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!