Applications of GIS and Remote Sensing for Beach Morphology Analysis. Overview

Transcription

1 Brian Maggi NRS 509 Concepts in GIS & Remote Sensing University of Rhode Island 15 December 2016 Applications of GIS and Remote Sensing for Beach Morphology Analysis Overview Worldwide there is an increased frequency and severity of weather events. Combined with future projections for sea level rise (SLR), soft coastlines not protected by resilient man-made, geologic, or environmental systems are extremely vulnerable. Due to the density of human populations and fragile ecosystems along many of these coastlines, there is a significant concern about the future of these areas. As a result, numerous GIS methods and Remote Sensing options have been employed to study coastlines worldwide to better understand their morphology, forecast future conditions, and determine if some level of intervention is required to make a section of coastline more resilient. Beaches are regularly the focus of these studies since they are very susceptible to human intervention and severe weather impacts while they are also typically a sought-after destination for recreational activities and generate revenue for local economies. In addition to beaches, more recent case studies have assessed all types of coastlines including river deltas to analyze the impact human intervention upriver has had on the delta environment. This paper will use eight case studies from the America s, Europe, Africa, and Asia to demonstrate the most basic applications of GIS and Remote Sensing technologies up to the most advanced research to improve coastline management policies and procedures. GIS methods and Remote Sensing options have been available for coastline management for decades. Historically, they ve been used to assess the vulnerability of sections of coastline typically where valuable infrastructure, real estate, critical habits, or natural environments were being threatened by erosion or the increased frequency and level of water surge. Fortunately, extremely capable GIS analysis software now provides the ability to merge historical nondigitized data, wind, wave, and other various forms of data with modern survey data and utilize compatible software extensions to significantly advance the coastal morphology research field. These GIS improvements combined with advancements in Remote Sensing technology have enabled researchers and coastal managers to be more proactive in better understanding the impacts of human intervention and identifying coastal areas vulnerable to SLR. Typically, the study of coastal morphology requires a trend analysis to look at historical changes and forecast future conditions based on the prevailing coastal dynamics. Since many of these analyses date back thirty plus years, older hardcopy aerial photos, topographic maps, and geologic maps all need to be scanned, georeferenced, and georectified to be useful for a digital analysis. ArcGIS, more specifically the ArcInfo tool, is an effective tool for this digitization, mapping, and analysis. One thing to note is that while the extraction of a coastline could be done automatically using this software, hand digitization was preferred in some of the case studies since a complex shoreline and auto-extraction performed by the software can generate many errors and lead to time consuming post-processing (Stanchev et al., 2013). This check and balance of advanced technology use is also relevant when extracting coastline information from satellite imagery. The Nile River Delta case study dated back to the start of the Landsat program so researchers were able to use specific bands of the Multispectral Scanner, Thematic Mapper, Enhanced Thematic Mapper Plus, and Operational Land Imager sensors to

2 develop the historical coastline information. When combining this process with some of the very high resolution images from QuickBird (QB) and WorldView-2 (WV-2), the QB and WV-2 images displayed unnecessary details that complicated the analysis. In addition to using historical data, researchers or coastal managers with larger budgets are able to employ modern LiDAR or Structure-from-Motion (SfM) photogrammetry methods to produce high resolution Digital Elevation Maps (DEMs) or Digital Surface Maps (DSMs) to compare current conditions to the historical data. LiDAR is the current industry standard for producing high resolution surveys, but due to the high cost associated with the equipment and typical platform used for a survey (i.e. plane) many researchers are looking for lower cost and more flexible methods to produce high resolution maps. This led to the development of vessel-based LiDAR surveys and SfM surveys. For each of these methods a root mean square (RMS) analysis is used to compare the accuracy of the collected data points to Real-Time-Kinematic GPS (RTK-GPS) points surveyed on the shoreline. Both of these methods produce extremely accurate results, within a few centimeters, which is comparable to results obtained through an airborne LiDAR survey. The change in platform for the LiDAR equipment does have some drawbacks though. Due to the low, horizontal viewpoint, vessel-based LiDAR can miss flat spots above the level of the sensor and topographic lows landward of high coastal features (Quan et al., 2013). SfM surveys on the other hand are able to pick up these features but require more post data collection processing to develop the dense data clouds produced by the LiDAR equipment. Various commercial and non-commercial software is available for the SfM technique and improvements in computer vision algorithms have made object recognition systems more reliable and effective (Brunier et al., 2016). The workflow for the SfM technique includes: picture synchronization with GPS data, picture alignment and bundling of the pictures using the Scale Invariant Feature Transform algorithm, georeferencing using Ground Control Points established during the data collection phase, and the development of a dense point cloud that has a density similar to a LiDAR point cloud, 100 to 300 points/m 2 (Brunier et al., 2016). DSM exports with a 10 cm resolution and an orthophoto with a 5 cm resolution were then produced from this SfM point cloud. In addition to the GIS and RS means and methods discussed above, various analysis tools are being used to tailor datasets to specific goals of a study. An example of one of the analysis tools, is the Decision Tree Classification (DTC) approach used to partition the Nile River Delta dataset to develop a land use, land cover map for the area since one did not exist. For this DTC, three indices that rely on Landsat bands to extract the sought-after data were used. The Red and Near-Infrared (NIR) bands were used for the Normalized Difference Vegetation Index, the Green and Mid-Infrared (MIR) bands were used for the Modified Normalized Difference Water Index, and the MIR and NIR bands were used for the Normalized Difference Building-up Index. The Thermal Infrared band was also used to separate the built-up areas from the coastal beaches (Ghoneim et al., 2015). The land use, land cover map produced from this process was then effectively used to assess the impact erosion has had and project the impact it will have on future land use on the Nile Delta. Due to the heightened concern about the impact coastline morphology may have on future land use, researchers are looking for ways to automate change detection and predict future vulnerabilities along the coastline. While visual interpretation and the least-cost flow path (LCP) algorithm are effective for extracting morphological features from aerial and satellite images they have their shortcomings. Most notably they are time intensive for large-study areas and they lack the ability to identify important dune features (Wernette et al., 2016). A multi-scale automated approach using relative relief and convergence/divergence analysis of topographic features has proven to be an effective tool based on the recent research by Wernette et al. Combined with historical trend analysis

3 and wind and wave data, this automated approach may help coastal managers more quickly identify potential hotspots along their coastline. The only limit on GIS and Remote Sensing applications in beach and coastline morphology analysis is time and effort. As evidenced by the case studies highlighted here, an effective coastline analysis can be developed from free, readily accessible data and displayed using basic GIS tools. On the other hand, depending on a project s budget more extensive, potentially even automated analyses can be developed from LiDAR or SfM surveys using highly capable GIS and other modelling software. GIS and RS technologies are invaluable to a coastal manager and efforts should continue to be made to bring down the costs of some of the more advanced technologies so they can be implemented on a wider scale. The resilience of coastlines very much depends on shoreline change monitoring and detection. If coastal managers develop a better understanding of the coastal dynamics effecting their shorelines the implementation of sound solutions that account for climate change and SLR can be implemented. Annotated Bibliography Brunier, G., J. Fleury, E. J. Anthony, A. Gardel, and P. Dussouillez Close-range airborne Structure-from-Motion Photogrammetry for high-resolution beach morphometric surveys: Examples from an embayed rotating beach. Geomorphology 261: In this paper, Brunier et al. present an in-depth discussion of workflow and case study analysis of the Structure-from-Motion (SfM) technique to produce high resolution Digital Surface Models (DSM). Based on the error analysis provided in the paper, the DSMs produced from SfM compare in accuracy to LiDAR but the cost is significantly less since the camera used to collect the aerial photos is less expensive than typical LiDAR equipment. The detail of the workflow and flight path development provides a good example that correlates well to the course material. Additionally, the paper effectively highlights how ground control points and ground truth points are used to georeference and analyze the aerial photos. There are some ground conditions that produce unsatisfactory values and the authors do a good job highlighting these issues in their DSM quality assessment. Overall, the authors of this paper thoroughly present the SfM technique, how it is used to study the morphology of a beach in French Guiana, how it compares to LiDAR, and some areas of potential improvement to further improve the accuracy of the DSMs. Ghoneim, E., J. Mashaly, D. Gamble, J. Halls, M. AbuBakr Nile Delta exhibited a spatial reversal in the rates of shoreline retreat on the Rosetta promontory comparing pre- and post-beach protection. Geomorphology 228:1-14. In this paper, Ghoneim et al. discuss in detail the use of Landsat, QuickBird, and WorldView2 images to analyze the shoreline retreat of a Nile River Delta. Since the impacts of human intervention on the Nile River are well documented along with the subsequent erosion that has occurred on the delta, the authors really focus on the data and methodology that is used for the Remote Sensing and GIS processing of the data. As a result, this paper is very relevant to the Remote Sensing part of the course since spectral resolution and the use of different spectral bands was discussed in great detail throughout the paper. The authors also highlight how the very high spatial resolution of the QB and WV2 images

4 displayed many unnecessary coastal details which made the analysis more difficult. In addition to being used to create shapefiles of the shorelines, ArcGIS and an ArcGIS extension (Digital Shoreline Analysis System) developed by the USGS were used to calculate the annual rates of shoreline change. The authors were also interested in assessing the erosion impact on different land uses so a decision tree classification (DTC) approach was used to partition the datasets into subsets that they were interested in. The DTC was based on three indices and a Land Use Land Cover map was produced. What made this paper unique, was that all the data used for the analysis was free. There was good agreement of shoreline change rates between the Landsat-derived shorelines and the shorelines derived from the free images acquired from the QB and WV2 satellites. Therefore, regardless of budgets, coastal managers have effective tools for coastal change detection and they should use these tools to identify vulnerable, hotspot areas. Jeanson, M., F. Dolique, and E. J. Anthony A GIS-based coastal monitoring and surveillance observatory on tropical islands exposed to climate change and extreme events: the example of Mayotte Island, Indian Ocean. Journal of Coastal Conservation 18: In this paper, Jeanson et al. discuss how GIS and remote sensing technology is being used along with field measurements to characterize various coastal environments on Mayotte Island. A degradation of the coastal zone and the reef-lagoon system on the island has resulted from a significant increase in population and development pressure. There are concerns that this degradation will reduce the resilience of coastal communities and environments especially since there has been a documented increase in the frequency and intensity of extreme weather events. As a result, the authors discuss how a policy of integrated and sustainable coastal management has been developed to use remote sensing to monitor shoreline changes since 1949 using aerial photographs and more recently orthophotos. They then present the process of how GIS is being used to translate the acquired information into a framework to inform the general public and influence researchers and decision-makers to implement rational coastal management and conservation. The observations presented in the paper show how mangrove cutting has resulted in the instability of a shoreline section and how the equilibrium of a beach section has been maintained despite noticeable seasonal changes. The authors claim that each of these observations have proven to be useful in subsequent coastal management decisions and the goal is to further develop these tools to continue to provide effective decision support. This paper does a good job of highlighting the resilience issues effecting all coastal communities and how modern remote sensing and GIS tools can support sound decision making processes. Quan, S., R. G. Kvitek, D. P. Smith, G. B. Griggs Using Vessel-Based LIDAR to Quantify Coastal Erosion during El Nino and Inter-El Nino Periods in Monterey Bay, California. Journal of Coastal Research, Vol. 29, No. 3, This paper provides a different perspective on remote sensing. Instead of using a typical aerial platform for the LiDAR survey of the Monterey Bay coastline, Quan et al. used a vessel to perform their survey. The goal of this method was to reduce the cost associated with a typical LiDAR equipped flight and due to the availability of the vessel, improve the ability to respond to shore notice significant environmental events. Post survey ground-truthing verified the precision of the results and Digital Elevation Models were generated in ArcGrid format. The Digital Shoreline Analysis System was then used to calculate shoreline recession. Additionally, Coastal Data Information Program wave height models were merged into ArcGIS to create a wave height distribution for the strongest El Nino storms. Overall, the authors provide a well-supported alternative to the typical LiDAR survey and when it s

5 combined with wave energy distribution models it produces effective and efficient high resolution data to support coastal management. Saravanan, S., N. Chandrasekar, M. Rajamanickam, C. Hentry, V. Joevivek Management of coastal erosion using remote sensing and GIS techniques (SE India). International Journal of Ocean and Climate Systems, Vol. 5, No. 4, In this paper, Saravanan et al. discuss the use of Remote Sensing and GIS techniques at a very high level. This paper lacks the detail found in the other papers that were reviewed. It was a straightforward application that continues to highlight the diverse application of RS and GIS technology in various countries and scenarios. Similar to the Nile River Delta paper, it provides another example of how human intervention contributes to coastal sediment starvation and subsequent erosion.

6 Stanchev, H., R. Young, M. Stancheva Integrating GIS and high resolution orthophoto images for the development of a geomorphic shoreline classification and risk assessment A Case Study of cliff/bluff erosion along the Bulgarian coast. Journal of Coastal Conservation 17: In this paper, Stanchev et al. discuss the digitization of the Bulgarian coastline from topographic maps, geologic maps and orthophoto images to develop a shoreline classification and risk assessment. This paper effectively presents the GIS methods used and provides a good hierarchical scheme example used to segment the coastline based on geology, cliff heights, and other characteristics. The authors are then able to translate the digitized and classified data into a hazard classification tool to identify coastal areas prone to erosion. Based on the collected field data, there is good correlation between the classification tool and areas where erosion has occurred. Compared to some of the other papers that focus more on the Remote Sensing half of the course, this paper aligns well and discusses applications of many of the topics discussed during the GIS segement. Thomas, T., A. Williams, N. Rangel-Buitrago, M. Phillips, and G. Anfuso Assessing Embayed Equilibrium State, Beach Rotation and Environmental Forcing Influences; Tenby Southwest Wales, UK. Journal of Marine Science and Engineering 2016, 4, 30. Similar to the paper by Jeanson et al., in this paper Thomas et al. used aerial photos dating back to the 1940 s to monitor shoreline change in the UK. The methods to model the shoreline change and the process to reduce the errors in the aerial photos was presented in greater detail in this paper. The authors analysis of the historical shoreline change was more in depth as well. In addition to comparing historical wave and wind data against the shoreline erosion/accretion trends, the authors developed regression models to predict when the shoreline would reach equilibrium (circa 2061). Using this predicted shoreline, the authors conducted a topographic survey and developed a flood map to highlight areas of potential vulnerability. This paper is very detailed and well supported. The authors do an excellent job of thoroughly presenting a complex analysis and producing an end product that is a relatively simple tool that can be understood by all stakeholders. This simple method of assessment has the potential to be an effective coastal management tool. Wernette, P., C. Houser, and M. P. Bishop An automated approach for extracting Barrier Island morphology from digital elevation models. Geomorphology 262:1-7. In this paper, Wernette et al. discuss an automated approach to identify beach, dune, and barrier island morphology based on the average relative relief (RR) across multiple spatial scales of analysis. The authors first present the current methods and shortcoming of these methods for extracting morphological features from LiDAR data. Their review of the current methods is detailed enough to provide a good reference point to compare their multi-scale automated approach to. The methodology of the automated approach is thoroughly discussed and the analysis of the LiDAR derived Digital Elevation Model for a portion of North Padre Island, TX is a good example of how RR along with convergence/divergence approaches can be used to identify topographic features. This example also effectively demonstrates how the manipulation of computational scales can affect RR results and how the results compare to the contemporary methods used to assess the topographic features of a beach system. Overall, the proposed average RR approach is well supported by the authors and seems like a good alternative to current methods. My only concern is the computation processing requirements or time associated with running an automated analysis of an area. The authors highlight how the other methods can be time-intensive but fail to note the time or computational resources required for this multi-scale automated approach.