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
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
NASA Earth Observatory Image of the Day http://earthobservatory.nasa.gov
Fiji IslandsMODIS Image Jul 21, 2011
IOTD Aug 13, 2011
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
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
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
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
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
Vis. Interp. using Google Earth
Talasiga (Grassland)
Upland Rainforest
Agriculture
Division of labour: 35 Mapping Zones
Mapping zones created: Visually merged groups 2-3 Tikinas in ArcMap
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
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
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
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
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
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
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
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
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
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
Thank You!