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Geospatial Data Acquisition and Integration Overview

This lecture provides a comprehensive overview of data acquisition in geospatial analysis and modeling, covering mapping technologies, remote sensing, ground-based methods, georeferencing, and coordinate systems. The content includes examples of satellite and airborne sensors, data acquisition techniques, and the transformation of mapped data into georeferenced representations. It also discusses various coordinate systems, cartographic projections, and national/state systems used in geospatial analysis.

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Geospatial Data Acquisition and Integration Overview

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  1. Lecture notes Helena Mitasova, NCSU MEAS Data acquisition and integration Geospatial Analysis and Modeling MEA592 – Helena Mitasova

  2. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Outline Brief overview of what you should already know from the GIS Introductory courses • mapping: data acquisition • coordinate systems and transformations • geospatial data models: raster, vector • raster-vector conversions and resampling • geospatial formats and conversions • data repositories, interpreting metadata

  3. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Data acquisition • Mapping technologies: • which you have used for your work? • Passive and active aerial and satellite sensors • On-ground surveys : (RTK)GPS, total station, laser scanner • In situ thematic data collection: climate and air quality stations, water sampling stations, species mapping, soil sampling; georeferencing usually through GPS

  4. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Data acquisition: Remote Sensing Satellite examples: • LANDSAT 1-7 (since 1972), 30m multispec., 15m panchrom. • SPOT 1-5 (20-2.5m image, 30m DEM, France), • AVHRR(Adv. Very High Res. Radiometer 1km), • Terra: MODIS (500m, temp, aerosol), ASTER (30m, temp, DEM) • Iconos, Quickbird (0.60-2.4 m resolution) • SRTM Shuttle Radar Topography Mission, lidar (ICESAT I) Airborne examples • Photogrammetry: ortho, oblique, infrared, multispectral • Lidar Future: UAV, on-board processing, sensor networks

  5. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Satellite Remote Sensing Sensors: Data: SRTM LANDSAT

  6. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Airborne Remote Sensing Sensors: Data: x,y,z points 1 point per 0.3m Orthophotography 0.15m resolution

  7. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Data acquisition: ground-based • GPS, RTK-GPS • terrestrial photogrammetry static and mobile • laser scanners static or mobile on cars/robots • discipline specific monitoring and sampling stations (econet station, ISCO sampler) • Products: georeferenced points with attributes or “streetview” imagery

  8. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Data acquisition: ground based Ground based imagery Google Street view Satellite imagery

  9. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Data acquisition: ground based Data: airborne lidar + RTK GPS, groud-based laser scanner Equipment: RTK GPS, Sonar, laser scanner, ISCO sampler

  10. Geospatial Analysis and Modeling MEA592 – Helena Mitasova From mapping to GIS • georeferencing (real-time during mapping with GPS) • feature or theme extraction • building GIS data model representation (raster or vector with attribute database) Mapped data (imagery or points) are transformed into georeferenced, discrete representations of landscape features

  11. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Georeferencing • Georeferenced data: location on Earth is represented in a Coordinate Referenced System • MANY coordinate systems exist, they evolve over time as accuracy of the Earth measurements improves

  12. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Coordinate systems Geographic coordinate system (learn it if you don't know it!): • geoid -> ellipsoid –> (sphere) -> latitude/longitude • GPS, large regions, data exchange (USGS, Google) • units are ? degree-minutes-seconds • requires complex algorithms for distances, areas

  13. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Coordinate systems Projected Reference Systems - cartesian coordinate systems based on projections: • geoid – ellipsoid - developable surface – plane – x,y • developable surfaces: conic, cylindrical, azimuthal (plane) • type of distortion: conformal, equidistant, equal area image from Neteler&Mitasova, 2008&

  14. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Cartographic Projections To learn more about Projected Reference Systems please read: www.progonos.com/furuti/MapProj/Normal/TOC/cartTOC.html excellent, easy to understand material about projections and map properties with lots of graphics and mathematical foundations, and fun to read see also links to references in this document

  15. Geospatial Analysis and Modeling MEA592 – Helena Mitasova National and state systems National/State Coordinate systems defined by: • Reference spheroid/geoid and datum • Vertical datum • Projection Goal was to minimize distortions on maps that were used to measure distances and areas – less important now when distances and areas are computed directly from data

  16. Geospatial Analysis and Modeling MEA592 – Helena Mitasova National and state systems Reference geoid and datum: • North American: Clarke 1866 - NAD27, Grs80 - NAD83 • World geodetic system WGS84 • Vertical datums: NGVD29- National Geodetic Vertical Datum 1929, NAVD88– North american Vertical Datum 1988 Projections • Lambert Conformal Conic (LCC): states in US • Universal Transverse Mercator (UTM): USGS, military • Albers Equal Area (conic): USGS national map

  17. Geospatial Analysis and Modeling MEA592 – Helena Mitasova On-line mapping systems Spherical Mercator: cylindrical on sphere, large distortions • Official name: Popular Visualization CRS and sphere • Used by Google, Microsoft and others EPSG (group that maintains standardized list of parameters for official georeference coordinate systems ) did not like it: “We have reviewed the coordinate reference system used by Microsoft, Google, etc. and believe that it is technically flawed. We will not devalue the EPSG dataset by including such inappropriate geodesy and cartography.” In 1989, seven North American professional geographic organizations adopted a resolution that called for a ban on all rectangular coordinate maps (especially Mercator). http://geography.about.com/library/weekly/aa030201b.htm http://demonstrations.wolfram.com/WorldMapProjections/

  18. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Popular visualization CRS The reference system was eventually included under the code 3785 - not recommended for professional work Winkel tripel projection - hybrid, for world only http://www.math.ubc.ca/~israel/m103/mercator/mercator.html

  19. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Coordinate systems in GIS Representation of coordinate systems in GIS • Metadata file • ESRI PRJ file • EPSG codesprovided by OGP - Int. Org. of Oil and Gas Producers Surveying and Positioning Committee, formerly EPSG – european petroleum survey group • http://mapserver.gis.umn.edu/docs/faq/epsg_codes Vertical datum support often missing in GIS – specialized tools

  20. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Coordinate systems in GIS Coordinate system definitions for the dataset used for assignments ESRI PRJ file (readable ASCII) PROJCS["NAD_1983_StatePlane_North_Carolina_FIPS_3200", GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983", SPHEROID["GRS_1980",6378137.0,298.257222101]], PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]], PROJECTION["Lambert_Conformal_Conic"], PARAMETER["False_Easting",609601.22], PARAMETR["False_Northing",0.0], PARAMETER["Central_Meridian",79.0], PARAMETER["Standard_Parallel_1",34.3333333333333 PARAMETER["Standard_Parallel_2",36.16666666666666], PARAMETER["Latitude_Of_Origin",33.75],UNIT["Meter",1.0]] EPSG translated to input parameters of the PROJ software NAD83(High Accuracy Reference Network HARN) / North Carolina <3358> +proj=lcc +lat_1=36.16666666666666 +lat_2=34.33333333333334 +lat_0=33.75 +lon_0=-79 +x_0=609601.22 +y_0=0 +ellps=GRS80 +units=m +no_defs

  21. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Coordinate transformations Data often come in different coordinate systems: • USGS, federal agencies: Geographic coordinates, Albers equal area, UTM • State agencies: State Plane • Older data may have different datums (NAD27, NAD83) Coordinate transformations • x,y -> longitude, latitude -> x’,y’ • on-fly transformation may be time consuming, especially for raster : resampling/reinterpolation to regular grid is required

  22. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Geospatial data models Mapped, georeferenced data are transformed into discrete GIS representations using • raster (regular grid) • vector (feature: point, line, area/polygon) geospatial data models

  23. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Geospatial data models Two different types of objects/phenomena • continuous fields: w=f(x,y), w=f(x,y,z) each point in space is assigned a distinct value, change between two neighboring points is relatively small: elevation, precipitation represented by raster data model, but vector model is also used: meshes, TIN, isolines or points. • discrete objects/features: lines, points or areas with attributes represented by vector data model as geometry(shape) with attribute table or object based (geodatabase); raster representation is also used : roads, streams, census blocks, land use, schools

  24. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Geospatial data models: raster continuous: elevation, precipitation

  25. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Geospatial data models: raster continuous: elevation, precipitation discrete: land use, roads 5 developed 1 water 3 herbaceous

  26. Geospatial Analysis and Modeling MEA592 – Helena Mitasova 2D raster data model header + matrix of values (INT, FP, DP) • continuous field : value assigned to a grid point • discrete object : cat value assigned to pixel (area) • imagery - several bands Speed limit Elevations north: 225720 south: 223370 east: 639900 west: 637590 rows: 235 cols: 231 117.979 117.892 117.964 118.207 118.516 120.567 120.565 120.782 121.625 122.414 123.598 124.359 124.614 124.733 124.934 124.775 125.009 124.972 125.412 125.908 north: 225720 south: 223370 east: 639900 west: 637590 rows: 235 cols: 231 5 5 5 25 25 25 5 5 5 5 5 5 5 5 5 5 5 5 25 5 5 5 5 5 35 35 35 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 45 45 45 45 45 45 25 25 25 25 25 25 5 5 5 5 25 25 25 5 5 5 5 5 5 5 5 5 5 5 5 5 5

  27. Geospatial Analysis and Modeling MEA592 – Helena Mitasova 2D raster data model for volumes • multiple surfaces (set of 2D raster layers) can be used to represent soil horizons or geological layers • combined representation: • continuous (horizontally) • discrete (vertically)

  28. Geospatial Analysis and Modeling MEA592 – Helena Mitasova 3D raster data model % org. carbon header + 3D matrix of values vertical scale is usually much finer than horizontal mostly used for 3D continuous representation w=f(x,y,z) north: 225720 south: 223370 east: 639900 west: 637590 top: 130 bottom: 20 rows: 235 cols: 231 levels:10 soil pH contribution of real-world 3D data (point samples, layers, volumes) from NC to the dataset will be welcome

  29. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Raster data - changing resolution 30m to 10m: elevation Continuous data - reinterpolation Nearest neighbor Spline, bicubic polynomial

  30. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Raster data - changing resolution 30m to 10m: elevation geology Discrete data -resampling Felsic Mica Quartzite Quartz diorite Metam granite Amphibolite Nearest neighbor Spline, bicubic polynomial interpolation creates categories that do not exist

  31. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Raster: increasing resolution 10m 10m elevation 30m nearest neighbor slope in the center cell is zero! interpolation 10m – new image zk zj zi zm zi z0 zi=z0, i=1,…n zi=f(zk), i=1,…n; k=1,…m

  32. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Raster: increasing resolution 10m 10m elevation 30m nearest neighbor slope in the center cell is zero! interpolation 10m – new image geology 30m nearest neighbor 10m interpolation 10m

  33. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Raster: increasing resolution 20m elevation 30m nearest neighbor 20m, not all “flats” are square interpolation 20m no problem similar to 30m to 10m 20m geology 30m nearest neighbor 20m: area for each class may change but do not use interpolation !

  34. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Raster: decreasing resolution elevation 10m nearest neighbor 30m 20m For some applications average, min or max may be more appropriate, see also nearest neighbor operations

  35. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Raster: decreasing resolution elevation 10m nearest neighbor 30m 20m soilsID: min or max will work but not average

  36. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Geospatial data models: vector Discrete: streets, streams, geodetic points, census blocks

  37. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Geospatial data models: vector • Continuous: isolines, points Discrete: streets, streams, geodetic points, census blocks

  38. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Geospatial data models: vector vector data model - geometry: • [x,y,(z)] points representing points, lines, areas • topology:nodes, vertices, centroids, line, polyline, boundary, polygon • 3D vector data: face, kernel volume • areas • points, lines

  39. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Vector data: geometry + attributes • points, lines and areas are abstract representations of complex features (firestation – point, road – centerline, ...) • attributes are stored in data management systems geometry 633649.29 221412.94 1 628787.13 223961.62 2 629900.71 222915.80 3 L 9 1 630206.53 239151.59 629068.26 238374.22 …. B 10 641635.38 226175.44 641626.92 226020.09 ..... C 1 1 642246.66 225317.27 1 1

  40. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Vector data: geometry + attributes • points, lines and areas are abstract representations of complex features (firestation – point, road – centerline, ...) • attributes are stored in data management systems geometry attributes 633649.29 221412.94 1 628787.13 223961.62 2 629900.71 222915.80 3 Cat ID LABEL LOCATION CITY MUN_COUNT PUMPER PUMPER_TAN TANKER 21 0 RFD #20 1721 Trailwood Dr Raleigh M 1 0 0.... cat|MAJORRDS_|ROAD_NAME|MULTILANE|PROPYEAR| OBJECTID|SHAPE_LEN 1|1|NC-50|no|0|1|4825.369405 L 9 1 630206.53 239151.59 629068.26 238374.22 …. B 10 641635.38 226175.44 641626.92 226020.09 ..... Cat| OBJECTID| BLOCK_| BLOCK_ID|BLOCKNUM| TOTAL_POP| POP_1RACE| WHITE_ONLY| BLACK_ONLY|AMIND_ONLY|ASIAN_ONLY|HWPAC_ONLY|OTHER_ONLY| POP_2RACES|HISPANIC|MALE|FEMALE|P_UNDER_5|........ 1|83117|83118|83117|371830535013008|44|44|41|0|3|0|0| 0|0|5|25|19|1 ... C 1 1 642246.66 225317.27 1 1

  41. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Geospatial data models: 3D vector • 3D vector data (x,y,z): points, lines, areas and volumes • volumes: face, kernel volume • extrude from footprint by given elevation • full 3D model (CAD, Sketchup)

  42. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Geospatial data models: 3D vector Full 3D model with draped texture created in Sketchup Entire city - buildings extruded from footprints using height from associated database and stored as 3D vector data See 3D NCSU in Google Earth - http://delta.ncsu.edu/about/research_initiatives/3d_ole/google_sketchup/

  43. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Vector to vector data conversions • polygons to points: centroids or line vertices Data geometry is not modified: subset is selected and stored in a different data structure

  44. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Vector to vector data conversions • polygons to lines (boundaries) Data geometry is not modified: subset is selected and stored in a different data structure Topology building is required for conversions point to line, line to polygon

  45. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Vector to vector data conversions Generalization (downscaling) - geometry is simplified • roads, streams, contours, building footprints, urban areas,coastlines • line to simplified line • polygon (building footprint, urban area) to point symbol Both data geometry and type can be modified Needs to be considered when combining local, state and national scale data Streams: 1:2000 local, 1:24000 topomap, 1:1mil national

  46. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Conversions between data models Vector to Raster Raster to Vector

  47. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Vector -> Raster conversions • continuous: interpolation, covered in separate lecture; • discrete: nearest neighbor Streets to speed limit 30m resolution raster map, null replaced by 5

  48. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Vector -> Raster conversions • continuous: interpolation, binning; • discrete: nearest neighbor • areas: attribute value applies to the entire polygon – only complete polygons can be converted to be fully valid Streets to speed limit raster map, null replaced by 5 Census blocks to population 10m and 30m resolution

  49. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Raster->Vector data conversions • Continuous data: sampling points

  50. Geospatial Analysis and Modeling MEA592 – Helena Mitasova Raster->Vector data conversions • Continuous data: sampling points,isolines

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