2011 IEEE Conference on Open Systems (ICOS2011), September 25 - 28, 2011, Langkawi, Malaysia
Mangrove Mapping in Penang Island by using Artificial Neural Network Technique Beh Boon Chun, Mohd. Zubir Mat Jafri and Lim Hwee San School of Physic, University Sains Malaysia, 11800 Minden, Penang, Malaysia.
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submerged by tidal flows with their aerial or prop root system known as pneumatrophores, buttressed trunks, special bark and leaf structures and other unique adaption. Mangrove tree not only provide food to marine life and its surrounding community but also function as coastal resources for human being application such as firewood, charcoal and timber. Therefore, mangrove plays a vital role in the flora and fauna ecosystem with important biological, ecological and environmental values [3]. Over the past 40 years, mangrove habitat had been declined dramatically in area among the world for expansion of human settlements. Variety of anthropogenic disturbance such as exploitation of non-renewable resource and clearing of forest for commercial aquaculture production already affect the balance in mangrove ecosystem [4]. Such treats are leading to the demands for details mangrove mapping and monitoring of mangrove distribution for effective conservation and preservation in endanger mangrove species. As many mangrove forests are hard to access and penetrate due to soft sediment and soil environments, remotely sensed data imagery serve as a superior tool for the monitoring and mapping purpose in mangrove ecosystem. Application of remote sensing in mangrove mapping has been well establish in fundamental level however advanced application for finer level of mangrove mapping is difficult and limited. From the research which had been done by researcher, neural network analysis was recently become a common method used for land use land cover mapping, mangrove mapping and so on. A study done by Wang has utilized 3 methods including back-propagation neural network classifier, newly developed clustering-based neural network classifier (CBNN) and maximum likelihood classifier (MLC) to classify the mangrove species in mainland mangrove forest at Punta Galeta on the Caribbean coast of Panama by using high spatial resolution multi-seasonal Ikonos imagery. When only multispectral bands were included in the classification, MLC was proved is the best method for separate the mangrove species as CBNN is little bit consuming time. If textual information was added to the classification, CBNN show strong advantages over MLC [5]. In 2010, Mustapha has using neural network technique applied to Landsat-7 ETM+ data for land cover mapping in Madinah and high overall accuracy of 83.7% was obtained from the classified data [6]. In addition, five types of mangrove
Abstract—Environmental study is crucial in order to understand deeply about the flora and fauna living in the Earth. Mangrove forest is a unique and natural ecosystem that can be used to produce forestry product such as charcoal, timber, supply food to their surrounding marine life, and protect the inland from disturbance like erosion, flood, and tsunami. Due to the uncontrolled planning of human activity, many mangrove forests had been deforested for development of industry area, urban land and agriculture. In this study, Multi-layer Feed Forward/Multilayer Perceptrons (MLP) network system in Artificial Neural Network technique was used to map out the current state of mangrove trees. This network system require user to have a ground truth data such as in supervised classification in order to generate the training area for classification. Generally, this network comprise of a simple structure layer which consist of three layers namely input layer, hidden layer and output layer. Multi-layer Feed Forward algorithm has at least one hidden layer of neuron between the input and output layer. Each successive layer of neurons is fully interconnected with connection weight determine the strength of the connection. 2010 Thailand Earth Observing System (THEOS) satellite imagery was used as the source for the data processing with the aid of PCI Geomatica version 10.3.2 software packaging. The classification result of Multi-layer Feed Forward yield 5 category of classes. Post-classification analysis was further carried out to validate the classified data with reference data. High overall accuracy of 93.5% and kappa coefficient of 0.900 was obtained for the mangrove cover mapping. Final thematic map was produce to quantify and display the current distribution of mangrove land. The result indicates that neural network approach is suitable and reliable used for mangrove mapping. Keywords: Multi-layer Feed Forward network, multilayer perceptrons, artificial neural network, mangrove, THEOS.
I.
INTRODUCTION
Mangrove ecosystem is one of the important and unique systems present on the seashore zone. It can be serves as natural barrier for environmental protection from tidal wave, erosion, floods, storm and tsunami [1]. Mangrove is a salt tolerant species of woody plants or shrubs that survive along sheltered coastline area in tropical and subtropical climate country [2]. High salinity soil with brackish salt water in mudflats are common environmental for mangrove plant to exist. They live in such harsh condition and periodically
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2011 IEEE Conference on Open Systems (ICOS2011), September 25 - 28, 2011, Langkawi, Malaysia
species at Beihai Coast of Guangxi, P. R. China were identified by using three different classification technique based on maximum likelihood classification, spectrum angle classification and artificial neural network classification [7]. Higher overall accuracy of 86.86% was achieved by using neural network classification among the three classifiers. The aim of this paper is to investigate the potential of neural network algorithm used for mangrove studies in Penang Island Malaysia. II.
MATERIAL AND METHODS
A. Description of study area The study area is the Penang Island, Malaysia which lies on northwest region of Peninsular Malaysia and contained within latitudes 5º 12’ N to 5º 30’ N and longitudes 100º 09’ E to 100º 26’ E. The study site map was shown in Fig. 1. Penang state was divided into two parts, the small island (known as Penang Island) covering an area of 297km2 and a coastal strip on the mainland (known as Province Wellesley) with an area coverage of 751km2. George Town is the capital city in Penang state. The island and mainland are linked by a 13.5km long Penang Bridge system and ferry services. Due to the reason Penang state locate proximity to the equator line on earth surface, Penang experience an equatorial climate weather which consists of warm and sunny days throughout the year. The mean daytime and night time temperature is between 27ºC to 30ºC and 22ºC to 24ºC respectively with annual temperature range from 23ºC to 32ºC. The humidity in is relatively high (70%-90%) with mean annual rainfall of 267cm. Fortunately, Penang also free from major natural disaster such as earthquakes, hurricanes, tornados and volcanic disruption.
Figure 1. Study area location
B. Imagery sources and pre-processing Thailand Earth Observing System (THEOS) satellite data (29 January 2010) which have 15 meters spatial resolution in multi-spectral band (blue, green, red and infrared band) has been acquired from Geo-Informatics and Space Technology Development Agency (GISTDA) as the study site. The data obtained was processed in scene level 2A with ID Scene T1 M 2010/01/29 03:28:40.7 0266-0339 1559. The raw satellite imagery for Penang Island was shown in Fig. 2. The aim in data pre-processing prior to classification is attempting to correct or compensate the systematic error. The digital imagery was subjected to several corrections such as radiometric, atmospheric and geometric correction. Then the image was projected to a standard cartographic projection Universal Transverse Mercator World Geodetic System 1984 (UTM WGS84) datum using a geocoded image model. Enhancement was applied on the image to improve the visual interpretation. The image was filtered through a 3 x 3window filter to remove noise.
Figure 2. Raw data for THEOS image
C. Image classification and validation The intent of this step is to categorize all of the pixels in the imagery into several land cover classes. Artificial neural network technique will be used as the approach to classify the THEOS data. Three types of network commonly use are Hopfield, Kohonen and Multi-layer Feed Forward network. Each type of network is differ from each other and utilized in different application. For example, studies of stereo-matching and feature tracking have applied the usage of Hopfield network, and Multi-layer Feed Forward network also has been used widely for supervised image classification [8,9]. Due to self organizing properties in Kohonen network, it is usually suitable for unsupervised or semi supervised classification [10]. In this paper, we will focus only on multi-layer feed forward network analysis to classify our data. This network consists of a simple layer structure and the brief network system was shown in Fig. 3. Basically there will have at least one hidden
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2011 IEEE Conference on Open Systems (ICOS2011), September 25 - 28, 2011, Langkawi, Malaysia
where r is the number of rows in confusion matrix, xii is the number of observation in row i and column i (the major diagonal in confusion matrix), xi+ and x+i are the marginal totals of row i and column i respectively and N is the total number of observation [13]. III.
RESULTS AND DISCUSSION
The classification result based on neural network approach was shown in Fig. 4. Generally the land cover in Penang Island can be classified into five category consist of forest/grassland, water, urban land/area, bare soil and mangrove vegetation. As we can notice, the mangrove cover is concentrate along the mudflats region on the north western side of the Island.
Input Layer
Hidden Layer
Output Layer
Figure 3. Multi-layer Feed Forward network system
layers of neuron between the input and output layers. The successive layers of neurons are fully interconnected and connection weights controlling the strength of the connections. The input to each neuron in the consecutive layer is the sum of all its incoming connection weights multiplied by their connecting input neural activation value [11]. Neural network is a non-parametric classifier hence assumption on statistical data distribution for the image scene is unnecessary. This means that neural network does not require many training data in generation of training set. The distribution of training data was assumed to be Gaussian or normalized distributed. The probability density function is utilized to classify the pixel by computing the probability of the pixel value belonging to each class. Four multispectral bands in THEOS data were used as network input connected to hidden layer to produce an output consist of five classes. In post-classification analysis, accuracy assessment test was carried out to validate the degree of correctness of the classified data. Error matrix (known as confusion matrix) was generated to compare the reliability between the classified and reference data. The correctly classified pixels resemble by major diagonal in the matrix and the non-diagonal elements (column and row element) represent the omission and commission error respectively. Producer’s accuracy, user’s accuracy, overall accuracy and kappa coefficient can be acquired and evaluate through the confusion matrix interpretation [12]. Equation (1) shows the computation of kappa coefficient ( ):
Figure 4. Neural network classified map TABLE 1. STATISTICAL ANALYSIS OF DIFFERENT LAND COVER
Class Name
Area Cover (km2)
Forest/Grassland Water Urban Land Bare Soil Mangrove Total
175.56 296.47 70.61 45.44 18.56 606.64
Percentage of Image (%) 28.94 48.87 11.64 7.49 3.06 100.00
Table 1 show the statistical analysis of the different land cover. Based on table 1, the mangrove cover area in Penang Island on 2010 is about 18.56 square kilometers (sq. km) or 3.06% of the total image scene. The others land cover classes
(1)
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2011 IEEE Conference on Open Systems (ICOS2011), September 25 - 28, 2011, Langkawi, Malaysia
TABLE 2. ERROR (CONFUSION) MATRIX OF CLASSIFIED MAP
Classified Data Forest/Grassland
Water
Urban Land
Forest/Grassland Water Urban Land
56 0 3
0 98 0
2 0 18
Bare Soil Mangrove Total
0 2 61
0 0 98
Producers’s Accuracy (%) Overall Accuracy (%) Kappa coefficient
91.803
100.00
Reference Data Bare Soil
Mangrove
Total
0 0 2
0 0 0
58 98 23
4 0 24
11 0 13
0 4 4
15 6 200
75.000
84.615
100.00
User’s Accuracy (%) 96.552 100.00 78.261 73.333 66.667
93.5 0.900
signature) among them is quite similar. The high overall classification accuracy of 93.5% was obtained from the confusion matrix with kappa statistic of 0.900. The result indicates that mangrove forest in Penang Island can be map accurately by using Artificial Neural Network approach.
forest/grassland, water, urban area and bare soil covering an area of 175.56 sq. km, 296.47 sq. km, 70.61 sq. km and 45.44 sq. km respectively. Confusion matrix for the various land cover classes was shown in table 2 to test the accuracy of the mapping result. Confusion matrix is a square array of row and column in which rows and column represent one category of land class. Producer’s accuracy is calculated by dividing the number of correctly classified pixels in each class by the total number of reference pixels in that class (the column total). User’s accuracy is calculated by dividing the number of correctly classified pixels in each class by the total number of classified pixels in that class (the row total). Overall accuracy is calculated by dividing the total number of correctly classified pixels (the sum of all elements along the major diagonal in the matrix) by the total number of reference pixels. From table 2, we can noticed that the classified of mangrove cover is acceptable with producer’s accuracy of 100% and user accuracy of 66.67%. The low percentage of user’s accuracy in mangrove cover is due to misclassified pixels between the forest/grassland and mangrove classes as they almost have same Digital Number (DN) number. All classes had high producer’s and user’s accuracies (>73%) except for the user’s accuracy for mangrove category. Producer’s accuracy measure how well a specific land area has been classified whereas user’s accuracy measures the reliability of the produced classified. The user’s accuracies for each category of land cover classes were as followed: 96.55 for forest/grassland, 100% for water, 78.26% for urban land and 73.33% for bare soil. For the land classes between bare soil and urban area, there will always have certain misclassified area that the classifier classified the urban area into bare soil and vice versa. Pixels within the urban area usually mixed with other features so that it is quite difficult to classify. However, the lower percentage for these two classes is expected and hardly distinguish due to the spectral information (spectral
IV.
CONCLUSION
This paper gives an overview of the mapping of mangrove cover distribution in Penang Island in year 2010. The results obtained from accuracy assessment showed that neural network is appropiate and reliable used for mangrove mapping at generic level. The classified maps show the potentiality of neural network classifier for produced a high overall accuracy of 93.5%. Neural network method can be served as an alternative technique instead of apply commonly used supervised and unsupervised classification for mangrove mapping. Further study need to carry out to discriminate the mangrove species from mangrove area at species level. ACKNOWLEDGMENT The authors gratefully acknowledge the financial support from the short term Research University Postgraduate Research Grant Scheme (USM-RU-PGRS), account number: 1001/PFIZIK/843094 and USM fellowship used to carry out this project. We would like to thanks the technical staff who involved in this project. Thanks are also extended to USM for support and encouragement. REFERENCES [1]
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2011 IEEE Conference on Open Systems (ICOS2011), September 25 - 28, 2011, Langkawi, Malaysia
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