Farah Nusrat NRS 509: Concepts in GIS & Remote Sensing Due Date: December 15, 2016

Transcription

1 Farah Nusrat NRS 509: Concepts in GIS & Remote Sensing Due Date: December 15, 2016 Use of Geographic Information System (GIS) & Remote Sensing for the Assessment of Sea Level Rise Impact Sea level rise is a threat to the coastal community. The main reason for the sea level rise is the melting of glaciers and land-based ice caps and expansion of sea water due to the increase in average temperature. The rise of water level is not increased equally in places but varies due local hydrological factors, geographical factors, local terrain etc. The cities which are expanded near coastlines are the most vulnerable ones. High resolution satellite images can be the latest source to trace the most vulnerable locations among the coasts of any country. Use of Geographic Information System (GIS) for predicting the sea level rise and for creating climatic models to interpret the effect of sea level rise has started a long time ago. Now it is time to improve the GIS for more accurate prediction and modeling. Shoreline change is a significant effect of the sea level rise. The shoreline change has become a major social, economical and environmental concern to the countries along the coast due to its effect on human settlements. In the paper by Chand et al. (2010), described shoreline recession as a result of rising sea level has been recognized as a potential near future hazard by a number of countries. Their research showed the importance of accurate demarcation and monitoring of shoreline changes for understanding and deciphering the coastal processes operating in an area. They emphasized on the wide range of coastal studies such as coastal zone management planning, hazard zoning, erosionaccretion studies, analysis and modelling the coastal morphodynamics. Due to the high resolution, multispectral database, synoptic and repetitive data coverage and cost effectiveness in comparison to conventional techniques, GIS and remote sensing are regarded as useful tool s for shoreline changes studies. There are different papers which show the new techniques of using GIS and remote sensing for sea level rise prediction, assessing coastal vulnerability and shoreline changes. The paper by Cooper et al. (2013), discussed the use of Light Detection and Ranging (LiDAR) data for assessing the vulnerability due to sea level rise by using GIS and remote sensing. They used discrete-return, scanning airborne laser altimeters for capturing LiDAR data by both of U.S. Army Corps of Engineers (USACE) and National Oceanic and Atmospheric Administration (NOAA) with a 20,000 Hz pulse rate. Authors generated Digital Elevation Models (DEMs) of 2 m horizontal spatial resolution from the two datasets. Using tidal benchmarks by the authors to review the USACE LiDAR data and to find out if a calibration was required for NOAA LiDAR due to the deficiency of tidal benchmarks is an innovative procedure. From their research, it is clear that exploratory analysis of the USACE LiDAR ground returns (point data classified as ground after the removal of vegetation and buildings) indicated that another round of filtering could reduce commission errors. Apart from the remote sensing, vulnerability maps by using GIS can help to take proper measures to address coastal vulnerability. In the paper, Rao et al. (2008) discussed how coastal vulnerability index

2 can help in the assessment of coastal vulnerability assessment. The authors segmented the coastline into low, moderate, high and very high risk categories on the basis of coastal vulnerability index. Shape file of each variables were inserted into the ArcGIS software by the authors and the vulnerability ranks of all the coastal segments for the five variables were also entered into the corresponding attribute tables against the unique ID of each coastal segment. For analyzing the vulnerability by using GIS technique, Triangulated Irregular Network (TIN) method in 3-D Analyst module of ArcGIS and Overlay module were used. For remote sensing data, the authors used Indian Remote Sensing Satellite IRS P6 Advanced Wide Field Sensor data, Shuttle Radar Topographic Mission (SRTM) data and satellite images from IRS 1B Linear Imaging Self Scanning Sensor 2 for Geomorphology, Coastal slope and Shoreline Change respectively. Images from QuikSCAT satellite are also used in the research. The researchers used coast as a line feature instead of segmenting coasts into spatial grids which provided accurate picture of the vulnerability level. A study done by Zhang et al. (2011) has established a GIS model to estimate the inundated land area and the effect on its topography. By analyzing a DEM derived from airborne Light Detection and Ranging (LiDAR) data and airborne height finder measurements, it demonstrates that a 1.5 meter sea-level rise by 2100 would cause inundation of large areas of Miami-Dade County, southern Broward County, and Everglades National Park. For analysis, author has derivate inundation polygons and hypsometric curves for elevation and then done projection of future sea-level rise. The last step is to do derivation of hypsometric curves for inundation time. the researcher found that the faster sea-level rise accelerates, the sooner the inundation threshold is reached. From the study by Chand et al. (2010), an interactive relationship has been established between the shoreline changes and sea level rise by utilizing remote sensing and statistical techniques. Researchers used multi-resolution satellite data of Landsat series [Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM), and Landsat Enhanced Thematic Mapper Plus (ETM+) sensors] because Landsat is enriched with coastal data at free of cost. For transect wise shoreline changes analysis as well as digital and vector analysis, the authors use ERDAS and ARCGIS software which is very efficient for these types of analyses. The authors divided the methodology into two parts, one is to analyze and interpret the optical remote sensing data (Landsat) for shoreline change mapping and statistical techniques to find out shoreline change rate over the four decades and another one is to trace out the interactive relationship between sea level changes and shoreline change. The paper by Pramanik et al. (2014), uses GIS and remote sensing along with sea level data of the nearest tidal station to analyze the responses, migration, destruction and vulnerability of the deltaic mangrove ecosystem. From the assessment by the author, it is observed that, with the fall of sea level, mangrove margins migrate seaward and mangrove density will increase also with expansion of land area. With the rise of sea level, Mangrove s seaward and landward margins retreat landward which causes declination of land area. It also reduces the mangrove density and causes soil erosion. The importance of GIS and remote sensing in the field of predicting and modelling sea level rise cannot be denied. One problem is that we generally use global scenarios for any model or prediction. If we use local scenarios for modelling, it may give more accurate results. So a comparison by using local and global scenarios can be useful resource prediction.

3 References: Chand, P., and P. Acharya Shoreline change and sea level rise along coast of Bhitarkanika wildlife sanctuary, Orissa: An analytical approach of remote sensing and statistical techniques. International Journal of Geomatics and Geosciences 1: Cooper, H.M., Q. Chen, C. H. Fletcher, and M. M. Barbee Assessing vulnerability due to sea-level rise in Maui, Hawai i using LiDAR remote sensing and GIS. Climatic Change 116: Pramanik, M.K Assessment the Impact of Sea Level Rise on Mangrove Dynamics of Ganges Delta in India using Remote Sensing and GIS. Journal of Environment and Earth Science 4: Rao, K.N., P. Subraelu, T. V. Rao, B. H. Malini, R. Ratheesh, S. Bhattacharya, A. S. Rajawat and Ajai Sea-level rise and coastal vulnerability: an assessment of Andhra Pradesh coast, India through remote sensing and GIS. Journal of Coastal Conservation 12: Zhang, K Analysis of non-linear inundation from sea-level rise using LIDAR data: a case study for South Florida. Climatic Change 106: Annotated Bibliography Cooper, H.M., Q. Chen, C. H. Fletcher, and M. M. Barbee Assessing vulnerability due to sea-level rise in Maui, Hawai i using LiDAR remote sensing and GIS. Climatic Change 116: In this paper, Cooper et al. discussed how LiDAR was used to assess the vulnerability due to sea level rise by using remote sensing. The study areas of this paper are Kahului and Lahaina of Maui Island. The industrial center of the Maui Island is Kahului. Lahaina is located on the shore of West Maui and is an historic port town. The source of data for Kahului was the Joint Airborne LiDAR Bathymetry Technical Center of Expertise (JALBTCX) for the U.S. Army Corps of Engineers (USACE), which were collected in January through February of 2007 using the Compact Hydrographic Airborne Rapid Total Survey (CHARTS) system equipped with the Optech SHOALS-3000 sensor. EarthData Aviation for the National Oceanic and Atmospheric Administration (NOAA) collected the data for Lahaina in March of 2005 during Mean Lower Low Water (MLLW) tides using the Leica Geosystems ALS-40. Discrete-return, scanning airborne laser altimeters was used for capturing LiDAR data by both of USACE and NOAA with a 20,000 Hz pulse rate. By using Toolbox for LiDAR Data Filtering and Forest Studies (Tiffs) software, DEMs of 2 m horizontal spatial resolution were generated from the two datasets by the authors. The interesting part of this research is using tidal benchmarks to review the USACE LiDAR data and to find out if a calibration was required for NOAA LiDAR due to the deficiency of tidal benchmarks. In addition, high resolution SLR vulnerability maps can assist the coastal planners to take proper measures to the critical infrastructures vulnerable to inundation and erosion.

4 Rao, K.N., P. Subraelu, T. V. Rao, B. H. Malini, R. Ratheesh, S. Bhattacharya, A. S. Rajawat and Ajai Sea-level rise and coastal vulnerability: an assessment of Andhra Pradesh coast, India through remote sensing and GIS. Journal of Coastal Conservation 12: In this paper, Rao et al. did the vulnerability assessment of Andhra Pradesh (AP) coast, India by developing a coastal vulnerability index by integrating the differentially weighted rank values of the five physical variables (coastal geomorphology, coastal slope, shoreline change, mean spring tide range, and significant wave height). On the basis of the index, coastline is segmented into low, moderate, high and very high risk categories. The study area of the research is the 1,030 km long coast of AP State including the 300 km long Krishna Godavari delta front dominating its central part. The authors used Shape file of each variable and inserted them into the ArcGIS software and the vulnerability ranks of all the coastal segments for the five variables are also entered into the corresponding attribute tables against the unique ID of each coastal segment. For analyzing the vulnerability by using GIS technique, Triangulated Irregular Network (TIN) method in 3-D Analyst module of ArcGIS and Overlay module are used. For remote sensing data, the authors used Indian Remote Sensing Satellite-IRS P6 Advanced Wide Field Sensor data, Shuttle Radar Topographic Mission (SRTM) data and satellite images from IRS 1B Linear Imaging Self Scanning Sensor 2 for Geomorphology, Coastal slope and Shoreline Change respectively. Images from QuikSCAT satellite are also used in the research. The interesting part of the paper is, instead of segmenting the coast into spatial grids, they have taken the entire coast as a line feature in GIS in which every point along the coast is considered for the analysis. It represents a more accurate picture of the vulnerability level of any point along the coast. Chand, P., and P. Acharya Shoreline change and sea level rise along coast of Bhitarkanika wildlife sanctuary, Orissa: An analytical approach of remote sensing and statistical techniques. International Journal of Geomatics and Geosciences 1: Chand et al. finds out the interactive relationship of shoreline changes and sea level with the application of remote sensing and statistical techniques. They have selected Bhitarkanika Wild Life Sanctuary in central coast of Orissa as their study area which is a rich, lush green vibrant ecosystem lying in the estuarine region. They used multi-resolution satellite data of Landsat series (Landsat MSS, Landsat TM, and Landsat ETM+ sensors) because Landsat is enriched with coastal data at free of cost. For transect wise shoreline changes analysis as well as digital and vector analysis, the authors use ERDAS and ARCGIS software which is very efficient for these types of analyses. The authors divided the methodology into two parts, one is to analyze and interpret the optical remote sensing data (Landsat) for shoreline change mapping and statistical techniques to find out shoreline change rate over the four decades and another one is to trace out the interactive relationship between sea level changes and shoreline change. Among the various methods for shoreline extraction from optical imagery, the processed NIR bands of Landsat MSS, TM and ETM+ is used here for identification and delineation of shorelines. To calculate the shoreline change rates, End Point Rate (EPR) calculations, R 2, Net Shoreline Change (NSC) or linear

5 regression is mainly used by the authors. The rates determination in End Point Rate (EPR) calculation is based on the difference in position between the oldest and most recent shorelines in a given dataset. The result of the average rate of changes using a number of shoreline positions over time is represented as rates in linear regression. Though the increasing trend of MSL and high magnitude of shoreline shift occurred simultaneously, there are some other processes involved here rather than SLR. The authors found the relevancy of storm surge in this shoreline shifting mechanism which is a very turning point for this research and there are many more scopes to use GIS and remote sensing to address the uncertainty related to storm surge. Zhang, K Analysis of non-linear inundation from sea-level rise using LIDAR data: a case study for South Florida. Climatic Change 106: In this paper, Zhang et al. has worked to establish a GIS model to estimate the inundated land area and find the effect of topography and acceleration in sea-level rise on inundation for The 7 counties in the State of Florida, along with Everglades National Park. An investigation was done on the influence of the horizontal and vertical resolutions of DEMs on inundation analysis, and also to quantify the impact of sea-level rise induced inundation on property and population in South Florida. In this research, the census data consists of Topologically Integrated Geographic Encoding Referencing (TIGER)/Line files have been converted into a format compatible with GIS files. The researcher has produced DEMs from airborne LIDAR surveys organized by U.S. Army Corps of Engineers (USACE). The USGS 30 m DEMs and 10 m DEMs are also used for the research. For analysis, author has derivate inundation polygons and hypsometric curves for elevation and then done projection of future sea-level rise. The last step is to do derivation of hypsometric curves for inundation time. The attention-grabbing part is the observation of a non linear inundation process with accelerated sea-level rise in next century which shows that the faster sea-level rise accelerates, the sooner the inundation threshold is reached. Kumar, M Remote sensing and GIS based sea level rise inundation assessment of Bhitarkanika forest and adjacent eco-fragile area, Odisha. International Journal of Geomatics and Geosciences 5: Kumar et al. described here how to use GIS and remote sensing for inundation assessment. He also pointed up the vulnerable land cover and how adaptive measure for sea level rise can reduce that. He chose the deltaic mangrove swamps of Bhitarkanika Wildlife Sanctuary as the study area. Author used IRS P-6 LISS-III Resourcesat (2009) satellite image for preparing Land cover map of study area. He extracted the land cover map through visual interpretation technique following unsupervised classification method. And then, the land cover map was overlaid on the DEM to assess the impact of SLR due to inundation. Apart from other data, LISS-III satellite data, CARTOSAT-1 stereo pair PAN image and toposheets are also used for analytical purpose. The goal of the research is to do inundation

6 assessment on the basis of elevation but not ecologically important sites which can be the next consequence of this research. Hennecke, W.G GIS Modelling of Sea-Level Rise Induced Shoreline Changes Inside Coastal Re- Rntrants Two Examples from Southeastern Australia. Natural Hazards 31: Hennecke et al. discussed the potentiality of sea level rise to shoreline recession which can be considered as hazard in many countries. The author also mentioned that high resolution data at a particular elevation can be more costly as well as time consuming for inputting in any model routinely. The author has established a GIS-based coastal-behavior model to formulate simple algorithms for simulating the potential physical impacts of rising sea level on the coastal environment. This model has the capability to provide the estimation of shoreline change based on available data. The model has special focus on coastal re-entrants. A spreadsheet based hazard probability model is used for analyzing the rate of shoreline change. The model prepared by the author has the capacity to do a rapid assessment of the probability of shoreline changes instead of a single impact zone. The author has returned the hazard probability rates to the GIS to be displayed as a grading of risk instead of a single impact zone. Pramanik, M.K Assessment the Impact of Sea Level Rise on Mangrove Dynamics of Ganges Delta in India using Remote Sensing and GIS. Journal of Environment and Earth Science 4: Pramanik et al. uses GIS and remote sensing along with sea level data of the nearest tidal station to analyze the responses, migration, destruction and vulnerability of the four deltaic mangrove ecosystem (Bakhali, Bulcherry, Dalhausie and Bangaduni Islands). The researcher has used Landsat multispectral satellite images [Landsat Multispectral Scanner (MSS) data, Landsat Thematic Mapper (TM) data, Enhanced Thematic Mapper plus (ETM+) data and Operational Land Imager (OLI) data] from USGS (United States Geological Service) GLOVIS website which is freely available. These images helped to find out the vulnerable mangrove islands. Author has also used ASTER data of 2014 to determine the affecting area by sea level rise along the coastal area which bring a new dimension in the analysis. From the assessment it is observed that, with the fall of sea level, mangrove margins migrate seaward and mangrove density will increase also with expansion of land area. With the rise of sea level, Mangrove s seaward and landward margins retreat landward which causes declination of land area. It also reduces the mangrove density (NDVI) and causes soil erosion. Use of remote sensing images excels the evaluation by providing images of different time frames.