Prepared by: Dan Deneau Applied Geomatics Research Group. Submitted to: Tim Webster and Bob Maher Applied Geomatics Research Group

Transcription

1 Extracting 3D Coastlines from Remotely Sensed Data Prepared by: Dan Deneau Applied Geomatics Research Group Submitted to: Tim Webster and Bob Maher Applied Geomatics Research Group

2 Abstract This project is the second part of a 3-year venture with the Department of Fisheries and Oceans, Canadian Space Agency, the Centre of Geographic Sciences, and the Applied Geomatics Research Group (AGRG). The project s aim is to develop and evaluate methodologies for a high-resolution system for coastline definition and coastal flooding prediction, involving the use of remote sensing technologies (RADARSAT-1, ERS-1, IKONOS, LANDSAT, SPOT, CASI, LiDAR). The study area for this project is the Bay of Fundy coastal zone in Nova Scotia, primarily the Annapolis Basin and Port Lorne. Two methods of shoreline extraction were examined and implemented, namely the grey level thresholding method and the object-oriented approach (new method). The grey level thresholding method was used with CASI, LANDSAT, RADARSAT-1, IKONOS, and SPOT (panchromatic); the new object-oriented approach was implemented on a low tide IKONOS image. The object-oriented approach involves isolating an image object of the intertidal zone (waterline to the backshore), resulting in the extraction of two vectors: 1) the land/water boundary and 2) a coastline vector mimicking the mean high water vector produced by the Nova Scotia Geomatics Centre. The resulting image object of the intertidal zone, from the object-oriented shoreline extraction procedure, was further segmented to perform an object-oriented classification of the intertidal zone (classes: seaweed, sand, rocky/basalt, water, image shadow). Two tide gauge datasets were processed and used to calculate tide heights for any given time during the life of the tide gauges (summer of 2000). These heights were referenced to a geodetic datum (CGVD28); and atmospheric pressure data was extracted from METAR weather data for these times, to adjust the pressure readings from the tide gauge. These tide gauge derived heights were assigned to extracted shorelines from imagery that were acquired during the life of the tide sensor. These heights were compared to coastal LiDAR data, as well as Real-Time Kinematic GPS, in an effort to validate the tide gauge measurements. Results were better when extracting shorelines from low tide imagery, because of steeper slopes at high tide. Ground truth data (GPS and Photos) for the Minas Basin was compiled into its own database. In addition, all coastal ground truth datasets at the AGRG were separated and categorized. Elevation datasets, such as LiDAR and 70m Point DEMS, were processed and delivered to DFO, to be used in tidal and flood modelling simulation work carried out by David Greenburg. An inventory of recent ERS imagery was created and supplied to Joost van der Sanden at the Canada Centre for Remote Sensing. Main results of the project are: 1) geocoded imagery of various types, both satellite and airborne, for the Bay of Fundy study area, 2) processed tide gauge data in table format lending an orthometric height at the land/water boundary for the Port Lorne and Annapolis basin, every 5 minutes for the summer of 2000, 3) extracted land/water boundaries, as shapefiles, for all images used, with orthometric heights assigned to them. Applied Geomatics Research Group - 2 -

3 Table of Contents ABSTRACT...2 TABLE OF CONTENTS...3 LIST OF FIGURES...5 LIST OF TABLES INTRODUCTION BACKGROUND Overview of AGRG Research/Data Collection Literature Review on Shoreline Extraction Methods Synopsis of Shoreline Extraction Methods Study Area Location RESOURCES USED Source Data Software Used: Hardware Used: COMPILING GROUND TRUTH DATA DELIVERING DATASETS TO DFO/CCRS Metre Point DEMS for DFO LiDAR for DFO Inventory of ERS Imagery for CCRS (Using DESCW) IMAGE DATASET PREPARATION Geometric Correction of Imagery Mosaicking and Subsetting SHORELINE EXTRACTION GREY LEVEL THRESHOLDING Adding Image Channels to Imagery Filtering RADARSAT Imagery Vector Extraction Process Vector Extraction Process SHORELINE EXTRACTION OBJECT ORIENTED APPROACH Scaling Imagery Starting an Ecognition Project Multiresolution Segmentation, Creating Image Objects Creating Polygons, and Extracting Shoreline Vectors TIDE GAUGE DATA PROCESSING Tide Gauge Background Comparing Amplitude Curves of Levelling Data and Tide Gauge Data Using METAR Pressure Data to Adjust Tide Gauge Pressure Data Applied Geomatics Research Group - 3 -

4 9.4 Converting Adjusted Pressure Depth into Metres Using Levelling Measurements to Calculate Geodetic Depths ASSIGNING AND COMPARING ELEVATIONS Assigning Orthometric Heights to Extracted Vectors Comparing CASI Vector Elevations to LiDAR DEMS, Port Lorne Comparing CASI Vector Heights to Kinematic GPS, Annapolis Basin Comparing Kinematic GPS Elevations with LiDAR DEMS, Annapolis OBJECT-ORIENTED CLASSIFICATION OF INTERTIDAL ZONE Prepare IKONOS Image of Intertidal Zone Multiresolution Segmentation of Intertidal Zone Image Creating Classes and Classification Rules Classifying Classification Based Segmentation Export Classification to GIS CONCLUSIONS...62 CONCLUSIONS...63 ACKNOWLEDGEMENTS...64 REFERENCES...65 BIBLIOGRAPHY...66 APPENDIX 1: VBA CODE TO MAKE TIMES COLUMN EVERY 5 MINUTES FROM START TO FINISH...67 APPENDIX 2: VBA CODE TO INTERPOLATE METAR PRESSURE VALUES TO 5 MINUTE INTERVALS...68 APPENDIX 3: AML TO CREATE A DEM USING TOPOGRID...69 APPENDIX 4: EASI PROCEDURE TO SCALE MANY CASI IMAGES AND EXPORT AS A TIFF...70 APPENDIX 5: EASI PROCEDURE TO UNZIP MANY CASI TIFFS AND CREATE MRSID IMAGES...71 Applied Geomatics Research Group - 4 -

5 List of Figures FIGURE 1 - ENTIRE STUDY AREA FIGURE 2 - SPECIFIC STUDY AREAS FIGURE 3 - GROUND TRUTH GPS POINTS WITH HOT-LINKED PHOTOS FIGURE 4 ANNAPOLIS BASIN LIDAR FOR DFO FIGURE 5 MINAS BASIN LIDAR FOR DFO, EACH TILE IS APPROX. 4X4 KM FIGURE 6 RECENT ERS-2 IMAGES FOUND WITH DESCW FIGURE LOW TIDE CASI (AUGUST 28, 2000) AT PORT LORNE, GEOCODED TO NSGC VECTORS ALONE (BLUE LINE) FIGURE 8 COLOUR SHADED RELIEF OF LIDAR FOR PORT LORNE, WITH NSGC VECTOR 21 FIGURE 9 LOW TIDE CASI AT PORT LORNE (AUG 28, 2000) WITH NSGC VECTOR (BLUE) AFTER GEOCODING TO THE LIDAR DATA (WIDTH = 1 KM) FIGURE 10 TRUE COLOUR LANDSAT IMAGE OF THE ANNAPOLIS BASIN (JULY 20, 2000) FIGURE 11 TRUE COLOUR LANDSAT IMAGE OF THE MINAS BASIN (JULY 13, 2000) FIGURE 12 S2 RADARSAT IMAGE OF THE ANNAPOLIS BASIN (JULY 17, 2000) FIGURE 13 F1 RADARSAT IMAGE OF THE ANNAPOLIS BASIN (JUNE 19, 2000) FIGURE 14 F5 RADARSAT IMAGE OF THE ANNAPOLIS BASIN (JUNE 5, 2000), WIDTH = 30 KM FIGURE 15 SPOT (PAN) OF THE ANNAPOLIS BASIN (APRIL 17, 2000), WIDTH = 50 KM. 24 FIGURE 16 IKONOS IMAGE OF MARGARETSVILLE (JULY 21, 2000), WIDTH = 7 KM FIGURE 17 PORT LORNE CASI MOSAIC, HIGH TIDE (JULY 24, 2000), WIDTH = 17 KM FIGURE 18 PORT LORNE CASI MOSAIC, LOW TIDE (AUGUST 28, 2000), WIDTH = 12 KM26 FIGURE 19 A CASI STRIP FROM THE ANNAPOLIS BASIN (NEAR-INFRARED 810NM) JULY 7, 2000; WIDTH = 35 KM FIGURE 20 FILTERING S2 RADARSAT-1 DATA BEFORE SHORELINE EXTRACTION PROCESS FIGURE 21 FILTERING F1 RADARSAT-1 DATA BEFORE SHORELINE EXTRACTION PROCESS FIGURE 22 THRESHOLDING INTERFACE FIGURE 23 BITMAP ENCODING INTERFACE FIGURE 24 SIEVE FILTERING TO ELIMINATE LAKES AND UNWANTED IMAGE ERRORS FIGURE 25 SIEVE FILTERING TO ELIMINATE NON-ISLANDS OR IMAGE ERRORS IN THE WATER FIGURE 26 BITMAP ENCODING AND SIEVE FILTERING FIGURE 27 RASTER TO VECTOR CONVERSION USING RTV COMMAND FIGURE 28 RTV OF THE MINAS BASIN LANDSAT IMAGE, NOT EDITED (WIDTH = 62 KM) FIGURE 29 RTV OF THE MINAS BASIN LANDSAT IMAGE, EDITED (WIDTH = 62 KM) FIGURE 30 PORTION OF AN S2 RADARSAT IMAGE BEFORE AND AFTER EDITING, ANNAPOLIS BASIN (THORNES COVE), WIDTH OF 3 KM FIGURE 31 IKONOS IMAGE IN ECOGNITION FIGURE 32 MULTIRESOLUTION SEGMENTATION DIALOG SETTINGS FIGURE 33 MULTIRESOLUTION SEGMENTATION RESULT, IKONOS IMAGE OBJECTS Applied Geomatics Research Group - 5 -

6 FIGURE 34 RESULT OF OBJECT-ORIENTED SHORELINE EXTRACTION, TRUE COLOUR IKONOS IMAGE, WIDTH = 2 KM FIGURE 35 TIDE GAUGE LOCATIONS, FALSE COLOUR LANDSAT IMAGE (WIDTH OF 50 KM) FIGURE 36 AMPLITUDES OF LEVELLING DATA AND TIDE GAUGE DATA (ANNAPOLIS BASIN) FIGURE 37 AMPLITUDES OF LEVELLING DATA AND TIDE GAUGE DATA (PORT LORNE) FIGURE 38 CALCULATING AN AVERAGE ORTHOMETRIC HEIGHT FOR A TIDE GAUGE VIA LEVELLING, AN EXAMPLE FIGURE 39 USING AN AVERAGE TIDE GAUGE ORTHOMETRIC DEPTH TO CALCULATE THE ORTHOMETRIC HEIGHT OF THE LAND/WATER BOUNDARY AT A GIVEN TIME FIGURE 40 FINDING AN ORTHOMETRIC HEIGHT FOR A CERTAIN TIME, AN EXAMPLE FIGURE 41 USING CASI FLIGHT LINES TO ACQUIRE THE TIME AND ASSIGN HEIGHTS FIGURE 42 ELEVATION DIFFERENCE, LIDAR VS. TIDE GAUGE HEIGHT, HIGH TIDE, WIDTH = 13 KM FIGURE 43 ELEVATION DIFFERENCE, LIDAR VS. TIDE GAUGE HEIGHT, LOW TIDE, A BETTER FIT (WIDTH = 12 KM) FIGURE 44 FREQUENCY OF ELEVATION DIFFERENCE, LIDAR VS. TIDE GAUGE, HIGH TIDE FIGURE 45 FREQUENCY OF ELEVATION DIFFERENCE, LIDAR VS. TIDE GAUGE, LOW TIDE FIGURE 46 HIGH TIDE CASI, ELEVATION DIFFERENCE ERROR, WIDTH = 1 KM FIGURE 47 GPS LOCATION WITH HIGH AND LOW TIDE CASI VECTORS, CASI MOSAIC OF LOW TIDE IMAGE SWATHS (IMAGE WIDTH = 9 KM) FIGURE 48 ELEVATIONS OF HIGH TIDE (RED) AND LOW TIDE (GREEN) VECTORS WITH GPS FIGURE 49 ELEVATION DIFFERENCE BETWEEN GPS AND INTERPOLATED ELEVATIONS FROM A GRID (INTERPOLATED FROM 3 TIDE GAUGE ELEVATIONS) FIGURE 50 GRAPH SHOWING ELEVATION DIFFERENCE BETWEEN GPS AND ELEVATIONS INTERPOLATED FROM A GRID (INTERPOLATED FROM 3 TIDE GAUGE ELEVATIONS FIGURE 51 LIDAR ELEVATIONS VS. GPS ELEVATIONS FIGURE 52 ADJUSTED LIDAR ELEVATIONS VS. GPS ELEVATIONS FIGURE 53 CLASSIFICATION RULES, MEMBERSHIP FUNCTIONS IN ECOGNITION FIGURE 54 IKONOS, FALSE COLOUR COMPOSITE, IMAGE WIDTH = 1.5 KM FIGURE 55 IKONOS CRUE COLOUR COMPOSITE WITH CLASSIFICATION RESULTS, GPS AND PHOTOS TAKEN NEA R ACQUISITION TIME, IMAGE WIDTH = 1.5 KM Applied Geomatics Research Group - 6 -

7 List of Tables TABLE 1 - CASI IMAGERY TABLE 2 LANDSAT IMAGERY TABLE 3 RADARSAT IMAGERY TABLE 4 IKONOS IMAGERY TABLE 5 SPOT IMAGERY TABLE 6 LIDAR IMAGERY/DEMS TABLE 7 TIDE GAUGE RECORD, FINDING AN ORTHOMETRIC HEIGHT FOR A CERTAIN TIME Applied Geomatics Research Group - 7 -

8 1.0 Introduction A rise in the sea level has contributed to storm surge flooding, and substantial coastal erosion. Remotely sensed data linked with ocean, atmosphere and digital elevation models (DEMs) provide the potential for defining, monitoring, and predicting coastline position. Additionally, remotely sensed data can provide critical elevation data for the land/water boundary, so that storm surge models can be created to predict flooding events. The investigation of shoreline extraction algorithms using a variety of remote sensing datasets, will contribute to the foundation for subsequent applications in more remote areas such as the Arctic, where traditional sampling is difficult. The ability to use a tide gauge to predict the orthometric elevation of the land/water boundary, and assign these heights to shoreline vectors that are extracted from remotely sensed imagery to a certain degree of accuracy (validated by LiDAR and/or GPS) should prove useful. Some researchers have used the waterline method to determine the topography of the intertidal zone from extracted waterlines from remotely sensed data (Lohani et al, 1999), while others have used the waterline method in conjunction with hydrodynamic models to assign heights to waterlines (Mason et al, 2000). Applied Geomatics Research Group - 8 -

9 2.0 Background 2.1 Overview of AGRG Research/Data Collection During the summer of 2000, the Applied Geomatics Research Group conducted a major data collection project, consisting of the acquisition of various remotely sensed images and supporting ground reference data. Satellite imagery included IKONOS, RADARSAT-1, LANDSAT-7, and SPOT-4. Airborne imagery included CASI (Compact Airborne Spectrographic Imager, hyper-spectral data collection) and LiDAR (Light Detection and Ranging, high resolution relief data). Two tide gauges were deployed prior to most of these image acquisitions; one in the Annapolis Basin and the other in the Port Lorne area. These tide gauges measure the depth of water above the sensor in pounds per square inch (PSI); and were in the water from late June, until mid October, in the year 2000 (Sangster, 2001). Other sensors on the unit also measured temperature and turbidity. Students at the Applied Geomatics Research Group levelled the Port Lorne and Annapolis Basin land/water boundary in the fall of 2000 (Sangster, 2001, Spinney, 2001). The resulting data being orthometric heights at the waters edge at incrementing time intervals, for the duration of a tide cycle. 2.2 Literature Review on Shoreline Extraction Methods Various methods for coastline extraction are discussed in the literature, using RADARSAT and various optical sensors, ERS-1 SAR Interferometry, and Polarimetric SAR (Convair 580). These papers will be discussed here in order of relevant material applicable to this project. The most useful report by far is entitled, Satellite Remote Sensing for Monitoring Coastline Dynamics of the Canadian Beaufort Sea Coast (Van der Sanden et al, 2000). The purpose, very similar to this project, was to use remote sensing to predict the coastline structure in a remote area. They used a variety of imagery, both optical and radar: LandSat MSS and TM, SPOT Panchromatic, and RADARSAT-I. Their aim was to develop a method for shoreline extraction using these remote sensing technologies. They name their method Local Region Growing (van der Sanden et al, 2000). This region growing is an iterative process in which regions are merged based a homogeneity criterion, the criterion being a grey value threshold. This method can account for the variable land/water boundary in the image. Their conclusions, should prove very useful, and are several in numbers. They found that this method provided an overall good estimate of the coastline position in the individual images; but an overall automated procedure will not apply to both optical and radar sensors. They concluded that their method, which yielded best results, was not completely automated. Other, more specific results showed that for radar imagery, speckling in the image was the main problem. On the other hand, using optical imagery (without infrared capability), it was very difficult to discern land from water, due to the sediment load in the water. This problem creates a Applied Geomatics Research Group - 9 -

10 general lack of contrast between land and sea, which is needed for discrimination between land and sea. The next report is the summation document for the first phase of this project. Evaluation of Polarimetric SAR Data for Assessment and Monitoring of Coastal Areas, (Stockhausen, 2001), was a joint venture between the Canada Centre for Remote Sensing and the Applied Geomatics Research Group for submission to the Department of Fisheries and Oceans. This report covers in lengthy detail, the effort of using polarimetric SAR data, which contains four complex images, one for each polarization (HH, HV, VH, VV). The analysis included the task of identifying the land-water interface and extracting shoreline vectors, as well as attempts at classifying other terrestrial features. Again, the problem of speckle (noise) in the radar imagery was the main cause for error; however he discusses how using the cross polarization (VH, HV) in the polarimetric data can be used to distinguish moving objects; thereby differentiating land from water more clearly than standard detected imagery. His conclusions were that polarimetric SAR is the preferred method for automatically delineating the land/water boundary; and that the extracted coastlines should be heavily tested against vertical datum information. He suggests that the available high resolution LiDAR digital elevation data should be used for this analysis. This report on the first year of this project was submitted to Joost van der Sanden (CCRS) and Tim Webster (AGRG), who will be in collaboration for this second year of the ongoing project. Another report concerning shoreline extraction using Convair 580 polarimetric SAR is entitled, Shoreline Mapping from SAR Imagery: A Polarimetric Approach (Yeremy et al, 2001). This paper focuses on polarimetric methods for extracting a waterline. The analysis methods that were used were three fold: shoreline slope extraction, general shoreline extraction process, and the investigation of polarimetric techniques. Their technique for shoreline extraction included despeckling (filtering); contour algorithms were then used to extract shoreline vectors based on a threshold limit between land and water (Yeremy et al, 2001). They discuss that by selecting a certain threshold limit, lakes and other unwanted vectors appear, which wasn t a significant error in the Bay of Fundy (not many, and closed vectors could just be deleted); but in areas with complicated shorelines it will cause problems. The authors of this paper discuss how polarimetric discriminators can be used to further distinguish the land/water boundary. As the others discussed the radar systems: RADARSAT and Convair 580 polarimetric SAR, this next paper, Coastline Extraction Using ERS SAR Interferometry (Schwabisch et al, 1997), discusses the use of ERS-1 SAR imagery for coastline extraction. They describe in little detail, that a fully automatic procedure was developed to detect the line of highest water level between two coherent ERS SAR images. Their coastline extraction is purely grounded on an examination of the interferometric correlation of two ERS images; they use a 50m DEM of the intertidal zone for comparison. Their results were promising though their limitations were similar to the others. The limitations stated in the report are the temporal correlation of each image to the land surface, which is not often met (depending on cycle times). Furthermore, noise in the water SAR imagery can hinder results. Applied Geomatics Research Group

11 The fifth paper entitled, Derivation of a Tidal Inundation Model to Support Environmental Research in Roebuck Bay (Western Austrailia) (Carew & Hickey, 2000), discusses shoreline extraction using two Landsat TM 5 scenes, from different years and tidal states; roughly low tide and high tide. They ve used band 4 ( µm) to delineate the land/water boundary interface, and a binary image was created by thresholding. The waterline is extracted from this binary image by raster tracing (Carew and Hickey, 2000). The resulting vectors were then taken into Microstation (Bentley Systems) to simplify the vectors. The two vectors, low and high tide, were used in conjunction with predicted tide heights for the time of acquisition, to manually interpolate tide heights at any given time during the tide cycle. The acquisition of the waterlines from the Landsat imagery permitted the derivation of a continuous raster surface between the two contours, but assumptions were made. Image capture dates differed by 10 months, inferring that the intertidal zone has remained static for this time; and there was no in-situ data taken at the time of acquisition to verify that the predicted heights for the two vectors would be accurate. These papers provide much needed insight to the methodology of shoreline extraction using radar and optical imagery. The literature review was focused on shoreline extraction because other topics such as DEM creation and geocoding of imagery are intuitive and fairly common procedures. 2.3 Synopsis of Shoreline Extraction Methods The local region growing or grey level thresholding method to shoreline extraction refers to an algorithm where surrounding pixels are merged if they satisfy a homogeneity criterion. The region continues to grow until it encounters pixels that do not satisfy the criterion. The algorithm finishes when the region can grow no further (van der Sanden et al. 2000). This method uses PCI Image Analysis software. The object-oriented approach refers to the segmentation of an image into meaningful image objects. Multiresolution Segmentation (based on homogeneity criterion) is used to help the user extract the image objects that are needed (i.e. water, land). The outcome is an image object of the intertidal zone, so that two vectors may be extracted from this object: 1) the shoreline (i.e. the land/water boundary), and 2) the backshore. In most cases the backshore is a close representation of the mean high water (MHW) coastline from the Nova Scotia Geomatics Centre; a 1:10000 scale vector (NSTDB). This approach uses the Definiens ecognition software. Detailed explanations of these methods will be presented in their respective chapters (7 and 8) 2.4 Study Area Location The study area for this project is the Bay of Fundy, Nova Scotia (Fig. 1). In more specific terms: the Annapolis Basin (Fig. 2), Port Lorne Area (Fig. 2) and the Minas Basin (Fig.2). The tide gauges were located in the Annapolis Basin and Port Lorne area. Applied Geomatics Research Group

12 Figure 1 - Entire study area Bay of Fundy: Annapolis Basin to Minas Basin N Km Figure 2 - Specific study areas 0 40 Km Minas Basin Port Lorne Annapolis Basin Applied Geomatics Research Group

13 3.0 Resources Used 3.1 Source Data 3.11 Image Datasets Imagery used in the shoreline extraction of the Annapolis Basin includes: RADARSAT- 1, LANDSAT-7, CASI, and SPOT (panchromatic). IKONOS and CASI imagery were used for the Port Lorne area shoreline extraction, and LANDSAT-7 was used for the Minas Basin shoreline extraction. The files in the tables below are post-geocoding names. Table 1 - CASI imagery Filename (.pix) Location Date Time (ADT) O1ab1gc_2_band7 8:56 to 9:02 O1ab2gc_2_band7 9:10 to 9:20 O1ab3gc_2_band7 9:30 to 9:42 O1ab4gc_2_band7 10:18 to 10:31 O1ab5gc_2_band7 Annapolis July 7, 2000 Basin (low tide) 10:38 to 10:50 O1ab6gc_2_band7 9:46 to 9:56 O1ab7gc_2_band7 10:03 to 10:13 O1ab8gc_2_band7 8:38 to 8:46 O1ab9gc_2_band7 8:18 to 8:21 O1ab10gc_2_band7 8:09 to 8:11 O2ab1gc_band7 8:32 to 8:39 Ojul13_2ab_band7 8:50 to 9:01 Ojul13_3ab_band7 9:36 to 9:49 Ojul13_4ab_band7 10:00 to 10:14 Ojul13_5ab_band7 Annapolis July 13, :53 to 11:07 Ojul13_6ab_band7 Basin ligh tide 10:35 to 10:45 Ojul13_7ab_band7 10:19 to 10:28 Ojul13_8ab_band7 9:17 to 9:27 O2ab9gc_band7 11:32 to 11:34 O2ab10gc_band7 11:26 to 11:28 Spatial Res. (m) 2 Bands Used 18(NIR) 790nm-810nm 2 18(NIR) 790nm-810nm Oo28a233g_ Lidargcps Oo28a234g_ Lidargcps Oo28a235g_ Lidargcps Oo28a236g_ lidargcps Pl_mosaic_lt_16bit Port Lorne Aug 28, :20 to 16:55 Low Tide 2 2(Blue) 437nm-463nm 3(Green) 538nm-562nm 5(Red) 638nm-652nm 18(NIR) 790nm-810nm Applied Geomatics Research Group

14 Filename (.pix) Location Date Time (ADT) O1fs34agc_ht _1237 O1fs34bgc_ht _1237 O1fs35bgc_ht _1237 O1fs35cgc_ht _1237 Pl_mosaic_ht_16bit Port Lorne July 24, :10 to 17:45 High Tide Spatial Res. (m) 2 Bands Used 2(Blue) 437nm-463nm 3(Green) 538nm-562nm 5(Red) 638nm-652nm 18(NIR) 790nm-810nm Table 2 LANDSAT imagery Filename (.pix) Location Date Time (ADT) O20july00_30msub Annapolis Basin Spatial Res. (m) Bands Used July 20, : ,2,3,4 Ojul13_00_30m_sub Minas Basin July 13, : ,2,3,4 Table 3 RADARSAT imagery Filename (.pix) Location Date Time (ADT) s2_annab_12m_sub osar_f1_6m_anna_sub osar_f5_6m_anna Annapolis Basin Annapolis Basin Annapolis Basin Spatial Res. (m) Bands Used July 17,2000 7: C Band HH Polarization June 19, : C Band HH Polarization June 5, : C Band HH Polarization Table 4 IKONOS imagery Spatial Filename (.pix) Location Date Time (ADT) Bands Used Res. (m) ikonos_mid_sub_8bit Margaretsville July 21, :00 4 1,2,3,4 Table 5 SPOT imagery Filename (.pix) Location Date Time (ADT) ospot_04_17_00_sub Annapolis Basin Spatial Res. (m) Bands Used Apr. 17, :26 10 Panchromatic Applied Geomatics Research Group

15 Table 6 LiDAR imagery/dems Filename (.pix) Location Date Time (ADT) Spatial Res. (m) Bands Used Pl_lidarmos Port Lorne N/A N/A 2 Real Elevation Values Lidar_pl_chromo Port Lorne N/A N/A 2 Colour Shaded Relief For Image to Image Correcting low tide casi 3.12 Vector Datasets Existing vectors (UTM, WGS84) were used in the orthorectification process, and in the comparison between tide heights. These vectors include: the 1:10000 NSGC roads, coastline and lakes; post processed kinematic GPS points collected by Dennis Kingston from COGS, and GPS ground truth points for the Minas Basin coastal area Tide Gauge, Levelling, and METAR Data Spreadsheet data from two tide gauges (Annapolis Basin, Port Lorne), which had PSI measurements every 5 minutes (ADT time), were used in conjunction with levelling data (AGRG) from both sites (Sangster, 2001 and Spinney, 2001), and METAR pressure data (GMT time), to calculate the orthometric height of the waters edge for every 5 minutes during the life of the tide gauges. The processing of this data is explained in chapter 9 of this document. 3.2 Software Used: The software required to complete this project includes various GIS, Image Processing and Analysis, word processing, spreadsheet, database, image index, and graphical, computer programs. They are: PCI Image Processing/Analysis EASI/PACE, Imageworks, Orthoengine ESRI GIS Software ArcInfo 8.1, Arcview 3.2a DEFINIENS Object Oriented Image Analysis Ecognition 2.1 MICROSOFT 2000 Software- Word, Excel, Access ESA Image Index Software DESCW 4.34 COREL - Photo-Paint 9, DRAW Hardware Used: The computer used to complete this project had a Pentium 3 processor with 256Mb of RAM. The lack of RAM slowed the CASI image processing, and the object-oriented analysis. Applied Geomatics Research Group

16 4.0 Compiling Ground Truth Data All available in-situ data for the Minas Basin was compiled into its own Arcview Database with photos linked to each point. The shapefile is called gnd_truth_minas_coast.shp. The photos are linked a specific path as a record entry in the shape file table, for example: \avproject\data\fall_1999\photos\the_photo.tif Figure 3 - Ground truth GPS points with hot-linked photos In addition to the compilation of ground truth data for the Minas Basin, all coastal marine in-situ data at the Applied Geomatics Research Group was categorized for easier use in the future. It resides in a folder called Final_Coastal_Samples, and categorized into four folders: - Line_transects - Mud_samples - Tows - Water_samples Applied Geomatics Research Group

17 5.0 Delivering Datasets to DFO/CCRS Metre Point DEMS for DFO Scientists at the Department of Fisheries and Oceans were to create tidal models of the Bay of Fundy, in the Annapolis Basin, Minas Basin and Truro areas. To support the tidal modelling effort at DFO, point DEMS were needed for the Bay of Fundy, from the Annapolis Basin to the Parrsboro area. These files were obtained from Mike Donnelly at COGS as NTX files, approximately 150 tiles in all. These files were translated using the NTX2SHAPE utility from ESRI, to shape files. Once translated, the format was Easting, Northing, Height Above MSL (MTM) ; the requested format was for tab delimited text files with the columns, Longitude, Latitude, Height (above MSL). The DBF (from shapefile) files were saved as comma delimited format, then projected using the ArcInfo command PROJECT, using the FILE option. Once projected these files were converted to tab delimited text files in Microsoft Excel, one text file for each tile. 5.2 LiDAR for DFO In addition to the point DEMS, DFO also requested high resolution LiDAR relief data. This LiDAR has laser hits every 2 metres, producing an orthometric height for each hit; needless to say, the data consumes much disk space. LiDAR for the Annapolis Basin (Figure 4), Minas Basin (Figure 5), and Truro area were delivered to DFO personnel as XYZ data. The X, Y coordinates were UTM projected, and the elevations were orthometric (Z). Figure 4 Annapolis Basin LiDAR for DFO Applied Geomatics Research Group

18 Figure 5 Minas Basin LiDAR for DFO, each tile is approx. 4x4 km 5.3 Inventory of ERS Imagery for CCRS (Using DESCW) At the request of Joost van der Sanden at CCRS, a recent inventory of ERS imagery was needed. This inventory was to include all recently acquired (January 1, 2000 to the present) ERS-1 or ERS-2 imagery. Relevant specs of each image are also extracted during the process, i.e. orbit, date, time, image coordinates. The search areas were the Minas/Cape d Or area, and the Annapolis Basin. Two files were produced: - anna_basin_ _to_present.xls - minas_capedor_ _to_present.xls The following is a description on how to use the DESCW program (ESA) to find ERS imagery. After installation of the program, the DATA.EXE must be downloaded from the ESA website at: < >, and place it in the DESCW installation directory. This executable file contains the information on available imagery and is updated every week. Upon start up, click the area of interest on the Navigation Map, a zoom window will appear showing the area that has been selected. To define a search area, select Area under the Define menu. Draw a polygon in the Zoom Window that represents the specific area that ERS coverage is needed for. Next, the Mission must be defined, i.e. what dates, times, sensor, ascending or descending options and so on. Under the Define menu, choose Misson to invoke the Missons and Filters dialog box. Choose the Applied Geomatics Research Group

19 sensor of interest (e.g. ERS-2 SAR), and then fill in the Date Range (and other filters if so desired). Then press the Add button then Ok to search for imagery. If the Scene List Window is not already displayed, select Scene List under the View menu; this displays the information about each image found with the search. The screen should look something like Figure 6 below. The red polygon in Figure 6 is the study area; the purple polygon is the imagery swaths that have been found, in this case just one (multiple dates). Figure 6 Recent ERS-2 images found with DESCW To eliminate unwanted records, place the mouse on the swaths in the Zoom Window or the Scene List and right-click the mouse to remove them. Once the unwanted swaths have been weeded out, the parameters can then be saved by choosing Save Parameters option under the File menu. Save them as a text file, open the text file with Microsoft Excel and save it as an Excel file. Applied Geomatics Research Group

20 6.0 Image Dataset Preparation 6.1 Geometric Correction of Imagery In order to make any comparison with respect to shoreline extraction, the imagery must be geocoded as accurately as possible. This involves the collection of ground control points (GCP s) from existing vectors or accurate imagery and relating these points to the image pixel coordinates. A digital elevation model (DEM) is used to extract the elevation of each GCP and incorporate each elevation into the shifting process, using the SPLINE algorithm. Unless otherwise stated, the images were geocoded using the 1:10000 NSGC vectors, with a 20 metre DEM of Nova Scotia. Some images were previously geocoded (checked and redone or tweaked, if necessary), others were being geocoded for the first time. All GCP s were saved as text files for any future user who wishes to import them back into PCI and orthorectify more bands. The GCP file names mimic the file names of the images (Tables 1-5). The RADARSAT-1 and LANDSAT-7 images of the Annapolis Basin were previously orthorectified. The LANDSAT scene of the Minas Basin was geocoded using the SPLINE algorithm and the SPOT (pan) image was orthorectified using satellite orbital modelling Geocoding CASI The CASI for the Annapolis Basin and Port Lorne areas (high and low tide) were loosely georeferenced by the vendor (HDI) and needed improving. Upon inspection after the CASI was geocoded, the low tide CASI for the Port Lorne still had accuracy problems (because of the lack of available ground control, see Figure 7). To remedy the CASI, the geocoded images were again processed, but to a colour shaded relief image of the LiDAR DEM (Figure 8), collecting GCPS from exposed bedrock and coastal features that the LiDAR data had picked up and using the LiDAR DEM as an elevation source. This proved successful (Figure 9). Figure 7 Low tide CASI (August 28, 2000) at Port Lorne, geocoded to NSGC vectors alone (blue line). Applied Geomatics Research Group

21 Figure 8 Colour shaded relief of LiDAR for Port Lorne, with NSGC vector Figure 9 Low tide CASI at Port Lorne (Aug 28, 2000) with NSGC vector (blue) after geocoding to the LiDAR data (Width = 1 km) Applied Geomatics Research Group

22 6.2 Mosaicking and Subsetting Prior to shoreline extraction, some imagery needed to be clipped to a more specific study area or mosaicked (CASI). All mosaicking and subsetting took place after the geocoding process. The LANDSAT, SPOT, RADARSAT, and IKONOS images were subset to either the Annapolis Basin or Minas Basin to reduce the file sizes to conserve disk space, and to eliminate irrelevant land area. The IKONOS image of the Middleton area was clipped to the Margaretsville coastal area. These images are shown below in Figures Figure 10 True colour LANDSAT image of the Annapolis Basin (July 20, 2000) width = 67 km Figure 11 True colour LANDSAT image of the Minas Basin (July 13, 2000) width = 62 km Applied Geomatics Research Group

23 Figure 12 S2 RADARSAT image of the Annapolis Basin (July 17, 2000) width = 35 km RADARSAT data copyright Canadian Space Agency, 2000 Figure 13 F1 RADARSAT image of the Annapolis Basin (June 19, 2000) width = 40 km RADARSAT data copyright Canadian Space Agency, 2000 Applied Geomatics Research Group

24 Figure 14 F5 RADARSAT image of the Annapolis Basin (June 5, 2000), width = 30 km RADARSAT data copyright Canadian Space Agency, 2000 Figure 15 SPOT (pan) of the Annapolis Basin (April 17, 2000), width = 50 km Applied Geomatics Research Group

25 Figure 16 IKONOS image of Margaretsville (July 21, 2000), width = 7 km The Port Lorne CASI (high and low tide imagery), had covered a large part of the Fundy shore, and needed to be clipped. The CASI strips were clipped to similar extents in the Port Lorne Area, and then two true colour mosaics (high and low tide) were created using the MOSAIC procedure in PCI/Xpace. These files are called, Pl_Mosaic_ht_16bit.pix and, Pl_Mosaic_lt_16bit.pix. These mosaics are shown below in Figures 17 and 18. The Annapolis Basin CASI was not clipped in any way, but most image strips were previously mosaicked end to end. Due to disk space limitations, only one band was geocoded (near-infrared). The near-infrared band was chosen because it naturally provides the largest contrast between land and water. An example of a CASI strip used is shown in Figure 19. Applied Geomatics Research Group

26 Figure 17 Port Lorne CASI mosaic, high tide (July 24, 2000), width = 17 km Figure 18 Port Lorne CASI mosaic, low tide (August 28, 2000), width = 12 km Applied Geomatics Research Group

27 Figure 19 A CASI strip from the Annapolis Basin (near-infrared 810nm) July 7, 2000; width = 35 km Applied Geomatics Research Group

28 7.0 Shoreline Extraction Grey Level Thresholding 7.1 Adding Image Channels to Imagery Three 8-bit channels were added to each file in preparation for the process, except for the RADARSAT image, which required 3 more (a total of 6) for subsequent filtering and/or scaling before vector extraction (due to image speckle. Filtering was not done on the optical imagery because the Near-Infrared Bands were being used for their extractions (except SPOT). 7.2 Filtering RADARSAT Imagery Due to the amount of speckle in the water of the RADARSAT imagery (Figures 12-14), it was decided that filtering was required to further discern land from water. To filter the data, a 7x7 GAMMA filter was chosen. This type of filter aims to reduce high frequency noise (speckle) from Radar, while preserving image edges (e.g. the land/water interface). This filtering process was an iterative one, meaning the filter was used more than once in order to achieve appropriate results. The GAMMA filter was run three times on the S2 image; the results shown in Figure 20. The GAMMA filter was run two times on the F1 and F5 imagery, and then the imagery was scaled; the results in Figure 21. Figure 20 Filtering S2 RADARSAT-1 data before shoreline extraction process a) Original S2 Radar b) One Iteration width = 2.5 km c) Two Iterations d) Three Iterations Applied Geomatics Research Group

29 Figure 21 Filtering F1 RADARSAT-1 data before shoreline extraction process a) Original Radar b) 1 Iteration width = 1.5 km c) 2 Iterations c) Scaled to 8-bit Applied Geomatics Research Group

30 7.3 Vector Extraction Process 7.31 Local Thresholding The purpose of the thresholding process is to create a binary bitmap, with classes of 0 and 1 (land and water). The thresholding process was accomplished with the THR procedure in PCI. A range of values that are characteristic of water is chosen manually by looking at each image s values (DN) around the coastline. The minimum number in this value range (TVAL) should be 1, if 0 is chosen the no data portion of the image will be considered which isn t water area. In Figure 22 below, channel 4 of an image (NIR) is used to threshold the image. The water area in this image has values of 1,50 ; this creates a bitmap segment called thr with an appropriate description. Figure 22 Thresholding interface Again, the near-infrared channels of multispectral imagery are best for grey level thresholding and are used with the IKONOS, LANDSAT, and CASI. The SPOT (panchromatic) and RADARSAT images used do not have near-infrared. Threshold values are decided on a case-by-case basis Bitmap Encoding and Filtering The output from the thresholding process is a bitmap segment with values of 1 and 0. This bitmap must be encoded into an image channel, so that lakes and holes in the water bitmap can be filtered out. The MAP command in PCI encodes bitmap segments into an empty 8-bit channel. In Figure 23 below, the bitmap segment 2 is encoded into the empty channel 5. The VALU parameter is 1, meaning that the water (values 1,50) will be set as value 1, the rest will be 0. Applied Geomatics Research Group

31 Figure 23 Bitmap encoding interface Once an image channel exists with values of 1 and 0, this channel must be filtered, to rid the image of lakes and unwanted specs on the land area. A SIEVE filter was used, which removes raster polygons based on an input size. In Figure 24 below, the input channel 5 (encoded bitmap) is filtered into channel 6, an empty 8-bit channel. The STHRESH parameter is set to 250, meaning that it will remove all raster polygons that are 250 pixels and smaller. The KEEPVALU is set to 0 so that only areas of water (value 1) are removed and the 0 values are not touched. Figure 24 Sieve filtering to eliminate lakes and unwanted image errors The next step is to use the SIEVE filter again, to filter out image errors in the water area. In Figure 25, the output of the first SIEVE filter 6 is the input channel, with a KEEPVALU of 1 this time, so that errors within the water area are removed. The STHRESH value should be reduced significantly (15) so that real islands are not removed. The output of this filter is an empty 8bit channel 7. The results of each step are shown in Figure 26; some larger lakes still appear after the filter, but after vector extraction, they can easily be deleted. Applied Geomatics Research Group

32 Figure 25 Sieve filtering to eliminate non-islands or image errors in the water Figure 26 Bitmap encoding and sieve filtering a) Near-infrared Band of Landsat b) Encoded Bitmap (via MAP ) width = 62 km c) Sieve Filter (Keepval of 0) d) Sieve Filter (Keepval of 1) Applied Geomatics Research Group

33 7.33 Raster to Vector Extraction Once the second SIEVE filter has been done, and the classes of land and water have been discriminated, vectors can then be extracted. The raster tracing procedure RTV in PCI was used to draw vectors along the edges of the filtered bitmap. In Figure 27 below, the output of the second SIEVE filter (Figure 26d) is used as input for the RTV procedure. A new vector segment is created called rtv, with an appropriate channel descriptor. The POLYINFO parameter is set to LINES (the other option is points), and the BORDER parameter is set to OFF so that vectors are not drawn on the border of the image. Figure 27 Raster to vector conversion using RTV command 7.33 Vector Editing The new vector segment is described as, not edited, because inevitably some edits must occur (except with the IKONOS imagery). The imagery that used a near-infrared band to threshold the image needed very little editing (CASI, LANDSAT, IKONOS). Manual editing performed in PCI included selecting lakes and deleting them, or in the case of SPOT (pan) and to a lesser extent RADARSAT, some portions of the shoreline were interpreted and manually drawn in. Figure 28 shows the resulting vector from the above RTV (not edited), and Figure 29 shows the end result after editing. As expected, the SPOT and RADARSAT had difficulty with discerning land from water, usually in areas where tidal flats existed. Figure 30 shows an area of the RADARSAT image where the process identified a mudflat from water, and the end result from editing. Applied Geomatics Research Group

34 Figure 28 RTV of the Minas Basin LANDSAT image, not edited (width = 62 km) Figure 29 RTV of the Minas Basin LANDSAT image, edited (width = 62 km) Applied Geomatics Research Group

35 Figure 30 Portion of an S2 RADARSAT image before and after editing, Annapolis Basin (Thornes Cove), width of 3 km a) Tidal Flats are Identified (before editing) b) Edited Vector 7.34 Exporting Vectors to GIS After the process is completed, each image file has two vector segments: - shoreline not edited (vector before any manual editing) - shoreline edited (vector after manual editing) The edited shoreline was exported to a Arcview shape file using the FEXPORT task in PCI, by specifying the shoreline vector segment as the DBVS parameter. The FTYPE parameter is set to SHP (shape file), and the FOPTIONS parameter is set to ARC, meaning that the desired output is a line shape file. All extracted vectors are in the folder named extracted_shorelines, for each type of imagery. Applied Geomatics Research Group

36 8.0 Shoreline Extraction Object Oriented Approach 8.1 Scaling Imagery The object-oriented approach is performed with the ECognition Image Analysis software package. This software only accepts 8-bit imagery, therefore, the imagery must be scaled (linear). The IKONOS image of Margaretsville was used to investigate this approach to shoreline extraction. To scale 16-bit imagery to 8-bit, all values between the minimum and maximum values of each image channel are scaled to the 0,255 value range (OUTRANGE). 8.2 Starting an Ecognition Project The project was started by selecting New from the File menu. The IKONOS image, ikonos_mid_sub_8bit.pix, was chosen; this invokes the Import Image Layers dialog box. The image channels that were not needed were selected, and removed via the Remove button; the Create button then was pushed to show the image (Figure 31). Figure 31 IKONOS image in ECognition Applied Geomatics Research Group

37 8.3 Multiresolution Segmentation, Creating Image Objects The segmentation process breaks the image into objects, based on a homogeneity criterion. The user can control the relative size of the image objects, as well as assigning weights on each image band, if necessary. Under the Segmentation menu, Multiresolution Segmentation was chosen; this invokes the dialog box for segmentation (Figure 32). In the figure below, the NIR band (IKONOS band 4) is given a strong weight in order to distinguish a land object, a water object, and an intertidal object. The Scale Parameter was set to 500 to make the objects very large, again because only 3 objects were desired (water, intertidal, and land). The Homogeneity Criterion is mostly based on colour (0.8) instead of shape, because it was hoped that the difference between the waters colour (dark in the NIR band of IKONOS) would get grouped into one image object. Figure 32 Multiresolution Segmentation dialog settings The result of the segmentation process is shown below in Figure 33. The water was successfully segmented into one object, as was the intertidal zone. The land was segmented into 3 or 4 objects, which is far less important because all polygons except the intertidal polygon, will eventually be deleted. Applied Geomatics Research Group

38 Once an intertidal object exists, one can extract two vectors from this object: 1) the shoreline (land/water boundary), and 2) the backshore, or a close representation of the MHW Coastline from the NSGC. Figure 33 Multiresolution Segmentation result, IKONOS image objects 8.4 Creating Polygons, and Extracting Shoreline Vectors Polygons were created, by choosing Create Polygons under the Polygons menu; a dialog box appears. By setting both Thresholds to 0, the polygons will be the most accurate; this was done to ensure accuracy. The polygons were then exported to Arcview shape file format by choosing the Export, menu, then Image Objects. These polygons were then taken into Arcview and all polygons except the object representing the intertidal zone, were deleted. The polygon was then broken into lines, and two small lines that connect the shoreline and the backshore line area deleted. Figure 34 illustrates the resulting two vectors in red, and the NSGC province vector in green. Applied Geomatics Research Group

39 Figure 34 Result of object-oriented shoreline extraction, true colour IKONOS image, width = 2 km In the figure above, the resulting backshore vector in red relates well to the NSGC coastline vector (in green) and in most cases is within 10 metres (horizontal accuracy). The largest difference between the two coastline vectors is approximately 35 metres, in areas of image shadow primarily. Image shadow causes problems in the segmentation process because it is confused with vegetation above the backshore. When the objects are created, the image shadow is grouped with the land objects. The shadow may be grouped with the intertidal objects, perhaps after a classification of the image, the shadow may be isolated and merged with the intertidal object. This was not done for this demonstration, but further work with this method is surely warranted. Applied Geomatics Research Group

40 9.0 Tide Gauge Data Processing 9.1 Tide Gauge Background Figure 35 shows the tide gauge locations, as well as the tide gauge itself. These sensors measure temperature, turbidity, and pressure in Pounds per Square Inch (PSI). The tide gauges were deployed on June 28/2000, and extracted at different times in the fall of 2000, after levelling work was done. Figure 35 Tide gauge locations, false colour Landsat image (width of 50 km) Sensor Locations Port Lorne Annapolis Basin Applied Geomatics Research Group

41 9.2 Comparing Amplitude Curves of Levelling Data and Tide Gauge Data To ensure that clocks for the GPS levelling data and the tide gauge data were simultaneous, the amplitudes of both the orthometric heights of the shoreline during a tide cycle (Sangster, 2001), and the pressure measurements (converted from PSI to metres) of the tide gauge for the same times, were compared. This was done, by subtracting each value by the smallest value in its field. Once completed, amplitudes of pressure (m) and height (m) were compared for the Annapolis Basin data (Figure 36), and Port Lorne data (Figure 37). Figure 36 Amplitudes of levelling data and tide gauge data (Annapolis Basin) The Annapolis Basin data (above) shows a very good relationship between curves. Both curves start and stop at nearly the same time, meaning that the clocks are synchronous (no significant time shift exists), and the levelling data can be used to relate the tide gauge data to a geodetic datum (discussed later). The Port Lorne data (Figure 37), achieves synchronicity between the start and stop times of the GPS and the tide gauge, yet there is an anomaly of about 0.5m near the end of the tide cycle, with the levelled heights. This may be caused by human error, based on the levelling conditions at the time (i.e. levelling down a cliff, and over large boulders). Applied Geomatics Research Group

42 Figure 37 Amplitudes of levelling data and tide gauge data (Port Lorne) 9.3 Using METAR Pressure Data to Adjust Tide Gauge Pressure Data 9.31 Matching Time Units Between METAR and Tide Gauge Data Atmospheric pressure values were used to adjust the pressure values recorded by the tide gauge. This METAR pressure data is collected hourly, in GMT time; the tide gauge data was collected every 5 minutes, in local time (ADT). The date_time field of the atmospheric pressure data was converted from GMT to ADT time, by subtracting 3/24 (3 hours) Interpolating Hourly METAR Values to Match 5-Minute Tide Gauge Data In order to adjust the tide gauge pressure values with the atmospheric pressure values, the atmospheric pressure values needed to be interpolated to five-minute intervals to match the tide gauge. Within Microsoft Access, a date_time field was created by the VBA code in Appendix 1. The start date and time is entered, and the end date and time are entered, and the fiveminute time intervals are created. The values have not yet been interpolated, only the times. An SQL query was then done to place the new times field (every 5 minutes) next to another column containing the hourly METAR pressure values. Applied Geomatics Research Group

43 Another VBA code (Appendix 2) within Microsoft Access, was used to then interpolate the atmospheric pressure values at 5-minute intervals and populate the rest of that column. The result is a field that has atmospheric pressure values (in millibars) every 5 minutes at coincident times with the tide gauge data Normalizing and Converting the METAR Data to PSI The new fields were brought out of Microsoft Access and into Microsoft Excel with the tide gauge data, and then the METAR pressure data was normalized to an accepted average reference point. An average value of millibars (equal to one atmosphere) is considered an average pressure reference point. The data was normalized to this reference value by the equation: (pressure ) * (1/68.94). The value equals the number of millibars in one PSI. This normalizes the METAR data, and converts the data to PSI so that the units will match those of the tide gauge pressure measurements Adjusting Tide Gauge Pressure with Atmospheric Pressure The normalized PSI values (atmospheric pressure) were then subtracted from the tide gauge pressure values to remove the atmospheric pressure effect on the tide gauge measurements, resulting in adjusted tide gauge pressure values in PSI. 9.4 Converting Adjusted Pressure Depth into Metres The adjusted pressure values (PSI) were then converted to metres of water by multiplying the adjusted pressure values by This yielded a new column with a depth measurement of water in metres. The adjustment for atmospheric pressure, was somewhat insignificant, in most cases, a matter of millimetres. 9.5 Using Levelling Measurements to Calculate Geodetic Depths The resulting depth is not tied to a geodetic datum; it only represented an amount of water above the tide gauge in metres. The next step was to use the levelling measurements (related to CGVD28 vertical datum) that have an orthometric height at the waters edge for a tide cycle, and match the depth measurements of the tide gauge for the same times. Since the tide gauge data was collected every 5 minutes and the levelling data was approximately every 15 minutes, not all levelling measurements matched up exactly (need one minute measurements). For example: Using a levelling time of 15:33, the closest tide gauge time would be 15:35. These differences were insignificant. Subtracting the water depth in metres measured by the tide gauge from the shoreline orthometric heights (via levelling), for the same time, equals the orthometric height of the tide gauge (below geodetic datum). All of the levelling times were compared, and an average orthometric height for the tide gauge was calculated for both sites: Port Lorne = m, and Annapolis Basin = m. The process of calculating an average orthometric height for a tide gauge is depicted in Figure 38. Applied Geomatics Research Group

44 Figure 38 Calculating an average orthometric height for a tide gauge via levelling, an example Figure 39 Using an average tide gauge orthometric depth to calculate the orthometric height of the land/water boundary at a given time Applied Geomatics Research Group

45 These averages for both areas were then added to the adjusted depth values (m). This final process yields orthometric heights for the water level, every five minutes when the tide gauge was in the water (June Oct. 2000). The process of using an average orthometric height of a tide gauge, to calculate an orthometric height of a shoreline for a certain time; is depicted in Figure 39. Remotely sensed imagery captures the water level, planimetric position only (x,y), for an instant in time. By knowing the time of the image and using the tide gauge data, the land/water boundary can be assigned an orthometric height for that time. These concepts are discussed in the following chapter. Two files for both areas exist. One includes the amplitude curves, levelling data, and average orthometric height of the tide gauge. - amplitude_annapolis.xls - amplitude_portlorne.xls The other contains the tide gauge data, interpolated METAR pressure data, adjusted PSI values to METAR, depth measurements of water above the tide gauge, and orthometric heights of the water line every five minutes, during the tide gauge s duration. - Anna_Basin_Adjusted.xls - Port Lorne_Adjusted.xls Applied Geomatics Research Group

46 10.0 Assigning and Comparing Elevations 10.1 Assigning Orthometric Heights to Extracted Vectors Satellite Imagery The acquisition times for each image were determined, via the metadata file that raw images are delivered with. This time can be matched to the tide gauge record; yielding an orthometric shoreline elevation for that time. Table 7 and Figure 40 show how the orthometric elevation for July 19 at 15:00 ADT (for example) is found in the tide gauge record (2.768 m). Table 7 Tide gauge record, finding an orthometric height for a certain time This elevation was entered into a new field of the shoreline vector shapefile called Gauge_ht. Other fields were also added to each shapefile s table, such as: Time_adt, and Date (Figure 36). Applied Geomatics Research Group

47 Figure 40 Finding an orthometric height for a certain time, an example m Airborne Imagery (CASI) Assigning heights to vectors extracted from satellite imagery was a straightforward process because there is only one elevation to assign (the image is taken at one time). In the case of CASI data, however, multiple strips exist, and times vary within each strip. To accurately assign vectors to each shoreline segment extracted from a CASI strip, the flight lines were obtained, and used. These flight lines are GPS points from the aircraft, and have a time item (called Name by the data provider) that can be related back to the tide gauge record. Figure 41 below, shows the use of the flight line shape file (GPS points) to assign different heights to vectors extracted from the same CASI strip. Applied Geomatics Research Group

48 Figure 41 Using CASI flight lines to acquire the time and assign heights 14:02 GMT is closest to 11:00 ADT (-3 hrs) Notice in Figure 41, that the other two vectors (top right) are five minutes apart from the selected vector, and therefore a slightly different orthometric elevation Comparing CASI Vector Elevations to LiDAR DEMS, Port Lorne The elevations for land/water vectors derived from the tide gauge were checked against LiDAR DEMS for the Port Lorne area. The extracted shorelines were converted to a point coverage using the ARCPOINT command; these points were taken back into Arcview, where an existing Avenue script was run on to add X, Y coordinates for each point in the shapefile table. The points were then imported into PCI, and then transferred as a vector segment to the LiDAR DEM. The VSAMPLE command was then run in PCI, to sample the DEM under every coastline point, and output elevation values to a text file. Two new fields were added to the point shape file: Gauge_ht, and elev_diff (elevation difference between gauge height and LiDAR height). The points that correspond to each shoreline segment are assigned an elevation from the tide gauge. The output of grid sampling (a text file) was imported into Arcview and joined to the point shape file table into a new field, lidar_elev. The difference between the Gauge_ht and lidar_elev columns populated the elev_diff column. The two resulting shape files are within their respective Extracted_shorelines folder: Applied Geomatics Research Group

49 pl_ht_lidarelev_diff.shp high tide, point shape file showing elevation difference pl_lt_lidarelev_diff.shp low tide, point shape file showing elevation difference These points were then colour coded in Arcview, by elevation difference (Figures 42 and 43), illustrating problem areas, as well as areas that match nicely. Figures 44 and 45 show the frequency of elevation difference for the points. The frequency of elevation difference graphs and data are stored in two files within the Extracted_shorelines folder: pl_ht_elevdiff_freq.xls frequency of elevation differences, high tide pl_lt_elevdiff_freq.xls frequency of elevation differences, low tide Figure 42 Elevation difference, LiDAR vs. tide gauge height, high tide, width = 13 km Elevation Difference (m) Applied Geomatics Research Group

50 Figure 43 Elevation difference, LiDAR vs. tide gauge height, low tide, a better fit (width = 12 km) Elevation Difference The vectors extracted using the low tide CASI imagery (Figure 43) are clearly more accurate than the high tide vectors (Figures 42). This difference is could be the result of levelling error, as seen in Figure 37 (difference between amplitude curves), as a result of difficult levelling terrain. More likely, the source of the difference is due to registration of the imagery. The high tide CASI imagery of Port Lorne was geocoded to the NSGC vectors, and matched the roads well, but perhaps the registration was not good enough to match the LiDAR. The low tide CASI on the other hand was geocoded to the NSGC vectors, and then to the LiDAR. This may account for why the low tide vectors are much more accurate, in comparison to the LiDAR. In addition, if the registration is off in the high tide CASI imagery, the effects will be more in magnitude, because the vectors are nearer to the backshore (cliffs). For example, a shoreline vector that is meant to go around a wharf or cliff, may be on the cliff or wharf, according to the LiDAR DEM (Figure 46). If the high tide image is accurately geocoded, the vertical measurement can still be ambiguous because each level (different times) may have the same X, Y location. These reasons are why the low tide imagery provides more accurate results. Applied Geomatics Research Group

51 Figure 44 Frequency of elevation difference, LiDAR vs. tide gauge, high tide High Tide Mean = m StDev = 3.36 m Applied Geomatics Research Group

52 Figure 45 Frequency of elevation difference, LiDAR vs. tide gauge, low tide Low Tide Mean = m StDev = 1.41 m Applied Geomatics Research Group

53 Figure 46 High tide CASI, elevation difference error, width = 1 km Elevation Difference (m) More elevation difference near steep inclined features As seen in Figure 44, the range of elevation difference for the high tide vectors is approximately 8 to 33 metres; most likely due to the vectors being in close proximity to steep features (Figure 46). The range of elevation difference for the low tide vectors is approximately 8 11 metres. Registration errors are most likely not severe enough to affect the low tide vectors (as much) as they are quite a bit further away from any abruptly inclined features on the coast Comparing CASI Vector Heights to Kinematic GPS, Annapolis Basin A kinematic GPS transect of a tidal flat was performed on the north shore of the Annapolis Basin, in an effort to assist in validation of the tide gauge data. Figure 47 shows the low (green) and high (red) tide shorelines, as well as the GPS transect (blue) for the Annapolis Basin. Note that because of the nature of the CASI imagery (many strips), vectors were extracted from each CASI strip, which were acquired at different times; therefore several vectors at different elevations exist in areas where there was image overlap. Applied Geomatics Research Group

54 Figure 47 GPS location with high and low tide CASI vectors, CASI mosaic of low tide image swaths (image width = 9 km) Unfortunately, the GPS transect doesn t reach the low tide vector, but an interpolation can be done, assuming that the tidal flat is a flat surface. Figure 48 shows the elevations of the high and low tide vectors with the GPS transect, at a larger scale. In an effort to compare the GPS with the tide gauge elevations of the high and low tide vectors, a surface was interpolated using the two high tide vectors and the one low tide vector shown in Figure 48. The GPS points were used to sample this interpolated surface, and then colour coded by elevation difference (Figure 49). A graph showing a profile of elevation difference (Figure 50) for the points (north-south) shows that elevation difference increases as the distance from the high tide vectors increases; then decreases as it nears the low tide vector. As the points approach the extracted low tide vector from which the sampled grid was interpolated from, the elevation difference decreases. The absolute elevations were compared with the elevations of the extracted vectors, and it is currently unclear why the elevation difference decreases when approaching the low tide vector. Applied Geomatics Research Group

55 Figure 48 Elevations of high tide (red) and low tide (green) vectors with GPS 2.74 m 2.64 m Image seam of CASI strip GPS Heights m Figure 49 Elevation difference between GPS and interpolated elevations from a grid (interpolated from 3 tide gauge elevations) Applied Geomatics Research Group

56 Figure 50 Graph showing elevation difference between GPS and elevations interpolated from a grid (interpolated from 3 tide gauge elevations Error 10.4 Comparing Kinematic GPS Elevations with LiDAR DEMS, Annapolis LiDAR coverage exists for the Annapolis Basin, and does cover the area of the GPS transect. However, this LiDAR coverage did not meet the accuracy specifications stated by the vendor (Webster, 2001 and Sangster, For this reason, the LiDAR for this area should not be used in a direct comparison to the tide gauge elevations. A comparison was done between the kinematic GPS and the LiDAR in an effort to correct the LiDAR, and possibly use the corrected LiDAR in a comparison of the tide gauge elevations. The LiDAR grid was sampled using the GPS points, and the elevations were plotted on a graph, seen in Figure 51. In Figure 51, the LiDAR seems to be at a constant offset to the GPS elevation; the average of elevation difference between the LiDAR and GPS was 2.64 metres. This average was added to the LiDAR in an effort to match both datasets. The result of this adjustment, seen in Figure 52, shows that although the LiDAR matches much better than before, areas are still off in excess of 0.45 metres. The average difference between the GPS points, and the adjusted LiDAR is approximately 1.45m, but this is due to a stray GPS point that is most likely an error (tallest spike). If the average elevation difference Applied Geomatics Research Group

57 between the GPS and the adjusted LiDAR is calculated without the stray GPS point, it is 0.11 metres. Perhaps the entire LiDAR grid can be corrected with a bulk shift of 2.64 metres, and then compared to the tide gauge elevations; adjustment of the entire Annapolis Basin LiDAR coverage, may require more GPS transects in the Annapolis Basin to validate results. Figure 51 LiDAR elevations vs. GPS elevations Applied Geomatics Research Group

58 Figure 52 Adjusted LiDAR elevations vs. GPS elevations Applied Geomatics Research Group

59 11.0 Object-Oriented Classification of Intertidal Zone 11.1 Prepare IKONOS Image of Intertidal Zone In chapter 8.4, one result of the object-oriented shoreline extraction process was a polygon of the intertidal zone. This polygon was converted to a grid in ArcInfo with the Polygrid command, with a 4-metre cell size (IKONOS resolution). The grid was imported into PCI, and the PROUTM command was run to ensure that the georeferencing was identical to the original image. The intertidal image was then mosaicked into an empty 8-bit channel in the original IKONOS image. An image bitmap was then created for the intertidal zone using the THR (threshold) in PCI. Four 8-bit channels were added to the image. The IIIBIT command in PCI was used to transfer the imagery (all four channels) under the intertidal bitmap into the 4 new channels. These channels were subset or clipped into its own PIX file, and then imported into ECognition Image Analysis software Multiresolution Segmentation of Intertidal Zone Image The image was segmented into image objects using Multiresolution Segmentation with a Scale of 10 (small image objects), and a weight of 10 on the NIR band of the IKONOS Creating Classes and Classification Rules Classes were created by right-clicking the mouse on the Class Hierarchy and then clicking the Insert Class option. Classes like: 1) Seaweed, 2) Sand, 3) Basalt/Rocky, 4) Water, 5) Shadow, and 6) Unclassified; were created. A few training samples were created for each class, by clicking Input Manual Samples and clicking the appropriate class in the Class Hierarchy, and then clicking the desired image object to define it as a sample. The classification will use a standard nearest neighbour method to classify the image. This classification will most likely mis-classify similar image objects. By creating classification rules or Membership Functions, the classification will be much more accurate because it is based on image object values and/or size etc. For example, if seaweed image objects are reflecting image values between in the NIR band, one can specify that for the Seaweed class, any image objects within that range (in the NIR band) should be classified as seaweed. These rules are extremely powerful and should be used carefully, because if a rule is based on only one image objects value, or the user isn t sure what the object really is, the class may be misrepresented. In Figure 53, a membership function for the Seaweed class is defined as values that are approximately 50 to 100, in the NIR band. Applied Geomatics Research Group

60 Figure 53 Classification rules, membership functions in ECognition 11.3 Classifying A Standard Nearest Neighbour method was used in conjunction with the class rules defined with Membership Functions. Under the Classification menu, Apply Standard NN to Classes was chosen. To perform the classification, the Classify option was chosen under the Classification menu, choosing With Class-Related Features so that the membership functions are used Classification Based Segmentation Once the classification was completed, some image objects that were adjacent to each other are classified the same. To simplify the classification, Classification Based Segmentation was performed from the Segmentation menu. This creates a new simplified classification; adjacent image objects that are the same class are merged. Once Classification Based Segmentation was done, classification was done again so that image polygons could be created from the new image objects. Applied Geomatics Research Group

61 11.5 Export Classification to GIS The classification was exported as polygon shape files via the Export menu. Once in Arcview, the polygons can then be displayed based on the best_class field. Figures 54 and 55 below illustrate part of the image before and after the classification. Figure 54 IKONOS, false colour composite, image width = 1.5 km Applied Geomatics Research Group

62 Figure 55 IKONOS True colour composite with classification results, GPS and photos taken near acquisition time, image width = 1.5 km Applied Geomatics Research Group