john lowry and students from gs211 (remote sensing i) semester 2, 2013
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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. - PowerPoint PPT PresentationTRANSCRIPT
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Learning remote sensing by doing: A student
generated land use/cover map of the Fiji Islands using MODIS imagery
John Lowry and Students from GS211 (Remote Sensing I) Semester 2, 2013
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NASA Earth Observatory Image of the Day http://earthobservatory.nasa.gov
Fiji IslandsMODIS Image Jul 21, 2011
IOTD Aug 13, 2011
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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
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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)
Band
Bandwidth Description
1 0.62-o.67 µm Red
2 0.84-0.87 µm Near IR
3 0.46-0.48 µm Blue
4 0.55-0.56 µm Green
5 1.23-1.25 µm Mid IR
6 1.63-1.65 µm Mid IR
7 2.10-2.15 µm Mid IR
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10 Elements of Visual Interpretation
Primary ElementsColor
Tone (light-dark)
Spatial Arrangement of Tone and Color
Size
Shape
Texture
Pattern
Based on Analysis of Primary Elements
Height
Shadow
Contextual ElementsSite
Association
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Classification Legend (Scheme) Principal Vegetation Types of Fiji from
Mueller-Dombois and Fosberg (1998)10 Cloud Forest20 Upland Rainforest30 Lowland Rainforest40 Mixed Dry Forest50 Talasiga (grassland)60 Mangrove forest and scrub70 Plantation & Production
71 Hardwood Plantation72 Softwood Plnatation73 Coconut Palm
80 Anthropogentic Landscapes
81 Urban/Developed82 Agriculture
90 Waterscapes91 Water92 Coral reef
100 Cloud cover
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Fiji Landcove
r 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
patchesPattern: 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 RAINFORES
T
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
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Vis. Interp. using Google Earth
Talasiga (Grassland)
Upland Rainforest
Agriculture
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Division of labour: 35 Mapping Zones
Mapping zones created: Visually merged groups 2-3 Tikinas in ArcMap
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Sample collection using Google Earth, guided with MODIS pixel grid
Footprint created: Conversion Tools > From Raster > Raster to PolygonConverted to KML: Conversion Tools > To KML > Layer To KML
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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 ESRI Geodatabase
Conversion to Geodatabase: Conversion Tools > From KML> KML to Layer (Batch)Then, Data Management > General > Merge
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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
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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 SignatureClassification: Spatial Analysts > Multivariate >Maximum Likelihood Classifier
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Accuracy Assess. MODIS Image
Reference Data 10 20 30 40 50 60 81 82 91 92 100
Mapped Data
Cloud For. 10 21 20 63 1 105 20% Up. Rainfor 20 3 10 3 5 21 14% Lo. Rainfor 30 4 52 4 2 2 2 66 79% Mix Dry For. 40 1 4 10 14 3 15 47 21% Talasiga 50 1 4 3 11 1 2 7 2 29 38% Mangrove 60 1 1 3 13 1 1 22 59% Developed 81 1 8 1 10 80% Agriculture 82 1 1 5 1 1 32 1 42 76% Water 91 1 13 14 93% Coral Reef 92 3 22 25 88% Cloud Cover 100 1 2 5 8 63%
23 31 136 21 34 26 12 57 19 25 5 389 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.com
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Spectral Signatures for “natural” Vegetated Classes
0
500
1000
1500
2000
2500
3000
3500Cloud Forest
Up Rainforest
Lo Rainforest
Mix Dry Forest
Talasiga
Mangrove For
Brig
htne
ss V
alue
Create signatures: Spatial Analyst > Multivariate > Create Signature Graph in Excel
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Use of Ancillary Data: Data Fusion
Elevation: 100 m resolution
Ave July Precip: 100 m resolution
Resample to 500 m: Data management> raster > resampleNormalized to same range as imagery: Spatial analyst > map algebra > raster calculatorCreate “Layer stack”: Data management > raster > raster processing > composite bands
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Spectral Signatures for “natural” Vegetated Classes (w/ Ancillary Data)
Create signatures: Spatial Analyst > Multivariate > Create Signature Graph in Excel
0
1000
2000
3000
4000
5000
6000
7000
8000 Cloud Forest
Up Rainforest
Lo Rainforest
Mix Dry Forest
Talasiga
Mangrove For
Nor
mal
ized
Bri
ghtn
ess
Valu
e
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Accuracy Assess. W/Ancillary data
Reference Data 10 20 30 40 50 60 81 82 91 92 100
Mapped Data
Cloud For. 10 18 15 6 39 46% Up. Rainfor 20 4 12 10 1 1 29 41% Lo. Rainfor 30 1 2 108 3 3 3 2 122 89% Mix Dry For. 40 5 14 14 4 8 41 34% Talasiga 50 3 2 15 1 5 25 60% Mangrove 60 1 1 1 15 1 1 23 65% Developed 81 10 3 13 77% Agriculture 82 1 3 3 2 39 1 49 80% Water 91 14 15 93% Coral Reef 92 3 23 26 89% Cloud Cover 100 1 1 5 7 71%
23 31 136 21 34 26 12 57 19 25 5 389 78% 39% 79% 67% 44% 50% 83% 68% 68% 92% 100%
Overall Accuracy: 70.18% Kappa Coefficient: 64.39
Accuracy Assessment: Kappa Stats tool (Python script) from http://arcscripts.esri.com
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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
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Thank You!