Angelica Murdukhayeva NRS509 Report Fall 2010 GIS and Coastal Environments

Angelica Murdukhayeva NRS509 Report Fall 2010 GIS and Coastal Environments The coastal zone contains unique natural resources that are critical to biological and economic productivity. It is also subject to the effects of both human induced and natural longterm and short term changes. Studies of the coastal environments around the world have and continue to benefit greatly from recent developments in GIS software and applications and remote sensing technology. Important issues in coastal science and management are shoreline change, erosion/accretion patterns, sea level rise, storms and tsunamis and assessments of coastal vulnerability. The following paper highlights the use of geospatial information and tools in addressing these issues. In coastal studies, the most widely used dataset is the Digital Elevation Model (DEM). Elevation models are important for assessing risk of inundation from tsunamis, storm surge, and sea level rise. Sometimes DEMs are used as input in complex simulation models that predict flooding and other coastal hydrological processes. Other times, they are entered as a base layer in a simple GIS bath tub flooding analysis, where the elevation in each cell is compared against a predicted sea level and all cells with values lower than the predicted sea level are considered flooded (Demirkesen et al. 2008). Some DEMs are derived from LiDAR (light detection and ranging), a remote sensing technology that measures distance to ground targets. For example, Poulter and Halpin (2008) used LiDAR derived elevation data to perform sea level rise simulations on the North Carolina coast. A widely used source for elevation data is the U.S. Geological Survey s National Elevation Dataset (NED); it is used in SLAMM, sea level affecting marshes model, a simulation model used to study wetlands response to long term sea level rise (Craft et al. 2009). However, these datasets have limited vertical accuracies. The NED is accurate to + 2.4 m (Gesch 2007) and LiDAR elevational data are accurate from 15 centimeters to 1 meter (Gao 2007). Scientists predict a sea level rise of up to 1 meter by the year 2100 (Harvey and Nicholls 2008). When simulations of sea level rise are performed these datasets, the resulting outputs need to be closely scrutinized. In order to perform more robust analyses, researchers studying sea level rise need to focus on obtaining elevation data with greater accuracy. Another important data source used for coastal studies is aerial photography. Maps derived from aerial photographs have been used as a way to study historical shoreline change and patterns of erosion and accretion on wetlands and beaches. Hapke (2010) combined recent LiDAR data and maps derived from aerial photography (from the 1800s, 1930s, and 1970s) to perform a historic shoreline change rate analysis. Aerial photography (if it can be obtained) can be a valuable tool for studying historical patterns in many coastal areas. Another data source that can be used for coastal studies is satellite imagery. Chen et al. (2005) compared Landsat Thematic Mapper (TM) satellite imagery from 1978, 1988 and 1998 to study coastline movement and urban expansion in the Linding Bay, an estuary of the Pearl River in China. Remote sensing technologies are developing rather quickly, especially in the field of coastal studies. The Multi Resolution Land Characteristics Consortium developed a consistent, comprehensive national land cover map. Using that product as a base, NOAA s Coastal Change

Analysis Program (C CAP) created land cover map products of all coastal areas of the United States that show detailed areas of marshes and other coastal wetlands. These map products are available for the years 1992, 1996, 2001 and 2005 and have been used extensively to monitor land cover change and wetland conversions near the coast (Ramsey et al. 2001; Borde et al. 2003). It is clear that the data model used most frequently is the raster data model. Digital elevation models, aerial photography and satellite imagery (all raster format) have been extremely valuable in studies of the sea level rise, coastal inundation, shoreline change and land cover change. Raster data are also used for mapping pollutants in coastal wetland sediments. When sediment samples are analyzed for heavy metals and other pollutants, their concentrations are mapped to points (vector data) where the samples were taken. Then, methods of interpolation are applied for example, inverse distance weighing (Affian et al. 2008) and kriging (Zhou et al. 2007). The resulting raster data maps show concentrations of pollutants across a large area, not just at the sampling locations. In response to the threat of global sea level rise and climate change, scientists and managers have been experimenting with a new analytical procedure that uses GIS, namely coastal vulnerability assessments (Richmond et al. 2001; Torresan et al. 2008; Harvey and Woodroffe 2008; Hammar Klose et al. 2003; Pendleton et al. 2004). The vulnerability assessment process includes dividing the length of the coastline into uniform fragments and ranking each fragment s value for a series of vulnerability indicators. For example, Pendleton et al. (2004) performed an assessment of Assateague Island National Seashore and ranked 1.5 kilometer grid cells on geomorphology, erosion and accretion rates, regional coastal slope, mean significant wave height, tidal range and relative sea level change. Other assessments use different or additional vulnerability indicators, such as coastal population density and wetland migratory potential (Torresan et al. 2008) or historical tsunami wave height (Richmond et al. 2001). In the future, GIS and remote sensing applications in coastal studies will benefit greatly from widely available elevation data with increased vertical accuracy and the development of more refined coastal vulnerability indicators. The accuracy of coastal elevation data needs to be within centimeters in order for sea level rise inundation models to robustly predict flooding and other impacts. I believe that within the next ten years, GPS technology will be able to do this at low cost and labor. Furthermore, I believe than in the future, coastal vulnerability assessments will be performed more widely and more often around the world. Currently there is a need to monitor coastlines and the impacts of large events on coastlines. As more data on these impacts is collected, scientists will uncover which coastal hazards and events are most prevalent in their study areas and use these to develop a meaningful measure of coastal vulnerability in different areas of coastline. 2

Annotated Bibliography Affian, K., M. Robin, M. Maanan, B. Digbehi, E.V. Djagoua, and F. Kouamé. 2008. Heavy metal and polycyclic aromatic hydrocarbons in Ebrié lagoon sediments, Côte d Ivoire. Environmental Monitoring and Assessment 159(1 4): 531 541. This paper examines the spatial distribution of heavy metals and polycyclic aromatic hydrocarbons (PAH) in a coastal lagoon in Côte d Ivoire using GIS and GPS. Surface sediment samples were collected at 44 georeferenced locations in three bays of the lagoon; they were analyzed for Zn, Fe, Cu, Cd, Mn and PAH. The data were input on a grid based map and interpolated using the inverse distance weighted (IDW) method. The results showed high heavy metal concentrations in the Bietri bay and Koumassi Bay which are both impacted by anthropogenic activities. When the pollutant maps were overlayed with maps of road systems and buildings, the presence of an oil refinery on the shore of the Bietri bay shed light on a possible point source of pollution. I am really impressed with this simple and elegant GIS application for lagoon sediment analyses. It seems that more and more scientists around the world are georeferencing their samples and using maps to represent their data. Furthermore, they are integrating social and economic data sets (roads, industrial sites, buildings) with their collected data, which can help coastal managers and urban planners make better decisions. Craft, C., J. Clough, J. Ehman, S. Joye, R. Park, S. Pennings, H. Guo, and M. Machmuller. 2009. Forecasting the effects of accelerated sea level rise on tidal marsh ecosystem services. Frontiers in Ecology and the Environment 7(2): 73 78. This paper uses a simulation model called SLAMM ( sea level affects marshes model ) in a GIS environment to predict wetland conversions and shoreline modifications during long term sea level rise on tidal marshes of the Altamaha River, Georgia. The inputs include: the USGS National Elevation Dataset (NED), NOAA tidal data, and USFWS National Wetlands Inventory. The output predicts an overall reduction in salt marsh along with an increase in tidal freshwater marsh. The best part of this study is that it pairs model outputs with field measurements of ecosystem services (biomass and nitrogen accumulation). The model predictions are used to predict reductions or increases in ecosystem services in the tidal marshes. Modeling the effects of sea level rise on coastal ecosystems is extremely difficult because of the limitations in data accuracy and because validation of the predictions is impossible. The paper acknowledges these limitations and attempts to address them. Hapke, Cheryl J. Integration of LiDAR and Historical Maps to Measure Coastal Change on a Variety of Time and Spatial Scales, in Remote Sensing of Coastal Environments, ed. Yeqiao Wang (Boca Raton, FL: CRC Press, 2010), 79 101. This chapter in Remote Sensing of Coastal Environments describes two case studies that integrate modern LiDAR (Light Detection and Ranging) data and historical maps (derived from aerial photos) to calculate long term coastal change rates and map erosion hazard areas. The first study uses the datasets to examine impacts of hurricanes on the shoreline retreat on a barrier island in the Gulf Islands National Seashore, and the second study uses the same data sources to perform a statewide analysis of coastal cliff retreat in California. These studies demonstrate a good method for combining modern remote sensing data and historical data 3

sources (maps and aerial photographs) to study long term coastal processes. The most impressive aspect of these studies is the different scales at which they were applied (small scale: 300 km of California coast and large scale: 25 km of Santa Rosa Island coastline). Furthermore, it seems that coastal managers can apply this approach to any coastline if they can acquire the necessary data sets. Pendleton, E.A., S.J. Williams, and E.R. Thieler. 2004. Coastal vulnerability assessment of Assateague Island National Seashore (ASIS) to Sea Level Rise. (U.S. Geological Survey Open File Report 2004 1020), http://pubs.usgs.gov/of/2004/1020/images/pdf/asis.pdf This paper summarizes a method used by USGS coastal geologists to assess coastal hazard vulnerability along the Atlantic, Pacific and Great Lakes coastlines of the United States. It also reports the results of a vulnerability assessment for Assateague Island National Seashore. The Coastal Vulnerability Index (CVI) ranks geological variables (geomorphology, historic shoreline change rate, coastal slope) and physical process variables (wave height, tidal range, historical rates of sea level change) and yields a quantitative measure of the park s vulnerability to the effects of sea level rise. Predicting future coastal processes and vulnerability to change is extremely difficult because of the many factors involved, but the CVI system takes many of these factors into account and develops a map that shows which parts of the Assateague barrier island are more susceptible to change than others. The analysis at Assateague found that geological variables show the most variability and have the most influence on CVI. I am impressed by the methodology used to calculate the index and the map products that are published in the appendix of this report. Using the CVI can be a good first step for coastal park managers interested in the vulnerability of their coasts. Poulter, B. and P.N. Halpin. 2008. Raster modeling of coastal flooding from sea level rise. International Journal of Geographical Information Science 22(2): 167 182. This paper uses DEMs derived from lidar to model coastal flooding from sea level rise. There are two DEMs of coastal North Carolina; the first has a 6 meter horizontal resolution and the second has a 15 meter horizontal resolution. There are three modeling rules used. The first is a zero side rule (also known as, a bathtub approach) where cells flood if their elevation is lower than projected sea level. The second and third rules use connectivity: cells flood if their elevation is lower than projected sea level AND if they are connected to a cell that is flooded or open water; they are differentiated by how they define connectivity, either on 4 sides or 8 sides. The study found that the 15 meter DEM predicted more inundation areas than the 6 meter DEM. Using the connectivity rules resulted in lower inundation estimates; furthermore, the 8 side rule predicted greater areas of inundation than the 4 side rule. This paper demonstrates a simple raster model for predicting flooding from sea level rise using lidar data and highlights that the extent of inundation is sensitive to the horizontal resolution of the elevation data and the modeling of hydrological connectivity. However, the modeling does not consider vertical accretion in marshes and wetland forests that could mitigate inundation. This approach could be used for a first round inundation study of any coastal area where such elevation data is available. 4

Richmond, B.M., C.H. Fletcher, E.E. Grossman, and A.E. Gibbs. 2001. Islands at Risk: Coastal Hazards Assessment and Mapping in The Hawaiian Islands. Environmental Geosciences 8(1):21 37. This paper attempts to systematically quantify, rank and map the distribution coastal hazard potential in the Hawaiian coastal hazards. In order to do this, the researchers study the specific history of hazard phenomena and local environmental processes. For example, tsunamis occur on average once every two years in Hawaii. As a result of years of observations, the researchers learned that tsunami waves are higher in long funnel shapped bays and dissipate in barrier reefs. This information, along with information about the characteristic patterns of coastal stream flooding, seasonal high waves, high winds, marine overwash, shore erosion, and volcanism, was used in developing the hazard map. This paper was successful in synthesizing the history of coastal hazards over the past 40 years and creating a map that displays potential future hazards. Other references (not included in Annotated Bibliography) Chen, S., L. Chen, Q. Liu, X. Li, and Q. Tan. 2005. Remote sensing and GIS based integrated analysis of coastal changes and their environmental impacts in Lingding Bay, Pearl River Estuary, South China. Ocean & Coastal Management 48(1): 65 83. Borde, A.B., R.M. Thom, S. Rumrill, and L.M. Miller. 2003. Geospatial habitat change analysis in Pacific Northwest coastal estuaries. Estuaries and Coasts 26(4): 1104 1116. Demirkesen, A.C., F. Evrendilek, and S. Berberoglu. 2008. Quantifying coastal inundation vulnerability of Turkey to sea level rise. Environmental Monitoring and Assessment 138: 101 106. Gao, J. 2007. Towards accurate determination of surface height using modern geoinformatic methods: possibilities and limitations. Progress in Physical Geography 31(6): 591 605. Gesch, D.B. 2007. Chapter 4 The National Elevation Dataset, in D. Maune Ed., Digital elevation model technologies and applications: The DEM User s Manual 2nd ed., 99 118. Bethesda, MD: American Society for Photogrammetry and Remote Sensing. Hammar Klose, E.S., E.A. Pendleton, E.R. Thieler, and S.J. Williams. 2003. Coastal Vulnerability Assessment of Cape Cod National Seashore (CACO) to Sea Level Rise. (U.S. Geological Survey, Open file Report 02 233), http://pubs.usgs.gov/of/2002/of02 233/ Harvey, N., and R. Nicholls, R. 2008. Global sea level rise and coastal vulnerability. Sustainability Science 3(1): 5 7. Harvey, N., and C.D. Woodroffe. 2008. Australian approaches to coastal vulnerability assessment. Sustainability Science 3(1): 67 87. 5

Ramsey, E.W., G.A. Nelson, and S.K. Sapkota. 2001. Coastal change analysis program implemented in Louisiana. Journal of Coastal Research 17(1): 53 71. Zhou F., G. Huaicheng, and Z. Hao. 2007. Spatial distribution of heavy metals in Hong Kong s marine sediments and their human impacts: A GIS based chemometric approach. Marine Pollution Bulletin 54(9):1372 1384. 6