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John Lowry and Students from GS211 (Remote Sensing I) Semester 2, 2013 Slide 2 NASA Earth Observatory Image of the Day http://earthobservatory.nasa.govhttp://earthobservatory.nasa.gov Dubai, UAE ASTER Image Feb 8, 2010 IOTD Mar 12, 2010 Slide 3 Sydney Hall Road Fire Linksview Road Fire State Mine Fire NASA Earth Observatory Image of the Day http://earthobservatory.nasa.govhttp://earthobservatory.nasa.gov Fires Around Sydney, Austraila MODIS Image Oct 17, 2013 IOTD Oct 18, 2013 Slide 4 NASA Earth Observatory Image of the Day http://earthobservatory.nasa.govhttp://earthobservatory.nasa.gov Sundarbans, Bangladesh Landsat 7 Image Nov 24, 1999 IOTD Oct, 2006 Slide 5 NASA Earth Observatory Image of the Day http://earthobservatory.nasa.govhttp://earthobservatory.nasa.gov Fiji Islands MODIS Image Jul 21, 2011 IOTD Aug 13, 2011 Slide 6 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 Slide 7 Launched in 1999 Terra & Aqua Satellites 36 spectral bands 250 m (bands 1 & 2) 500 m (bands 3-7) 1000 m (bands 8-36) BandBandwidthDescription 10.62-o.67 mRed 20.84-0.87 mNear IR 30.46-0.48 mBlue 40.55-0.56 mGreen 51.23-1.25 mMid IR 61.63-1.65 mMid IR 72.10-2.15 mMid IR Slide 8 Primary Elements Color Tone (light-dark) Spatial Arrangement of Tone and Color Spatial Arrangement of Tone and Color Size Shape Texture Pattern Based on Analysis of Primary Elements Height Shadow Contextual Elements Site Association Slide 9 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 Slide 10 Fiji Landcover Key Colour: Black, Blue & Grey Colour & tone: surface is smooth, uninterrupted blackish-blue expanse 91_WATER Colour & tone: Surface varies between greenish- blue and turquoise with irregular prey patches 92_CORAL REEFS Colour: Green, Brown and White Colour & tone: Surface is less than 95%, of a lighter tone and also consists of brown patches Pattern: Surface pattern of rows of "bumpy" shapes, spaced at regular intervals 73_COCONUT PLANTATION Pattern: Surface mainly covered with flat expanse with few scattered aggregates of darker green 50_TALASIGA GRASSLANDS Colour & tone: Surface is at least 95-100% green, of medium dark to very dark tone Texture & pattern: Surface appears medium to highly coarse/rough, consisting of large aggregates /masses of green covering 80-100% of image area Site: Elevation above 400m Site & association: Located at 600-900m, on ridges. 10_CLOUD RAINFOREST Site: located at 400-800m 20_UPLAND RAINFORST Site: Located below 600m 30_LOWLAND RAINFOREST Site: Elevation below 400m Site & association: Found on leeward side of slopes 40_MIXED DRY RAINFOREST Site & association: Found exclusively near water bodies 60_MANGROVE RAINFOREST Texture & pattern: Surface appears lightly or finely coarse/rough, with smaller aggregates of green covering less that 80% of image. White regular shapes of buildings present Pattern: Large aggregates of white buildings, curvilinear road networks, exposed bare patches of soil. 81_URBAN/SUBURBAN /DEVELOPED Pattern: Few scattered houses. Landscape largely divided into regular rid shapes with greenery and patches of brown exposed soil 82_AGRICULTURE Texture & pattern: Surface appears medium coarse with aggregates of greenery broken up by network or roads, buildings near the edges. 72_SOFTWOOD PLANTATION Slide 11 Talasiga (Grassland)Upland Rainforest Agriculture Slide 12 Mapping zones created: Visually merged groups 2-3 Tikinas in ArcMap Slide 13 Footprint created: Conversion Tools > From Raster > Raster to Polygon Converted to KML: Conversion Tools > To KML > Layer To KML Slide 14 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 ESRI Geodatabase Conversion to Geodatabase: Conversion Tools > From KML> KML to Layer (Batch) Then, Data Management > General > Merge Slide 15 Roughly 900 sample polygons total After cleaning, 790 sample polygons total Randomly divided: 50% Training 50% Accuracy Randomized division: Geostatistical Analyst > Utilities > Subset Features Slide 16 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 Slide 17 Slide 18 Reference Data 10203040506081829192100 Mapped Data Cloud For. 10212063 1 10520% Up. Rainfor 20 3103 5 2114% Lo. Rainfor 30 452422 2 6679% Mix Dry For. 401 410143 15 4721% Talasiga 50 143111272 2938% Mangrove 60113 131 1 2259% Developed 81 1 81 1080% Agriculture 82 1 1511321 4276% Water 91 1 13 1493% Coral Reef 92 322 2588% Cloud Cover 100 1 25863% 2331136213426125719255389 91%10%38%48%32%50%67%56%68%88%100% Overall Accuracy: 48.84% Kappa Coefficient: 41.92 Accuracy Assessment: Kappa Stats tool (Python script) from http://arcscripts.esri.comhttp://arcscripts.esri.com Slide 19 Create signatures: Spatial Analyst > Multivariate > Create Signature Graph in Excel Slide 20 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 Slide 21 Create signatures: Spatial Analyst > Multivariate > Create Signature Graph in Excel Slide 22 Slide 23 Reference Data 10203040506081829192100 Mapped Data Cloud For. 1018156 3946% Up. Rainfor 2041210 11 2941% Lo. Rainfor 3012108333 2 12289% Mix Dry For. 40 514 4 8 4134% Talasiga 50 32151 5 2560% Mangrove 60 1 1115 11 2365% Developed 81 103 1377% Agriculture 82 1332391 4980% Water 91 14 1593% Coral Reef 92 323 2689% Cloud Cover 100 1 15771% 2331136213426125719255389 78%39%79%67%44%50%83%68% 92%100% Overall Accuracy: 70.18% Kappa Coefficient: 64.39 Accuracy Assessment: Kappa Stats tool (Python script) from http://arcscripts.esri.comhttp://arcscripts.esri.com Slide 24 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 Toblers (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 http://blogs.esri.com/esri/arcgis/2010/12/12/on-map-scale-and-raster-resolution/ http://www.scanex.ru/en/monitoring/default.asp?submenu=cartography&id=det Slide 25 Thank You!