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John Lowry and Students from GS211 (Remote Sensing I) Semester 2, 2013

John Lowry and Students from GS211 (Remote Sensing I) Semester 2, 2013. Learning remote sensing by doing: A student generated land use/cover map of the Fiji Islands using MODIS imagery. Fiji Islands MODIS Image Jul 21, 2011 IOTD Aug 13, 2011.

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John Lowry and Students from GS211 (Remote Sensing I) Semester 2, 2013

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  1. John Lowry and Students from GS211 (Remote Sensing I) Semester 2, 2013 Learning remote sensing by doing: A student generated land use/cover map of the Fiji Islands using MODIS imagery

  2. Fiji Islands MODIS Image Jul 21, 2011 IOTD Aug 13, 2011 NASA Earth Observatory Image of the Day http://earthobservatory.nasa.gov

  3. Highlights of this presentation • Student learning more meaningful with “hands-on” learning through project-based activities • Remote sensing students at USP create first MODIS-based land use/cover map for Fiji Islands • Learning/teaching fundamentals of remote sensing accomplished using tools in ArcGIS & Google Earth

  4. MODIS (Moderate Resolution Imaging Spectroradiometer) • Launched in 1999 • Terra & Aqua Satellites • 36 spectral bands • 250 m (bands 1 & 2) • 500 m (bands 3-7) • 1000 m (bands 8-36)

  5. Primary Elements Color Tone (light-dark) Spatial Arrangement of Tone and Color Size Shape Texture Pattern Based on Analysis of Primary Elements Height Shadow Contextual Elements Site Association 10 Elements of Visual Interpretation

  6. Classification Legend (Scheme) • Principal Vegetation Types of Fiji from Mueller-Dombois and Fosberg(1998) 10 Cloud Forest 20 Upland Rainforest 30 Lowland Rainforest 40 Mixed Dry Forest 50 Talasiga (grassland) 60 Mangrove forest and scrub 70 Plantation & Production 71 Hardwood Plantation 72 Softwood Plnatation 73 Coconut Palm 80 Anthropogentic Landscapes 81 Urban/Developed 82 Agriculture 90 Waterscapes 91 Water 92 Coral reef 100 Cloud cover

  7. Vis. Interp. using Google Earth Talasiga (Grassland) Upland Rainforest Agriculture

  8. Division of labour: 35 Mapping Zones Mapping zones created: Visually merged groups 2-3 Tikinas in ArcMap

  9. Sample collection using Google Earth, guided with MODIS pixel grid Footprint created: Conversion Tools > From Raster > Raster to Polygon Converted to KML: Conversion Tools > To KML > Layer To KML

  10. Sampling continued... • Each student: • Digitizes 25-40 polygons in mapping zone • Interprets homogenous land use/cover types that are 3+ footprint grid cells in size • Assigns numeric label to each sample polygon • Converted & Merged to ESRIGeodatabase Conversion to Geodatabase: Conversion Tools > From KML> KML to Layer (Batch) Then, Data Management > General > Merge

  11. Reference Data (Sample Polygons) • Roughly 900 sample polygons total • After cleaning, 790 sample polygons total • Randomly divided: 50% Training 50% Accuracy Randomized division: Geostatistical Analyst > Utilities > Subset Features

  12. Maximum Likelihood Classifier • Students experimented with EQUAL and SAMPLE prior probabilities • Produced classified maps and error matrices • Compared results visually & quantitatively Create signatures: Spatial Analyst > Multivariate > Create Signature Classification: Spatial Analysts > Multivariate >Maximum Likelihood Classifier

  13. Accuracy Assess. MODIS Image Accuracy Assessment: Kappa Stats tool (Python script) from http://arcscripts.esri.com

  14. Spectral Signatures for “natural” Vegetated Classes Create signatures: Spatial Analyst > Multivariate > Create Signature  Graph in Excel

  15. Use of Ancillary Data: Data Fusion Elevation: 100 m resolution Ave July Precip: 100 m resolution Resample to 500 m: Data management> raster > resample Normalized to same range as imagery: Spatial analyst > map algebra > raster calculator Create “Layer stack”: Data management > raster > raster processing > composite bands

  16. Spectral Signatures for “natural” Vegetated Classes (w/ Ancillary Data) Create signatures: Spatial Analyst > Multivariate > Create Signature  Graph in Excel

  17. Accuracy Assess. W/Ancillary data Accuracy Assessment: Kappa Stats tool (Python script) from http://arcscripts.esri.com

  18. Summary • Students experienced land use/cover classification project start-to-finish • Learned skills & understand theory by practice • Visual interpretation, sampling, spectral signatures, supervised classification, data fusion, accuracy assessment • 1:1,000,000* scale land use/cover map of Fiji Islands (2011) • Improvements with more training samples • Further experimentation, PCA, 250 m res. * Based on Tobler’s (1987) Rule of Thumb that map scale is 1,000 times double the pixel size (http://blogs.esri.com/esri/arcgis/2010/12/12/on-map-scale-and-raster-resolution/) Another useful website: http://www.scanex.ru/en/monitoring/default.asp?submenu=cartography&id=det

  19. Thank You!

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