Fundamentals of Remote Sensing REMS5001 CAPSTONE PROJECT Due Date December 10, 2014 W0279429
TABLE OF CONTENTS 1.0 Introduction.. 1 2.0 Study Area.. 1 3.0 Procedures...... 2 Vector National Road and Hydro Networks.. 2 Digital Elevation Model (DEM)... 3 Orthorectification).. 3 Land Cover Classification. 4 Filtering and Vectorization.... 6 4.0 Conclusions... 6 References... 7 Appendix 1 Orthorectification Metadata.. 8 Appendix 2 Land Cover Classification Metadata.. 10 Appendix 3 SPOT 5 image and Land Cover Classification Map. 8 Appendix 4 Data Organization and Data Catalogue 9 LIST OF FIGURES Figure 1: Regional Digital elevation model for Nova Scotia illustration location of multispectral SPOT 5 satellite imagery used in this study. 1 Figure 2: Google Earth Image showing location of SPOT 5 imagery relative to nearest regional center and Cape Breton Highlands National Park.. 2 Figure 3: Screen grab illustrating compilation ArcGIS 10.2.2 workspace associated with this project. 2 Figure 4: Screen grab illustrating overlap of vector road-hydro reference data and final orthorectified SPOT 5 Imagery 3 Figure 5: Screen grab of 1-2-4 false colour composite and correlation to final information class types... 4 Figure 6: Comparison of Herbaceous and Deciduous information classes illustrating spectral overlap...... 5 Figure 7: Google Earth, SPOT 5 1-2-4 false colour composite and final land cover classification images for the Wetland information class 5 Figure A1-1: Screen grab illustrating the distribution of ground control points (pink) and check points (yellow) used to orthorectify the SPOT 5 imagery. 8 Figure A2-1: Spectral profile of final 8 information classes. 12 Figure A2-2: X-Y plot of band 1 vs. 4 illustrating distribution of information classes at 2 standard deviations.. 12 i
Figure A2-3: Final thematic classification compared to SPOT 5 image for Developed information class illustrating the overlap with Barren land cover class.. 12 Figure A2-4: Final classification compared to SPOT 5 image for Developed information class illustrating the overlap with Barren land cover class. 13 Figure A3-1: False colour composite and land cover classification of SPOT 5 Imagery.... 15 Figure A4-1: Schematic diagram summarizing folder hierarchy and data storage..... 16 Figure A4-2: Road and Hydro networks trimmed to SPOT 5 scene footprint underlain by DEM mosaic... 17 LIST OF TABLES Table A1-1: Check Point residual and RMS data used to orthorectify SPOT 5 Imagery. 8 Table A1-2: Ground Control Point residual and RMS data used to orthorectify SPOT 5 imagery 9 Table A2-1: Summary of representative and final merged spectral classes that characterize 8 information classes... 10 Table A2-2: Best Average Separability data calculated using Euclidean Distance. 10 Table A2-3: Classification Accuracy Assessment Filtered Results 11 Table A2-4: Classification Accuracy Assessment Filtered Results 11 TABLE A4-1: Summary of vertical and horizontal accuracies for individual DEMS used to create final mosaic DEM.... 17 ii
1.0 Introduction The following report provides an overview of a Geographic Information Systems (GIS) compilation package, focussing on SPOT5 multispectral imagery from the northern region of Cape Breton Island, Nova Scotia (Figure 1). In addition to the GIS compilation, two core deliverables of the project were the (i) ortho-rectification and (ii) unsupervised land classification of the SPOT 5, four band multi-spectral (red-green-blue-near infra-red), imagery. In order to carry out these tasks, Digital Elevation Model data (DEM) and road, hydrology and landuse validation site vector datasets were also acquired. Each dataset is briefly discussed with respect to data sources and the associated data manipulation and/or image processing techniques employed prior to final compilation in an ArcGIS 10.2.2 workspace. Datasets were both downloaded from several internet locations and also made available from existing in-house databases. Site specific modifications to vector datasets took place in ArcGIS 10.2.2. Ortho-rectification and unsupervised classification of the SPOT 5 imagery was completed using the ERDAS IMAGINE software packaged (Version 14.00.0100, build 715; Intergraph Corporation). Final conversion from raster to polygon and polygon smoothing was completed in ArcGIS 10.2.2. Metadata pertaining to orthorectification accuracy, classification separability and accuracy are provided in Appendices 1 and 2. An 11x17 in map illustrating the results of the unsupervised classification on the SPOT 5 data is provided in Appendix 3. All data are in the NAD83 CSRS datum and projected using UTM Zone 20. Figure 1: Regional Digital elevation model for Nova Scotia illustration location of multispectral SPOT 5 satellite imagery used in this study. Nova Scotia Digital Elevation model sourced from http://novascotia.ca/natr/meb/ download/dp055dds.asp. 2.0 Study Area The SPOT 5 (56512560709201512141j.img) data is located between the latitudes of 46.901780 o N and 46.216853 o N and longitudes of -59.9979876 o W and -61.004689 o W. The largest regional centre closest to the scene footprint is town of Sydney (46.139887 o N, -60.193194 o W, Figure 1). The region encompassed by the SPOT 5 imagery is sparsely populated, characterised by relatively minor land use activities (compared to other regions of Nova Scotia). Logging and minor farming activities are visible in the southern half of the scene. The increase in percentage of exposed bedrock coincides with an increase in overall relief in the northern half of the imagery. The Cape Breton Highlands National Park broadly overlaps this part of the SPOT 5 Image (Figure 2). 1
Figure 2: Google Earth Image showing location of SPOT 5 imagery relative to nearest regional center and Cape Breton Highlands National Park 3.0 Procedures The final compilation ArcGIS 10.2.2 workspace (Landcover_VBennett.mxd) consists of six vector files (3 line, 1 point, 1 polygon) and four raster datasets and (Figure 3). Each dataset is discussed below. Figure 3: Screen grab illustrating compilation ArcGIS 10.2.2 workspace associated with this project. Vector National Road and Hydro Networks The National Road and Hydro network datasets (NRN, NHN) represent vector line data encompassing all of Nova Scotia. The two vector datasets were used as the reference dataset to orthorectify the raw SPOT 5 imagery. The data were first clipped to the scene footprint and subsequently merged for use in Erdas Imagine. The compilation file is provided in the final ArcGIS 10.2.2. workspace in addition to the national road and hydro shape files. The hydro dataset was particularly useful in the Cape Breton area due to the paucity of roads across the scene area. 2
Digital Elevation Model (DEM) A DEM mosaic that coincides with the footprint of the SPOT 5 data was created from individual DEM files publicly available from http://www.geobase.ca. A table summarizing vertical and horizontal accuracies for each DEM used in the mosaic is provided in Appendix 4 (Table A4-1). For this project, the final mosaic DEM was assumed to have a horizontal accuracy of 3 m and a vertical accuracy of 9.6 m. Fifteen individual DEM s were used to create a mosaic encompassing the entire SPOT 5 scene. No DEM data were available for a small portion of the NE corner of the multispectral image which was entirely located over ocean. A separate ArcGIS workspace (DEM_Mosaic_N83CSRS), an associated geodatabase (DEM_NAD83CSRS.gdb)housing the individual DEMS and a final mosaic (in NAD83CSRS datum) in addition to metadata information are included in the data deliverables for this project. The individual DEM s were merged into a mosaic in ArcGIS 10.2.2 using the Mosaic to New raster tool. The final mosaic was clipped to the scene footprint and re-projected to NAD83CSRS UTM z20 for use in ERDAS Imagine. The clipped mosaic is located in the (Landcover_VBennett.mxd) workspace. Orthorectification The SPOT 5 scene (651_256) was imported into ERDAS Imagine, in addition to the merged and clipped Road_Hydro dataset. Ortho-rectification was completed using the SPOT geometric model which is a custom mathematical model allowing accurate orthorectification of SPOT panchromatic or multispectral data that uses a pushbroom sensor type. Fifty five ground control points were selected from the SPOT Image and the vector reference imagery. Nine additional check points were defined across the image and reference data to assess resultant model accuracy. Ground Control Point and CP summary tables with residuals and Root Mean Square (RMS) calculations, in addition to a screen grab illustrating the distribution of the control points are located in Appendix 1 (Tables A1-2, A1-3, Figure A1-1). For accurate ortho-rectification, the average RMS value should be less than the spatial resolution of the SPOT 5 imagery (i.e. <20m; (FRS Oct 2014, Lecture 8, slides 20-21). Final average RMS values calculated for GCP and CP datasets were 4.1712 (m) and 2.4316 (m), respectively. These values are significantly below the 20 m resolution of the SPOT 5 imagery for both the GCP and CP datasets. The RMS results indicate the selection of control points was sufficiently evenly distributed to allow for an accurate geometric model and final geocorrection of the dataset. Figure 4 shows the results of the ortho-rectification for the SE corner of the SPOT 5 image, centred on the town of Sydney Mines. The overlay of the road vector network shows good visual correlation with the underlying roads within the raster image. Figure 4: Screen grab illustrating overlap of vector road-hydro reference data and final orthorectified SPOT 5 Imagery 3
Land Cover Classification A land cover classification was carried on the SPOT 5 imagery in order to generate both raster and vector thematic imagery for use in ArcGIS 10.2.2. Statistical recognition methodologies are used to group the multispectral data into to spectral and ultimately, more simplified information classes. For this study, the intent was to integrate land cover classifications completed on SPOT imagery covering all of Nova Scotia and carried out by several different analysts. To successfully achieve this, a consistent classification method and classification scheme was adopted that was based on the GeoBase Land Cover 2000 project (see Appendix 4) Thirteen information classes and associated universal codes were provided to classify to the 651_256 SPOT 5 image. Eight of the thirteen information classes were identified in the 651_256 SPOT 5 scene. An unsupervised classification, in which the software allows the computer to group pixels into unique clusters based on spectral similarity (Jensen 2005), was completed on the dataset. Time was not available to conduct the appropriate field verification, nor was the analyst an expert in land cover types in this particular region of Nova Scotia. The K means algorithm was used to complete the unsupervised classification. Fifty classes (i.e data cluster centres) were assigned after 10 iterations of the k-means algorithm. The algorithm assigns each pixel to a cluster center where the value of the pixel is closest to the mean of the cluster center. After a preliminary visual examination of land cover class types occurring across the SPOT 5 scene, each recognized land cover type was subsequently examined to identify representative spectral classes and how many spectral classes characterised a land cover type. A summary table is provided in Appendix 2 illustrating the association between land cover type (information class) and associated spectral class (Table A2-1). Digital surficial geology data for Nova Scotia (http://novascotia.ca/natr/meb/) was used to identify land feature types in the northern part of the imagery. Additionally, in-house, digital forestry data was also utilized to identify broad distribution of vegetation species. A 1(red gun)-2(green gun)- 4(blue gun)false colour composite was particularly useful for first-order subdivision of the imagery (Figure 5). Figure 5: Screen grab of 1-2-4 false colour composite and correlation to final information class types. A significant problem in the final classified data that uses only 4-band multispectral data is the high degree of spectral overlap between many information classes. In several instances, the representative spectral class was the same for 2 (or more) information classes. Figure 6 illustrates the overlap problem using the Herbaceous vs. Deciduous Information classes where spectral class 49 was most diagnostic for both information classes. 4
Figure 6: Comparison of Herbaceous and Deciduous information classes illustrating spectral overlap Separability metadata illustrating the spectral overlap between information classes is provided in Appendix 2 (Table A2-2, Figure A2-1, A2-2). As the Herbaceous land cover class could not be resolved spectrally from Deciduous, it was removed from the final classification. Similar problems occur between, (i) Exposed/barren vs. Shrubland (Appendix 2, Figure A2-3) (ii) Exposed/barren vs. Developed (Appendix 2, Figure A2-4) (iii) Wetland vs. Barren and Deciduous, Mixed Wood and Shrubland. The wetland information class was the least spectrally distinct of all final 8 information class defined for the final classification. A review of the 1-2-4 false colour composite and the counterpart Google Earth imagery (Figure 7) illustrates the wetland class represents several other spatially resolveable information classes including barren/exposed, shrubland, deciduous. Figure 7: Google Earth, SPOT 5 1-2-4 false colour composite and final land cover classification images for the Wetland information class. The high degree of spectral overlap between several land cover information classes degraded the resultant classification accuracy if allowances were not made during accuracy assessment. Accuracy assessment was carried out using a point validation dataset acquired in-house. The validation data represents a synthesis of 5
1:50,000 scale digital datasets including the Land Cover for Agricultural regions of Canada dataset, the Nova Scotia Topographic Database (NSTDB) and the Department of Natural Resources (DNR) Forest Inventory. Information on the Land Cover for Agricultural Regions of Canada data is located at http://www.agr.gc.ca/eng/?id=1343256785210. Information on the NSTSB is found http://www.novascotia.ca/geonova/services/nstdb_wms.asp and details of the Nova Scotia Forestry inventory is located http://novascotia.ca/natr/forestry/programs/inventory/. Two accuracy assessments were completed, the first involved an assessment of classified image using nine information classes and ignoring the spectral overlap problems (i.e. Herbaceous included). The resultant accuracy of the classified data was 56.7% (See Appendix 2, Table A2-3). A second accuracy assessment was also performed that conducted the following modifications: 1. The Herbaceous information class was removed from the accuracy assessment. Where the Deciduous class (220) occurred in place of Herbaceous (100), this was considered a match. 2. Where the Barren class (33) was classified as Developed (34) and vice versa, this was also deemed a match. 3. Where the Barren (33) was classified as Shrubland (50), this was considered a match. 4. Where the wetlands (80) overlapped with barren (30) this was considered a match. When these allowances were made, the resultant accuracy assessment was 78.5 % (Appendix 2, Table A2-4). Filtering and Vectorization Prior to export of the classified image to ArcGIS 10.2.2, filtering of the classified data was carried out to reduce erroneous pixels and smooth individual class boundaries. Clumping statistics were calculated for the image, in which contiguous groups of pixels were clumped into one thematic class. Various filters were then applied to the clumped data. The eliminate filter removes a user-specified size of clumps and replaces them with values of the adjacent (majority) clump. A pixel size of 50 was set as it provided the best compromise between loss of data and excessive pixel detail (noise). A statistical filter was then applied to the data in which a 3x3 pixel window was assigned a value based on the most frequently occurring DN value in that 3x3 pixel block (majority function). The final classified, filtered and smoothed imagery was imported into ArcGIS where it was subsequently vectorized using the raster to polygon tool. Both the vector and raster classified image can be found in the Landcover_VBennett.mxd. A layer file is also provided in the case that thematic coding is corrupted or lost during transfer of data. 4.0 Conclusions The GIS data compilation product associated with this report contains the results of ortho-rectification and land cover classification conducted on SPOT 5 imagery acquired in 2007 and focussed on the northern part of Cape Breton. The scene footprint overlaps the location of the Cape Breton Highlands National Park and is characterized by comparatively minor anthropogenic land use activities (mainly logging and farming), with respect the rest of Nova Scotia. Accurate ortho-rectification was achieved using a reference dataset comprised of merged and clipped line data from the National Road Network for Canada (10 m accuracy) and the National Hydro Network for Canada (30 m maximum accuracy). A Digital Elevation Model (DEM) mosaic was created from 15 individual DEM and used to resample the SPOT 5 imagery to permit subsequent land cover classification. An unsupervised classification was completed using the K-Means Algorithm provided in the Erdas Imagine software package. Eight information classes were defined from 50 spectral classes. An accuracy assessment using externally supplied validation sites highlighted the significant spectral overlap that occurs between information classes, such that accurate resolution of certain information classes was not possible (e.g. deciduous vs. herbaceous). Allowances were made during a second round of accuracy assessment that took into account the spectral overlap occurring in the four-band multispectral data. Improvement on the overall classification accuracy is unlikely because of the limited number of multispectral bands available to fingerprint each information class. Additional field verification may help with overall accuracy. Application of a different 6
classification scheme that is more amenable to the resolution the four band multispectral imagery provided may represent a longer term solution to achieving better classification results. Final filtering, smoothing and vectorization of the classified image were completed prior to final export into ArcGIS 10.2.2. All pertinent datasets that may be required by the user are located in a series of folders (see Appendix 4 for detail), and a compilation ArcGIS 10.2.2 workspace is provided for easy access to final generated data products. References Jensen, J. R. Introductory Digital Image Processing, 3 rd edition, Prentice Hall, 2005. Fundamentals of Remote Sensing Oct 2014, Lecture 8 (geocorrection, slides 20-21). Unpublished lecture, Centre of geographic Sciences. 7
Appendix 1 Orthorectification Metadata Figure A1-1: Screen grab illustrating the distribution of ground control points (pink) and check points (yellow) used to orthorectify the SPOT 5 imagery. PointID Xinput Yinput Xref Yref Xresidual _meter Yresidual _meter RMS Error Contribution PointID Xinput Yinput Xref Yref Xresidual _meter Yresidual _meter RMS Error Contribution GCP #2 702818.27 5187089.9 702582.91 5187204.7 0.275-1.15 1.183 0.486 GCP #9 676849.37 5181777.7 676651.71 5181818.3 1.776 0.385 1.817 0.747 GCP #13 699487.87 5169675.4 699255.03 5169780.4-1.436-0.907 1.698 0.698 GCP #31 689828.07 5143065.6 689586.84 5143150.8 2.906-1.66 3.347 1.376 GCP #37 692696.7 5157736.3 692446.88 5157830.7 2.963-1.102 3.162 1.3 GCP #39 668162.4 5165615.9 668004.75 5165634.1 0.416 0.09 0.426 0.175 GCP #56 671394.2 5186945.8 671281.1 5186948.3-1.645-1.201 2.037 0.838 GCP #61 707514.79 5125639.1 707414.93 5125676 1.019 2.794 2.974 1.223 GCP #65 662823.9 5142470.7 662784.29 5142496.5 2.407 2.337 3.355 1.38 1.8923 1.5269 2.4316 Table A1-1: Check Point residual and RMS data used to orthorectify SPOT 5 imagery 8
PointID Xinput Yinput Xref Yref Xresidual _meters Yresidual _meters RMS Error Contribution GCP #1 675702.05 5185992.1 675537.9 5186021.3 1.95-0.016 1.95 0.468 GCP #3 713937.85 5123279.5 713942.21 5123281.6 3.183 0.474 3.218 0.771 GCP #4 656397.19 5140620.9 656497.46 5140615.4 3.09-1.051 3.264 0.783 GCP #5 657072.7 5148440.4 657119.96 5148425.2 1.543-1.244 1.982 0.475 GCP #6 662179.97 5160006.6 662108.06 5159998.2 2.622-3.2 4.137 0.992 GCP #7 665571.1 5178342.3 665480.89 5178323-3.139-2.241 3.857 0.925 GCP #8 690328.69 5182314.2 690068.87 5182409.3 0.898 3.903 4.005 0.96 GCP #10 678937.61 5193034.8 678792.86 5193074 2.922 2.233 3.678 0.882 GCP #11 672679.71 5192699.6 672602.09 5192705.3 1.783 3.643 4.055 0.972 GCP #12 703772.34 5183293.9 703547.45 5183407.7 1.284-1.624 2.07 0.496 GCP #14 668965.04 5141328.1 668836.04 5141376.8 3.564 0.427 3.589 0.861 GCP #15 669921.96 5147188.2 669756.73 5147241.8-4.516 6.901 8.248 1.977 GCP #16 672654.66 5155101.9 672454.73 5155154.8 3.591 3.057 4.716 1.131 GCP #17 671952.86 5153435.1 671757.04 5153483.4-1.205-0.44 1.283 0.308 GCP #18 661247.15 5153206.5 661194.08 5153210.3-4.188 3.061 5.188 1.244 GCP #19 697472.03 5129442.7 697269.95 5129508 1.95-2.4 3.093 0.741 GCP #20 710291.77 5129561.9 710241.78 5129600.7-4.87 5.777 7.556 1.811 GCP #21 712187.06 5127638.1 712172.93 5127658.9 1.791-0.078 1.793 0.43 GCP #22 704858.83 5132373.3 704731.33 5132427.1-3.982-2.555 4.731 1.134 GCP #23 683301.61 5130933.5 683066.65 5131008.9-4.466-2.485 5.11 1.225 GCP #24 675097.28 5133818.3 674911.91 5133888.9-4.425 2.478 5.072 1.216 GCP #25 673003.34 5134645.6 672837.25 5134707.9-4.713-1.172 4.857 1.164 GCP #26 667690.24 5135633.6 667601.87 5135677.6 6.655-4.483 8.024 1.924 GCP #27 667703.1 5135736.5 667609.79 5135792.8 2.27 7.951 8.269 1.982 GCP #28 662828.83 5137036.9 662812.57 5137062.5 2.389-4.184 4.818 1.155 GCP #29 686919.41 5135725.8 686677.98 5135801.7-0.44-5.823 5.84 1.4 GCP #30 680241.12 5139914.9 680006.23 5139993.8-4.858 2.285 5.369 1.287 GCP #32 676968.33 5146208.4 676747.15 5146279.1 1.937 1.893 2.708 0.649 GCP #33 686588.53 5150195.4 686329.4 5150277.6-0.931-4.539 4.633 1.111 GCP #34 679767.83 5156440.3 679513.31 5156511.6-1.913-0.786 2.068 0.496 GCP #35 682446.86 5160531.7 682178.39 5160606.8-3.834-2.529 4.594 1.101 GCP #36 681785.81 5159743.9 681521.31 5159819.4-2.144-0.601 2.227 0.534 GCP #38 692209.9 5156891.2 691954.52 5156986-1.656 0.11 1.66 0.398 GCP #40 680778.06 5167432.7 680519.11 5167503.6 0.054 1.604 1.605 0.385 GCP #41 678791.7 5176979.6 678560.12 5177033.4 0.504-0.374 0.628 0.15 GCP #42 692357.74 5174486.5 692095.58 5174583.2 5.251-1.021 5.349 1.282 GCP #43 683523.98 5163449.8 683255.95 5163532.3 0.192 3.074 3.08 0.738 GCP #44 696758.91 5166819.4 696515.15 5166918.2 0.701-3.784 3.849 0.923 GCP #45 699679.36 5162064 699465.73 5162162.1 1.356-3.251 3.523 0.844 GCP #46 692391.7 5162691.8 692132.89 5162788.4 1.502-0.095 1.505 0.361 GCP #47 686827.34 5171240.1 686556.88 5171329 1.115 3.545 3.716 0.891 GCP #48 676495.05 5171126.3 676265.4 5171180.2 0.678 3.662 3.724 0.893 GCP #49 701400.98 5176133.4 701167.47 5176248.7-3.993 4.851 6.283 1.506 GCP #50 680052.17 5186982.4 679855.68 5187030.8 1.951-1.133 2.257 0.541 GCP #51 683831.5 5190912.6 683627.15 5190970.8-1.606-4.255 4.548 1.09 GCP #52 695591.32 5187446.6 695336.1 5187547.9-2.615-2.092 3.349 0.803 GCP #53 696857.36 5180284.8 696601.28 5180391.7-0.439 0.764 0.882 0.211 GCP #54 671394.41 5168076.7 671204.19 5168102.4 0.979-5.085 5.178 1.241 GCP #55 664872.94 5173408.9 664777.83 5173393.3 2.614-3.002 3.981 0.954 GCP #57 669681.32 5190535.9 669620.64 5190520.5-1.694-2.386 2.926 0.701 GCP #58 670818.93 5192215 670759.26 5192205.5-0.598-0.832 1.025 0.246 GCP #59 671357.5 5192634.3 671297.52 5192627 2.491-1.323 2.821 0.676 GCP #60 670498.45 5184216.8 670380.08 5184222.2 0.986 2.647 2.825 0.677 GCP #62 705404.5 5127124.6 705278.94 5127167.9 0.75-1.036 1.279 0.307 GCP #63 695380.15 5133421.3 695167.35 5133500.8 2.178 3.395 4.034 0.967 2.8281 3.0661 4.1712 Table A1-2: Ground Control Point residual and RMS data used to orthorectify SPOT 5 imagery 9
Appendix 2 Classification Metadata Information Class Code Representative spectral classes Overlapping Information Classes Merged Classes Deciduous 220 31, 44, 45, 46, 49 Herbaceous, Shrubland, Mixed Wood, Wetland 43, 44, 46 Coniferous 210 23,19, 20, 21, 27 Mixed Wood, Wetland Mixed Wood 230 27, 29, 31, 33, 35, 37, 38, 40, 43, 44, 46 Deciduous, Coniferous, Shrubland, Wetland 10, 16, 19, 20, 21, 23, 24, 27 7, 11, 12, 13, 14, 15, 17, 18, 22, 25, 26, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40 Shrubland 50 31,38, 44, 45, 46, 47, 48, 50 Herbaceous, Deciduous, Exposed Land, Developed 48 Water 20 1, 2, 3, 4, 5,6, 8 No overlap 1, 2, 3, 4, 5,6, 8, 9 Exposed Land 33 42, 45, 47, 48, 50 Shrubland, Developed, Herbaceous 41, 45, 47 Developed 34 32, 28, 42, 45, 47, 48, 50 Exposed Land, Herbaceous 42, 50 Wetland 80 16, 17, 18, 21, 26, 28, 29, 30, 32, 34, 36, 39, 41, 45, 47 Spectral Variation extreme 28. 39 Herbaceous 100 48, 49 Exposed Land, Developed no designation. Overlap with deciduous Table A2-1: Summary of representative and final merged spectral classes that characterize eight information classes used for classification of SPOT 5 imagery. Bands AVE MIN Class Pairs 1: 2 1: 3 1: 4 1: 5 1: 6 1: 7 1: 8 2: 3 2: 4 2: 5 2: 6 2: 7 2: 8 3: 4 3: 5 3: 6 3: 7 3: 8 4: 5 4: 6 4: 7 4: 8 5: 6 5: 7 5: 8 6: 7 6: 8 7: 8 1 2 3 4 73 4 72 85 84 67 86 132 75 51 55 51 24 108 4 12 26 73 159 51 19 79 163 56 75 154 53 88 23 109 Table A2-2: Best Average Separability data calculating using Euclidean Distance, using bands 1-4 simultaneous. Classes - 1 Shrubland; 2 Wetland ; 3 Developed; 3 Developed; 4 Barren/Exposed; 5 Deciduous; 6 Coniferous; 7 10
Class Reference Classified Number Producers Users Name Totals Totals Correct Accuracy Accuracy ---------- ---------- ---------- ------- --------- ----- Class 20 13 13 13 100.00% 100.00% Class 33 13 10 3 23.08% 30.00% Class 34 12 10 6 50.00% 60.00% Class 50 8 15 7 87.50% 46.67% Class 80 12 2 1 8.33% 50.00% Class 210 15 13 12 80.00% 92.31% Class 220 16 30 16 100.00% 53.33% Class 230 10 14 3 30.00% 21.43% Totals 107 107 61 Overall Classification Accuracy = 57.01% Table A2-3: Classification Accuracy Assessment Filtered Results Class Reference Classified Number Producers Users Name Totals Totals Correct Accuracy Accuracy ---------- ---------- ---------- ------- --------- ----- Class 20 13 13 13 100.00% 100.00% Class 33 10 10 10 100.00% 100.00% Class 34 13 10 10 76.92% 100.00% Class 50 14 15 13 92.86% 86.67% Class 80 8 2 1 12.50% 50.00% Class 210 15 13 12 80.00% 92.31% Class 220 24 30 22 91.67% 73.33% Class 230 10 14 3 30.00% 21.43% Totals 107 107 84 Overall Classification Accuracy = 78.50% Table A2-4: Classification Accuracy Assessment Filtered Results 11
Figure A2-1: Spectral profile of final 8 information classes. Figure A2-2: X-Y plot of band 1 vs. 4 illustrating distribution of information classes at 2 standard deviations. Figure A2-3: Final thematic classification compared to SPOT 5 image for Developed information class illustrating the overlap with Barren land cover class. 12
Figure A2-4: Final classification compared to SPOT 5 image for Developed information class illustrating the overlap with Barren land cover class. 13
Appendix 3 Final Landcover Classification 11 x 17 Map 14
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Appendix 4 Data Organization and Data Catalogue Figure A4-1 provides a schematic outline of the folder structure housing all relevant data for the image processing and GIS Compilation project. The parent folder housing all the data is called P2_Bennett_Venessa, and within that folder are 6 subfolders that contain the different datasets for the project. A digital pdf copy of this report is located in the Reports subfolder. Figure A4-1: Schematic diagram summarizing folder hierarchy and data storage. ArcMap Portal: An ArcGIS 10.2.2 workspace contains the main data products generated from this report including, the orthorectified SPOT 5 image, the land cover classification (vector and raster), that are housed in the 651_526 group layer and the DEM mosaic, SPOT 5 footprint, and the Road and Hydro network vector files which are located in a group layer called reference. The workspace is in the NAD83CSRS UTM zone 20 projection. Reference Data-Vector: Figure A4-2 illustrates the final DEM mosaic and trimmed road and hydro vector datasets. The data can be downloaded from http://www.geobase.ca/geobase/en/index.html. The original shape files provided for the project were in the NAD83CSRS datum. The national road data has an estimated accuracy of + 10 m. Additional metadata details for the NRN dataset is found at http://www.geobase.ca/geobase/en/metadata.do?id=c0db1b2e-bdaf-6998-8b8e- E569E5D39D6B. The hydrographic dataset was created from 1:50 000 scale datasets (either provincial or federal datasets). The maximum accuracy for the data is 30 m, but can be accurate to a few meters if provincial data is the source data. For this project, an accuracy of 30 m was assumed. Further details on the NHN meta data can be found at http://www.geobase.ca/geobase/en/data/nhn/index.html. Reference Data-DEM: A DEM mosaic that coincides with the footprint of the SPOT 5 data was created from individual DEM files publicly available from http://www.geobase.ca. The Canadian Digital Elevation Data (CDED) comprises regularly spaced ground elevations derived from 1:50 000 1: 250 000 digital data associated with the National Topographic Data Base (NTDB). Ground Elevations are recorded in metres relative to Mean Sea Level (MSL), based on the North American Datum 1983 (NAD83) horizontal reference datum. A full metadata summary is located at 16
http://www.geobase.ca/geobase/en/metadata.do?id=3a537b2d-7058-fced-8d0b-76452ec9d01f. Table A4-1 summarizes vertical and horizontal accuracies for each DEM used in the mosaic. For this project, the final mosaic DEM was assumed to have a horizontal accuracy of 3 m and a vertical accuracy of 9.6 m. CDED Region Horizontal Accuracy Vertical Accuracy 011K01, SYDNEY 3 4.2 011K06, MARGAREE 3 5.3 011K07, ST. ANNS HARBOUR 3 8.7 011K08, BRAS D'OR 3 2.1 011K09, INGONISH 3 2.1 011K10, CHÉTICAMP RIVER 3 9.6 011K15, PLEASANT BAY 3 5 TABLE A4-1: Summary of vertical and horizontal accuracies for individual DEMS used to create final mosaic DEM Figure A4-2: Road and Hydro networks trimmed to SPOT 5 scene footprint underlain by DEM mosaic Orthorectified SPOT image: SPOT 5 scene (56512560709201512141j.img) has a spatial resolution of 20 m and consists of 4 bandwidths (blue, green, red and near infra-red). The 10 m resolution panchromatic data was not used in this project. Full details about the accuracy of the SPOT 4 and 5 datasets for Canada is located at http://www.geobase.ca/geobase/en/metadata.do?id=17fb0a31-d9fe-a35b-3bdd-b8afb93bdb66. The SPOT 5 image used in the project was collected on September 20, 2007 at 15:12:14 pm using the High Resolution Visible sensor number 1. A map of the orthorectified SPOT 5 image is provided in Appendix 3. A false colour 4-3-2 composite is displayed in the image. Landcover Classification: A land cover classification was carried on the SPOT 5 imagery in order to generate both raster and vector thematic imagery for use in ArcGIS 10.2.2. Eight Information classes were used to create both filtered and smoothed raster and vector datasets for display in ArcGIS. The thematics maps are classified and coloured according to standards defined by the GeoBase Land Cover 2000 project that was primarily developed for vectorization of Landsat 5 and 7 imagery. A description of the Geobase Land Cover, 2000 data is located http://www.geobase.ca/geobase/en/data/landcover/csc2000v/description.html. A map of land cover classification results in vector format image is provided in Appendix 3. 17