional Development Authority (IRDA). The region encompasses an area of 2217 km
2; it involves five local government authorities with five distinctive “Flagship Zones” or developmental focal points to guide its overall development (
Figure 2a).
Currently the natural environment (forest, mangrove, rivers and water bodies) covers ~24% (56,719 ha) of the total IM (Comprehensive Development Plan ii—unpublished). The Ramsar Convention (formally the Convention on Wetlands of International Importance especially as Waterfowl Habitat) is an international treaty signed in 1971 for the conservation and sustainable utilization of wetlands; it came into force in 1975. There are over 2000 Ramsar sites worldwide of which 6 are in Malaysia, namely: Tasek Bera in Pahang, Kuching Wetlands National Park in Sarawak and Lower Kinabatangan-Segama Wetland in Sabah, while the other three are found in Johor in the IM region (see
Figure 2b). Geographically, the mangrove forests in the region are distributed along the estuaries which can be broadly classified into three areas as shown in
Figure 1 and
Figure 2b. Pulau Kukup, Sungai Pulai and Tanjung Piai (Tg Piai) mangrove areas (
Figure 2b) found in IM were designated as Ramsar Sites in 2003. Mangrove forests are important in this region for shoreline protection, ecology, bio-diversity and as a source of income for local people.
3. Data and Methodology
We downloaded several scenes of Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM) and Operational Land Imager (OLI) images from the United States Geological Survey (USGS) website, available for free at [
32]. The images covered 1989, 2000, 2005, 2007, 2009, 2013 and 2014 of Johor state in Peninsular Malaysia. These periods were chosen because during the 1980s a new economic policy was implemented in Malaysia where the government focused on urbanization and industrialization that caused major changes in land cover. Data from the 2000s were important, because the growth of the Iskandar Malaysia (IM) region started in 2006 and massive developments are still continuing now.
The total cloud cover of the study area in each scene was not more than 20% (
Table 1). The images were subset to the IM region (
Figure 2) and the digital numbers were converted to reflectance following the method provided in the NASA Landsat 7 Science Data Users Handbook, available at [
33]. Clouds were masked out based on the brightness temperature information of the Landsat thermal band. We calculated and examined the brightness temperature [
34] of clouds in each image and masked them out by using the thresholds as shown in
Table 1. Clouds are assumed to be colder than these thresholds [
35]. We did not perform atmospheric correction because the images were not too hazy and the training data is from the image to be classified [
36]. We ran a co-occurrence matrix texture measurement mean filter 3 × 3 window by using ENVI software (Exelis Visual Information Solutions, Boulder, Colorado) to smooth the spatial variation in the study area and thus improve the classification results [
37].
Table 1.
Cloud coverage and brightness temperature threshold values used to mask out clouds in Landsat images.
Table 1.
Cloud coverage and brightness temperature threshold values used to mask out clouds in Landsat images.
Landsat Images | Cloud Coverage (%) | Brightness Temperature (°C) |
---|
13 September 1989 | 6 | <16 |
28 April 2000 | 10 | <17 |
4 May 2005 | 2 | <16 |
10 May 2007 | 5 | <17 |
8 February 2009 | 0 | - |
27 June 2013 | 10 | <18 |
13 May 2014 | 20 | <18 |
There are over a dozen image classification algorithms that have come into use in recent years. Li
et al. [
38] carried out extensive tests on the performance of 15 of these algorithms and concluded that “when sufficiently representative training samples were used most algorithms performed reasonably well.” To classify the Landsat images in our data set we have chosen two of the most commonly used of these algorithms, namely the Maximum Likelihood Classification (MLC), because it is simple, and the Support Vector Machine (SVM), because it is widely held to be “better” than other algorithms [
39]. MLC uses a parametric logic which assumes that the data is normally distributed and the classes are trained based on the probability density function [
40]. The probability of each pixel belonging to any particular class is calculated, and then the pixel is assigned to the class with the highest probability. SVM uses a non-parametric machine learning logic where no assumption is made on the data distribution [
39]. SVM discriminates the data into a discrete number of classes by projecting the data into a feature space with hyperplanes by using a kernel function. Machine learning involves iterations to find the finest border line to discriminate the data. It was reported that the result of SVM is promising even with limited training samples [
39].
We selected training samples from the images (
Figure 3) by carefully selecting homogeneous pixels so that every land use/land cover (LULC) class (forest, oil palm, rubber, mangrove, urban, and water bodies) has three sets of training samples (10, 20 and 30 polygons where each polygon contains about 40–60 pixels). The number of pixels (40 or 60) selected for each polygon is dependent on the size of the land use. For example larger number of pixels was selected for oil palm and fewer pixels were used for rubber. Different training samples (10, 20 and 30 polygons) were used to test if MLC and SVM can produce higher accuracy with increased number of training samples. This size of training samples follows the guide where training sample size for each class should be not fewer than 10–30 times the number of bands [
40]. We used all the spectral bands of Landsat sensors except for the thermal band for the classification with both MLC and SVM. For SVM we tested all kernel types
i.e., radial, polynomial, linear, and sigmoid [
41] and after several trials we chose values of the following parameters that produced the highest accuracies (Bias in Kernel function = 1, Gamma in Kernel function = 0.167, penalty parameter = 100, pyramid level = 0 and class probability threshold = 0). The overall classification accuracies produced by MLC and SVM using 10, 20 and 30 samples were compared using Analysis of Variance (ANOVA).
The classification results were validated using another independent set of polygons (10 polygons with 40 to 60 pixels—
Figure 3) distributed across the study region which we referred to the Johor land use maps produced by the Department of Agriculture, Peninsular Malaysia (scale 1: 250,000) of 1990, 2000, 2006, 2008 and 2010. Similar to the training dataset we selected more validation pixels for oil palm and fewer pixels for rubber. The Johor land use maps were considered as ground-truth because these maps were produced from aerial photos and SPOT images, and verified by extensive field work. Our local knowledge of several locations also helped us to verify the results. We also used land cover reports produced by the Comprehensive Development Plan ii (unpublished) report produced by the Iskandar Regional Development Authority (IRDA) for years 2013 to 2025. Relative Predictive Error (RPE) was used in this study to quantify the mean percentage difference between land cover classified by digital classification techniques and land cover data produced by the Department of Agriculture (DOA) and IRDA. RPE provides the direction of changes (underestimation or overestimation) in predicted values compared to measured values.
Figure 3.
The distribution of test samples (30 polygons) and validation samples (10 polygons) for each land cover types in the study area. Yellow color symbols show the test samples and blue color represents the validation samples respectively.
Figure 3.
The distribution of test samples (30 polygons) and validation samples (10 polygons) for each land cover types in the study area. Yellow color symbols show the test samples and blue color represents the validation samples respectively.
The accuracy of the classified images was assessed using confusion matrices and kappa coefficients [
42]. The overall accuracy in the confusion matrix is calculated by summing the number of pixels classified correctly and dividing by the total number of pixels. The kappa coefficient is calculated from [
42] as follows:
where
is the KHAT statistic (an estimate of KAPPA),
r is the number of rows in the matrix,
xii is the number of observations in row
i and column
i,
xi+ and
x+i are the marginal totals of row
i and column
i, respectively, and
N is the total number of observations [
42].Since the classified images suffered from the “salt and pepper” effect we ran post classification to remove these pixels. We used clump classes from the post-classification in the ENVI software (Exelis Visual Information Solutions, Boulder, Colorado) to clump closest and similarly classified areas. The pixels in 3 × 3 window size were clumped together by first carrying out a dilate operation then an erode operation on the classified image.
The classified images with highest overall accuracy and Kappa coefficient were selected to calculate the total area covered by each LULC types. We created shape files with a polygon feature for each LULC type using ArcCatalog software. All the LULC boundaries were delineated and their total areas were calculated.
6. Conclusions
Overall, mangrove areas in Iskandar Malaysia (IM) have decreased at an alarming rate (33%) from 1989 to 2014. The major causes of mangrove destruction in this region are the development of the coastal region (construction of a port, industrial area, water front project, etc.), intensified erosion, local hydrodynamic conditions and development of aquaculture activities. On the other hand, a small increase of 710 ha of mangrove occurred in this region and the possible reasons for the gain could be replanting, especially in the Ramsar sites, and regrowth. The loss of about 241 ha per year of mangroves was associated with a steady increase in urban land use (1225 ha per year) from 1989 until 2014. Action is necessary to protect the existing mangrove cover from further loss.
Systematic monitoring and control measures are urgently needed to protect these sites from further coverage loss in the future. Regular monitoring and mapping can be performed with remote-sensing technology particularly with digital classification techniques. In this study, MLC produced higher overall accuracies and Kappa coefficients and less “salt and pepper effect” compared to SVM for all 7 years of data.
Gazetting of the remaining mangrove sites as protected areas or forest reserves and introducing tourism activities in mangrove areas can ensure the continued survival of mangroves in IM as mangrove forests are valuable ecological and economic resources and they play an important role to ensure economic and environmental sustainability [
61].
Therefore, gazetting of the remaining mangrove sites as protected areas or forest reserves at the least can be the basis, with a detailed management plan that indicates the permitted land use activities with different levels of access within these areas is crucial. On the other hand, introducing tourism activities is able to justify the continued conservation of mangroves through the economic values it offers bearing in mind the contribution of tourism to the job opportunities and currency exchange in Malaysia. However, the tourism activities must be carefully planned and centered on environmental education to raise the public awareness of the importance of mangroves, which would later have an influence on the physical development in the region.