UTILIZING SPOT 5 IMAGERY FOR WATER QUALITY STUDY IN BATANG LASSA (LASSA RIVER) UPPER STREAM, SARAWAK

Transcription

1 UTILIZING SPOT 5 IMAGERY FOR WATER QUALITY STUDY IN BATANG LASSA (LASSA RIVER) UPPER STREAM, SARAWAK Kasumaiya Mahajir, Ahmad Nadzri Mohamed, Norizan Abdul Patah and Abdul Majid Wahab Malaysian Remote Sensing Agency, Ministry Science Technology and Innovation of Malaysia, No 13, Jln Tun Ismail, Kuala Lumpur, Malaysia, kasumaiya@remotesensing.gov.my, nadzri@remotesensing.gov.my, norizan@remotesensing.gov.my and majid@remotesensing.gov.my KEY WORDS: Water Turbidity Mapping, Remote Sensing, Batang Lassa River. ABSTRACT: Batang Lassa is among the lowland protected mangrove areas which contribute significantly to flora and fauna ecology, aquatic habitat, provision and preservation of water quality. Traditionally, measurement of water quality and turbidity for monitoring and managing river network were carried out by collecting water parameters from ground sampling. This method is relatively costly and time-consuming and not practical for extensively large areas. However, utilising Remote Sensing technology, which is widely used in detecting water turbidity, enable the process to be carried out in a much shorter duration of time and in a more cost-effective manner. The main objective of this study is to map and classify water turbidity using spectral reflectance of SPOT 5 imagery based on Water Quality Standard issued by the Department of Environment, Malaysia (DOE). This study demonstrates that turbidity concentration levels can be quantified using band 3 [NIR] ( µm), the best single band to represent water turbidity differences. The result of this study shows that SPOT 5 imagery can be utilised for monitoring water quality and water turbidity classification in lowland protected mangrove areas. 1. INTRODUCTION Water quality refers to the chemical, physical, biological, and radiological characteristics of water. It is a measure of the condition of water relative to the requirements of one or more biotic species and or to any human need or purpose (Wikipedia). Water quality is measured by several factors, such as the concentration of dissolved oxygen, bacteria levels, the amount of salt or salinity, or the amount of material suspended in the water turbidity. In some bodies of water, the concentration of microscopic algae and quantities of pesticides, herbicides, heavy metals, and other contaminants may also be measured to determine water quality. Although scientific measurements are used to define water quality, it is not a simple thing to say that water is good or that water is bad. So, the determination is typically made relative to the purpose of the water is it for drinking or to wash a car with or for some other purpose? Poor water quality can pose a health risk for people. Poor water quality can also pose a health risk for and ecosystems. It is most frequently used by reference to a set of standards against which compliance can be assessed. The most common standards used to assess water quality relate to health of ecosystem, safety of human contact and drinking water. Turbidity is one of the optical properties of water that causes light to disperse and absorbed instead of being passed in straight line. This happens due to sediment. Turbidity level can be measured and identified by observing how far can light travel through the air. The more the water contains sediment, the harder it is for the light to travel through it. Based on this observation, turbidity level becomes higher when the amount of suspended solid from various sources such as decayed plants and mud from peat forests that will cause turbidity level to rise. Turbidity is measured using nephelometer in nephelometric unit (NTUs) In Malaysia, water quality is measured using Water Quality Indexs (WQI). Generally, WQI is a standard used by Department of Environment to measure the cleanliness and the quality of the water supply that can be consumed for domestic, aquaculture and irrigation purpose (JAS, 2001). There are a number of parameters that is usually subjected to analyzation to determine Water Quality Index WQI which includes the value of dissolved oxygen (DO), ph, ammoniacal nitrogen (NH3 -N), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD) and suspended solids (SS) (Nurfadzlina, 2012). Water Quality Index (WQI) can be done by in situ measurement and any techniques. Remote Sensing is one of the best techniques available that can be used to manage and monitor the water quality. Remote sensing and GIS provides effective tools in monitoring of water quality parameter aid with in situ measurement data (Norsaliza, 2010). Various studies have been reported on the usefulness of remote sensing as tools in monitoring of water quality (Ritchie, J.C and Schiebe, F.R. 2000). Remote Sensing can be defined as a science and art of obtaining information about an object, area, or phenomena through the analysis of data acquired by device that is not in contact with the object, area, or phenomena under investigation (Thomas et al., 2004). In

2 Malaysia, the use remote sensing technology to study the water quality is still new (Hasmadi, 2010). Remote Sensing is a tool used to perceive the water quality and the earth surface by the coverage electromagnetic energy emitted. (James B. Campbell, 2007). It is within its capabilities to inspect and detect large area. Remote Sensing technology not only can inspect and detect over a huge area but includes sources that are reliable and time-efficient to study a classify the level of water quality compared with fieldwork monitoring that consumes time and huge number of workforce when it comes to a large area also costly. It has been upsurge used in water resources management since the early 1980 s (Mancino et al. 2009). The water quality parameters were including turbidity, ph, salinity, chlorophyll, total phosphorus (TP) and total suspended solids (TSS). Based on earlier research, Remote Sensing technology has been applied to classify turbidity; level of River Klang Peninsular, Malaysia (Mohd Hasmadi, 2010). In addition, Ainon Nisa, (2007) has also completed a study on utilizing satellite-based remote sensing data for water quality assessment to review existing models which relates the spectral reflectance of satellite image and water quality parameters and to map and compare water quality of six reservoirs in Selangor based on existing models. Hence, objective of this study is to map the water turbidity classes using spectral reflectance techniques using SPOT 5 image based on Interim National Water Quality Standard (INWQS) issued by the Department of Environment, Malaysia (DOE). Water classification could help to rate the quality level of turbidity Batang Lassa upper stream. This study also process that the water quality at the study area is still preserved and is suitable to be qazetted as National Park. In this case, mangrove forest helps to preserve water quality. 2. STUDY AREA Water turbidity mapping studies classes has been conducted over an area at Batang Lassa upper stream in Matu, Sarawak. The area is planned to be gazetted as a National Park. It covers over an area of 13, hectares. The earth surface then 20 m from sea level. This area is located between 2 o 30 0 N and 111 o 25 1 E of the Borneo Archipelago and has the yang equatorial climate, which is hot and humid all year round. Batang Lassa upper stream is surrounded by various types of plants such as mangroves forest, peat swamps, sago and palm oil. The study area is concentrated in mangrove forest. Mangrove forest habitat have contributed to the maintenance of ecological flora and fauna, aquatic habitat and are very important in maintaining and providing quality water supply. Figure 1 shows the location of the study area and its surrounding. Figure 1: Location of the Study Area

3 3. MATERIALS AND METHOD 3.1 Satellite Data Satellite image used is a SPOT 5 satellite image with a resolution of 10 m dated 12 August 2011 (path 290/raw 345). SPOT 5 is a multispectral satellite image prepared by Spot Image and was launched in SPOT 5 consists of 4 Spectrum bands such as Band 1 - green (0,50-0,59 µm), Band 2- red (0,61-0,68 µm), Band 3 - Near Infrared (0,78-0,89 µm) dan Band 4 SWIR (1,58-1,75 µm). 3.2 Land cover Classification The analysis begins with pre-processed the SPOT 5 image, followed by classify the land cover classes. The task of geometrical rectification of the original image has been conducted using PCI software by orthorectification method. The process of rectifying the image s geometry in carried out based on ground control point (GCP) taken during ground verification work using the Rectified Skewed Ortomorphic (RSO) system with the spheroid of Everest (Sabah & Sarawak) and refering to Timbalai As many as 15 reference points has been picked as ground control point ang RMS error obtained is less that 0.5 pixel. Erdas Imaging 2013 has been exploited to classify SPOT 5 image. Image classification is the process of assigning pixels to classes. By comparing pixels to one another and to those of known identity, it is possible to assemble groups of similar pixels into classes that match the informational categories of interest to users of remotely sensed data. These classes form regions on a map or an image. Visual interpretation and digital image classification techniques have been used to identify and classify land cover within the study area. A selection of class sampling has been carried out based on land cover available within the study area which includes mangrove, water, open area, mixed agriculture and settlements. Sampling selection was performed using AOI Region technique and Seed Drawing Region; which contains 5 samplings for every class. Image classification for all the four spectrum bands has carried out using Supervised Classification technique to produce output with Parametric condition: Maximum Likelihood. It is selected based on the probability that every pixel of every class are taken into account. The evaluation of random sampling selection has been accomplished by generating feature space image, histogram and plot mean to make sure the sampling is uniformly distributed. ArcGIS is also used to process Generalisation which takes into account the eliminating process to remove polygons by merging them with neighboring polygons that have the largest area or the longest shared border. Eliminate is often used to remove small sliver polygons that are the result of overlay operation (ESRI). In this study, an area < 0.05 hectare has been removes and assigned to the area next to it. Then dissolve process has been carried out to dissolve for aggregates features based on specified attributes (ESRI). The technique of multipart to single part been used to creates a feature class containing single part features generated by separating multipart input features. Finally, topology technique been used for more accurate model geometric relationships. Topology is a collection of rules that, coupled with a set of editing tools and techniques used in geodatabases to arrange and defines how point, line, and polygon features share coincident geometry. (ESRI) 3.3 Water Quality Index Classification Generally, river pollution is caused by natural factors and various human activities (Davis, 1991). There are two main components that caused water pollution; irregular source of pollution and regular source of pollution. (Novotny, 1995). Irregular source of pollution is caused by pollution components that cannot be traced such as agriculture waste, farming activities, and so on. (O'Shea, 2002). ERDAS Imagine software is used to reclassify the thickness and the quality of the river water using band 3 spectrum based on the classification of Water Quality Index that has been specified by Interim National Water Quality Standards (INWQS) from the Department of Environment Malaysia (DOE). Figure 2 shows INWQS employed by DOE.

4 Figure 2: Interim National Water Quality Standards In this study, band 3 spectrum, near infrared ( µm) has been used to classify water turbidity since band 3 is the best spectrum band available to create levels of water turbidity (Mohd Hasmadi, 2010). Also based on Mohd Hasmadi, 2010, water quality classification has been classified to be used for the purpose of remote sensing analysis based on INWQS; it has been classified into three (3) classes out of five (5) classes. There are low turbidity class, medium turbidity class and high turbidity class. Table 1 shows band spectrum classification based on INWQS class by Hasmadi However, very low turbidity class with the value of < 50 DN Value has been included into this study due to the availability of that particular class in the result of DN value classification. Meanwhile, the rest of the turbidity class includes low turbidity (51-57 DN Value), Medium turbidity (58-64 DN Value), and High Turbidity (65-78 DN Value). Table 1: Reclassify Index of Interim National Water Quality And Band Spectrum Classes CLASS 1 CLASS II CLASS III INWQS Low Water treatment is not needed. NTU < 5 Moderate Conventional treatment required. NTU 6-50 High Extensive t r e a t m e n t i s n e e d e d f o r water supply. NTU > 50 Spectrum Band (DN Value) Low Moderate High RESULTS AND DISCUSSION 4.1 Land Cover Classification from Imagery Land cover classification was carried out in order to investigate the impact of classes with water quality level of Batang Lassa River. The result in Figure 3 showed mangrove class occurs the highest percentage of areas (87.85%), followed by water class (11.41%), mixed agriculture (0.42%), open area (0.24%) and settlement are the lowest (0.08%). Settlements of native people in this area can be mapped and located at downstream river. Open area and mixed agriculture classes were found in several small patches as farming activities by native community.

5 Figure 3: Supervised Classification of the Study Area. 4.2 Mapping of Water Turbidity Maps Figure 4 shows statistic calculation of turbidity areas classes of Batang Lassa River. Two classes can be mapped with class of very low polluted comprise largest area of 2,200 m 2 while low polluted consist of 120 m 2. Figure 5 shows river channels and water turbidity level of upper stream of Batang Lassa River of band 3. All river streams to the main trunk comprises DN value within 0 47, which classed as very low. This expected results can be categorize by two factors, which are: (1) Land cover - due to apparent of naturally mangrove mainly nypa palm along riverbanks and ramin species (Gonystylus spp.) as intermediate natural forest barriers of the surface runoff and erosion. (2) Physical topography condition which ranging below 20 meter elevation were entirely uniform within this area also considered as a contribution in preventing surface runoff within this catchments. Therefore, it is noted that water quality of river network within upper stream Batang Lassa River were sustainable preserve for flora and fauna as well as useful in providing clean water supply for local community surrounding. Figure 4: Statistic Calculation of Turbidity Areas

6 Figure 5: Turbidity Map of Batang Lassa River On the other hand, main river trunk shows staging changing water turbidity level from upper to downstream flow of this area. Although there were some human activity shown nearby several river channel, the water turbidity level still sustainably while mixed agriculture, sewage result of the settlements of native people, land development/settlement and human activities located at downstream changing water turbidity level to low polluted DN value separately. The land cover classification (Figure 2) therefore suggested the nature mangrove is highly protected the flow of surface sedimentation, which intact water turbidity thus preserving water quality. Human activities by native people are very nominal which not significantly contribute on water quality pollution. The result showed that water quality for upper stream Batang Lassa River are significantly influence by mangrove classes map which was conducted using supervised classification technique. Consequently, based on Water Quality Index (WQI), very low turbidity class has been classified as very clean and highly suitable to the sensitive aquatic life. It does not require any treatment apart from river eradication or through the method of boiling only (INQWS). The class shows that water quality in Batang Lassa upper stream is still preserved to its surrounding. Meanwhile, low turbidity class has been classified as clean but it need of regular treatment. 5. CONCLUSION The study concluded that the potential of SPOT 5 imagery in mapping water turbidity of upper stream of Batang Lassa River into two classes namely; low and very low based on reclassifications index from INWQS (DOE, 1993). Batang Lassa upper stream river classified under class 1 which is very clean and have no need for any treatment where the water is preserved to its surrounding. Consequently, the availability of spatial data from remote sensing is very useful as a tool in evaluating and monitoring water quality. In this case, remote sensing data provide accurate and timely information on water quality which have providing an alternative means of studying and monitoring water quality of both temporal and spatial scales. The existent of protected mangrove in the area was significantly important to preserve water quality level from turbidity thus providing appropriate habitat environments for natural flora and fauna as sources of fresh water

7 supply. It also recommended that the use of advance imagery such as SPOT 6 and real time ground truth for more precise analysis and reliable data to representing water quality classes accurately. Future research should emphasize on the use of higher resolution and near real time remote sensing data to assess the water turbidity precisely, timely and can be used to analyse of sea surface temperature. Apart from that, this study suggests that the analysis to the parameters of the water quality (Nurfadzlina, 2012) such as PH, dissolved oxygen (DO), biochemical oxygen demand (BOD) chemical oxygen demand (COD, suspended solids and ammonia nitrogen is performed using remote sensing technique. 6. REFERENCE Davis, J.M., & Cornwell, D.A. (1991). Introductin to Envirnment Engineering, Boston Massachusset: P.W.S ESRI South Asia, (2002). Building Gedatabase 1. Interim National Water Quality, Standard for Malaysia (INWQS), Department of Environment,1993. James B. Campbell, "Introduction To Remote Sensing, Fourth Edition The Guilford Press. Mohd Hasmadi (2010). "Analysis of SPOT- 5 Data for Mapping Turbidity Level of River Klang Peninsular Malaysia". Norsaliza, (2010). Use of Remote Sensing and GIS in Monitoring Water Quality. Novotny & V. (1995). Non-Point Pollution and Urban Stormwater Management. Technomic Pubishing Comny. Inc.Pennsylvania Nurfadzina, (2010). Nurfadzlina Mohd Rozi, Kajian Kualiti Air Sungai Bedi Berdasarkan "Water Quaity Index, (WQI). O'Shea, L. (2002). An Economic Approach to Redicing Water Pollutin: Point and diffuse Sources. Water Resurces. Ritchie, J.C and Schiebe, F.R. (2000). Water quality. In: G.A. Schultz and E.T. Engman (eds.), Remote sensing in hydrology and water management, Springer-Verlag, Berlin: Germany, ; Thomas, M.L., I., and Shandley, J. (2001). Detection of offshore plankton blooms with AVHRR and SAR imagery. Int. J. Remote Sensing. 22, Water Quality Data (WQI), Department of Environment, 1993.