RS-based change detection of coastal processes and elements at risk mapping in Sagar Island, West Bengal, India

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RS-based change detection of coastal processes and elements at risk mapping in Sagar Island, West Bengal, India Mahmoud Ibrahim Mahmoud (19865) Applied Earth

1 RS-based change detection of coastal processes and elements at risk mapping in Sagar Island, West Bengal, India Mahmoud Ibrahim Mahmoud (19865) Applied Earth Science Department (AES) Disaster Geo-Information Management (DGIM) Faculty of Geo-Information Sciences and Earth Observation (ITC) University of Twente 1 6 April, 2011

2 Table of Contents 1. Introduction Remote sensing Application in Coastal studies Change Detection Aim and objectives Study Area Data set Software Methodology Structure of methodology Image preprocessing and processing Results Usefulness of landsat Archives Remote sensing data integration for coastal mapping Coastline change in the recent past Coastline change detection for erosion hazards and risk mitigation Conclusions...9 Reference...10 List of Annexures Annexure I. Optimal Index factor table.13 Annexure II. Change detection analysis table.14 2

3 1.0 Introduction Numerous changes occur and can be detected in the surroundings of coastal environments. These changes are results of natural or anthropogenic processes. There are varieties of different approaches for change detection analysis to study coastal processes, map vulnerability and elements at risk currently in practice. Remote sensing (RS) based approach is one of the most popular and promising methods for land use/land cover changes detection and can be adapted for mapping dynamic coastal activities and fluvial processes taking place in coastal areas such as coastal flooding, erosion/accretion, sedimentation, land subsidence, salt water intrusion, water pollution and cyclone hazards etc. It is for these cogent motivations this project seeks to apply RS techniques to study the dynamic changes going in coastal fringe of Sarga Island of India and as well map the various elements at risk with emphases on erosion and accretion processes. 1.1 Remote sensing applications Using remote sensing technology the dynamics of coastal processes and their effect on coastal regions can be monitored. In a hazard context remote sensing is also helpful in evaluating the changes in land use activities corresponding to these processes. RS of the coastline, monitoring and management capabilities have been demonstrated in many regions of the world in recent past. (Bandyopadhyay, 1997,) established the utility of landsat MSS-1 and other different map sources in a comprehensive study to understand the nature and management of natural environment hazards in the Sargar island. A demonstration based on multidated satellite data of Indian Remote Sensing Satellite -IC Linear Imaging Self-scanning Sensor (LISS) IIIERS-1 data further confirmed to study the natural and anthropogenic processes taking place in the coastal zone of Sagar Island by (Gopinath and Seralathan, 2005). (Mukhopadhyay, 2007) in a technical report explored reconnaissance survey approach for vulnerability and damage assessment of the effect of the cyclone Sidr of 15th November 2007 which occurred in Sargar island in the recent past. 1.2 Change detection This project explores the use of multi-temporal satellite images for coastline change analysis of Sarga Island. (Li and Damen, 2010) demonstrated the use of RS data for coastline mapping and coastline change detection and further sufficed the method as critical for safe navigation, sustainable coastal resource management and environmental protection. Change detection analysis is classified into several categories according to the theme, frequency of changes, data source, and remote sensing application methods. Advancement in RS has been shown that bridging the gap between geoscience data and the perceptions of individuals concerning environmental change can reduce the resilience of these individuals. To complete the purpose of this technical report a couple of technical information sources, academic papers, lecture and practical exercises describing remote sensing based change detection were studied and applied for coastal process change detection and mapping of element at risk of the study area. The resulting output was also used for implementing and presenting a spatio-temporal change detection monitoring analysis calculated from the variety of Lansat MSS, TM and ETM as well as ASTER satellite images provided. Several map algebra calculation methods and image classification and slicing methods to achieve the project objectives prescribed were tested. 3

4 1.3 Aim and objectives This project aims to carry out a change detection analysis of coastal processes, evaluate what has been the effect of these changes on different elements at risk with emphasis on erosion and accretion. Attention was paid to elements at risk in the study area. The specific objectives to achieve results include the following: 1. To assess the usefulness of the available Landsat image data for spatial and temporal analysis of the selected hazardous processes and elements at risk; 2. To assess the possibility and relevance of integrating Landsat images with image data obtained from Google Earth; 3. To collect and use additional remote sensing data (E.g. Aster) from more recent dates for change detection; 4. To design an approach for change detection of a selected hazard example, the effect of changes on elements at risk and proposed mitigation measures. 1.4 Study Area and Geology Sagar Island in India is uniquely referenced on ( to N lat. and to E long), Area 30 km 2 ). It is the largest island of the Sundarbans deltaic complex, located on the continental shelf of the Bay of Bangal. This island is also known as Sagar or Gangasagar, situated about 150 km south of Kolkata, India. It is a large estuarine island, traversed by a major tidal creek and several smaller ones. The island has an elevation averaging 6.5m above the mean sea level (Mukherjee 1983) cited in (Gopinath and Seralathan, 2005). Due to its low elevation during cyclone and surges, the margins of the island are usually inundated (Mukhopadhyay, 2007). Fluvial, marine, tidal and Aeolian processes are the chief agents actively shaping the narrow coastal belt. It has 43 villages and a Parganas of over 1,60,000 cited in (Mukhopadhyay, 2007) with a population that thrives on agriculture, prawn seeds collection and fishery. 4 Fig. 1. Location map of the study area.

5 1.5 Dataset In this sub-section dataset provided for the project are described. Two distinct types of datasets were provided for this project. These include spatial and nonspatial dataset. Table 1: Tabulated datasets for this project. Spatial Non-spatial 1.6 Tools and Software This sub-section describes tools and software packages used to execute the project. Hewlett-Packard high end EliteBook 8540p laptop machine was used in this project. Erdas Imagine 2010 licensed to ITC, University of Twenete was used for Remote sensing image preprocessing and processing of the MSS, TM, ETM and Aster dataset. Ilwis 3.3 was also used to inspect, analyze and select most suitable bands of the image for the by computing the (Optimal Index Factor OIF and Variance-Covariance). ArcGIS 10 also licensed to ITC was used GIS operations to extract information for the change detection analysis, delineate the coastline and map elements at risk and compare other images with Digital globe Google earth 2010 of the image of the study area. Microsoft (MS) Visio was used to graphically represent the logical framework of the project; MS-word was used for computerizing the report in a document format and MS-excel was used to implement the change detection matrix analysis. 5

6 2 Methodology This chapter describes the method considered to process the dataset provided for this project as shown in (table.1), and accesses data suitability for use to address the principal aim and set objectives explained in the preceding chapter. Firstly, this involved critically inspecting the data and performing some fundamental pre-processing steps to standardize the data as a rule of thumb for the intended analysis as shown in (fig 2). Best band selection criteria were applied by implementing the Optimal Index Factor (OIF) before enhancing and transforming the data sets for Digital Image Pre-Processing and processing for future extraction. Mapping of the coastline changes in time and possible element at risk was done by digitizing of objects of interest (fig 2). 2.1 Structure of methodology This sub-section presents a stepwise methodological framework adopted to achieve the specific objectives listed in the introduction chapter of this report. Firstly, a concise literature review from a variety of scientific and technical papers were consulted for gaining better theoretical base. Secondly, the remote sensing component of this project was initialized by carrying out a data quality inspection check and data ordering according to years of collection on the provided dataset (table1). The logical frame work is organized in blocks all containing a process and subprocess as the step may require. Enlisted bellow is a sequenced order of proceedings and is also illustrated diagrammatically in (fig 2). 2.2 Image preprocessing and processing Image pre-processing is the process of making images more interpretable for a particular application. For this project it was necessary do a rigorous preprocessing operation chain considering the multi-temporal and multi sensor image data involved. a. Geometric correction was the first pre-processing step initialized as schematized in (fig 2). The MSS, TM, ETM, ASTER images were already georeferenced from source. However, geo-correction process was applied on the Digital globe s, google earth image downloaded. It was georegistered in ArcGIS10 at (RMSE 0.1 pixel). Reference points were collected from google earth map and implemented using the WGS 1984 datum and the UTM zone 45 projection formats. To test for the georeferencing accuracy all data set were visualized in ArcGIS10 and exported to KML and visualized on google earth global system. Result showed conformity and overlaid perfectly on the online google map which was an indication of a good georefernced dataset. b. Resampling of MSS 60m to 30m was done to make MSS comparable to TM and ETM datasets. c. Atmospheric correction process was experimental and exceptionally good in Aster 2005 imagery due to its darkness by computing for haze reduction. Same operation generated poor results for other images. 6

7 7 d. Radiometric Correction being a prerequisite for creating high-quality science data and information was implemented on all dataset through conversion of Digital Number DN to at sensor reflectance and to Top of Atmosphere TOA) using the model maker in ERDAS. e. Band Suitability selection is a good practice to consider as an optimal band combination rule for multi-temporal and multi-spectral remote sensing images when doing digital image processing. Hence the Optimal Index Factor (OIF), the variance-covariance matrix routine were considered for selecting most suitable band combination for the four sensors images provided for this project using the IlWIS software. For MSS bands (124) with (18.74 OIF) were found most suitable considering their highest ranking factor for the required analysis of this project (annexure I table 1). Bands (145) of TM with OIF of % ranking were considered (annexure I table 2). The ETM computed OIF suggested bands (124) with percent ranking and was accepted coupled with the bands applications. OIF computation for the ASTER was somewhat strange because the band 3b continued to re appear in the highest index ranking, however the suitability of using band 3b of ASTER imageries is not acceptable due to the fact that it is a backward looking band and might have effect on the analysis. Therefore closest possible OIF highest in the visible near inferred bands were considered. For the four years image scenes bands (123n) were used all through the project. Attached in the annexure are tables of the OIFs, correlation matrix and variance-covariance matrix computation of all the imageries for verification. f. Image enhancement and transformation was computed by doing histogram equalization, and experimental Principal Component Analysis (PCA) was done for the TM, ETM and ASTER. Different band false colour combinations were tested for coastline and river bank extraction. g. Visual and Digital image analysis change detection using ERDAS model maker was done but results were very much uncertain as what object changed to what and how much change occurred. Consequently, a Cross matrix change analysis was done to quantify changes in the island. This was achieved by running an unsupervised classification due to none availability of field validation data for change detection analysis on the datasets. h. Feature extraction process by image slicing was applied to the classification result to discriminate, identify and compare trends along the coastline of Sagar Island. i. Mapping and delineation by sub-setting the Image close to the exact size of the island, digitizing the coastline, classification and identification of element at risk specifically done using the extracted google earth image in comparison with landsat and ASTER data. j. Report presentation basically comprised the map compilation processes of the coastal landcover, change and element at risk maps, coastline trends analysis and report documentation.

8 Project Start Project objectives MSS image (1975) MSS image (1989) ETM (2000) ASTER (2000,2003, 2005 & 2010) Google Earth (2010) Spatial dataset ordering Literature review Resampling Georeferencing Geometric correction Image preprocessing-1 Literature review Atmospheric Correction Haze reduction Sun Angle calculation using NOAA online facility Image preprocessing-2 Conversion form At Sensor radiance to Top of Atmosphere (TOA) Conversion form DN values to At Sensor radiance values Radiometric Correction Image preprocessing-3 Pre-processing Map list of all Images Optimal Index factor Variance- Covariance Matrix Band Composite Layer Stack Band Suitability Selection Principal Component Analysis (PCA) Histogram Equalization Image Transformation Digital image enhancement and transformation Classifcation Unsupervised Classification Image slicing Feature extraction Change detection statistics Export routine to Excel for change detection analysis Cross matrix/ intersection in Ilwis/ ArcGIS Change detection Quantitative Change detection Image Processing Image subsetting Digitization/overlay of mapped coastlines Coastal zone classification Element at risk mapping Mapping/ delineation Report Coastline change/ land cover trends maps Coastline /zone change elements at risk map Map compilation Report Compilation and presentation GIS mapping operations/result compilation End project Figure 2: Diagram illustrating logical method detailing project work flow. In this diagram plain rectangles are depicting process while striped rectangles represent sub-processes in a process. The processes/sub processes highlighted in green were more or less experimental but useful. 8

9 3.0 Result and discussion 3.1 Usefulness of landsat archives Considering the phrase the past is the key to the future the availability of landsat data were useful for delineating the coastlines of the years under review base on the OIF and experimental PCA which suggested bands 1, 4, 5 in TM and 1, 2, 4 in ETM. These bands were most valuable for mapping coastline and observed spatio-temporal land cover processes going on in the Island. ASTER bands 1, 2, 3n were used for the coastline mapping at the preliminary stages as well. After extracting coastlines from Landsat MSS image of 1975, Landsat TM image of 1989, Landsat ETM image of 2000 and ASTER images of 2000, to They were overlaid in ArcGIS for visualization to detect changes in the coastline of various years (fig.3). Bands 3, 2, 1 for RGB were found most suitable for suspended material which indicated erosion process going. This was observed mostly from the north of the Island. Composite 4, 3, 2 for RGB was useful in identifying vegetation and seasonal variation base of image collection date. Landcover maps was of 1975 and 1989 image were composed to demonstrate the usefulness of landsat data for this studies base on unsupervised classification (fig.4). Fig. 3 Extracted coastlines of 1975 to 2010 in Map A and coastal zone delineation depicting elements at risk, particularly in the west of Sagar Island. Additionally, changes were more in the south-east and south west of the Island due to erosion process which originates from the north with a lot of suspended sediment visible in the ASTER images. Observation shows that sediments are the major source of beach formation in the coastline. Visual interpretation of the images shows massive sedimentation from the north and results obtained from the crossmatrix change detection confirms massive deposition in the south west which increases accretion and beach formation reflecting bright tones on the satellite images and represented in yellow (Figs. 6 &7). 9 Fig. 4 Land cover maps of change in vegetal cover of 1975 and 1989.

3.2 Remote sensing data integration for coastal mapping Disaster managers are left with no choices than to take advantage of data fusion since data integration is an unavoidable situation to achieve

In this project landsat data were useful but for temporal analysis which necessitated the need to use google earth image as a complimentary data sources which served as high resolution image and was

10 3.2 Remote sensing data integration for coastal mapping Disaster managers are left with no choices than to take advantage of data fusion since data integration is an unavoidable situation to achieve required results. In this project landsat data were useful but for temporal analysis which necessitated the need to use google earth image as a complimentary data sources which served as high resolution image and was used to better visualize processes in the coastline and aided mapping of elements at risk in the coastal zone as shown in map B of (fig.3) which depicts extracted elements at risk. Indeed the google earth image was useful for visual identification and interpretation as well mapping the coastal zone and elements at risk (fig5). Figure 5. Delineation of elements at risk in the coastal zone and overlay of coastlines Figure 6. Land cover and coastline change maps from 1975 to Coastline Change in the recent past Result of unsupervised classification of ASTER image was further explored based on the third objective of this project to implement a change detection analysis (figs.7). The ArcGIS toolbox was used to convert the unsupervised classifications in raster format to vector, then the intersection operation was prompted to cross the before image with after sequentially. After which a cross matrix was done for all the images. Tables generated were exported to excel for statistical analysis (tables 10 to 18). From the change analysis table it was observed that from 2005 to 2010 there was significant increase in changes from coastal farmland and forest to beach along the coast. While in the same years no decrease in sand to farmland and forest class. In the change analysis trends table generally there was increase in sand/beach formation and there was little or no decrease from sand/beach to other classes. Statistics of the unchanged landcover analysis shows 10 Figure 7. Overlaid coastline on land cover change detection 2000 to 2005

that while other classes were changing from one class to the other, sand/beach remained unchanged retaining the highest unchanged threshold value (fig8). Figure 8.

11 that while other classes were changing from one class to the other, sand/beach remained unchanged retaining the highest unchanged threshold value (fig8). Figure 8. Sand/beach & sediment unchanged trend in red cycle from 2000 to 2010 Figure 9. Coastline/coastal zone comparison with 2005 to 2010 landcover change for 2005 to Coastline change detection for erosion hazard and risk mitigation Effect of the expansion of land due to increased sedimentation and reclamation increases the chances of a narrowing river channel. This is particularly the case in the northern and south western part of Sagar Island. Also outlets and the canals between Sagar Island, kakdwip and Namkhana were clearly visible on the Landsat ETM and ASTER multi-temporal images given. The severe erosion and accretion processes caused during the last decades were due to more severe flooding in the upper parts of the Island. Hence mitigation of the erosion can be improved by protecting the coastal zone by vegetating the coastline, operational coastal management policies and other landuse best practices. 4.0 Conclusions The coastlines extracted from landsat satellite images in 1975, 1989, 2000 and, as well as from Aster scenes in 2000, 2003, 2005 and 2010, and google earth of 2010 were overlaid to establish the coastline changes in the Sagar, Island, India. The main conclusions were summarized as the following: 1. The most obvious coastline changes since 1975 occurred at the tip of the island, south east and the entire western coastline. The earlier mainly due to erosion while the latter mainly result from accretion in the south western part and construction of harbour in the north east of the Island. 2. One of the most important impacts of coastline change was expansion due to the sedimentation and reclamation in the outlets. And narrower and elongated river channels in the creeks. Generally intense flooding was observed in the upper Sagar Island and sedimentation and beach formation in the south. 11

12 5.0 References BANDYOPADHYAY, S. 1997,. Natural Environmental hazards and their Management: A case study of Sangar Island, India. Singapore Journal of Tropical geography,, 18, GOPINATH, G. & SERALATHAN, P Rapid erosion of the coast of Sagar island, West Bengal - India. Environmental Geology, 48, LI, X. & DAMEN, M. C. J Coastline change detection with satellite remote sensing for environmental management of the Pearl River Estuary, China. Journal of Marine Systems, 82, S54-S61. MUKHOPADHYAY, P A reconnaissance based vulnerability and damage survey report Sagar islands, West Bengal-Effect of the cyclone SIDR of 15th November Technical Visit report undertaken to Sagar Island,

13 Annexure I Optimal Index Factor of project imageries 13

14 Annexure I I Change detection Analysis Table 10 Table 14 Table 11 Table 15 Table 12 Table 16 Table 13 Table 18 14

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