GIS & Remote Sensing in Mapping Sea-Level Rise (SLR)

Joe McGuire NRS-509 Concepts in GIS & Remote Sensing Professors August & Wang Due 12/10/2015 11:30am GIS & Remote Sensing in Mapping Sea-Level Rise (SLR) The ever-present threat of global warming and a changing climate has brought with it an array of issues that all levels of government must acknowledge. One of the major issues that must have developing strategies is the topic of sea-level rise (SLR). The change in climate has caused federal agencies such as the Army Corps of Engineers to issue guidelines for both the direct and indirect effects of climate impacts, like SLR, in regards to planning, managing, designing, operating and constructing federal projects (Marcy et al, 2011). There are three primary contributing factors of SLR, and they have been cited as: ocean thermal expansion, glacial melt (specifically from Greenland & Antarctica), and change is terrestrial storage. Until recently it was ocean thermal expansion that was regarded as the dominating factor, however recent studies have shown how glacial melt has a much more significant impact. These ice sheets of Greenland and Antarctica contain enough water to raise sea-levels almost 70 meters in height (Dasgupta, 2007). However, the National Climate Assessment has projected global sea levels to rise another 1 to 4 feet based on current climate change patterns (NCA, 2014). The impacts of such sea-level rise would have catastrophic outcomes; therefore GIS and Remote Sensing are technologies that can provide the necessary models preparing for such impacts. Before the mapping of potential inundated areas is carried out it is important to consider the areas of interest themselves. Human settlements in low elevation coastal zones must be considered to the extent of the size, populations, and distribution internationally (McGranahan, Balk, & Anderson, 2007). More importantly it is crucial to gain an understanding of how these coastal settlements change over time. For example, looking at the urban development and the hardships inherent in the shifting direction of populations adapting to increasing risk brought about by climate change. Improvement is perhaps the best means of avoiding climate change related impacts such as SLR, but due to the already increasing effects of climate change it has become too late to rely solely on urban improvement. Therefore, migration away from potential inundated regions will be crucial, but costly, so modification of prevailing coastal settlements from seaward hazards will also be vital (McGranahan et al, 2007). However, it must also be recognized that coastal ecosystems will also face further stress due to SLR. The reliance of urban populations to that of coastal ecosystems only furthers the need for monitoring and adapting coastal settlements (McGranahan et al, 2007). Utilizing GIS and remote sensing to map SLR is imperative, but so also is the mapping of where urban improvements can be made to keep settlements from being totally transferred. When looking at GIS and how it can be utilized to aid in the mapping of SLR it is important to also compare findings with population datasets. Haskell Indian Nations University, a small university, has been contributing to the study of SLR. The case study that Haskell has produced looks at both elevation in relation to mean sea level and the connectivity to the existing ocean (Kostelnick et al., 2008). The model that was created inputs a global DEM, a grid of elevation points, and then calculates all grid cells that would be inundated based on user-defined SLR increments. The end result is a model that overlays population estimates of currently living people inside inundation zones that range from an SLR of 1 to 6 meters at global levels. This sort of modeling allows for basic understanding of potential risks at a global level, but with a risk of error given the size of the regions being modeled. When mapping SLR accuracy is everything and given a smaller region of interest the increase in accuracy is greatly heightened. A case study that looked specifically at the region of Fiji did so with the interest of SLR and storm surge. This study is worth noting due to the fact that its sole focus was not so much on population and SLR relationships, but rather SLR and storm surge. The methodology differs from that of the Haskell University study in the way that it incorporated storm surge based on tidal levels. The concept in this case study revolved around the use of a DEM and design water level (DWL) of created tidal heights (Gravelle & Mimura, 2008). The introduction of the DWL layer was based on three different components. Utilizing the maximum spring tidal range (the highest level), the effect of SLR based upon

the IPCC projections of 2001 and 2007, and storm surge estimates based on residual water levels before and after a storm created the DWL layer (Gravelle & Mimura, 2008). The DWL layer appears similar to that of a DTM or coastal terrain model, but includes storm surge as one of its elements. Here GIS not only maps SLR, but also the inundation of regions based on storm surge with an ever-rising sea level, which only highlights the power of GIS further. The benefits from mapping SLR are impressive by any standard, but developing countries are perhaps the regions that benefit the most from GIS based SLR models. The Development Research Group of World Bank produced research on SLR in developing countries back in 2007. Included with the research was the methodologies and data sources utilized by the group. GIS was employed by the research group in order to overlay analytical features such as population, land, agriculture, urban extent, wetlands, and GDP (Dasgupta, 2007). Once overlaid the inundation zones for a projected 1 to 5-meter rise in sea level were incorporated. Interestingly enough, the data sources used were all various public sources such as the Center for Environmental Systems Research, the Center for International Earth Science Information Network, the National Oceanographic and Atmospheric Administration (NOAA), etc. Once the data was acquired and the analytical elements overlaid, a six-step process was carried out to complete the research. The following are the six-steps that were employed for mapping SLR: 1. Preparing country boundaries & Coastlines The National Geospatial Intelligence Agency was utilized for the worldwide coverage of shorelines and boundaries of international scale. The country coastlines were built by sub-setting polygons from the World Vector Shoreline polygon (Dasgupta, 2007). 2. Building coastal terrain models (DTM) The terrain models were derived from the International Centre for Tropical Agriculture (CIAT). Using the SRTM 90 meters DEM released in 2005, the zipped files were downloaded from the CIAT website, converted to raster format, and then mosaiced based on country boundaries in ArcGIS (Dasgupta, 2007). 3. Identifying Inundation Zones These zones were calculated from the coastal terrain model (DTM) when the value of pixels in the DTM was given a value of 1 for the various scenarios examined in the study. The pixels that were not connected to coastlines, for example inland wetlands, were masked out manually (Dasgupta, 2007). 4. Calculating Exposure Indicators To create estimates for each indicator a calculation was completed by overlaying the inundation zone with the applicable exposure surface dataset. Two GIS models were built for acquiring the exposed value for the exposure grid surfaces. 5. Adjusting Absolute Exposure Indicators In order to obtain the exposed value for exposure indicators such as land area, population and GDP, which have the measured country totals, an adjustment is made to reflect its real value by utilizing a designated formula (Dasgupta, 2007). 6. Conducting Data Quality Assurance & Control Quality control was necessary to adjust for errors caused by overlaying grid surfaces of different resolutions (Dasgupta, 2007). This basic overview of the process for mapping SLR effects on developing countries proved that even a 1 meter SLR would significantly impact the countries observed. This six-step process also serves a primary example of the process involved with efficient and effective SLR modeling. Of course more in depth explanations of these steps would need to be researched, but here we see the structure and level at which SLR modeling is to be preformed. Creating a stronger bond with remotely sensed data provides a bright future for SLR mapping. GIS work that incorporates remote sensing will only improve the resulting models that are produced. Remote sensing can provide the necessary accuracy required for creating SLR models. The implementation of light detection and ranging (Lidar) data serves as a principal example. In recent years vast amounts of high quality elevation data have come from the use of Lidar (Gesch, 2009). The advantage of Lidar elevation data is that it provides significantly better spatial resolution and vertical accuracy compared to traditional methods. This accuracy is essential to delineating lands that could potentially become inundated due to SLR. Lidar has also shown that it can provide higher levels of accuracy when looking at densely vegetated regions compared to that of traditional aerial mapping methodologies

(Gesch, 2009). This is due to the fact that Lidar can provide increased levels of point density, which allows for the potential of some returns from the ground back to the sensor. These returns allow for Lidar to be used for creating Digital Surface Models. There are two types of models that Lidar can create, reflectivesurface DEM s which show returns for elevation of above ground features and then bare-earth DEM s for accurate measurement of the earth s surface (Gesch, 2009). Lidar is both a fast and cost-effective method that provides accurate data for more localized areas of interest. It is also worth noting that Lidar should not replace traditional methods, but compliment these methodologies to improve data results. GIS and remote sensing are technologies that share a common thread and therefore combining efforts from both fields will only improve SLR mapping as a whole. Annotated Bibliography Dasgupta, S., B. Laplante, C.M. Meisner, D. Wheeler, and D.J. Yan. 2007. The Impact of Sea Level Rise on Developing Countries: A Comparative Analysis. World Bank Policy Research Working Paper 4136: 3-9. In this paper Dasgupta et al. discuss the issue of sea-level rise (SLR) in regards to climate change as a global threat. The various factors that contribute to SLR are discussed to highlight the major reasons for how climate change is related to such a global threat. They specifically discuss the assessment of developing countries most likely to be affected by SLR through the use of GIS software. The use of GIS reviewed the various applications of the software for highlighting not only inundated areas due to SLR, but also the relationship between socioeconomic factors and SLR. Their review expresses a number of findings, but for those countries with SLR impacts the outcome is catastrophic. The best section of the paper is the overall breakdown of how the GIS software can be utilized to preform a similar assessment. The way in which the authors are able to briefly describe a sis-step process to better understand how a trained mind may utilize so much data is uncanny. These authors also provide the data sources that are readily available for any users that wish to access similar data sets. This paper was very well presented due to the ability of the authors to provide detailed explanation on various points to the GIS mapping process. Gesch, D.B.. 2009. Analysis of Lidar Elevation Data for Improved Identification & Delineation of Lands Vulnerable to Sea-Level Rise. Journal of Coastal Research 53: 49-58. In this paper a very well drawn out explanation of how remote sensing can be tied into the use of SLR mapping took place. The importance of accuracy is a key point to this paper, and the use of remote sensing data such as Lidar data is a fundamental part to SLR mapping s future. Gesch provides an understanding of how topography is a crucial influence on coastal change, and therefore up-to-date, high resolution, high-accuracy elevation data is required. This paper does a great job expressing how Lidar data is acquired from sensor based platforms and then transformed to become reliable elevation data. The use of Lidar data to create digital elevation models (DEM s) allows for much more accurate vertical accuracy assessment data. Therefore, Gesch makes the point that Lidar can be used to compliment the use of GIS to create accurate SLR mapping scenarios to better relate inundated areas. This paper was very well presented based on the fact that it provides the topic of GIS and remote sensing as combining data to better improve the future of SLR mapping. Gravelle, G., and N. Mimura. 2008. Vulnerability Assessment of Sea-Level Rise in Viti Levu, Fiji Islands. Sustainability Science: 3: 171-180. Gravelle and Mimura present a specific case study to the topic of SLR mapping to a region of the world that will see the true impacts of SLR and climate change as a whole. Fiji and its vast coastline are expected to come under increasing pressure as the years pass. Therefore, Gravelle and Mimura conducted a study that utilizes GIS to identify specific high-risk locations. This case study best highlighted the use of Lidar data and GIS software as complimenting methods for mapping SLR. The best section of this paper came

from the methods section due to its basic overview of how the process was carried out. This paper also touches upon the issue of storm surge, which is often overlooked when considering SLR. These authors point out that storm surge will also see an increase in impact due to the fact that with a higher sea-level indicates any future storms will bring with it higher levels of storm surge on inland regions. The layer used in the case study for storm surge was also broken down to show the factors used to create this layer for the study. This was a great paper for a specific case study in a localized region, which is where Lidar data is most effective. Kostelnick, J., R.J. Rowley, D. McDermott, and C. Bowen. 2008. Sea Level Rise Modeling with GIS: A Small University s Contribution to Understanding a Global Dilemma. IEEE Earthzine 31262 1: 1-4. This case study is a prime example of how universities are beginning to develop GIS programs on campus for mapping impacts of climate change. Haskell Indian Nations University acknowledges climate change and its global impacts, which is what motivated this specific case study of mapping global population datasets and inundated areas. The parameters for this case study included elevation in relation to mean sea level and the connectivity of regions to the existing ocean. Again we see the use of DEM based on Lidar data being utilized to highlight once again the complimenting factor that both GIS and remote sensing have towards one another. The best section of this paper comes about when authors discuss how not only were SLR maps created, but also results included static maps, map animations, and inundation layers that could be seen in Google Earth. This paper also mentions the difference that exists between mapping inundated areas globally and locally. Many variables can either be included or must be taken away due to accuracy when looking at global or local level mapping. Nonetheless, this paper does a great job showing how small universities can provide impactful research for global levels. Marcy, D., W. Brooks, K. Draganov, B. Hadley, C. Haynes, N. Herold, K. Waters, M. Pendleton, K. Schmid, J. McCombs, S. Ryan, and M. Sutherland. 2011. New Mapping Tool & Techniques for Visualizing Sea Level Rise & Coastal Flooding Impacts. National Oceanic & Atmospheric Administration (NOAA) Coastal Services Center: 2-3. This particular paper provided the necessary background on climate change and its overall impact. These authors draw on the various scenarios that government task forces are required to consider when mapping SLR. Being a source from NOAA the use of GIS to map SLR not only includes areas where human impact will be felt, but also the potential ecological sensitivity that exists. Marcy et al. highlight the use of mapping technologies via the Web. These maps provide an interactive experience that truly draws on the visualization of mapping. The use of flooding viewers is discussed as the next generation of these interactive maps is produced. The authors, as future goals of interactive flood mapping, also discuss the use of higher resolution data. This paper was great for providing the processes used by government agencies and how they are tasked with the issue of SLR mapping. McGranahan, G., D. Balk, and B. Anderson. 2007. The Rising Tide: Assessing the Risks of Climate Change & Human Settlements in Low Elevation Coastal Zones. Environment and Urbanization 19 no.1: 17-37. McGranahan et al. provide a more comprehensive understanding of settlements in low lying areas and their relationship to SLR. This paper helps to understand the way in which areas are defined as areas of interest for SLR mapping. Highlighted are the percentages of the various types of settlements based within less than 10 meters above sea level. These authors highlight the disproportionate distribution of human settlements along coastlines that are susceptible to inundation. McGranahan et al. also discuss the issue of ecological stress on various settlements. These ecosystems are already under immense stress and therefore even impacts of SLR on these regions of coastlines, while not damaging to the settlements, will cause stress on readily available resources that typically are seen in coastal ecosystems. The best section of this paper is where these authors go in depth about how human settlement has always been drawn to coastal regions, and therefore the various impacts to really consider when looking at SLR are highlighted.

This paper was very useful due to the fact that these authors were able to coherently draw on such a brief topic in the grand scheme of SLR mapping. Walsh, J., and D. Wuebbles. 2014. Sea Level Rise. National Climate Assessment: 19-67. This source was utilized primarily for the findings of scientific projections of SLR. Having provided extensive research on projections of SLR by the year 2100, this particular source draws on research from accumulation of cited sources. The overall decision is that SLR will be between 1 to 4 feet by 2100 from the findings of this dataset.