Reduce Exposure to Reduce Risk

Figure 4. Transects along region 2 - northern Viet estuary. In region 2, there are 145 transects which -0.4 m/yr by EPR and -0.6 m/yr by LRR. Near the were set up along the coast. The shoreline change mouths of the rivers, high positive rates can be seen rate in this region is shown in Figure 5. The with values of +5.6 m/yr and +6.1 m/yr by EPR positive rate means that shoreline tended to and LRR respectively. It has shown that shoreline accretion and the negative value means that has been strongly affected by sediment source from shoreline tended to erosion. In general, annual rate river and hydrodynamical processes at the river of change in this region is about +1.4 m/yr by EPR mouth. Comparing the rates between two methods, and +1.3 m/yr by LRR. Therefore, shoreline has they have a same trend with a quite small tended to accretion. However, some places have difference in value. had small negative rates but values were not excess 10.00 + Accretion 8.00 - Erosion EPR LRR 6.00 Shoreline change rate (m/yr) 4.00 2.00 0.00 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 -2.00 Viet estuary Tung estuary -4.00 Longshore distance (km) Figure 5. Shoreline change rates in region 2. In region 4, 305 transects were built along were not over -2m/yr by EPR and -1m/yr by LRR. 30 km coast. The shoreline change rates in this As same trend in the southern part of Tung estuary, region are shown in Figure 6. Average rates of southern Viet estuary tended to accretion with high change are +0.9 m/yr (EPR) and +1.0 m/yr (LRR) rates up to +8.9m/yr and +6.9 m/yr by EPR and in the period of 1989-2010. Shoreline tended to go LRR respectively. The magnitude of rates in this offshore, except some places at the middle of the region is higher than in region 2, especially at the area was eroded. The maximum rates of change river mouth. Reduce Exposure to Reduce Risk 190

10.00 + Accretion 8.00 - Erosion 6.00 EPR LRR Shoreline change rate (m/yr) 4.00 2.00 0.00 5.0 10.0 15.0 20.0 25.0 30.0 0.0 Longshore distance (km) -2.00 Viet estuary -4.00 Figure 6. Shoreline change rates in region 4. Normally, results by LRR are smaller than [1] Brooks S.M., Spencer T., 2010. Temporal value of EPR. In this paper, the biggest difference and spatial variations in recession rates and between two methods is 1.3m/yr (near the end of sediment release from soft rock cliffs, Suffolk region 4 – at km 28 in Figure 6). It is easy to coast, UK. Geomorphology 124:1-2, 26. understand because LRR is based on the linear regression calculation using all shoreline data while [2] Cesar Augusto Arias Moran, 2004. Spatio- EPR just uses two shorelines, the earliest and latest Temporal Analysis of Texas Shoreline ones. However for average rate of change for each Changes Using GIS Technique. Master region, the difference between EPR and LRR is Thesis. only  0.1 m/yr. [3] Frederick Ato Armah, 2011. GIS-based Assessment of Short Term Shoreline 5. Conclusions Changes in the Coastal Erosion-Sensitive Zone of Accra, Ghana. Research Journal of In summary, using Landsat images can help Environmental Sciences, 5: 643-654. us to monitor shoreline change in long-term period. At the river mouths as Tung and Viet estuary, [4] Hai Hoa Nguyen, David Pullar, Norm Duke, shorelines changed significantly due to the Clive McAlpine, Hien Thu Nguyen and interaction of complex processes between river and Kasper Johansen, 2010. Historic Shoreline coastal zone. In addition, the presence of artificial Changes: An Indicator of Coastal constructions such as jetties was affected so much Vulnerability for Human Landuse and to sediment discharge in these regions. In region 2 Development in Kien Giang, Vietnam. Poster and 4, shoreline tended to accretion in general with in Asia Association on Remote Sensing. the rates around +1m/yr during 1989-2010. The highest rates are near the river mouth with value [5] Hapke, C. J. and Reid, D., 2007. National over +5m/yr. Assessment of Shoreline Change Part 4: Historical Coastal Cliff Retreat along the Although there are some benefits when California Coast. Open File Report 2007- using remote sensing and GIS techniques, the 1133, 79pp. results would be more accuracy if high resolution images were used. In addition, some data is needed [6] Hapke, C. J., Reid, D., Richmond, B. M., such as beach profile and ground control points. Ruggiero, P. and List, J., 2006. National After having enough good data, shoreline can be Assessment of Shoreline Change: Part 3: predicted for the future. Historical Shoreline Change and Associated Coastal Land Loss along Sandy Shorelines References of the California Coast. Open File Report 2006-1219, 79pp. [7] Himmelstoss, E.A., Kratzmann, M., Hapke, C., Thieler, E.R., and List, J., 2010. The national assessment of shoreline change: A Reduce Exposure to Reduce Risk 191

GIS compilation of vector shorelines and Engineering 38 (2011) 1141–1149. associated shoreline change data for the New England and Mid-Atlantic Coasts. [18] US Army Corps of Engineers, 2003. Open-File Report 2010–1119. Engineering and Design: Remote Sensing. Engineer Manual No. 1110-2-2907. [8] Jensen J. R., 1996. Introductory Digital Image Processing: A Remote Sensing [19] Yuri Gorokhovich and Anthony Leiserowiz, Perspective, Prentice Hall. 2012. Historical and Future Coastal Changes in Northwest Alaska. Journal of [9] Maio, C. V., 2009. Rainsford Island Coastal Research: Volume 28, Issue 1A: pp. Shoreline Evolution Study (RISES). 174 – 186. Graduate Masters Theses. 86p. [10] Morton, R. A., Miller, T. L., and Moore, L. J., 2004. National Assessment of Shoreline Change: Part 1: Historical Shoreline Changes and Associated Coastal Land Loss along The U.S. Gulf Of Mexico. Open File Report 2004-1043, 45pp. [11] Morton, R. A., Tara Miller, 2005. National Assessment of Shoreline Change: Part 2, Historical Shoreline Changes and Associated Coastal Land Loss along The U.S. Southeast Atlantic Coast. Open File Report 2005-1401. [12] Nguyen Van Cu et al., 2008. Study on the integrated solution for environmental protection, preventing sedimentation for flood flushing and navigation from Cua Viet port to Dong Ha port. Final report of research. Archived at Institute of Geography, Hanoi. [13] Pham Thi Phuong Thao, Ho Dinh Duan and Dang Van To (2008). “Integrated Remote Sensing and GIS for Calculating Shoreline Change in Phan Thiet Coastal Area”, Proceedings of International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences 2008. [14] Sheik, M., Chandrasekar, 2011. A Shoreline Change Analysis along the Coast between Kanyakumari and Tuticorin, India, Using Digital Shoreline Analysis System. Geo- spatial Information Science, Volume 14, Issue 4, pp 282-293. [15] Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, Ayhan, 2009. Digital Shoreline Analysis System (DSAS) version 4.0 — An ArcGIS extension for calculating shoreline change. U.S. Geological Survey Open-File Report 2008-1278. [16] To, D.V., and Thao, P.T.P., 2008. A Shoreline Analysis using DSAS in Nam Dinh Coastal Area. International Journal of Geoinformatics, Vol. 4, No. 1, March, 2008, pp 37-42 [17] Tuncay Kuleli, Abdulaziz Guneroglu, Fevzi Karsli, Mustafa Dihkan, 2011. Automatic detection of shoreline change on coastal Ramsar wetlands of Turkey. Ocean Reduce Exposure to Reduce Risk 192

CHARACTERIZATION OF THE FEBRUARY TO APRIL 2011 MUANG PHAYAO DISTRICT, THAILAND FIRE DISASTER USING SATELLITE REMOTE SENSING AND GIS Phaisarn Jeefoo Geographic Information Science, School of Information and Communication Technology, University of Phayao, 19 Moo 2, Maeka, Muang, Phayao 56000, Thailand, Tel. (+66) 54 466666 ext., 2312, E-mail: [email protected] / [email protected] ABSTRACT Data from the NASA’S MODIS (Aqua and Terra) satellite sensor is analysed to characterise the geographic and temporal evolution during 3th February to 23th April in the 2011 fire disaster in Muang district, Phayao Province, Northern Thailand using a geographic information system (GIS). A total of 65 active fire hotspots were detected by MODIS satellite. The results indicate complex differences in spatial fire distribution, behaviour and risk within the province and the effect of sensor differences. The highest number of MODIS active fire counts per day were 5 times (day 12: date 22/02/11) in Ban Tam subdistrict. The highest number of 26 fires was found in the month of February (days 1 to 17) with 40%. Approximately 74% of the fires occurred in February and March (days 1 to 30). Apronounced fire diurnal cycle with a broad afternoon peak between 14.00-15.00 local time is observed, ingeneral agreement with observations from the region. Despite their limitations, the study demonstrates the importance and usefulness of remotely sensed data and GIS technology for fire disaster and risk assessment for a developing country, where fire monitoring resources are scarce. KEY WORDS: Disaster, Fire, GIS, MODIS, Muang Phayao, Thailand 1. INTRODUCTION satellites thus offering the opportunity to observe Forest fires can cause substantial damage to natural fire activity both day and night. MODIS Terra scans resources and human lives regardless of whether it the Southern African region between 10:00–11:30 is caused by natural forces or human activities. To am and at night around 22:00 pm whereas MODIS minimize threat from wildfires, fire managers must Aqua scans in the afternoons between 14:00–15:30 be able to plan protection strategies that are pm and also in the early morning at 03:00 am appropriate for individual local areas. A prerequisite (Giglio et al., 2006). The MODIS sensor also for the planning is the ability to assess and map includes bands specifically selected for fire and forest fire risk zones across both broad areas and cloud detection and allows the retrieval of sub-pixel local sites. Forest fire risk zones are locations where fire area and temperature. Although the capabilities a fire is likely to start, and from where it can easily of current geostationary satellites are limited, they spread to other areas (Jaiswal et al., 2002). can provide valuable local, regional and global fire products in near real time, and are critical for fire The forest fires are the major environmental detection and monitoring in remote locations and problems worldwide and have become particularly developing countries. Under ideal conditions, the severe in recent decades with rapid economic performance of these sensors is somewhat developments of northern Thailand especially satisfactory and such conditions occur when a fire is Phayao, Chiangmai, Chiangrai, and Maehongson observed at (or near) nadir on a fairly homogeneous provinces. surface, or when no other significant fires are nearby, or when the scene is free of clouds, heavy Significant progress was made with the launch in smoke or sun glint. In these circumstances, the 1999 and 2002 of the Moderate Resolution Imaging smallest flaming fire that can be routinely detected Spectroradiometer (MODIS) instrument on board (i.e. near 100% probability of detection) is the morning descending Terra and afternoon approximately 50 sq.m. in size (Giglio, 2007). The ascending Aqua polar-orbiting earth observation Reduce Exposure to Reduce Risk 193

accuracy of the fire detection by MODIS has been fire detection algorithm from the MODIS Rapid observed to reach up to 90% and even higher (Wang Response System (http://maps.geog.umd.edu/) at a et al., 2003). The main purpose of this study is to spatial resolution of 1 km (Justice et al., 2002). The describe the temporal (daily and diurnal) variation MODIS active fire data is based on Giglio, of the February, March, and April 2011 fires as Descloitres, Justice, and Kaufman’s (2003) recorded by MODIS satellite-based fire detection enhanced contextual fire detection algolithm which system. uses the 4 µm and 11 µm bands and classifies every pixels as missing data, cloud, water, non-fire, or 2. METHODS unknown. A full decription of the algolithm is given by Giglio et al. (2003). The MODIS active fire 2.1 Study area: Muang Phayao District, Phayao product contains information about the detected fire Province, Thailand pixels including location, observed brightness Muang Phayao, a district (Amphoe) in the northern temperature, pixel size, and fire confidence. The fire part of Thailand (Figure 1), had the highest fire confidence is calculated by a system of equations disaster in Thailand very year province. Muang within the algolithm and is expressed as a district comprises 13 subdistricts (Tambon) consist precentage (Giglio, 2007). The confidence levels of Mae Pim, Ban Mai, Tha Jam Pi, Ban Tam, Tha reported in MODIS active fires during the period Wang Thong, Ban Tom, Wiang, Ban Sang, Ban under investigation were explored and it was found Tun, Jam Pha Wai, Mae Sai, Ban Na Rae, and Ban that even pixels in areas where the fires were intense Ka with 163 villages. The district is located 690 km (plantations) were given a zero confidence, most north of Bangkok, and covers an area of 887 sq km likely due to the obscuration by the thick smoke with geographical location between 18° 53’ 59’’ N cover over almost the whole country. From the to 19° 24’ 26’’ N and 99° 24’ 0’’ E to 100° 1’ 12’’ MOD14A1 (MODIS active fire) data, the smoke E. The district has a population of about 125,820 was found to have been classified as “non-fire clear people (Department of Province Administrator, land”. Therefore, MODIS fire pixels with a 2011). It is mostly covered with forest mountain, confidence level of 0% were also considered as with an approximate elevation of 840 meters about individual fire counts (Figure 2). mean sea level. Figure 1: The study area: Muang Phayao district, 194 Thailand 2.2 Satellite data analysis The active fire datasset, MODIS, from 1th February to 30th April 2011 were colledted and collated. The MODIS dataset is based on the version 4 contextual Reduce Exposure to Reduce Risk

Figure 2. MODIS active fire hotspots between February-April 2011 in Northern of Thailand 3. RESULTS burning area) of the fires occurred in February and 3.1 Temporal distribution March (days 1 to 30). Temporal analysis reveals that a majority of the fires were detected from the period 3th February to 23th Table 1. the Number of fires and percentage (%) April 2011 or 41 days of MODIS activity fires. Actually, MODIS detected 40% of the February during February, March, and April 2011 and the highest MODIS activity fire was 13:00– 16:00 pm with 36.69%. Number Percentage Area of of fires (%) burning Figure 2. MODIS active fire counts for 3th February Month (sq.m.) to 23th April 2011 in Muang district, Thailand Table 1, the highest number of 26 fires was found in February 26 40.00 166,400 the month of February (days 1 to 17) with 40% or 166,400 sq.m. of total buring area. It was followed March 25 33.85 147,200 by 25 fires in March (days 18 to 30) or 33.85%, 147,200 sq.m. of total buring area and 17 fires (days April 17 26.15 142,400 31 to 41) of the MODIS active fire counts in that order. Approximately 74% (313,600 sq.m. of Total 65 100.00 456,000 Temporal analysis reveals that a majority of the fires were detected from the period 3th February to 23th April 2011 (Figure 3). Maps of daily fires were generated, which disaster the dynamics of fire distribution through time during 41 days for the months of February, March, and April for the year 2011. A total of 65 active fire hotspots were recorded and geo-referenced (Figure 3). It was observed that the temporal dynamics of MODIS active fire continued to the forest area (i.e. deciduous forest). The highest number of MODIS active fire counts per day were 5 times (day 12: date 22/02/11) in Ban Tam subdistrict. Reduce Exposure to Reduce Risk 195

Figure 3. Temporal dynamics in space and time for Figure 4. Mapping MODIS active fire hotspots the 41 days of MODIS active fire counts between 3th February to 23th April in the year 2011 4. CONCLUSION AND RECOMMENDATIONS The MODIS activity fires hotspots were illustrated by generating the fire areas over the space, as shown The usefulness, including the unique strengths and in Figure 4. These map show clear spatial limitations, of remotely sensed active fire data and distribution of fire areas that were concentrated in geostationary satellites in understanding the west (Ban Tam, Ban Tom, Ban Sang, Ban Na Rae, geographic and temporal characteristics of fire is and Ban Tun subdistricts) of the study area during demonstrated in this study. Despite the observed February and while in April they were mostly limitations, satellite monitering of fires proves to be shown in the middle (Ban Tam, Ban Tom, and Ban useful for a resource-constrained developing Sang subdistricts) of Muang Phayao district with country such as Thailand. There is an observed 12,800 to 16,000 sq.m. of total burning area. The distinctive spatial distribution pattern in the fire highest density of MODIS activity fire hotspots activity during the month of the Phayao Province, occurred within the forest areas. Thailand fire disaster. In general, the highest proportion of fire hotspots were detected in the west of Muang district, where a majority of the devastating firers raged through the region’s plantation forests thus indicating the high fire risk in these areas. The high temporal resolution detected more fires in the west especially Ban Tam, Ban Tom, Ban Sang, Ban Na Rae, and Ban Tun subdistricts respectively and reveals a distinct diurnal variation in fire activity where peak activity is observed mainly in the afternoon between 13:00– 16:00 or 36.69% when fires are most likely to burn. The burning patterns presented in this study could be used as input information for the further analysis of current and future fire regimes and fire risk according to local and global change. Increasing observational and technological skill and experience at recognizing dangerous fire activity will offer significant lead time to anticipate future disasters and minimize losses and environmental impacts from such disasters. The capabilities of remote sensing data, such as demonstrated in this study, offwe exceptional value for early warning and Reduce Exposure to Reduce Risk 196

disaster assessment even for developing countries 197 like Thailand. ARCKNOWLEDMENTS I would like to express my sincere gratitude to School of Information and Communication Technology (ICT), University of Phayao, Thailand for providing financial support to this study. REFERENCES Jaiswal, R.K., Mukherjee, S., Raju, D.K., et al. 2002. Forest fire risk zone mapping from satellite imagery and GIS lJ1. International Journal of Applied Earth Observation and Geoinformation, 4: 1-10. Justice, C. O., Giglio, L., Korontzi, S., Owens, J., Morisette, J. T., Roy, D., et al., 2002. The MODIS fire products. Remote Sensing of the Environment, 83, 244–262. Giglio, L., Csiszar, I., & Justice, C. O., 2006. Global distribution and seasonality of active fires as observed with the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Journal of Geophysical Research, 111, G02016. doi:10.1029/2005JG000142. Giglio, L., Descloitres, J., Justice, C. O., & Kaufman, Y., 2003. An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment, 87, 273–282. Giglio, L., 2007. MODIS collection 4 active fire product user’s guide version 2.3. http://maps.geog.umd.edu/products/MODIS_Fire_U sers_Guide_2.3.pdf>. Wang, S., Zhou, Y., & Wang, L., 2003. A research on fire automatic recognition using MODIS data. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS03), 4. Toulouse, France: IGARSS. 2502–2504. Reduce Exposure to Reduce Risk

MODELLING THE EFFECT OF COASTAL LANDUSE ON TSUNAMI INUNDATIONS PATTERNS ALONG THE SELECTED STRETCHES OF SOUTH INDIAN COAST K.Srinivasa Raju1, G.Gopinath1, M.Ramalingam1 and Bhoop Singh2 1Institute of Remote Sensing, Anna University, Chennai, India 2Department of Science and Technology, Government of India, New Delhi ABSTRACT Indian Ocean Tsunami 2004 has invoked the interest in Asian Scientific Community in Tsunami Modelling for understanding the tsunami dynamics. Several Models were developed for modelling Tsunami Wave Propagation in Open Seas and Off-shore and for Tsunami Inundation Modelling of on-shore. Non-availability of High Resolution topographic and bathymetric data is of main concern for modellers particularly for inundation modelling where the inundation patterns are highly influenced by the resolution of topography and friction for flow. This research concentrates on use of a Tsunami Inundation Model developed in MIKE-21 Environment using Hydrodynamic Module for modelling the effect of coastal landuse on Tsunami Inundation pattern. High Resolution Digital Elevation Models and Bathymetric data derived from Remote Sensing and Ground based surveys was used for development of the model. The Indian Ocean Tsunami caused by Earthquake off Sumatra Coast in December 2004 were used as baseline data for model building. The model is validated with field observations made by other researchers and output of similar models developed. Inferring Indian Ocean Tsunami Inundation Patterns helped in understanding the relationship between the landuse and extent of inundation. Bays, Creeks and Rivers act as Carriers of Tsunami Inundation causing larger extent of inundation along rivers, channels. Similarly coastal plantations like Eucalyptus acted as Barriers for Tsunami Inundation causing minimal damage along coastal stretches protected by coastal plantations. Various scenarios of coastal landuse were developed with high resolution imagery was induced into the Tsunami Inundation Model to model the behaviour Tsunami waves for changes in coastal landuse/landcover practices. This helped to provide information for coastal landuse planning essential for Planners and Administrators for mitigation of future tsunami damages. COASTAL LINE CHANGED DETECTION IN THAILAND Wutjanun Muttitanon Faculty of Engineering, Mahidol University, Thailand, E-mail: [email protected] ABSTRACT Land use/land cover of the Earth is changing severely because of human activities and natural disasters. Information about changes is useful for updating land use/land cover maps for planning and management of natural resources. Land loose is one of the principal troubles that found in coastal area in SamutPrakarn. Several methods for land use/land cover change detection using time series Landsat imagery data were employed and discussed. Landsat 5 TM color composites of 2002 and 2007 were employed for locating coastal line for supervised classification in the coastal areas of Bangkok, SamutPrakarn, and SamutSakhon, Thailand. It is illustrated an decreasing trend of land area in the extremely rate with coastal line changed. The NDVI composite images are used to detected land use changed. The several NDVI composite techniques are discussed. KEY WORDS: Changed detection, Coastal line, NDVI, Classification, Image detection Reduce Exposure to Reduce Risk 198

GENERATION OF KATABATIC WIND ALONG THE LEE SIDE OF THE MOUNTAIN ADJACENT TO THE EAST SEA OF KOREA Doo Sun Choi1, Hyo Choi1 and Soo Min Choi2 1Dept. of Atmospheric Environmental Sciences, Gangneung-Wonju National University Gangneung, 210-702, Korea, E-mail: [email protected] 2Department of Computer Science and Engineering, Gwangju University, Gwangju 503-703, Korea ABSTRACT The evolution of windstorm over 25m/s was investigated using a three-dimensional Weather Research and Forecasting Model-WRF 3.3, in the mountainous coast near Gangneung city in the east of Korea from October 24 through 26, 2003. From 0900LST to 2100LST, October 24, cold front lay in the Bohai Sea, northwesterly synoptic-scale moderate wind prevailed in the Korean east coastal region. After 0900LST, October 25, when cold front passed by the study area, synoptic-scale wind direction was shifted from moderate northwesterly wind into more intensified westerly or southwesterly wind. After sunset on October 24 before sunrise on October 25, the downslope wind associated with mountain wind along the mountain slope and land breeze in the coastal basin generated by nocturnal cooling of the ground surface should be stronger along the eastern slope of Mt. Taegulyang in the west of the city. Simultaneously, as the core of negative geopotential tedency of - 141m/day at 500hPa level (ie; 500 hPa geopotential height change for 24 hours) in the left of cold front was closing to the eastern coast of Korea, the atmospheric depth between 500 hPa and the ground surface near Gangneung city should reduce to 50m, that is, 50m/day. The air flow in the shrunken layer became faster and could make a great contribution to the increase of westerly wind speed near the surface. Thus, the faster southwesterly wind overrode the top of a steep mountain and moved down along the eastern slope of the mountain toward Gangneung city, resulting in stronger downslope wind (katabatic wind; downslope wind storm) over than 25m/s. The strong downslope wind storm produced the propagation of lee side-internal gravity waves bounding upward in the eastern slope and eastward above the coastal sea. A maximum wind speeds were detected at 1200LST, October 25 with 7.5m/s on the top of the mountain, 25.58m/s along the eastern slope of the mountain and 5.6m/s near the ground surface in the city, respectively. This kind of the downslope wind storm along the slope maintained until 1700LST, October 25. KEY WORDS: WRF-3.3 model, Katabatic wind, Cold front, Geopotential tendency, Wind storm COOLING OF SEA SURFACE WATERS INDUCED BY STRONG MARINE WIND UNDER THE INTENSIFICATION OF A COASTAL LOW PRESSURE NEAR THE EAST SEA OF KOREA Hyo Choi1 and Soo Min Choi2 1Department of Atmospheric Environmental Sciences, Gangneung-Wonju National University, Gangneung 210-702, Korea, E-mail: [email protected] 2Department of Computer Science and Engineering, Gwangju University, Gwangju 503-703, Korea ABSTRACT Temporal variations of sea surface temperatures along the eastern coastal sea of Korean peninsula under strong wind filed were investigated from March 28 through 30, 2004, using NOAA MCSST sea surface temperature (SST) satellite pictures and a three-dimensional Weather Research & Forecasting Model (WRF)-version 3.3. Wind and air temperature at a 3 km (fine-mesh domain), 9km (second coarse-mesh domain) and 27 km (first coarse-mesh domain) horizontal grid points horizontally and every 35 level on 10m ~ 10km height, vertically using one-way nesting technique in the model simulation were calculated in the mountain, coastal basin and both coastal and open seas. On March 28, as westerly wind under a high pressure prevailed in the Gangneung coast and the open sea, wind driven current could be southward, resulting in the intrusion of cold waters such as the North Korea Cold Current toward the south along the coast. Under this situation, SST near Gangneung coast and open seas were 110C. On March 29, a low pressure in Bohai Sea between China and Korea produced a Reduce Exposure to Reduce Risk 199

cyclonic air flow, which could produce southwesterly marine surface wind in the eastern coastal sea of Korea and induce a strong southeastward wind driven current, resulting in the intrusion of cold water from the northeastern coastal sea of Korean peninsula toward south along the coast and reaching near Gangneung coast. On March 30, the low pressure was more intensified with a decrease of 5 hPa from 1013 hPa to 1008 hPa, and the pressure pattern induced both northerly wind along the coast and westerly wind in the offshore could cause the intrusion of cold waters from the northeastern coast of Korean peninsula into the southeastern sea, resulting in the occurrence of 90C SST near Gangneung coastal sea. This work was funded by the Korea Meteorological Administration Research and Development Program under Grant CATER 2006-2308 “Generation mechanism and prediction of windstorm in the mountainous coast” for 2006 ~ 2008. KEY WORDS: NOAA MCSST SST satellite picture, WRF-3.3 model, North Korea Cold Current COASTAL EROSION HAZARD ASSESSMENT, SOUTH WEST COAST, SRI LANKA Bandula Wickramarachchi1 and Talpe Liyanage Chanaka Vinodh2 Coast Conservation & Coastal Resource Management Department, Sri Lanka E-mail: [email protected], [email protected] ABSTRACT Naturally the coastal areas are dynamic due to natural processes. The coastal sediment process is one major physical process, which determines the stability status of the shoreline. The sediment process is driven by natural factors such as climate, shoreline geometry and sediment availability etc. The ultimate state of the sediment process is either accretion or erosion. Through the analysis of active drivers on the coasts, the susceptibility to coastal erosion shall be assessed. The southwest coast of Sri Lanka is subjected to vigorous wave climate of southwest monsoon, resulting severe coastal erosion. The wave climate remains same for the entire southwest coast, but sediment availability and shoreline geometry are varied locally. Hence the erosion assessment was done for each sediment cell, which is physical sediment unit outlined by shoreline geometry. For the last period the coping capacities of coastal cells are increased through physical protections, and those capacities were also assessed for the final erosion hazard assessment. Geo-Information Technologies were applied in the erosion hazard assessment, and the hazard rankings were mapped at 1:50,000 scale. Results were verified by the historical incidents. Due to the limitations of data and approach, the results are qualitative, and the hazard levels are relative. KEY WORDS: Coastal Erosion, Sediment, Wave, Shoreline, Hazard Map DEVELOPING A DECISION SUPPORT SYSTEM FOR FOREST FIRE RISK MODELING IN PRAVIYANGALA AREA OF WALAWE BASIN N. Saranga Vithanage 1 K.V.D. Edirisooriya 2 1Faculty of Graduate studies, University of Sri Jayewardenepura, [email protected] 2Faculty of Social sciences and Languages, Sabaragamuwa University of Sri Lanka, [email protected] ABSTRACT A forest fire is a natural disaster consisting of a fire which destroys a forested area. Forest fires, also known as wildfires, vegetation fire, grass fire, brush fire or bush fire, is common in vegetated areas of Australia, South Africa, United States and Canada, where climates are sufficiently moist to allow the growth of trees, but feature extended hot and dry periods. The problem of forest fires in Sri Lanka can be summarized by examining weather conditions, fuel types in the forests, and human attitude in the area. Fire hazard in Sri Lanka is very high in forest plantations especially in Eucalyptus and Pine (Pinus spp.) plantations. Over the past 40 years 18,000 ha of pines and 19,000 ha of Eucalyptus have been planted. Besides being a pyrophytical species most of the pine plantations are situated in the steep slopes of central highlands. This situation creates a very high fire Reduce Exposure to Reduce Risk 200

hazard. GIS is very useful and important in forest fire modeling. In this study to determine the risky areas used following variables were tried to be obtained and manipulated into ArcGIS to make related analyses. Dem, slope, aspect, and hill shade maps were generated to determine the topological variables. Land use map was used to identify the forest cover, residential areas, road network and power lines ect. All those parameter layers were assigned different weight ages depending upon their impact, in identification of fire risk zone. Thus, high to low fire risk zones can be identified and another GIS analysis used to determined forest fire visibility locations. Upper Walawe watershed is very valuable ecosystem for the region. This application was very useful to understand the capabilities and easiness of GIS on fire risk zonation mapping and develop an information and decision support system to monitor and predict forest fire activity, and to enhance fire management efficiency. KEY WORDS: Dem, Forest fire, forest plantation, visibility location. REGRESSIVE PREDICTION OF PM10, PM2.5 AND PM1 CONCENTRATIONS AT A KOREAN EASTERN COASTAL CITY UNDER THE DUST TRANSPORTATION FROM GOBI DESERT Mi Sook Lee1 and Hyo Choi2 1Research Institute of East Sea Life Sciences, Gangneung-Wonju National University Gangneung 210-702, Korea, E-mail: [email protected] 2Dept. of Atmospheric Environmental Sciences, Gangneung-Wonju National University Gangneung 210-702, Korea, E-mail: [email protected] ABSTRACT Using hourly concentrations of PM10, PM2.5 and PM1 measured by a GRIMM-1107 aerosol sampler in Gangneung city in the eastern Korea, fractional analysis and correlation coefficients of PM10, PM2.5 and PM1 were invested from May 6 to 10, 2007. Before dust transportation from Nei-Mongo in the northern China to the Korean eastern coast, correlation coefficients of PM2.5 to PM10, PM1 to PM2.5 and PM1 to PM10 were 0.91, 1.00 and 0.90. The fractions of (PM10-PM2.5)/PM2.5 and (PM2.5-PM1)/PM1 showed 0.32 and 0.35, which implied generally fine particulate matter (< 2.5μm) rather than coarse particulate (10~2.5μm) matter to make a great contribution to the distribution of PM10 concentration in the Korean coastal city. On the other hand, during the dust transportation from May 7~8, correlation coefficients of PM2.5 to PM10, PM1 to PM2.5 and PM1 to PM10 concentrations were 0.65, 0.93 and 0.35. The fractional ratios of (PM10-PM2.5)/PM2.5 and (PM2.5-PM1)/PM1 were 3.88 and 0.88, which implied coarse particulate matter (10~2.5μm) rather than fine particulate matter (< 2.5μm) to make a great contribution to the formation of high PM concentrations at this city. Coarse particulates transported from the northern China could cause mainly the increase of PM10 concentration at the coastal city. Correlation coefficients after the dust storm period, similarly to ones before the dust period, were 0.82, 1.00 and 0.81. In order to investigate the transportation of dusts from Gobi Desert toward the Korean eastern coastal city and horizontal and vertical profiles of wind fields, MTSAT-IR satellite picture, NOAA HYSPLIT-backward trajectory model and streamlines generated by a three-dimensional numerical model-WRF-version 3.3 were used. This work was funded by the Korea Meteorological Administration Research and Development Program under Grant CATER 2006-2308-“Generation mechanism and prediction of windstorm in the mountainous coast” and continued in 2012. KEY WORDS: PM1, PM2.5, PM1, Fractional analysis, Coarse particulate matter, Fine particulate matter, MTSAT-IR satellite picture, HYSPLIT-backward trajectory model, WRF-3.3 numerical model Reduce Exposure to Reduce Risk 201

FORMATION OF SEA AND COASTAL FOGS BY THE INTRUSION OF WARM AIR OVER COOL SEA SURFACE BY CYCLOGENESIS IN THE YELLOW SEA Hyo Choi Dept. of Atmospheric Environmental Sciences, Gangneung-Wonju National University Gangneung 210-702, Korea, E-mail: [email protected] ABSTRACT Sea and coastal fogs along the western coast of Korea under marine wind shift and cooling of sea waters was investigated using a three-dimensional Weather Research & Forecasting model (WRF)-version 3.3 and NOAA- MCSST satellite pictures from February 21 through 23, 2005. As an initial input data for WRF model, global meteorological data set (NECP) was adopted and meteorological elements such as wind, relative humidity and air temperature were evaluated on each 3 km, 9km and 27 km of a horizontal grid interval with a grid number of 91 x 91 in three different model domains. As supplementary materials, QuikSCAT wind data was also used for analyzing marine wind in the Yellow Sea. On February 21, marine winds in the coastal sea of the western Korea and the Yellow sea were northwesterly with about 5m/s under a high pressure, but on February 22, as a low pressure passed by the Bohai Sea and intruded into the Yellow Sea, it was much developed, showing a rapid drop in atmospheric pressure of 6 mb/12hour (cyclogenesis). Thus, marine wind was changed from northwesterly into southwesterly like counterclockwise wind and was more intensified into 10m/s. Counterclockwise wind could induce wind driven current to move from southwestward into southeastward, where the area of sea surface temperatures (SST) below 50C extended toward the south of the Yellow Sea and along the southwestern coast of Korea. Especially, the cool water of 100C initially located in the central part of the Yellow Sea moved into south and southeast, sequentially. Under the influence of the cold water intrusion in the range of 20C ~ 50C, the area of below 50C extended to about 100 km away from the Chinese eastern coast toward the central part of the Yellow Sea. For instance, SST in the coastal sea of Mokpo city on February 21 was 80C and the SST decreased to 60C. Since fog with relative humidity (RH) over 85% due to salty condensation nuclei was found at 0300 LST, February 22 near the coastal sea of Inchon city, the area of fog formation extended along the coast toward south with time, until the next day morning of February 23. As marine wind was shifted from southwesterly on February 21 into southeasterly on February 22, warm air intruded from the southern Yellow Sea into the western coastal sea of Korean peninsula, where cold sea waters existed. Thus, sea and coastal fogs could be generated as the intruded warm air overrode cold sea surface should cool down and reach its saturation to condense. In general, RH should be 100% for the formation of fog in the inland, but RH greater than 85% is sufficient for the fog formation over the sea, as salty nuclei can act as cloud nuclei. In order to distinguish fog from cloud, vertical profile of RH was depicted from 10m, 500m and 1km heights over the sea surface. The comparison of evaluated meteorological elements by the numerical model with observed ones was carried out at the western coastal observatories of Korea Meteorological Administration. This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MEST) (No. 2009-0094). KEY WORDS: WSea and coastal fogs, WRF-3.3 model, NOAA-MCSST satellite picture, NECP, QuikSCAT wind data, Yellow Sea, Cyclogenesis, Relative humidity Reduce Exposure to Reduce Risk 202

Technical Session-9 [Hall B]: Health Hazard Identification of Dengue Risk Zones in Kelaniya Divisional Secretariat Area of 204 Gampaha District in Sri Lanka using Geo-informatics Techniques 211 Nadarajapillai Thasarathan, N. D. K. Dayawansa, Ranjith Premalal De Silva 211 212 Geographical Information System (GIS) Based Approach to Monitor Epidemiological 213 Disaster: 2011 Dengue Fever Outbreak in Punjab, Pakistan Shahbaz Ahmad Muhammad, Bilal Sadiq, Tanzeem AkbarCheema, Qaisar Abbas and Muhammad Shahzad Sarfraz Geographic Information of Fish-borne Parasitic Metacercaria in Chi River, Mahasarakham, Thailand Choosak Nthikathkul, Chalobol Wongsawad, Pipat Rungsaeng, Pissamai Homchumpa, Sirinart Tongsiri, Anothai Trivanich Mapping of Liver Fluke for Reduce exposure to Reduce Risk at Phutthaisong District, Burirum Province, Thailand Sarawut Jampapunt, Pissamai Homchumpa, Sompong Jarungjitanuson, Somsak Sripakdee, Choosak Nithikathkul Mapping and Surveillance of Opisthorchiasis Study in Khon Kaen : Ban Non Model Choosak Nithikathkul, Pipat Reungsaeng, Bangon Changsap, Supaporn Wannapinyosheep, Direk Panitsupakamon, Trivanich, Chalobol Wongsawad Reduce Exposure to Reduce Risk 203

IDENTIFICATION OF DENGUE RISK ZONES IN KELANIYA DIVISIONAL SECRETARIAT AREA OF GAMPAHA DISTRICT IN SRI LANKA USING GEO-INFORMATICS TECHNIQUES Nadarajapillai Thasarathan1, N. D. K. Dayawansa2 & Ranjith Premalal De Silva3 1Postgraduate Institute of Agriculture, University of Peradeniya, Sri Lanka, E-mail: [email protected], 2Department of Agricultural Engineering, University of Peradeniya, Sri Lanka, E-mail: [email protected], 3Uva Wellassa University, Badulla, Sri Lanka,E-mail: [email protected] ABSTRACT Dengue arboviral disease has become a serious epidemic in Kelaniya Divisional Secretariat (DS) Division in Gampaha District of Sri Lanka. Kelaniya DS Division is a highly urbanized area with the total extent of 21.8 sq. km and is located adjacent to the Colombo Metropolitan area. This study intended to identify the risk zones and level of risk for the dengue outbreak by identifying and mapping factors affecting dengue outbreak in the area using geo-informatics techniques. Factors affecting dengue outbreak are primarily the conducive environmental conditions for mosquito breeding such as Land Surface Temperature (LST), presence of wetlands and fresh water bodies; and human induced factors like presence of solid waste collection centers, slum areas, and residential, industrial and commercial areas. Population density is another major risk factor increasing the pace of dengue spread. Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were calculated using Landsat ETM+ and SPOT 5 satellite images respectively. Weighted maps of each factor corresponding to its risk attributable to dengue were overlaid to identify the risk areas. These risk areas were classified into 3 classes according to risk level as low, moderate and high. In order to validate the model output, the actual dengue incidences in the Grama Niladari (GN) divisions during 2007 to 2010 were mapped. A Neighborhood Analysis was performed to incorporate the potential influence of the neighboring GN divisions. Then the resultant map was ranked as low, moderate and high. Finally, the ranked model and actual dengue incidence maps were compared using Spearman Rank Correlation. Since the correlation between the model and actual data is significant at p = 0.01, the model can be identified as reliable to assess the dengue risk in the study area. KEY WORDS: Dengue outbreak, NDVI, LST, Overlay, Spearman Rank Correlation 1. INTRODUCTION In Sri Lanka where dengue is still being a major issue in country‟s health care and economy, it is important Dengue fever is an arboviral disease transmitted to to identify the dengue risk areas. During recent times, humans by female mosquito of Aedes aegypti. The the dengue cases are increasing gradually in Kelaniya virus infection has become one of the priority health DS division but there are no mechanisms to forecast issues in Sri Lanka. Incidences of Dengue Fever the disease occurrence. Spatial risk models help to (DF), Dengue Hemorrhagic Fever (DHF) or Dengue identify the risk zones and to take necessary actions Shock Syndrome (DSS) have increased in Sri Lanka immediately to the appropriate region. These days in recent decades. Actually, dengue virus is found in government, community and nongovernmental tropical and sub-tropical regions around the world organizations who are involved in dengue control (Bhandari et al., 2008). World Health Organization programs welcome this type of research that (WHO) currently estimates there may be 50 million facilitates them to easily focus of high risk areas. cases of dengue infection worldwide every year. The Within this background, the present study was carried pattern of dengue changed in Sri Lanka after 1989, out with the following objectives. with an exponential increase in the incidences of DHF (Kulatilaka and Jayakuru et al., 1998). Recent 1.1 Objectives dengue outbreaks in Sri Lanka show two trends: yearly increase of total number of dengue incidence  To map the occurrence / prevalence of the and increasing dengue outbreaks outside the endemic dengue fever in Kelaniya Divisional urbanized areas in the south and the west (Pathirana Secretariat area of Gampaha District. et el., 2009). In Sri Lanka, the outbreak of dengue has increased in Gampaha district. Statistics show that  To compare the developed dengue risk map Gampaha was the worst affected district in Western with the actual dengue incidence map to Province. In this district, most of the dengue cases validate the results. were identified in Kelaniya DS area. Reduce Exposure to Reduce Risk 204

2. METHODOLOGY dengue risk assessment. Table 1 show the criteria used in the study. Using ERDAS Imagine software, 2.1 Study area NDVI was derived from SPOT- 5 (20 m spatial resolution) image. NDVI information was used to Kelaniya; a highly urbanized area in Gampaha differentiate vegetation and non-vegetation areas. It district is situated between 60 95‟ and 70 00‟ North was identified that non vegetation areas are mostly latitude and between 790 88‟ and 790 94‟ East suitable for dengue mosquito breeding than vegetated longitude. The location of the study area is given in areas. LST was calculated from a cloud free Landsat Figure 1. Kelaniya DS area lies in the wet zone and ETM+ data (30 m spatial resolution). When the LST receives rainfall mainly from south west monsoonal is suitable, the mosquito eggs will breed quickly. rain between months of May to September. In Gampaha district, Kelaniya DS division is Buffers were introduced to determine the proximity comparatively small administrative area with an of places to fresh water bodies, wetlands, and solid extent of 21.8 square kilometers and with a total waste collection points. When the places are located population of 146,636 in 2009 (Statistical Hand close proximity to these places, mosquito spreading Book, 2009). The area is highly urbanized and the was considered high. Three buffer zones were created population density was 6726/km2 in 2009. It has within 200 meter distances from the area. Buffer bounded North by Mahara and Wattala DS divisions, distances were identified considering life span and East by Biyagama and South and West by Kelaniya average distance travelled per day of the Aedes river. There are 2 local authorities in the study area aegypti mosquito. The average life span of female namely Peliyagoda Urban Council and Kelaniya mosquito is 8-15 days and the mosquito can fly about Pradeshaya Shaba. 30-50 meters per day. This indicates that the Aedes aegypti can moves around 240-600 meter range in 2.2 Data used their life time (Nakhapakorn and Tripathi et el., 2005). Therefore, the buffer zones were created in Satellite images of Landsat ETM+, SPOT-5 were 200 meter distances and ranked as high, medium and collected from the USGS website and GIS unit, low according to the proximity to the breeding site. Survey Department of Colombo respectively. A All the buffered vector maps and other raster images maximum clouds free Landsat ETM+ geometrically (vector and raster) were ranked in to 3 classes as corrected image (Path-141, Row-055, and Date- High risk (3), Moderate risk (2) and Low risk (1). 2001.03.14) was downloaded from the USGS After ranking, the vector maps were converted to website. Some paper maps (slums and solid waste raster format with the same pixel sizes. Finally, all collection areas) were collected from Urban maps were overlaid using raster calculation and the Development Authority, Gampaha and Peliyagoda dengue risk zones were identified. Urban Council. Land use for Kelaniya DS Division was collected from the Survey Department, The final comparison was made between the dengue Colombo. Statistical data of population distribution, risk map and the actual dengue cases. Zonal mean density and land use details were collected from the was calculated for each GN division according to the Planning Division of Kelaniya Divisional Secretariat. risk map of dengue and then according to the zonal means, the GN divisions were classified into 3 Data on Dengue incidences were collected from the classes as high, moderate and low. In order to Medical Officer of Health (MOH) office, Kelaniya validate this model, the actual dengue incidences and the Deputy Director of Health Service (DDHS), during 2007 to 2010 in the GN divisions were Gampaha. The data included the information about mapped. A Neighborhood Analysis was performed DF/DHF/DSS cases reported at Grama Niladhari to incorporate the potential influence of the Divisions from 2007 to 2010 in the Kelaniya DS neighboring GN divisions. After the Neighborhood division. Arc GIS 9.2, ERDAS Imagine 9.2 and Analysis, the final sum ratio was classified into 3 Microsoft Excel software were used to perform the classes and ranked as high, moderate and low. data analysis and to develop maps. For final Statistical package of SPSS was used to calculate the comparison between the risk map and actual dengue Spearman correlation coefficient to assess the cases, the statistical analysis package of SPSS was correlation between potential risk and actual risk. used to calculate the Spearman Rank Correlation Figure 2 presents the methodology followed in the Coefficient. Spatial data layers were developed in the study. same coordinate system for analysis. 3. RESULTS AND DISCUSSION 2.3 Criteria of the study 3.1 Land Surface Temperature (LST) A number of factors were identified as the most important parameters to develop the criteria in According to Figure 3, the range of the LST was from 30 0 C- 410 C. The ranges were classified in to Reduce Exposure to Reduce Risk 205

3 classes as <30 0 C, 30 0 C- 34 0 C and > 34 0 C. M) from the water body area was ranked as 3 (high High, moderate and low risk temperature classes risk) and 200 – 400 meters was ranked as 2 were identified based on the literature information. (moderate risk) as well as areas located beyond 400 The most favorable land surface temperature range meters were ranked as 1 (low risk). Figure 5 shows for mosquito breeding in Sri Lanka is 30 – 34 0 C the fresh water bodies and the buffer zones identified according to the Entomology Department, Colombo. according to the proximity. Therefore, this range was given a higher rank (rank 2) and other temperature ranges were given a lower Wetlands are another dengue mosquito breeding sites rank (rank 1). in the study area. Wetlands have stagnating waters for at least considerable time duration of the year. In Figure 3, the results show that Land Surface Therefore, they act as breeding sites for the dengue Temperature over the Kelaniya area. The temperature mosquito very prominently when they have fresh on the buildup area is higher than other land areas. water soon after the rain (especially swamps). High temperature (in red) can be identified in highly Western part of Kelaniya DS division consists of urbanized area. White area equate to either water wetlands. Further in this area, most of the unplanned body or vegetation. When compared with the actual settlement areas (slums area) also located near the dengue cases, most of the cases were recorded in wetlands. These people dump plastic bottles, bags areas with temperature within 30 – 34 0 C range. and containers in to the wetlands, making them more Accordingly, rank 2 was given to this temperature suitable for mosquito breeding. Accordingly, less range. The results show that the LST is an than 200 meters (< 200 m) from the wetland area was important environmental factor that helps to ranked as 3 (high risk) and areas located 200 – 400 determine the potential areas for dengue risk. meters were ranked as 2 (moderate risk) and areas located beyond 400 meters were ranked as 1 (low 3.2 Normalized Difference Vegetation Index risk). Figure 6 shows the wetlands and their buffer (NDVI) zones. Normalized Difference Vegetation Index was Solid waste collection centers or open dumping sites calculated to identify the vegetation and non- are potential breeding sites for dengue mosquito after vegetation cover in the study area. SPOT 5 images rainfall events due to the stagnation of water in the was used to derive the NDVI value. Figure 4 shows waste materials (cans, tires, plastic containers, the NDVI variation of Kelaniya DS division where buckets, polythene bags etc.) for significant time the built up areas are appeared in red color while the duration. Risk is high when compared to the wetlands other areas (light green) refer to vegetation covers. and water bodies. Poor garbage disposal practice and Aedes aegypti is a species that is closely associated dumping along the road sides also increases the with humans and breeds in locations associated dengue cases. In this study, the solid waste collection with human habitation, and it is not ecologically centers were considered as risk areas and identified linked to forests (Sithiprasasna et al., 1997). three different levels of risks zones around the Furthermore, Kelaniya area is mostly urbanized centers. According to the risk condition, less than 200 hence the vegetation and built up area had to be meters (< 200 m) from each collection center was separated for this study. Roads, buildings, settlements ranked as 3 (high risk) and the areas located 200 – areas were considered under built up areas. 400 meters were ranked as 2 (moderate risk) and area Therefore, in the Figure 4, the vegetation area were located beyond 400 meters were ranked as 1 (low ranked as 1 (low risk) and built up areas were ranked risk). Figure 7 shows the dengue risk around the as 2 (high risk). solid waste collection centers. In the study area more than 10 major solid waste collection centers are 3.3 Breeding sites of Dengue mosquito located in the middle and the western part of the area. Water bodies, Wetlands and Solid waste collection 3.4 Suitable environment for dengue mosquito centers are possible dengue mosquito breeding sites. survival Dengue risk is high close to these sites. Hence buffer analysis was carried out for these mosquito breeding Slum areas, residential areas, commercial and sites to determine the risk associated with the industrial areas were considered as the suitable proximity. In Kelaniya DS division, middle and the environment for mosquito survival. Actually these south western part mostly contain the fresh water land uses mostly related with the urban area where bodies including ponds and lakes. Actually these are the population is very high. It is resulted rapid clean waters hence they are acting as favorable sites dengue transmission. Furthermore, the dengue for dengue mosquito breeding. Comparatively, the mosquito breeds outside but it rests in indoor. water bodies have high risk over the wetlands as they Therefore, the chances are low to fly the mosquito to are perennials in nature. However, in principle water outside from these areas. Due to this, the factors were bodies also act as breeding sites and harbour the directly ranked according to their risks condition. mosquitoes. Three different vulnerable zones were demarcated in the study. Less than 200 meters (< 200 In this study, the slum areas were ranked as 3 (high rank) because these areas are under high risk due to Reduce Exposure to Reduce Risk 206

high population density and mismanagement of At this part, Spearman‟s Rank Correlation analysis resources. Further, poor drainage system of the slum area will create pools of stagnant water and many was used to validate the map. Actual dengue cases people are living within the short flight range of the vector. The study area is very small but highly were collected from different GN divisions. urbanized where higher population density and interconnection of houses could lead to more Neighborhood Analysis was performed to efficient transmission of the virus and thus increased exposure to infection. But compared with slum area, incorporate the potential influence of the the risk is low hence the residential areas were ranked as 2 (moderate risk). Comparatively neighboring GN divisions. Subsequently the results commercial and industrial areas have low risk level. Therefore, the commercial and industrial area was were ranked as high, medium and low based on the ranked as 1(low risk). Figure 8 shows the risk level of the above land uses in the study area. Dengue incidences. Eventually the Spearman‟s rank 3.5 Population density and dengue risk correlation analysis was done between the actual Population density is another major factor which dengue data and map data (Table 2). In the years supports dengue disease. Kelaniya DS division is relatively a small area but population density is very 2007, 2008, 2009 and 2010, a significant (0.01> p) high per square kilometer. Population density in Grama Niladhari divisions in 2009 was considered positive correlation was observed between actual for this study. Densely populated area stands a higher chance of experiencing a dengue outbreak because cases and risk map data. This indicates that the GIS Aedes aegypti mosquito does not have to travel far to search its victims. The flight distance of the mosquito model prepared to assess the dengue risk is suitable could range from a few meters to more than 50 meters in a closed urban environment (Reiter et al., in assessing dengue incidences in Kelaniya DS 1995). Due to this the disease can easily spread to people. Figure 9 shows the population density of division. According to the Table 2, 2009 actual study area and associated dengue risk in 2009. dengue cases were highly correlated as 0.635 with According to this density map, 6 GN divisions had more than 11501-16500 persons /Sq kilometer. the risk map and also the correlation was positive as Hunupitiya North, Nahena, Polhena, Pattiya-West, Peliyagodawatta and Peliyagoda Gangabada have 0.617 in 2010. Therefore, the comparison of the high density of population. This range of the population density was ranked as 3 (high risk). 16 study has proved that the final map and the actual GN divisions contained 1500-6500 persons/Sq kilometer. This range was ranked as 1(low risk). dengue cases are correlated positively and the risk Other GN divisions were categorized in to moderate risk (6501-11500 person/Sq km). According to the map will be a model for this disease to make density map, high density of population was identified in northern, southern and western parts of predictions in the future. the study area. 4 CONCLUSIONS 3.6 Identification of dengue risk zones Kelaniya DS division is a very small administrative The dengue risk zones connecting all the above area but the dengue incidences are becoming very mentioned factors were identified and presented in high. The high risks GN divisions were identified in Figure 10. According to the risk map, Pattiya- West, the western part of the study area. Specially, Pattiya- Pattiya- East, Peliyagoda, Peliyagodawatta, West, Pattiya-East, Peliyagoda, Peliyagodawatta, Peliyagoda Gangabada, Peliyagoda Gangabada-East Peliyagoda Gangabada, Peliyagoda Gangabada-East and Meegahawatta GN divisions are identified as and Meegahawatta GN divisions are in high risk high risk divisions and Kendahena, Dalugamgoda, zone. The level of risk is high towards the western Warakanatta, Koholvila, and Polhena divisions are part of the Divisions provided that, the associations identified as low risk GN divisions. Others fall under of the water features and solid waste collection moderate risk zone. Figure 10 shows the Dengue centers have high influence on the prevalence of risk zones in Kelaniya Divisional Secretariat area. dengue since other factors like population density, LST, NDVI and mosquito survival areas are 3.7 Comparison of dengue risk and actual relatively homogeneous. The spatial patterns of the risk level changes are localized and relatively dengue cases continuous due to the neighborhood effects. Correlation analysis confirms that this model can be effectively used to assess the possible dengue incidences in the study area and also may be applicable to other areas after initial testing. With the dengue risk map, the local authorities and other health agencies can predict and mitigate the dengue risk and it helps to carry out surveillance activities in the high risk GN divisions. 5 ACKNOWLEDGMENTS The authors wish to thank the Urban Development Authority, District office of Gamapaha for providing spatial data for this study. They would also acknowledge the MOH office Kelaniya for providing data on actual dengue cases and other information in Kelaniya GN divisions. A special thank goes for the Reduce Exposure to Reduce Risk 207

Peliyagoda Urban Council for providing useful Pathirana S., Kawabata M and Goonetilake R. comments during data collection. (2009). Study of potential risk of dengue 6 REFERENCES disease outbreak in Sri Lanka using GIS and statistical modeling, Journal of Bhandari KP., Raju PLN., and Sokhi BS. (2008). Rural and Tropical Public Health, Volume-8: pp. 8- Application of GIS Modeling for Dengue fever prone area based on socio-cultural and environmental 17. factors- A case study of Delhi city zone. The International Archives of the Photogrammetry, Reiter P., Amador M.A., and Anderson R.A. (1995). Remote Sensing and Spatial Information Sciences, Short report: dispersal of Aedes aegypti in urban Volume-XXXVII, Part B8: pp. 165-170. areas after blood feeding as demonstrated by rubidium-marked eggs. American Journal of Tropical Medicine and Hygine, 52. pp. 177-179. Kulatilaka A., and Jayakuru WS. (1998). Control of Sithiprasasna R., and Linthieum KJ. (1997). Use of dengue/dengue haemorrhagic fever in Sri Lanka. Geographical Information System to study the Dengue Epidemiology of Dengue Haemorrhagic Fever in Bulletin, WHO 22. pp. 53-59. Thailand. Dengue Bulletin, 21. pp 68-73. Nakhapakorn K., and Tripathi N. K. (2005). An Statistical Hand Book. (2009). Planning Division, Information Value Based Analysis of Physical and Kelaniya Divisional Secretariat, Sri Lank Climatic Factors Affecting Dengue Fever and Dengue Haemorrhagic Fever Incidence, Asian Institute of Technology, Thailand [online] Available from internet http://creativecommons.org/licenses/by/2.0. Table 1: Criteria of the study Factor Risk Level Rank LST (in degree < 30 (Low) 1 Celsius) 30- 34 (Moderate) 2 NDVI > 34 (Low) 1 0 - 1 (Vegetation areas 1 Fresh Water Bodies (Buffer Zones) - Low) 2 -1 - 0 (Non Vegetation Wetlands 1 (Buffer Zones) areas - Moderate) 2 > 400 meters (Low) Solid Waste 3 Collection Centers 200 – 400 meters 1 (Buffer Zones) (Moderate) 2 Land Uses (others) < 200 meters (High) 3 1 Population Density/ > 400 meters (Low) 2 sq Km 200 – 400 meters 3 (Moderate) 1 < 200 meters (High) > 400 meters (Low) 2 200 – 400 meters 3 (Moderate) 1 2 < 200 meters (High) Commercial & Industrial 3 areas (Low) Residential areas (Moderate) Slum areas (High) 1500 – 6500 (Low) 6501 – 11500 (Moderate) 11501 – above (High) Reduce Exposure to Reduce Risk 208

Figure 1: Location of the study Figure 10: Final output of the study area showing the dengue risk levels in Kelaniya DS division Table 2: Spearman‟s Rank Correlation (Nonparametric Correlations) between risk map and actual dengue cases in Kelaniya DS Division after the Neighborhood Analysis C or relatio ns Spearman's rho MAP Correlation Coef f icient MAP D2007 D2008 D2009 D2010 Sig. (2-tailed) 1.000 .476** .448** .635** .617** N . .003 .005 .000 .000 37 37 37 37 37 1.000 .375* .375* .418* D2007 Correlation Coef f icient .476** . .022 .022 .010 37 37 37 37 D2008 Sig. (2-tailed) .003 .375* .536** .402* N 37 .022 1.000 .001 .014 Correlation Coef f icient 37 . 37 37 .448** .375* .708** .022 37 1.000 .000 Sig. (2-tailed) .005 37 .536** . 37 .418* .001 1.000 D2009 N 37 .010 37 . Correlation Coef f icient .635** 37 37 .708** 37 Sig. (2-tailed) .000 .402* .000 .014 N 37 37 37 D2010 Correlation Coef f icient .617** Sig. (2-tailed) .000 N 37 **. Correlation is signif icant at t he .01 lev el (2-tailed). *. Correlation is signif icant at t he .05 lev el (2-tailed). Reduce Exposure to Reduce Risk 209

Environmental Anthropogenic Factors Factors LST NDVI Wat Wetla Solid Other Land Populat Land (SPOT - er nds Waste Uses ion sat Bod Collectio Density ETM+ 5) ies Buffers n Centers - Slum Area (3 - Residential Classif Buffers Buffers Area y (3 Classes) (3 - Commercial (3 Classes) Classes) Reclass Reclassi Proximity Maps ify fy (3 (2 Ranking High risk – 3 Moderate risk – 2 Low risk - 1 Ranked Maps Convert to Raster Actual Dengue Raster Calculation Cases Overlaid Map GN Division Reclassify wise Neighborhood Analysis Ranking Reclassifi Identifying Ranking Spearman‟s ed the (3 Rank Correlation Risk Map High Risk classes) (Comparison) Zones Figure 2: Flow diagram of the methodology Reduce Exposure to Reduce Risk 210

GEOGRAPHICAL INFORMATION SYSTEM (GIS) BASED APPROACH TO MONITOR EPIDEMIOLOGICAL DISASTER: 2011 DENGUE FEVER OUTBREAK IN PUNJAB, PAKISTAN Shahbaz Ahmad1, Muhammad Bilal Sadiq2, Tanzeem AkbarCheema2, Qaisar Abbas3 and Muhammad Shahzad Sarfraz4 1Department of Computer Science, National Textile University, Faisalabad,Pakistan ([email protected]) 2Food Engineering & Bioprocess Technology,Asian Institute of Technology, Bangkok, Thailand 3Department of Computer Science, National Textile University, Faisalabad, Pakistan ([email protected]) 4Remote Sensing & Geographical Information System, Asian Institute of Technology, Bangkok, Thailand ABSTRACT Epidemiological disaster management, using geo-informatics, is an innovative field of rapid information gathering.Dengue fever,a vector-borne disease, also known as break bone fever, is a lethal re-emerging arboviral disease. Its endemic flow is adding serious effects to the economy and health concern issues at global-level.Still many under-developing and developing countries like Pakistan are lacking the necessarygeo- informaticstechnologies to monitor such health issues. The objective of this study was to enhance the effectiveness of developing countries disaster management capabilities by using these state of the art technologies, which provides the measures to relief the disaster burden on public sector agencies. Temporal changes and regional burden of this disease distribution were mapped using geo-information tools. Such types of studies may widely be used for the prevention of disaster burden once it occurs, or can be also helpful to give relief to the effectives. Moreover, public sector institutes can use such information tools for surveillance to identify the risk areas for possible precautionary measures to be taken forcommon public. GEOGRAPHIC INFORMATION OF FISH-BORNE PARASITIC METACERCARIA IN CHI RIVER, MAHASARAKHAM, THAILAND Choosak Nthikathkul1,2, Chalobol Wongsawad2, Pipat Rungsaeng3, Pissamai Homchumpa1, Sirinart Tongsiri 1and Anothai Trivanich4 1Graduate Studies Division, Faculty of Medicine, Mahasarakham University, Mahasarakham, Thailand 2Biology Department, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand 3Computer Science Department, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand 4Statistic Department, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand ABSTRACT 211 Fish-borne infections continue to be a major public health problem, with more than 50 million people infected throughout the world. Fish-borne parasites of human and animals. They are dorsoventrally flattened and hermaphroditic and require one or more intermediate hosts. Fish-borne trematodes were found in the small intestines of several definitive hosts such as bird, cat, dog, rat and human. Human and definitive hosts were infected by eating raw freshwater fish containing encysted metacercariae. Thus, this study was to investigate the geographic information of the prevalence of fish-borne trematode metacercariae in 10 freshwater stations, (N 16°18'23.0'' E 103°18'58.6'', N 16°17'0.7' E 103°21'4.2'', N 16°13'52.4'' E 103°16'15.4'', N 16°14'11.1'' E 103°06'55.1'', N 16°15'10.1'' E 103°04'30.5'', N 16°13'53.5'' E 103°17'56.6'', N 16°08'10.7'' E 103°18'34.8'', N 16°13'0.6'' E 103°19'54.3'', N 16°12'51.7'' E 103°18'23.4'', N 16°11'24.9'' E 103°18'24.4'') Chi river area in Mahasarakham province. A total number of 420 samples of freshwater fish of 19 species were randomly collected and examined for fish-borne trematode metacercaria. A total metacercaria in fish were found infected with 3 fish-borne trematode metacercariae, namely; Opisthorchis viverrini, Haplorchis taichui, and Haplorchoides sp. 19 species of freshwater fishes were collected and investigated harbouring fish borne trematode metacercaria. The prevalence of fish-borne metacercariae were 28.33% (119/420). Opisthrochis viverrini 1.67% (7/420) and intensity was 0.80 per one fish. These were Barbodes gonionotus, Barbonymus Reduce Exposure to Reduce Risk

altus, Barbonymus gonionotus, Cyclocheilichthys enoplos, Cyclocheilichthys repasson, Epalzeorhynchos chrysophekadion, Hampala dispar, Henicorhynchus siamensis, Labiobarbus siamensis, Mystus nigriceps, Osteochilus vittatus, Paralaubuca typus, Pristolepis fasciatus, Puntioplites proctozysron, Puntius brevis, Thynnichthys thynnoides, Trichogaster pectoralis, Trichogaster trichopterus, and Yasuhikotakia sidthimunki. Our study shows fish-borne trematode metacercariae in fishes, relating to Opisthorchis viverrini, H. taichui, and Haplorchoides sp. The geographic information (latitude and longitude) associated with the infection rates among susceptible species of fresh water fish was recorded and used to build a geographical information system. A number of environmental parameters such as mean yearly temperature, rainfall level, and land use were imported to the system as well. The development of GIS can be useful in establishing a prevention strategy for the transmission of food borne diseases from infected fish in water catchment areas. KEY WORDS: Fish-borne diseases, fresh water fish, Opisthorchis viverrini, small intestinal fluke, GIS MAPPING OF LIVER FLUKE FOR REDUCE EXPOSURE TO REDUCE RISK AT PHUTTHAISONG DISTRICT, BURIRUM PROVINCE, THAILAND Sarawut Jampapunt1, 2 , Pissamai Homchumpa3, Sompong Jarungjitanuson4, Somsak Sripakdee4 and Choosak Nithikathkul3 1Faculty of Public Health, Mahasarakham University, Mahasarakham Province, Thailand 2Phutthaisong Public Health Office, Burirum Province, Thailand 3Graduate Studies Division, Faculty of Medicine, Mahasarakham University, Mahasarakham, Thailand 4Burirum Province Public Health Office, Burirum Province, Thailand ABSTRACT The present situation was performed in order to mapping of liver fluke to reduce exposure to reduce risk of infection with Opisthorchis viverrini in two areas of Phutthaisong district, Burirum Province, Neighboring districts are (from the south clockwise) Khu Mueang of Buriram Province, [WGS84 15° 32′ 54″ N, 103° 1′ 30″ E15.548333, 103.025 , UTM 48P 288187 1719954 , Thailand. Eight hundred and seventy three subjects including all age-groups from below 15 years of age to more than 60 years. The mapping of infection varied in the two investigated areas and ranged from 2.29 % to 7.12, with the growing of evidence linking environmental exposure to cholangiocarcinoma [CCA], the public is become increasingly aware of unacceptable burden of CCA resulting from environmental and occupational exposure that could have been prevented through appropriate national action. The focus group and participatory action research was particularly concern to find that the true burden of environmentally induced CCA has been underestimated. Liver fluke is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Data regarding spatial geographic information and epidemiologic characteristics of the population were thought to be useful in the prevention by reduce exposure to reduce risk and development of a strategy to control and eradicate infections in a cost-effective manner. Reduce Exposure to Reduce Risk 212

MAPPING AND SURVEILLANCE OF OPISTHORCHIASIS STUDY IN KHON KAEN: BAN NON MODEL Choosak Nithikathkul1, Pipat Reungsaeng2, Bangon Changsap3, Supaporn Wannapinyosheep3, Direk Panitsupakamon, Trivanich2 and Chalobol Wongsawad4 1Faculty of Medicine, Mahasarakham University, Mahasarakham Province, Thailand 2Faculty of Science, Khon Kaen University, Khon Kaen Province, Thailand 3Faculty of Science and Technology, Huachewchalermprakiet University, Samut Prakan Province, Thailand 4Faculty of Science, Chiang Mai University, Chiang Mai Province, Thailand E-mail: [email protected] ABSTRACT Opisthochiasis caused by Opisthorchis viverrini remains a major public health problem in many parts of Southeast Asia including Thailand, Lao PDR, Vietnam and Cambodia. The epicenter of this disease is located in northeast Thailand, where high prevalence coexists with a high incidence of cholangiocarcinoma (CHCA): a major primary carcinoma of the liver with a very poor prognosis. The current study was conducted to determine the surveillance characteristics of Opisthorchis viverrini infection in Northeast Thailand. One hundred and sixty four stool sample from Ban Non, Sum Song district, Khon Kaen province were used in this study. All age- groups from twenty years of age to more than 60 years. The result showed opisthorchiasis was 1.8 % (3/164) and Strongyloides stercoralis was 6.7% (11/164). The floods accompanied by the increased helminthological contamination of the upper soil layer may lead to a higher human risk for contamination with helminthic diseases. As a result of this study, the Provincial Health Officer concluded that mass treatment for Opisthorchiasis in the Thai population targeting high risk individuals may be a cost-effective way to allocate limited funds. Perhaps this type of approach and further study on the correlation of symptoms with infection may offer a comprehensive strategy to the helminthes dilemma. Reduce Exposure to Reduce Risk 213

Reduce Exposure to Reduce Risk 214

Technical Session- 10 [Hall C]: IT and NDM Design and Implementation of Web Based GIS for Flood Disaster Management: Case 216 Study of Nowshera District 220 Fayaz Ali Shah 230 Natural Disaster Management - using Mobile Mapping with GPS and High 236 Resolution Satellite Data 243 Ganesan Veerappan Goundar, Muralidharan, Maha lakshmi, Devi, Sakthivel, 247 Ghouse Mohamed Shaik ,Vajravel Tamilselvan 247 248 Semantic Integration of Geospatial Services In SDI for Mobile GIS Based Disaster Management RMKGSPB Koswatte Development of a Geoportal Framework for Natural Disaster MMR Related Activities Hamid Mehmood, Nitin K. Tripathi Automated Disaster Management System for Early Recovery through GIS M. P. A. W. Gamage, K. P. D. H. De Silva, S. Janahan, S. T. Mackmillian, R. H. Daniel, S. D. T. N. Siyambalapitiya Cloud Computing as a Novel Approach to Natural Disaster Managements in Sri Lanka J.M.D.R.Menike, Ranjith Premalal De Silva Predicting the Distribution of Port-AU-Prince’s IDP Camps James F. Bramante Land Use / Land Cover Mapping of Nagapattinam Block and Disaster Management using Geomatics Tamil Selvan Vajravelu, Ramakrishnan, Ramalinagam, Ganesan.G Ghouse M ohamed Shaik Reduce Exposure to Reduce Risk 215

DESIGN AND IMPLEMENTATION OF WEB BASED GIS FOR FLOOD DISASTER MANAGEMENT: CASE STUDY OF NOWSHERA DISTRICT Fayaz Ali Shah Institute of Geographical Information System, National University of Sciences and Technology, Pakistan E-mail: [email protected] ABSTRACT Natural hazards (i.e. earthquakes, floods, landslides) become disaster when there are human as well as financial losses. To effectively manage and reduce these losses on the earth we need a comprehensive approach, called disaster management. For this approach a user may need the spatial and non spatial data like: real time weather information, earthquake information, flood information, buildings, roads, hospital etc, to make a better decision about the pre-disaster and post-disaster situations. To take a better decision in such like situation decision makers may need to handle these information’s, which is helpful for the benefit of all human beings who live on this earth. In any disaster there is need of spatial data which can be mapped for easily understandable form for all users. So a Geographical Information System (GIS) can support disaster management as a powerful tool for collecting, storing, analysis, modeling and displaying large amount of data. Many governmental, national and international organizations which involve in disaster management, require to access to the right data in the right time to make the right decisions. So designing a GIS to distribute geospatial information on a network such as Web, gives a chance to the managers of organizations to easy access information about disaster anytime and anywhere they are. This paper outlines different steps of developing a Web-based GIS to manage and response to flood in a Nowshehra district of Khyber Pakhtunkhwa as a case study. In this project, the applications of the system in the four phases of disaster management include mitigation, preparedness, response, and recovery, Which has various capability such as publishing mitigation maps, Flood category maps like major, Moderate, minor and below, displaying real-time weather information, and automatically producing response reports of disasters, which had been established in a Web-GIS platform. KEYWORDS: Disaster management, Web-GIS, Decision Makers INTRODUCTION • Instant Feedback and updating: The current One of a common natural disaster in the world is status can be updated from moment to Flood, Which causes terrible damages to people’s moment lives and properties. Flood becomes a disaster when it destructs the man-made environment, such as Web-based GIS play a vital role in this aspect buildings, roads and crops etc. The current study is providing timely and right information to the focused on the impacts and mechanism of floods in concerned people and the emergency managers for Kabul River during the unprecedented monsoon rains taking necessary actions. Web-based GIS is a from 2000 to 2010 in the districts of Nowshera. From centrally managed and distributed computing design. this study one can find the major flood extent areas, It is urgently necessary to make a plan for response moderate flood extent areas, and minor flood extent phase of flood disaster management in order to areas. To give this spatial data access to everyone, reduce human losses and physical losses from there is need of distributed system through which all damage. Gathering relevant data through all of users may use these information’s on right time. This governmental departments, organizations and input distributed system is called Web GIS. Need of Web this data in a GIS as an appropriate tool to process GIS in Disaster management: and analyze it, can help managers to make a better decision during and just after of a flood. One • Centralized Control: A web GIS can important need for any disaster management effort is distribute information from a control room to have the spatial information accessible to a larger which can reach everyone. group of people, in a fast, easy and cost-effective manner. The use of GIS on the web can help a lot in • Changes made in the map are reflected achieving these objectives. This paper describes everywhere design and development of a web-based GIS for disaster response. • No need for a GIS Software with the users • No need for training the users in GIS Reduce Exposure to Reduce Risk 216

DATA COLLECTION  Free or open source software (FOSS); The 2010 floods in the Nowshera were chosen as a  Management tool provided for hierarchical suitable case study for the development of a prototype disaster support system. Marking the layer management; maximum flood level with the help of GPS co-  Capabilities of query and measurement tools; ordinates on either side of the channel at different  Capabilities for scale management and search stations in the study area. Plotting the GPS data on a satellite image base map of 5 m resolution. Digital functionality; Elevation Model of 30 m resolution to delineate the  Programming languages supported (Java and floodplains according to the intensity of risk of flooding in the area. C#);  Support for the OGC-compliant services, WMS Platform Selection and Platform Design The system platform selected for the disaster support and WFS; and system was based on the choice of the Web client for  Data interoperability (vector, raster, PostGIS). the Web-GIS. The Web client provides the interface that allows the users to visualize, navigate and Based on the above criteria, p.mapper was selected as analyze the geospatial data via the Web. it meets the majority of the requirements, and a The selection of the Web client was based on the prototype tool was developed, a detailed explanation following criteria: of which is presented in the results section below. The p.mapper server is a MapServer application based on PHP/Mapscript. The freely available MapServer for Windows package (MS4W) was selected, installing the pre-configured Web server environment required to establish a p.mapper Reduce Exposure to Reduce Risk 217

application (comprising the Apache Web server, DISCUSSIONS MapServer, PHP, Mapscript, and the Geospatial Data The Web-GIS platform mapserver was selected as Abstraction Library, or GDAL). Once installed the mapping engine as it satisfied best the operational MS4W provides the possibility for updating criteria. The questionnaire results provided key individual components without affecting or insights as to stakeholder requirements for receiving modifying the rest of the installed applications – a spatial displays and maps, as well as to the concern for managing ongoing support for such an infrastructure information required in order to application platform.Once MS4W was established, develop effective, time bound strategies for disaster the object-relational database system PostgreSQL management. The final prototype system exhibits and its extension PostGIS was also installed to several advantages for its ongoing implementation. provide the underlying data repository for the Firstly, it does not require specialist software such as platform. A key stage then involved the conversion GIS to be pre-installed on user computers. Secondly, and export of the Nowshera to Khairabad geospatial it adopts open-source ‘FOSS’ software tools, which data, from shapefiles, into the PostGIS database reduce significantly the costs of implementation. format. Finally the p.mapper application was Thirdly, it can operate with both vector and raster customized to meet the needs of the prototype. spatial data formats, making its integration with wider raster models and future satellite imagery STUDY AREA possible. Nowshera known locally as Nowkhaar is the chief city of Nowshera District in the Khyber CONCLUSION Pakhtunkhwa Province of Pakistan. It is also one of A Web-based GIS system has been developed for the largest cities of the province and lies on supporting flood disaster management of kabul river the Grand Trunk Road 27 miles due east of from nowshera to khairabad. This system helps to Peshawar at 34°0'55N 71°58'29E. The study area estimate the extent and size of damages, different starts from nowshera to khairabad, where the river categories of flood and flooded areas, just after the Kabul fell in the Indus river. flood. It also, helps the managers to fast response to flood disaster. This takes a lot of advantages to the managers by decreasing the cost and response time to make better decision during and just after the flood. Kabul River from Nowshera to Khairabad 218 Expected Outcomes:  Maps of Expected Damage.  Flood extent maps (major flood, moderate flood, minor flood, below flood).  WEB-GIS Module is to make geographic data and thematic maps available to specific end- users and, potentially, to the public.  Multi-source data and GIS integrated analysis can contribute to a better emergency planning, providing fundamental information for immediate response when future disasters will occur.  A web-based disaster management system for data sharing, data exchange and data analysis Reduce Exposure to Reduce Risk

REFERENCES A. Rajabifard, A. Mansourian, M. J. V. Zoej, and I. Williamson, Developing Spatial Data Infrastructure to Facilitate Disaster Management. In proceeding of GEOMATICS83 6 p. (2004). T. J. Cova, GIS in Emergency Management, in Geographical Information System: Principles, Applications, and Management (P.A. Longley, M.F. Goodchild, D.J. Macguire, D.W. Rihind, Eds.) Jonh Wiley & Sons, New York, p.p. 845- 858 (1999). Amdahl, G. (2002). Disaster response: GIS for public safety, Published by ESRI, Redlands California. http://www.esri.com/news/arcnews/winter0102ar ticles/gis-homeland.html - (visited on October 2002). Davies, J., 2003. Expanding Spatial Data Infrastructure model to support spatial wireless applications, PhD thesis, Department of Geomatics, The University of Melbourne, Melbourne, Australia, 210 pp. Flood Report, 2009. Pakistan Meteorological Department, Retrieved from http://www.pakmet.com.pk/FFD/cp/fr2009.pdf, November, 2010. Reduce Exposure to Reduce Risk 219

Natural Disaster Management - using Mobile Mapping with GPS and High Resolution Satellite Data Ganesan Veerappan Goundar$1, Muralidharan $2,Maha lakshmi,$3 Devi $4 Sakthivel$5, Ghouse Mohamed Shaik* ,Vajravel Tamilselvan** $ Asst Prof, MCA, [email protected], 2, [email protected] [email protected], [email protected], sreesakthi09 @gmail. com*, * Director Research, [email protected], Sri Venkateswara College of Engineering and Technology, Thirupachur, ** [email protected], Inst of remote sensing, Anna University Chennai 600025, India ABSTRACT: The pervasive use of Geographic Information Systems with Remote sensing satellite data, advances in Global Positioning System and Mobile positioning systems are strengthening and reinforcing the association between databases modeling, the day-to-day management of land with mobile mapping in rural and urban areas. Image analysis helps in interpretation of images in satellites and includes the cadastral information land parcels to push in to mobile computing for better use of remotely sensed satellite image. The nation can use mobile geographical information system for sharing the tiny land parcel information and to use data in of mobile handsets of millions of farmers and scientist assisting their growth. This paper suggests the visual enhancement of land parcel information in the mobiles for the pilot area in Arasanatham village, near Namakkal, India a part of South Asia. The information of owner ship of land parcels which are smaller than 0.1Ha the rural farm roads, housing settlement, ground water levels in open wells, soil moisture, crops raised, stage of crop growth, pest attack, weather data, micro irrigation and crop husbandry etc., are essential for profitable farming. High resolution remote sensing images are which provide detailed information are of immense use to farmers owning tiny land parcels of even less that 0.1 ha. The cadastral maps of the pilot study area are digitized, with GPS survey. The lat /log values of the corners of tiny land parcel are identified and included as layer over the Satellite data available in the Geo eye image from Google earth web site. The spatial data with non-spatial data such as land parcel owner ship, soil moisture, climate, cropping condition, irrigation, pest attack, failure due need of fertilizer, damage due to drought and failure of crop, flood damage, damage due to high winds in Pre Monsoon season, need of timely irrigation depend on need of crop, will be added to attribute table for creation of mobile disaster managements in farm lands. Using mobile mapping of land information on crop failure due to natural disasters at pilot study area is possible by using high resolution data of multi type satellites and by comparison of signal strength measurement with the combination of various Band analysis, the field levels, height of the crop, surrounding building, the distance from nearest tower, efficiency mobile services provider if the area is covered with more than tower. In Geographic Information System environment, we use viewshed analysis which explain about terrain model, on a map which can be seen from a given point(s), line or area. The study will provide required information for managing the farm land parcels by integration of GIS, Remote sensing data with mobile communication to stabilize agriculture production with land use spatial data in the event of natural disasters. The success of the study is assured as every farmer owns a mobile phone with 2G or 3G technology which can provide him to view his land details with attribute data with map, satellite data, terrain data and tiny picture or video of his land on the date of browsing. The farmer can take action to have a better crop by managing the land, irrigation and timely crop husbandry to protect from drought, cyclone, and floods. Adding agriculture marketing information such as transport availability, roads, marketing center, and non-spatial information such as marketing prices etc., in this mobile disaster management agriculture GIS, will enable sustainable returns from farming. The research in using multi date and multi type data to give reliable information to farmers to adopt this user friendly technology to improve his economy and living standards which keep India growing event nation faces worst climatic condition and natural disasters. KEY WORDS: Mobile Geographical Information System, Land Parcel, GPS Survey, Disaster Management, Viewshed Analysis, Satellite Data, Google earth website, Agriculture GIS. 1. INTRODUCTION System and Mobile positioning systems are strengthening and reinforcing the association The pervasive use of Geographic Information between databases, modeling the day-to-day Systems with Remote sensing satellite data, management of land with mobile mapping in rural advances in Global Positioning and urban areas. Image analysis helps in interpretation of images in satellites and includes Reduce Exposure to Reduce Risk 220

the cadastral information land parcels to push in to Google's mapping engine prompted a surge of mobile computing to better use of remotely sensed interest in satellite imagery [6]. satellite image. The nation can use mobile geographical information system for sharing the 1.2 GeoEye-1 High-Resolution Satellite Imagery tiny land parcel information and to use data in of mobile handsets of millions of farmers and scientist GeoEye-1, the world‘s highest-resolution [9], assisting their growth. This is also provide latest commercial color imaging satellite. This satellite land parcel information in the event of natural offers extraordinary detail, high accuracy and disasters and mobilize resources using data from enhanced stereo for DEM generation in comb high resolution satellites and protect the farm land. ination with stereo satellite data. GeoEye-1 will simultaneously collect panchromatic imagery at 1.1 Google Maps for High-Resolution Data 0.41m and Multispectral imagery at 1.65m Licensing, GeoEye-1 has the capacity to collect up Google Maps provides high-resolution satellite to 700,000 square kilometers of panchromatic images for most areas in the world. The current imagery (and up to 350,000 square kilometers of satellite imagery is over 5 years old and thus do not Pan-Sharpened Multispectral imagery) per day. have latest accurate data With the introduction of The table 1: Explains about the GeoEye-1 HRSI. an easily pannable and searchable mapping and satellite imagery tool as shown in figure 1. GeoEye-1 Specifications Imaging Mode Panchromatic Multispectral Spatial .41 meter 1.65 meter GSD Resolution GSD at Nadir Spectral Range 450-900 nm at Nadir Swath Width 15.2 km 450-520 nm (blue) 520-600 nm Off-Nadir Up to 60 degrees DImyangaimngic Range 11 bit per pixel Mission Life Expectation > 10 years Revisit Time Less than 3 day Orbital Altitude 681 km Table 1: GeoEye – 1 Specifications for satellite picture Figure 1: Google maps digitizer tool 221 Reduce Exposure to Reduce Risk

2. LAND PARCEL INFORMATION FOR 2.2. STUDY OF AREA ARASANATHAM This paper suggests the visual enhancement of land The following table contains the land parcel parcel information in the mobiles for the pilot area information includes land owners details, name, in Arasanatham village, Namakkalm India as survey numbers, Irrigation, crop in the land, shown in the figure 2. latitude in tiny pilot area of the Arasanatham village. It may be a tiny village but have lot of The information on land parcel owner ship which natural resources which fully suitable for the as small as 0.1Ha the rural farm roads, housing agriculture. The resource includes water storage, settlement, ground water levels in open wells, soil and water levels for agriculture, weather at seasons, moisture, crops raised, stage of crop growth, pest rain ratio are all completely different in year by attack, weather data, micro irrigation and crop year. Here my survey taken part of implementation husbandry etc., are essential for profitable in the land parcel information system by mobile farming. using web based high resolution image. The table 2 explains about the web HRSI land parcel data. Table 2: Land parcel information about Arasanatham Sur Land Are Irrig Crop Lat. a in atio Coordinat vey a’cr n es No. Owner es 270 Veerappan 5.10 well Groun 11°5'24\"N d nut 78°11'20\" E 175 Veerappa 1.60 Rive Sugar 11°5'22\"N goundar r cane 78°11'25\" E 269 Kandasam 4.50 Rive Cocoa 11°5'27\"N y r/We nut ll 78°11'19\" E 267 Kalianna 6.20 Well Sugar 11°5'31\"N goundar cane 78°11'23\" E 266 Ramasam 2.00 Well planta 11°5'34\"N y in 78°11'18\" E 390 Kaliannan, 4.10 Well Turm 11°5'30\"N eric Ramasam 78°11'18\" y E 268 Veerappan 2.65 Rive Wet 11°5'27\"N r 78°11'24\" Reduce Exposure to Reduce Risk 222

E 271 Rajasekar 2.65 Well Groun 11°5'24\"N d nut 78°11'27\" E 273 Senthil 2.70 Well Cocoa 11°5'24\"N nut 78°11'29\" E 275 Jaganatha 3.15 Well Groun 11°5'21\"N n / d nut Rive 78°11'25\" rE 280 Periyasam 4.13 Well Groun 11°5'20\"N y d nut 78°11'24\" E 281 Subramani 8.45 Well Sugar 11°5'18\"N / cane Rive 78°11'28\" r E 2. 3.HIGH RESOLUTION SATELLITE DATA disaster management ). Spatial statistics typically result primarily from observation rather than High resolution remote sensing images are experimentation. providing detailed information. It is of immense use to farmers owning tiny land parcels of even less 5. ADVANCED OPERATIONS that 0.1 ha. The cadastral maps of the pilot study area will be digitized, with GPS survey. The lat Geospatial analysis goes beyond 2D mapping /log values of the corners of tiny land parcel will be operations and spatial statistics. It includes: identified included as layer over the Satellite data available in the Geographical website [1]. For eg.,  Surface analysis —in particular analysing the Geo eye data Google Earth web site. properties of physical surfaces, such 4. GEOSPATIAL ANALYSIS FOR MOBILE COMMUNICATION as gradient, aspect and visibility, and Geospatial analysis, using GIS, was developed for analysing surface-like data ―fields‖; problems in the environmental and life sciences, in  Network analysis — examining the properties particular ecology, geology and epidemiology. It has extended to almost all industries including of natural and man-made networks in order to defense, intelligence, utilities, Natural Resources (i.e. Oil and Gas, Forestry etc), social sciences, understand the behavior of flows within and medicine and Public Safety (i.e. emergency and around such networks.  Geovisualization — the creation and manipulation of images, maps, diagrams, charts, 3D views and their associated tabular datasets. Reduce Exposure to Reduce Risk 223

Figure 3: High resolution satellite data Arasanatham Namakkal India. 6. NTEGRATION WITH GIS AND GPS Climate, cropping condition, irrigation, pest/ SURVEY FOR MOBILE MAPPING fertilizer needs will be integrated for creation of mobile mapping applications. This spatial information along with non-spatial data such as land owner ship, soil moisture, Figure 4. Agriculture in land parcel-Study Area The mobile mapping of land information of pilot field levels, height of the surrounding building, the study area will study the advantages of using high distance from nearest tower, efficiency mobile resolution data in detail using multi type satellite services provider if the area is covered more than data by comparison of signal strength measurement tower with all cadastral data of land parcels[2]. with the combination of various Band analysis, the 7. VIEW SHED ANALYSIS IN GEOGRAPHIC INFORMATION SYSTEM ENVIRONMENT The viewshed analysis is the result of a function to stabilize agriculture production with land use which explains about terrain model, where areas on spatial data. The study shows that each farmer who a map can be seen from a given point(s), line or has a mobile phone with 2G or 3G technology can area to provide all information required for view his land details with attribute data along with managing the farm lands by integration of Arc GIS, map, satellite data, terrain data and tiny picture or Remote sensing data with mobile communication video of his land on the date of browsing [9] [10]. Reduce Exposure to Reduce Risk 224

National GIS at NIC is a very large repository of spatial data which incorporates images from Foreign and Indian satellites with different spatial spectral resolution along with the Maps developed from Survey. Figure 5: Village level mapping of demography The application incorporates All India Mosaic of IRS-P6 satellite panchromatic (Black/white) image with 5.80m ground resolution. This data has been integrated with extensive taxonomy from Survey maps for administrative boundaries for states and districts. National Highways, Railway lines and over 10 lac points describing locations for villages, habitations, headquarters, major towns etc., are shown in fig-5. Program: 1 View shed program for vision in farmer mobile phones. A viewshed analysis can be performed using one of cell, each cell between the viewpoint cell and target many GIS programs, such as GRASS cell is examined for line of sight. Where cells of GIS (r.viewshed) & (r.los), SAGA GIS (Visibility), higher value are between the viewpoints and target TNT Mips, ArcMap, Maptitude, ERDAS cells the line of sight is blocked. IMAGINE. To determine the visibility of a target 7.1 Coding For Viewshed Analysis <meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\"/> 225 <meta http-equiv=\"X-UA-Compatible\" content=\"IE=7\" /> <title>GP Viewshed Task</title> <link rel=\"stylesheet\" type=\"text/css\" href=\"http://serverapi.arcgisonline.com/jsapi/arcgis/1.6/js/dojo/dijit/themes/tundra/tundra.css\"> <script type=\"text/javascript\" src=\"http://serverapi.arcgisonline.com/jsapi/arcgis/?v=1.6\"></script> <script type=\"text/javascript\"> dojo.require(\"esri.map\"); dojo.require(\"esri.tasks.gp\"); var map, gp; /*Initialize map, GP*/ function init() { var startExtent = new esri.geometry.Extent(-122.7268, 37.4557, -122.1775, 37.8649, new esri.SpatialReference({wkid:4326}) ); Reduce Exposure to Reduce Risk

map = new esri.Map(\"mapDiv\", { extent: startExtent }); var streetMap = new esri.layers.ArcGISTiledMapServiceLayer(\"http://server.arcgisonline.com/ArcGIS/rest/services/ESRI_StreetMap _World_2D/MapServer\"); map.addLayer(streetMap); gp = new esri.tasks.Geoprocessor(\"http:// sampleserver1.arcgisonline.com/ArcGIS/rest/ services/Elevation/ESRI_Elevation_World/GPServer/Viewshed\"); gp.setOutputSpatialReference({wkid:4326}); dojo.connect(map, \"onClick\", computeViewShed); } function computeViewShed(evt) { map.graphics.clear(); var pointSymbol = new esri.symbol.SimpleMarkerSymbol(); pointSymbol.setSize(5); pointSymbol.setOutline(new esri.symbol.SimpleLineSymbol(esri.symbol.SimpleLineSymbol.STYLE_SOLID, new dojo.Color([255,0,0]), 1)); pointSymbol.setColor(new dojo.Color([0,255,0,0.25])); var graphic = new esri.Graphic(evt.mapPoint,pointSymbol); map.graphics.add(graphic); var features= []; features.push(graphic); var featureSet = new esri.tasks.FeatureSet(); featureSet.features = features; var vsDistance = new esri.tasks.LinearUnit(); vsDistance.distance = 5; vsDistance.units = \"esriMiles\"; var params = { \"Input_Observation_Point\": featureSet, \"Viewshed_Distance\":vsDistance}; gp.execute(params, drawViewshed); } function drawViewshed(results, messages) { var polySymbol = new esri.symbol.SimpleFillSymbol(); polySymbol.setOutline(new esri.symbol.SimpleLineSymbol(esri.symbol.SimpleLineSymbol.STYLE_SOLID, new dojo.Color([0,0,0,0.5]), 1)); polySymbol.setColor(new dojo.Color([255,127,0,0.7])); var features = results[0].value.features; for (var f=0, fl=features.length; f<fl; f++) { var feature = features[f]; feature.setSymbol(polySymbol); map.graphics.add(feature); }} dojo.addOnLoad(init); </script> The farmer can take action to have a better crop by as marketing prices etc. in this mobile agriculture managing the land, irrigation and timely crop GIS, will enable sustainable returns from farming husbandry. Provision of agriculture marketing which is being neglected due to industrialization information such as details of transport, roads, and urge of rural to migrate to urban areas to have a marketing centre, and non-spatial information such better income from urban employment. Reduce Exposure to Reduce Risk 226

Legend \" sf no namakkal.JPG RGB Green: Band_2 Blue: Band_3 path Red: Band_1 fiield Figure 6: Survey numbers and owner of the land. This research using multi date and multi type data These tasks are handling geo-spatial data using can give reliable information to farmers to adopt remote sensing and GIS techniques. this user friendly technology to improve his economy and living standards to keep India Digital mobile mapping constitutes primary role of growing as advanced nation. the development process of managing land parcel area, and mapped data is the common product of The express development of wireless analysis of remotely sensed data. High-resolution communications requires efficient network space-borne remote sensing image data show a planning of cellular mobile communication. The high level of detail and provide opportunities to be initial process in the Mobile communication integrated into mapping applications. To perform networks includes network site identification and the exact mobile mapping, the geometric planning, signal strength measurements with prospective and information about the satellite coverage estimation for the expansion of system. images are essential. Legend 121 192 270.C 122 & sf no 123 198.A 271 175 path 175A 199 275 fiield 175B 175C 199.A 285 <all other values> SF_NO 263.D 285.A 270 namakkal.JPG 270.1 RGB Red: Band_1 Green: Band_2 Blue: Band_3 Figure 7: Mobile Mapping crop and pestides and irregatrion in Arasanatham. 227 Reduce Exposure to Reduce Risk

Nowadays commercially hybrid resolution satellite and its environs for the impact of Soil resistively, imagery (HRSI) offers the potential and accurate Vegetation, Water and Land use, Crop raised, spatial information for a wide mixture of mapping Transport, Housing settlement, crop growth, soil and GIS applications. The extraction of metric moisture and Land cover on the mobile map information from images is possible due to suitable identification and planning using Remote sensing, sensor orientation models, which describe the GIS and GPS for disaster management. relationship between two-dimensional image coordinates and three dimensional object points for The major part of the work has been carried out by agriculture use [5]. making use of the satellite data such as google map, google eye etc.. In the present study, a In this paper, an attempt has been made to study in planning strategy. and around Arasanatham village near Namakkal Legend No Crop Sugercane & sf no Cocomut No Crop Sugercanr Coconut path Dry No crop Topiaca fiield Graden Ground nut Paddy Topiaco <all other values> crop Scattered Trees ground nut Sugarcane sugarcane namakkal.JPG RGB Red: Band_1 Green: Band_2 Blue: Band_3 Figure 8: Area of the survey for land parcel communications using remote sensing and GIS is For establishing awareness about the mobile using demonstrate. 3G technologies for the purpose of mobile This information derived from the satellite data was 8. MOBILE MAPPING VIEW OF integrated with raster GIS modeling. The study IRRIGATION clearly demonstrates that the satellite data could be utilized for planning agriculture. Figure 8: Mobile mapping view of the irrigation systems 228 Reduce Exposure to Reduce Risk

9. RECOMMANADTAIONS analysis for mobile communication site planning using world view-2 high The research is use full for farmers is who resolution satellite data‖. have tiny land holding. It is demonstrated here that 2. Amitabh, B. Gopala krishna, T P even data of few ha of agri land data of each farmer srinivasan and P K srivastava, ―An can be sent by mobile using the High resolution integrated approach for topographical data and Arc GIS software. The information on mapping from space using cartosat-1 and soil, moisture, crop, irrigation crops raised, stage of cartosat-2 imagery‖ the international crop growth, pest attack, weather data, micro archives of the photogrammetry, remote irrigation and crop husbandry etc., are essential for sensing and spatial information sciences. profitable farming can be given to farmer by using Vol. Xxxvii. Part b4. Beijing multidate data and updating with field visit. 3. .Albertz, J. and R. Tauch, \"Mapping from space cartographic applications of satellite This attempt will become a modern communication image data\", geojournal 32.1, pp. 29-37 tool for disaster management for Agriculture. The (1994). research in using multi date and multi type data to give reliable information to farmers to adopt this 4. Akiyama, M., \"Topographic mapping user friendly technology to improve his economy method using spot imagery\", International and living standards which keep farmers growth. archives of photogrammetry and remote sensing, 29, b4 pp. 336-341 (1992). 10. ACKNOWLEDGMENT 5. M. W. Lake, P. E. Woodman and S. J. We are grateful to Dr.K.C.Vasudevan Chairman Mithen, ―Department of Archaeology, and Dr Sursesh Mohan Kumar Principal Sri University of Reading, Whiteknights, Venkateswara college of engineering and Reading, U.K‖ Journal of Archaeological Technology, Thiruvallur District, Tamilnadu for Science (1998) 25, 27–38. encouraging and allowing us to take up this work. Also, authors express their gratitude to all the 6. http://en.wikipedia.org/wiki/Google_Maps project team members and Google Earth for use of #Satellite_view. high resolution data and land survey dept for using the data. 7. www.Google support forum Clouds over Delhi. 11. REFERENCES 8. Hows does Google Maps work\", 1. B. Naveenchandra, K. N. Lokesh, Usha, Techpogo.com. 2009-01-25. Retrieved and H.Gangadhara Bhat, ―Geospatial 2010-01-12. 9. http://cen.gisserver.nic.in/default.asp Reduce Exposure to Reduce Risk 229

SEMANTIC INTEGRATION OF GEOSPATIAL SERVICES IN SDI FOR MOBILE GIS BASED DISASTER MANAGEMENT RMKGSPB Koswatte Department of CPRSG, Faculty of Geomatics, Sabaragamuwa University of Sri Lanka, P.O. Box 02, Belihuloya, Sri Lanka, [email protected] ABSTRACT: In disaster management, the value of accurate and reliable information together with available resources and facilities on the current situation of the disaster event is crucial. Spatial Data Infrastructures (SDIs) benefited for collaboration of geospatial data and services enabling to share information among wider community involved. SDIs are gradually evolving from data centric and process based to user centric models (Sadeghi- Niaraki et al., 2010). The “Semantic Web” will enable machines to comprehend data and services (Berners- Lee et al., 1999). Semantic based SDIs will enable machines to automatically search and process geospatial data and services, but to make this a reality suitable semantic services and user domain ontologies are required (Delgado and Capote, 2008). Generally, SDI service providers publish and retrieve geospatial information based on the background knowledge. Geospatial data are highly heterogeneous and the heterogeneity arises both at the syntactic and semantic levels (Paul and Ghosh, 2008). Ontologies are means to ensure semantic interoperability in dynamic environments (Gwenzi, 2010). Through this research, a method developed to semantically integrate distributed heterogeneous geo-services required in Mobile GIS based action planning, resource sharing, rescue and evacuation missions and various decision making activities etc. in emergency situations. In here, it is applied Structure Preserving Semantic Matching (SPSM) technique of the OpenKnowledge system in discovering geo-services from specific geographic information catalogs. Within the study, a set of distributed WMS and WFS geo-services were developed as such services were not available in the Sri Lankan context. The prototype is tested in a potential flooding event in the area of Ratnapura town, Sri Lanka. The scenario is based on past events occurred in the selected area as collected from interviews and related documents. The results show distributed heterogeneous geospatial service integration is effective through SPSM technique and possible to apply for disaster management activities successfully using Mobile GIS. KEY WORDS: Semantic SDI, Ontologies, SPSM, Mobile GIS, Disaster Management 1. INTRODUCTION administrative purposes, the party involving in a 1.1 Background particular task needs other’s data to A disaster is defined as a serious disruption of the well being of a community or a society causing Mitigation widespread human, material, economic or environmental losses where the demands exceed Preparedness Disaster Recovery coping capacity of the affected community (ISDR, Management 2003, Shultz, et al., 2007). Because of the negative impacts of disasters on societies, disaster Response management has long been recognized as a cycle of activities including mitigation, preparedness, Figure 1: Disaster Management Activity Cycle response and recovery (Figure 1). Spatial data has (Rajabifard et al., 2004) proven to be vital for disaster management in a way that, one cannot expect effective and efficient disaster management without spatial data (Cutter et al., 2003 and Amdahl, 2002). For events like disaster management, infrastructure facilitation or Reduce Exposure to Reduce Risk 230

incorporate in their work. It is required to collect of dispersed heterogeneous data and conducting accurate and timely spatial data with minimum geo-processing services. By allocating available waste of time. In here, a collaborative effort would resources in a distributed environment to process be benefited by saving time and cost while make the geographic data, can solve complex geospatial work more efficient (Mansourian et al., 2006). problems. The phenomenon is called Distributed Spatial Data Infrastructure (SDI) is denoted as the GIS and the key benefit is the user does not need to relevant base collection of technologies, policies have any highly sophisticated hardware or software and institutional arrangements, which provides the (Lazarova and Angelova, 2008). Processing data in access to spatial data (Nebert, 2004) and services such a system referred as Distributed Geographic (Sadeghi-Niaraki et al., 2010). It provides the basis Information Processing (DGIP). It is processing of for facilitation and coordination and exchange of geographic information across dispersed processing spatial data and service sharing among stakeholders units through computer networks and other from different jurisdictional levels in the spatial data communication channels, mostly over the internet community (Rajabifard and Williamson, 2001; by Web Services (Yang and Raskin, 2009). A Web McDougall et al., 2005; 2009; Sadeghi-Niaraki et Service is a software system designed to support al., 2010). interoperable machine-to-machine interaction over a network. The dream of computer scientists is to Generally, in a country, various authorities may automatically process such information over the have their own set of geospatial data stored in networks. Web Services provide a platform central or distributed servers. The governmental and independent functionality through a Web-accessible nongovernmental organizations may interest in interface using a programming language (Yu et al., collecting, processing, analyzing and publishing 2008). Distributed GIServices (Geographic geospatial data (Amdahl, 2002). The full use and Information Service) provide the geo-processing of benefits of such distributed geospatial data would be distributed data by interact with heterogeneous diminished without an SDI. Many countries are systems (Fallahi, 2006). GIServices provide the interested in setting up of geo-portals and SDIs in technical requirement for the development of SDI Global (GSDI), Regional (RSDI), National (NSDI), by enabling services to the users to integrate State (SSDI) and Local (LSDI) levels (Rajabifard et distributed information and functions (Aditya and al., 1999). Currently there are lots of successful SDI Lemmens, 2003). implementations (Figure 2) in all the above levels. Recently, there is a growing interest of spatially The dynamic and time-sensitive nature of disaster enabling governments through SDI implementations response demands for timely and rapid infield data like Geospatial One-Stop (GOS) in the USA, collection, in order to update decision makers about Service New Brunswick (SNB), etc. (Masser et al., the current status of emergency situations for proper 2010). decisions to be taken. Generally, SDI service providers publish and retrieve geospatial Figure 2: Spatial Data Infrastructure technological information based on the background knowledge. Implementation (Vaccari et al.,2009) Geospatial data are highly heterogeneous and the heterogeneity arises both at the syntactic and The fast proliferation of computer networks, semantic levels (Paul and Ghosh, 2008). Ontologies development of database technology and distributed are means to ensure semantic interoperability in computing technology have enabled the integration dynamic environments (Gwenzi, 2010). The research investigated the means of integrating distributed Geospatial services in SDI using semantics and ontology matching for facilitating disaster management. It is argued that the semantic based service integration in Mobile GIS context can assist disaster management agencies to improve the quality of their decision-making and increase efficiency and effectiveness in all levels of disaster management activities. 1.2 Disaster Management and GIServices In a disaster situation many people may trying to do quickly what they do not ordinarily do, in an Reduce Exposure to Reduce Risk 231

environment which they are not familiar (Tierney,  Bind: How an application connects to, and 1985a; Shultz et al., 2007). The magnitude of an interacts with, a web service after it is been event is not always a measure for a disaster. An found. event to be a disaster it should be affected an area of human development. Also it may differ according to Currently, the web and mobile mapping systems use the location as damage may be differing place to GIServices largely. The use of web or mobile place. Disasters generally cannot be adequately services in mapping ends up with higher managed merely by mobilizing more personnel and performances and greater flexibility. material (Heide, 1989). As the emergency or The most common natural hazards affecting in the disaster situations are dynamic in nature, they selected area Ratnapura, Sri Lanka are water related requests timely updating of a variety of required hazards. Through this study, an effort was made to data/information from various organizations as identify the most vulnerable locations within the single agency cannot provide all updated study area by interviewing knowledgeable elderly information (Rajabifard et al., 2004). persons and investigating related documents published. The approach was Volunteer Geographic Geo-services or GIServices are a type of web Information (VGI) which ended up with services which contain geo-operations and operate encouraging results. The main cause of flood in on geospatial data (Fallahi, 2006). Similar to web Ratnapura district has identified as the water level of services, GIServices communicate with each other, “Kalu Ganga” which is the third longest river in Sri through exchanging messages in XML in order to Lanka and it is discharges the largest volume of publish, discover and invoke them in a water to the sea. heterogeneous environment. The message can be exchanged in a standard manner according to a set 1.3 Mobile GIS in Disaster Response of computer networking protocols including UDDI Mobile GIS frees the user from office to the field. (Universal Description, Discovery and Integration), This enables the infield applications possible which WSDL and SOAP (Newcomer, 2002). are harder with traditional GIS. Disaster monitoring is also in the same list which is not possible with Service office based approaches. Since emergency response Registry is the most critical phase in Disaster Management, mobile GIServices can play a very important role in Publish Find evacuation, dispatch and entity tracking. Further, mobile GIServices will update the critical Service Bind Service information more effectively and efficiently than the Provider Consumer traditional methods (Tsou and Sun, 2006). There are technical issues (like standards and interoperability Figure 3: The Web Services Architecture models) and non-technical issues (like social, (Tidwell et al., 2001) cultural and institutional) on spatial data production, sharing and exchange (McDougall et al., 2009). SDI In the web services architecture (Figure 3), the provides the facility of coordination of the exchange “Service Provider” publishes a description of the and sharing of spatial data and services between service(s) which offers via the “Service Registry”. stakeholders in the spatial data community. The “Service Consumer” searches the “Service Registry” to find a service that meets their needs. If possible to overcome technical and non-technical The service consumer could be a person or a issues of spatial data sharing by using a proper program (Tidwell et al., 2001). framework incorporated with an emerging technology like Mobile GIS, it would immensely Further, Fallahi (2006) explains the publish, find benefitted to the emergency response (Mobaraki et and bind operations as; al., 2007). Through Mobile GIS, users could access to GIS anywhere at any time (Li et al., 2002a).  Publish: How the provider of a web service Mobile GIS more suits to events like disaster registers itself. response since a real-time infield engaging is feasible.  Find: How an application finds a particular web service. Reduce Exposure to Reduce Risk 232

2. SEMANTIC SDI AND ONTOLOGY Equation 1 MATCHING 2.1SDI and Semantics where: S = set of the allowed tree edit operations Spatial Data Infrastructures (SDIs) benefited for ki = number of ith operations necessary to collaboration of geospatial data and services convert one tree into the other enabling to share information among wider Costi = semantic distance community involved. SDIs are gradually evolving from data centric and process based to user centric The similarity between two trees T1 and T2 will be: models (Sadeghi-Niaraki et al., 2010). Road Equation 2 The “Semantic Web” will enable machines to Access Path comprehend data and services (Berners-Lee et al., 1999). Semantic based SDIs will enable machines to FID FID automatically search and process geospatial data and services, but to make this a reality suitable semantic Class A Cost services and user domain ontologies are required B Width (Delgado and Capote, 2008). Interoperability problems emerge when exist heterogeneity in data Speed sets or services. Heterogeneity can be characterized by the conflicts that occur when two resources (data Lanes Minor Category sets and/or services) are combined. Semantic Major heterogeneity is the focus of this work and considers the contents of an information item and its intended Figure 4: Graph like structures used in meaning. SPSM algorithm Since the starting of the notion of Semantic Web The user query is converted to meaningful statement (Berners-Lee et al., 1999) and its suburb as an ontological descriptive query (Figure 5). Then applications like Semantic SDI various approaches the ontological searching operation is started and the were tested to solve the related issues over the last identified services were semantically matched with years. The use of ontologies for the explication of using the OK’s SPSM algorithm. If the calculated implicit and hidden knowledge is a popular score exceeds the threshold value the services were approach to overcome the problem of semantic selected for integration. heterogeneity. Further, work on semantics and geo- ontologies have focused on semantic Is it safe Ontological interoperability between OGC services. This here for search for includes work on the role of ontology for spatio- more rain??? services and temporal databases, the notion of semantic reference semantic systems and the grounding of geographical Convert to matching categories, semantics-based and context aware Ontological retrieval of geographic information (Janowicz et al., descriptive 2008), ontology alignment (Cruz and Sunna, 2008), query as well as work on Semantic Geospatial Web services and their chaining. Specifically, semantic Ontological description of the feature matching is a technique for the identification of semantically related information. Given two graph- Figure 5: Semantic matchmaking for like structures (Figure 4) a semantic matching ontology-based service discovery operation identifies the pairs of nodes in the two structures that semantically correspond to each other. The Open-Knowledge (OK) Systems Structure Preserving Semantic Matching (SPSM) algorithm allows to find a map between two service descriptions and returns a score (McNeill et al., 2009). The cost of the overall map is calculated by; Reduce Exposure to Reduce Risk 233

3. METHODOLOGY REFERENCES A prototype Mobile GIS client is developed using J2ME programming language. It is consisting of Aditya, T. and Lemmens, R.L.G., 2003, Chaining GPS POI Manager, Image Manager, Google Maps Distributed GIS Services. Proceedings In: Client, Geo Analyzer, InfoCast, and Buddy Tracker components especially required in disaster Proceedings of XII Annual Scientific Meeting, management and rescue operations. In the process Society of Remote Sensing Indonesia, 29-30 of prototype system development, servers namely July 2003, Bandung. web servers, map servers, a GPS Tracking Servers, Amdahl, G., 2002, Disaster Response: GIS for a Mail Server and two database servers were Public Safety. ESRI, Redlands California. installed and configured. Several distributed http://www.esri.com/news/arcnews/winter0102a services of WMS and WFS were generated on rticles/gis-homeland.html GeoNetwork and ArcGIS server environments. Berners-Lee,T. Hendler, J. and Lassila, O., 2001, Also, slightly different versions of the original The semantic web, Scientific American, services were created for testing purpose. Two Geo- 284(5):28–37. portals delivering required map generating and Cutter, S.L., Richardson, D.B. and Wilbanks, T.J., map/feature downloading services were enabled. 2003, The Geographic Dimension of Terrorism, The GeoNetwork catalogue search function is Toutledge, New York. extended by adding semantic query processing Delgado, F. and Capote, F., 2008, Towards functionality that provides the semantic extension of Semantic Spatial Data Infrastructures: A the geo-catalogue. framework for sustainable development, GSDI 10, Ausustine, Trinidad. The OpenKnowledge (OK) systems Kernel used on Fallahi, G., 2006, Distributed Computing mobile device and had to use the OKs lightweight Architecture based on Geo Services; A Loosely trust model as a service where it required Coupled Method for Linking GIS and sophisticated computations. Environmental Models. Map Asia 2006. http://www.gisdevelopment.net/technology/gis/ The prototype is tested in a potential flooding event ma06_79.htm in the area of Ratnapura town, Sri Lanka. The Gwenzi, J., 2010, Enhancing Spatial web search scenario is based on past events occurred in the with Semantic Web Technology and Metadata selected area as collected from interviews and Visualisation, M.Sc. Thesis, International related documents. In here, the first event was very Institute for Geo-Information Science and Earch exciting as it was conducted in a way that any Observation Enschede (ITC), The Netherlands. knowledgeable person could contribute to the Heide, E.A., 1989, Disaster Response: Principles of knowledge base through Volunteered Geographic Preparation and Coordination. ISBN 0-8016- Information (VGI) system. Lots of people were 0385-4, St Louis, Mo, CV Mosby Co, 363. actively contributed and finally developed fairly ISDR, 2003, Basic Terms of Disaster Risk large reliable historical knowledge base. It Reduction, International Strategy for Disaster contained all historical events of flooding hazards Reduction. occurred within the study area with locations and http://www.adrc.or.jp/publications/terminology/t water level rises. op.htm Janowicz, K., Wilkes, M. and Lutz, M., 2008, 4. CONCUSIONS Similarity-Based Information Retrieval and Its The successful results reveals that the OK’s SPSM Role within Spatial Data Infrastructures, algorithm can be used for distributed service GeographicInformation Science, 5266, 151-167. integration for Mobile GIS based Disaster Lazarova, M. and Angelova M., 2008, GIS Web Management operations. It can be conclude that the direct rejection of low scored service pairs of the Services for Distributed Computing Systems, first pass of SPSM is not recommended. For International Scientific Conference Computer computationally intensive operations the OK kernel Science. is bulky and required lightweight version. Li, L., Li, C. and Lin, Z., 2002a, Investigation on the Concept Model of Mobile GIS. ISPRS, 34, 4, Commission IV. Reduce Exposure to Reduce Risk 234

Mansourian, A., Rajabifard, A. and Zoej, M.J.V., Rajabifard, A. and Williamson, I.P., 2001, Spatial 2006, SDI Conceptual Modeling for Disaster Data Infrastructures: Concept, SDI Hierarchy Management. ISPRS Workshop on Service and and Future Directions. Proceedings of the Application of Spatial Data Infrastructure, Geomatics 80 Conference, Tehran, Iran. XXXVI(4/W6), China. Sadeghi-Niaraki, A., Rajabifard, A., Kyehyun, K. Masser, I., Rajabifard, A., and Williamson, I., 2010, and Jungtaek, S., 2010, Ontology based SDI to Spatially Enabling Governments through SDI Facilitate Spatially enabled Society, GSDI 12 implementation, International Journal of Conference, Singapore. Geographical Information Science, 22, 5-20. Shultz, J.M., Espinel, Z, Flynn, B.W., Hoffman, Y. McDougall, ., Rajabifard, A. and Williamson, I.P., and Cohen, R.E., 2007, Deep Prep: All-Hazards 2005, Understanding the Motivations and Disaster Behavioral Health Training. Tampa, Capacity for SDI Development from the Local FL: Disaster Life Support Publishing. Level, From Pharaohs to Geoinformatics FIG Working Week 2005 and GSDI-8 Cairo, Egypt. Tierney, K.J., 1985a, Emergency Medical Preparedness and Response in Disasters: the McDougall, K., Rajabifard, A. and Williamson, I.P., Need for Interorganizational Coordination, In 2009, Local Government and SDI – Petak WJ: Emergency management: a challenge Understanding their Capacity to Share Data, for public administration, special issue, Public Netherlands Geodetic Commission 48, 205-218. Admin Rev 45:77. McNeill, F., Besana, P., Pane, J. and Giunchiglia, Tsou, M.H. and Sun, C.H., 2006, Mobile GIServices F., 2009, Service Integration through Structure Applied to Disaster Management . In: Preserving Semantic Matching, Journal on Drummond, J., Billen, R., Joao, E. and Forrest, Cases on Information Technology, 11, 4. D. (eds) Dynamic and mobile GIS: investigating changes in space and time, Taylor & Francis, Mobaraki, A., Mansourian , A., Malek, M. and London, 213-235. Mohammadi, H., 2007, Application of Mobile GIS and SDI for Emergency Management. Vaccari, L., Shvaiko, P. and Marchese, M., 2009, A ISPRS Commission Technique International geo-service semantic integration in Spatial Data Symposium, Marne-la-Vallée , FRANCE, 95- Infrastructures, International Journal of Spatial 100. Data Infrastructures Research, 4, 24-51. Nebert, D., 2004, Developing Spatial Data Yang, C. and Raskin, R., 2009, Introduction to Infrastructures: The SDI Cookbook. Distributed Geographic Information Processing http://www.gsdi.org/docs2004/Cookbook/cookb Research, International Journal of Geographical ookV2.0.pdf Information Science, 553-560. Newcomer, E., 2002, Understanding Web Services- Yu, Q., Liu, X., Bouguettaya, A. and Medjahed, B., XML, WSDL, SOAP and UDDI. Boston: 2008, Deploying and Managing Web services: Addison Wesley Professional, 1st edition, May Issues, Solutions, and Directions, The VLDB 13, 2002. Journal, 17, 537–572. Paul, M. and Ghosh, S. K. (2008) A Framework for Semantic Interoperability for Distributed Geospatial Repositories, Computing and Informatics, 27, 73–92. Rajabifard, A., Chan, T.O. and Williamson, I.P., 1999, The Nature of Regional Spatial Data Infrastructures. Proceedings of the 27th Annual Conference of AURISA Fairmont Resort, Blue Mountains NSW, 22-26. Rajabifard, A., Zoej, M.J.V. and Williamson, I.P., 2004, Developing Spatial Data Infrastructure to Facilitate Disaster Management. Proceedings of the Geomatics 83 Conference, Tehran, Iran. Reduce Exposure to Reduce Risk 235

DEVELOPMENT OF A GEOPORTAL FRAMEWORK FOR NATURAL DISASTER MMR RELATED ACTIVITIES Hamid Mehmood1, Nitin K. Tripathi 1Institute of Geographical Information Systems, National University of Sciences and Technology, Islamabad Pakistan, [email protected] ABSTRACT: People and organizations have always exchanged natural disaster related geographic information, but the practice has grown exponentially in recent years with the popularization on internet and the Web. An increased amount of geospatial data is being generated and collected during the course of a natural disaster by virtue of more user-generated content on the Web, in addition to data from satellites, ground and ocean sensors and GPS units. Geoportals – that is, websites where geospatial information can be discovered – make it easier for the users to find, access, and use geospatial information. This paper presents the functionalities, high and low level architecture, hardware and software requirements and prototype screen shots of the online national Geoportals (ONG) KEY WORDS: National Geoportal, Geodatabase Management, Web GIS 1. INTRODUCTION called a web server (Tait, 2005). A geoportal has a Geoportal is a web application which is essentially a three tier architecture consisting of data tier, logical client/server (C/S) architecture, in which the client is tier and presentation tier as shown in figure 1. specifically called a web client and the server is Database Web Server Client (Web browser, Server desktop or mobile) IInntteerrnneett Data tier Logical Tier Presentation Tier Figure 1: The basic architecture of a web application includes three tiers The basic workflow for accessing a geoportal is as processing, such as generating a map and performing follows (Goodchild, 2007) analysis. 1. A user uses a web client, usually a browser, and All possible geoportal architectures follow thick initiates a request to the web server by typing a client architecture and the same basic workflow for URL in the address bar or clicking a URL on a accessing a geoportal as give above. The difference web page. in the architectures is how the three tiers are located 2. The web server receives the request, parses the physically; based on this, there are three possible URL, locates the corresponding document or options script, and returns the document or else executes 1. Local / On site Setup scripts and returns the result of the script to the 2. Cloud based Setup client as a response. The response is commonly 3. Dedicated Remote Server Setup in HTML format. 3. The web client (the browser) receives the a. Unmanaged Remote Server Setup response, renders it and presents the geoportal b. Managed Remote Server Setup interface to the user. The pros and cons for each option are given below and recommendation is given regarding the selection In addition to this the geoportal follows the thin of the architecture for the deployment of online client architecture where the server performs most of national geoportals. The pros and cons and presented the work, leaving the client to do the least amount. considering that the geoportal would be providing the The client simply sends the user’s request to the following functionalities server. The server doesn’t the server does the Reduce Exposure to Reduce Risk 236

 A customizable geoportal web application Cloud computing corresponds to the delivery of for publishing, administering, and searching services which include computing and storage resources capacity to the end users. The end user is composed of heterogeneous community. Cloud computing  A live data previewer map interface for entrusts services with a user's data, software and viewing live resources computation over a network (Mell, Grance, & Grance, 2011).  Data extraction service customization for downloading data for a resource, with the The end user accesses the cloud based services ability to specify an extent, projection and through the web architecture. (Berry, 2009). The download format corner stone for cloud computing architecture is sharing of resources to achieve economies of scale  Search results exposed through the REST similar to a utility network (Kouyoumjian, 2010). API so resources can be easily shared Pros among applications and users  Cloud based setup eliminates the need of 1.1 Local / On Site Setup acquiring high end hardware and software and This setup would consist of setting up the whole hence the cost associated with it. network infrastructure at the premises of the  Cloud computing allows access to any software stakeholder premises (Esri, 2009). or program without having to have a customized Pros installation.  No sharing of servers, the stakeholder gets to run  Cloud computing allows the user to access to the services from anywhere in the world depending the entire software configuration upon the availability of the internet services,  Ability to update the software configuration or removing the need to carry customized machines to remote locations. any other application easily  Cloud computing hosting services ensure 100%  Additional level of in control physical security uptime meaning no loss of business.  A centralized repository makes it easy to manage layer the data and perform supporting operations on it. Cons Cons  A major disadvantage of server-based networks  A internet downtime would mean the cloud services would not be accessible to the end user is their high cost. These types of computer  Stakeholders would have security concerns on architectures are expensive to start up and knowing that their data is located at some remote expensive to maintain. Server-based networks location. have a centralized server, which is typically a  Cloud computing architecture is not flexible very large capacity CPU unit. This server alone enough for highly customizable solutions can be expensive.  Cloud computing has migration issues as large  Server based networks have cascading errors. amount of data has to be copied from a When the server is down or out of commission, heterogeneous network then the entire network is compromised and cannot function normally. 1.3 Dedicated Remote Server  Additional trained human resource is required to In addition to maintenance support a dedicated maintain the hardware and software remote server setup offers the user a better control,  In-house maintenance of software licenses and over their machines and services. There are two patches. models for dedicated remote server setup (Esri,  Setting up a dedicated facility to host the servers, 2009)(Esri, 2007a) the facility must have a reasonable temperature  Unmanaged Remote Server Setup so the machines do not overheat and must be  Managed Remote Server Setup accessible for maintenance.  Backups have to be taken and then moved to a 1.3.1 Unmanaged Remote Server Setup remote location. Unmanaged remote server setup involves acquiring a However, if the participating countries opt for a remote dedicated machine. country level setup up of Geoportal then, each Pros country will have to bear the full cost of hardware  Comprehensive security monitoring upto and software licenses, as the country level setup wouldn’t allow the sharing of resources in terms of physical infrastructure. hardware or software licenses. However, the major  Redundancy of networks and data-centers. advantage of such setup would be high level of  Remote access and management flexibility in data access and implementation of security policies. 1.2 Cloud based Setup This setup would consist of setting up the whole network infrastructure on the cloud by using services being provided by Amazon, Cloud.com, GoGrid, etc. Reduce Exposure to Reduce Risk 237

 Reporting on network, hardware, software, Cons applications usage including reports on security.  High cost of maintenance  Manual application of updates and patches  Fast access because of high bandwidth  Manual installation of software and licenses  Network storage based backup  Time and resource consuming architecture  Flexibility of designing and implementing high end architectures Table 1: Comparison of implementation approaches Maintenance Risk Involved Training Flexibility Cost Required (Esri, Required High-The local 2011) High- The setup is highly 60,000 Local / High – Because the High- As the organization flexible as both USD Online hardware and system is expected hosting the the hardware and Setup software setup are to up 99% of time, server have to software are in 45,000 locally installed and so an extended train their immediate access USD Cloud have to maintained power failure or workforce for and can be Based by the hosting natural disaster at maintaining updated and 30,000 Setup organization in terms the local site can server, taking modified USD of hardware disrupt the system, regular backups, according to the Dedicated upgrades, software in addition to this installing and requirements Remote patches, security, special security maintaining GIS Server server hosting arrangements are software Low-There is no Setup facility etc. required in form flexibility to (Managed of firewalls, and Medium-No change the Remote Low- Because the physical access to training is hardware or Server hardware and the servers required in software Setup) software and terms of specifications. maintained by the Medium - As the maintaining the company providing Geoportal will be operating Medium- MRSS the cloud services, deployed on a system, however allows remote so no maintenance is server sharing training is access to the required in terms of resources with required for server which hardware upgrades other applications using GIS allows the and software patch , so there is a risk Server software installed as they are that the server on the server to performed regularly might crash Low- No be modified by the cloud hosting because of some training is according to the company other application required in requirements being hosted, or terms of Low- As the there is a security maintaining the hardware and breach because of operating software are the shared model system, however maintained by the training is company providing Low- The server required for GIS the hosting services, hosting company Server so no maintenance is provides a required in terms of dedicated machine hardware upgrades, which can be software patch as accessed remotely they are performed and number of regularly by the periodic backups server hosting of the machine company state and data are taken, and it is ensured that the system will be available 99% of the time 1.3.2 Managed Remote Server Setup Reduce Exposure to Reduce Risk 238

Unmanaged remote server setup involves acquiring a more detailed descriptions of each information item remote dedicated machine, where the software or the full metadata record itself. installed on the machines are maintained by the From either the summary or detailed results displays, service provided in addition to number of other ONG enables the user to link directly to the Web site services such a periodic backup, remote access, etc. that hosts the cited information item if that option is (Esri, 2007b). made available by the information item publisher, Pros preview the information item if it is a \"live\" map In addition to the pros listed under the unmanaged available from a service maintained by the remote server setup the managed remote server setup information item publisher, or download the also provides the following pros. information item from within the portal if that option is made available by the information item publisher.  Hardware management support by the 2.3.2 Preview geospatial data resources produced service providers by others — The ONG provides inline map service preview functionality that enables users to discover  Software management support by the and view mapped data maintained on Web-accessible service providers map services (live maps) without launching a map viewer. This ability to preview a live map is provided  Application management which includes by a Preview button that automatically appears installation and management of applications together with the text description of each live map. 2.3.3 Obtain geospatial data resource produced by  Network management by installation of others — any information item that is cited in firewalls and other supporting services metadata published in ONG is obtainable if the publisher of the information item makes it available. Cons The information items can be obtained using the  In case of migration of the Geoportal to a option to link externally to the publisher's Web site or server other than the one provided by the the option to download the data from within the service provider, new software license will portal interface itself via an internal link provided by have to be purchased as they will not be the data producer. transferable to the new server. 2.3.4 Expose one's own geospatial data resources for discovery by others — The ONG enables Web- Table 1 shows the comparison of the implementation based geospatial information producers to publish models with respect to maintenece required, risk metadata describing their information if they are involved, training required, felexibility and authorized to do so by an ONG administrator. standardized implementation cost 2.3.5 Receive automatic notification of new geospatial data resources that meet pre- 2. FUNCTIONALITIES established criteria — The ONG will provide end This section categorizes the ONG users and lists the users with the ability to subscribe to a GeoRSS feed functionalities provided in the ONG. The users for that automatically notifies the user whenever a the ONG are identified as: metadata record describing a new geospatial data 2.1 Administrators resource meeting user-specified criteria is published The ONG administrator functionality enables the in the ONG. person or persons to approve or disapprove metadata The high level user interaction is shown in figure 2 prior to its release and undertake other related aspects of portal operations. Administrators are required to Administrator be registered users, and administrator function options are provided on the administrator's home Manage account page upon login based on the administrator's User-ID Manage metadata and password. Administrators will also be Manage geospatial data responsible for managing user accounts and access Manage security grant. …………………………... 2.2 Publishers Publisher functionality enables the publishers to Publisher User manage the metadata. Publishers become part of the geoportals by registering on the geoportal. Register account Search for resources 2.3 Users Create metadata View metadata 2.3.1 Discover geospatial data resources produced Publish metadata Download data by others — The ONG will enable end users to Secure metadata View map services discover and select information resources that are of …………………………….. ………………………………. particular interest to them. Searching uses term-based criteria entered by the user or geographic location Figure 2: ONG users’ interaction criteria the user designates on a map. The results of the search are displayed as summary statements derived from the metadata records citing each found information item. The user can then elect to display Reduce Exposure to Reduce Risk 239