Institute for Mine-Surveying and Geodesy Geomonitoring Group
COASTAL MONITORING USING REMOTE SENSING AND GEOINFORMATION SYSTEMS: ESTIMATION OF EROSION AND ACCRETION RATES ALONG GAZA COASTLINE Khaldoun ABU ALHIN , Irmgard NIEMEYER Geomonitoring Group, Institute for Mine-Surveying and Geodesy, TU Bergakademie Freiberg- Germany Khaldoun Abu Al Hin | Geomonitoring Group| Institute for Mine-Surveying and Geodesy | TU Bergakademie Freiberg Reiche Zeche, Fuchsmühlenweg 9 I D-09596 Freiberg Phone/Fax +49 (0)3731 39-2690/-3601 I
[email protected] I http://tu-freiberg.de/fakult3/mage/geomonitoring/
Objectives of the Study
• The objective of this study is to use Remote Sensing and Geoinformation Systems (GIS) to monitor and analyze the coastline dy a c du g e as dynamic during the last two decades o decades • Calculate the rate of the coastline change. • Test and evaluate different methods of the automatic coastline extraction. • Highlight the most threatened regions along the coastal zone and classify the coastal zone of Gaza according to the rates of change.
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Contents Study Area Methodology Outline Methodology Outline Methodology: Fusion using Pan‐sharpening Methodology: Coastline Automatic Extraction Coastline Extraction Accuracy Assessment Coastline Extraction: Accuracy Assessment Calculating the rate of change along Gaza coastal zone using DSAS Rate of Change Conclusions
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Study Area • The The coastline of the Gaza strip forms only a small section (42 km) of a larger coastline of the Gaza strip forms only a small section (42 km) of a larger concave system (a“litoral cell”) that extends from Alexandria at the west side of the Nile Delta via Gaza, to the Bay of Haifa. This litoral cell forms the south eastern corner of the Levantine Basin. f h • The coastline of the Gaza Strip, has been shaped over the last 15,000 years by the Nile river sediments which moves along the entire concave coastline in an anti‐clockwise direction, generally in a NE direction. • The coastal zone of Gaza is one of the most intensively used area and populated on the world.
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Study Area
• Gaza coast and beach has been changed and polluted due to many reasons including human activities (like the Gaza seaport, seaport electricity generator unit, sewage disposal from the domestic's area and seepage sewage on the beach). • In addition, Gaza shoreline is also affected and degraded by marine processes, such as wave erosion and deposition, and currents like alongshore l h current. • This study has been started despite of the shortage time of the studies, lack of the in situ data and disability to conduct the fieldwork so far due to the political problem in the region.
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Methodology Outline 1. Images preprocessing and preparation
2. Data fusion (pan‐sharpening) and q quality assessment of pan‐sharpened y p p images.
‐ Images preparation and layer stacking. Converting pixels value into ‐ Converting pixels value into radiance. ‐ Atmospheric correction using FLAASH atmospheric correction p Model. ‐ Image registration (RMS 0.2‐ 0.02)
3. Coastlines automatic extracting and its quality assessment
4. Calculating the rate of change along th G the Gaza coastal zone using DSAS t l i DSAS (Digital Shoreline Analysis System), USGS
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Methodology: Fusion using Pan-sharpening 2. Pan‐sharpening and quality assessment 2 Pan sharpening and quality assessment • Starting from the fact that the high spatial resolution satellite images are not available for 3. Coastlines extraction and quality assessment the Gaza region before the year 2002 and also because the limited financial budget we were not 4. Calculating the rate of change were not able to acquire expensive very high resolution optical imagery. able to l acquire l hexpensive f very h high resolution optical imagery • Since the study was performed using three different sets of medium spatial resolution images: a series of Landsat ETM+ images (30m resolution) for evaluating short‐term images: a series of Landsat ETM+ images (30m resolution) for evaluating short‐term changes, Landsat TM‐5 imagery (30m), SPOT panchromatic imagery (5m) for long‐term evaluation, a question came up whether it would be possible to get a reasonable result form medium resolution imagery especially for a narrow coastal zone as the Gaza coastal form medium resolution imagery especially for a narrow coastal zone as the Gaza coastal zone?
ETM 1999,true color SPOT2002,Pan.5m
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Methodology: Fusion using Pan-sharpening • Pan Pan‐sharpening sharpening is a pixel level fusion technique used to increase the spatial resolution of is a pixel level fusion technique used to increase the spatial resolution of the multispectral image using spatial information from the high resolution panchromatic image while preserving the spectral information in the multispectral image • In the study four different pan‐sharpening algorithms have been tested based on a set of three ETM Landsat images. ETM 1999 MS 30 m
ETM 2000 MS 30 m
ETM 2001 MS 30 m
ETM 2002 ETM 2002 MS 30 m
ETM 2003 ETM 2003 MS 30 m
ETM 2005 ETM 2005 MS 30 m
Panchromatic Band 15 m Sharpening Algorithms : •Gram‐Schmidt Spectral •PC Spectral Sharpening •Wavelet ARSIS DWT Wavelet ARSIS ATWT ARSIS ATWT •Wavelet
Pan‐Sharpening Quality Assessment, P Sh i Q lit A t Using Universal Image Qulatiy Index and Corellation Coeffecient CC where UIQI > 0.7
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Methodology: Fusion using Pan-sharpening • In order to evaluate the spectral and spatial quality of pan‐sharpening algorithms, the following analysis has been carried out: Variance image subtraction Variance image subtraction Universal Image Quality Index (UIQI) Correlation coefficient (CC) and Correlation coefficient (CC) and Image classification
Band b1-b1 b1 b1 b2-b2 b3-b3 b4-b4 b5 b5 b5-b5 b7-b7 Aver.
Pan-sharpened algorithm ATWT DWT PC GS Mean Mean Mean Mean 8 2 26 2,26 7 23 7,23 7 14 7,14 12,7 6,55 2,58 10,9 32,2 20,54 29,7 27,5 14,43 5,7 1,09 4,06 31 5 31,5 16 5 16,5 27 06 19,68 27,06 19 68 39 25,9 39,4 31,2 22,9 12,9 17,8 16,7
Variance image of ATWT
Variance image of the original MS image subtracted from variance image of pan‐sharpened images
Variance image of DWT
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Methodology: Fusion using Pan-sharpening Universal Image Quality Index (UIQI) Universal Image Quality Index (UIQI) A-trus Filter (ATWT) band General Urban
Agricult ural b1 0,80 0,65 0,72 b2 0,85 , 0,68 , 0,75 , b3 0,87 0,68 0,75 Avg. 0,86 0,67 0,74 Gram-Schmidt Spectral sharpening b1 b2 b3 Avg.
0,69 0,74 0,81 0,74
0,77 0,72 0,72 0,73
0,75 0,74 0,76 0,75
Daubechies Wavelet DWT band General Urban b1 b2 b3
0,92 0,54 0,93 , 0,57 , 0,94 0,57 0,93 0,56 PC Spectral sharpening b1 b2 b3
0,82 0,72 0,72 0,75
0,86 0,72 0,67 0,75
Agricult ural 0,82 0,81 , 0,81 0,81
0,85 0,73 0,71 0,76
Results of the universal image quality index
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Methodology: Fusion using Pan-sharpening
Correlation coefficient (CC) has been calculated between variance image of the panchromatic band and the variance image of pan-sharpened MS images
Pan-sharpening p g Algorithms
B1-Pan.
B2-Pan.
B3-Pan.
B4-Pan.
B5-Pan. B7-Pan.
ATWT DWT PC Gram Schmidt
0.65 0 45 0.45 0.60 0.60
0.65 0 51 0.51 0.64 0.64
0.60 0 51 0.51 0.60 0.59
0.63 0 42 0.42 0.52 0.59
0.65 0 49 0.49 0.58 0.53
0.65 0 52 0.52 0.57 0.52
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Methodology: Fusion using Pan-sharpening Classification a) Percentage of class changes
b) Percentage of class changes
original MS image vs. pan-sharpened image
panchromatic image classes vs. pan-sharpened image classes
15 classes
30 classes
2 classes
5 classes
ATWT
37.33
70.11
24.9
36.2
DWT
30.97
64.07
51
56.6
GS
58.53
75.3
26.9
66.29
PC
71.27
84.3
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31.4
Pan-sharpening P h i algorithms
a) Percentage of class changes between original MS and pan-sharpened image classes. b), Percentage of class changes between Sobel filtered pan-sharpened p p image g and p panchromatic images g
U Unsupervised i dK K-mean classification, l ifi ti ffrom th the lleft ft tto th the right: i ht ATWT ATWT, DWT DWT, PC and d GS
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Methodology: Fusion using Pan-sharpening
• Generally wavelet pan-sharpening algorithms preserves the spectral i f information ti off original i i l image i especially i ll DWT pan-sharpening h i algorithm, l ith on th the other hand the spatial quality of DWT is less than the other algorithms for medium resolution images. • PC spectral and ATWA pan-sharpening algorithms obtained the best spatial quality results respectively. • Therefore, the wavelet transformation ATWT(À trous wavelet transforms ) has been used to pan-sharpened the multispectral ETM+ images (30 m) to 15m spatial resolution.
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Methodology: Coastline Automatic Extraction • A coastline, by definition, is the line at the intersection of a body of land and the A coastline,2definition, is the line the intersection of a body of land and the 2by Pansharpening andat Quality assessment horizontal plane of the adjacent sea surface. 3- Coastlines extraction and Quality assessment y p • In study different methods have been performed to extract the coastline 4 4C Calculating l l i the h R Rate of f change h automatically, ‐ NDVI (Normalized Difference Vegetation Index) ‐ Tasseled Cap Transformation (Wetness) ‐ Band Ratio ‐ First Principle component PCA TM Images 1986,1998 Registered ,30 m ETM Images 1999 to 2005 R i t d ,15 Registered 15 m SPOT pan, Images 1992-94 2002 2008 1992-94,2002,2008 Registered, 5 m
NDVI Tasseled Cap Cap, Wetness Band Ratio 1st PCA
Histogram Thresholding 14
Methodology: Coastline Automatic Extraction, Quality Assessment Automatically A t ti ll extracted t t d Coastlines
manuall digitized di iti d coastline
Quality assessment using manual digitized coastline The coastlines have been manually digitized for each image. These manually digitized coastline assumed to be references coastlines to assess the automatic digitized coastline assumed to be references coastlines to assess the automatic extracted coastlines. (No in situ measurements, disability to conduct the fieldwork ) The problems of automatic coastline extraction: • Seawater foam & waves • High turbid seawater • Underwater sandbar (barriers)
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Seawater foam, waves, suspended sediments and underwater sandbar
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Coastline Extraction: Accuracy Assessment Coastline extracted using Tasseled Cap Transformation, wetness component Coastline extracted using Normalized Vegetation Index, NDVI l d l d d ST Coastline extracted using 1 Principle component Coastline extracted using Band Ratio Manually Digitized Coastline
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Coastline Extraction: Accuracy Assessment
50 m
Baseline Reference coastline ETM 2005 (Manually Digitized)
ETM 2005 coastlines coastlines
The minimum difference between the extracted coastline and the reference coastline is the “ proper“ method to use to calculate the rate of change. 18
Coastline Extraction: Accuracy Assessment • The calculated mean of the difference between two coastlines; the The calculated mean of the difference between two coastlines; the automatic extracted coastlines using different methods (PCA, NDVI, Tasseled cap wetness and Band‐Ratio) and its reference coastlines (manual digitized coastline for each image) digitized coastline for each image). • The closer mean to the zero is the more accurate method to extract the coastline automatically. 15 10 5
Mean values
0 1999 -5 5
2000
2001
2002
2003
2004
2005
NDVI
-10
TASSELED_WET
-15
Band_ratio
-20 -25
PCA
ETM Images
-30 30
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Calculating the rate of change along Gaza coastal zone using DSAS 2 Pansharpening and Quality assessment 23- Coastlines extraction and Quality assessment 4 Calculating 4C l l i the h R Rate off change h 1st PCA Image Thresholding
Raster to R t t Vector
Calculate Rate of Change Coastline Enhancement Using DSAS* * Digital Shoreline Analysis System Digital Shoreline Analysis System
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Calculating the rate of change along Gaza coastal zone using DSAS • The systems automatically estimates different statistical parameters such as the End Point Rate (EPR) by dividing the distance of shoreline movement by the time elapsed between the earliest and latest measurements. • LPR: A linear regression rate‐of‐change statistic can be determined by fitting a least squares LPR A li i t f h t ti ti b d t i d b fitti l t regression line to all shoreline points for a particular transect. The rate is the slope of the line.
baseline Shorline 1 Shorline 2
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Rate of Change The rate of change was calculated between 1999 and 2003 as well as The rate of change was calculated between 1999 and 2003 as well as between 1987 and 2008, for each 50 meter along the Gaza coastline Sh li Shorelines
R i Region
EPR ( / EPR (m/year) )
LPR ( / LPR (m/year) )
Shorelines 1987 – 2008 Image data set Image data set TM, ETM and SPOT
A (north Gaza)
‐0.06
‐0.32
B (northern Seaport) B (northern Seaport)
‐0 26 ‐0.26
‐0 16 ‐0.16
C (southern Seaport)
0.98
0.8
D (Wadi region)
‐0.26
‐0.36 *
E (south Gaza)
0.32
0.3
Entire shoreline
0.18
0.08
Shoreline e cl ded region C Shoreline excluded region
0 04 0.04
‐ 0.05 0 05
* Golik and Goldsmith (1984,1985) indicates rate of cliff retreat is 0,41 (m/year) at north of Khan yunis area.
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Rate of Change Shorelines
Region
EPR
Shorelines 2002 – 2008 SPOT
A (north Gaza)
‐ 0.14
B (northern Seaport)
‐ 0.28
C (southern Seaport)
1.9
A B
D (Wadi region)
0.25
E (south Gaza)
‐ 0.12
Entire shoreline
0.28
C
D Shoreline excluded region C
‐ 0.07
E
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Rate of Change, different rates for different regions The coastline changes in the period between 1993 and 2008, calculated from SPOT (5 m) images. 100 90 80 70 60 50 40 30 20 10 0 -10 20 -20 -30 -40 1 30 59 88 117 146 175 204 233 262 291 320 349 378 407 436 465 494 523 552 581 610 639 668 697 726 755 784 813 842 871 900 929 958 987 1016 1045 1074 1103 1132 1161 1190 1219 1248 1277 1306 1335
Shoreline change h (m) ( )
Distance along Gaza coastline (m)
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Conclusions ¾ The result shows a negative rates in general, which means that erosion has been the predominant process on the Gaza Coastal zone. ¾ Gaza coastal zone can be divided into four regions according to the rates of change, that means that the rate of change is not constant along the coastal one coastal zone. ¾ The result shows negative rates in northern side of Gaza seaport and positive rate in the southern side of the seaport, which means that the sediments transport ed along the coastal zone in the direction of NE. ¾ Band Band Ratio and first principle component could be used as a fast proper Ratio and first principle component could be used as a fast proper methods to extract the coastline. ¾ Medium resolution images could be very useful in order to calculate the coastal change in such narrow coastal zone.
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THANK YOU FOR YOUR ATTENTION THANK YOU FOR YOUR ATTENTION Any questions??.. yq
CONTACT:
Khaldoun Abu Al Hin Geomonitoring Group I tit t f Mi S Institute for Mine‐Surveying and Geodesy i dG d TU Bergakademie Frei berg Reiche Zeche, Fuchsmühlenweg 9 D‐09596 Freiberg Germany D‐09596 Freiberg, Germany Phone/Fax: +49 (0)37 31 39‐2690/‐3601
[email protected]‐freiberg.de http://tu‐freiberg.de/fakult3/mage/geomonitoring/ p // g / / g /g g/
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