Presentation [ppt]

National Mapping Division EROS Data Center U. S. Geological Survey

U.S. Geological SurveyEarth Resources Operation Systems

(EROS) Data Center

World Data Center for Remotely Sensed Land Data


USGS EROS DATA CENTERLand Remote Sensing from Space:

Acquisition to ApplicationsEarth Observation

SatellitesUSGS National

Archive ChallengeData

Applications

• Declassified Systems

• Landsat 1-5,7

• NOAA - POES

• Shuttle Radar

• TERRA (1999)

• NASA-EOS (1999)

• High Resolution Systems

• Preserve

• Provide Access

• Process

• Reproduce

• Distribute

• Hold in Trust

• Land Cover

• Environmental Monitoring

• Emergency Response

• Fire Danger Rating

• DOI Land Management

• Natural Hazards

• Coastal Zones

Expanding to over 18 million images of the earth!


USGS EDC Data Holdings Aerial Photographs

1940-present U.S. coverage > 9 million frames Scale: 1-2 meter

Natl. Aerial Photography Program (NAPP), Dallas/Fort Worth Airport

http://edcwww.cr.usgs.gov/napp/dfw2.html


USGS EDC Data Holdings Landsat Satellite Images

1972-present > 18 million frames Global coverage 15-80 meter

Landsat 5 MSS


USGS EDC Data Holdings AVHRR Satellite Images

1987-present Global coverage 1 km resolution

AVHRR Time Series

National Mapping Division EROS Data Center U. S. Geological SurveyFort Collins, Colorado - Landsat 7 - July 26, 1999

Using Landsat satellite imagery to estimate agricultural chemical exposure in an epidemiological

study

Susan Maxwell, PhD (USGS EROS Data Center)Interface 2002, Montreal, Canada

Collaborators: Dr. Jay Nuckols, EHASL, Colorado State University Dr. Mary Ward, National Cancer Institute Eric Smith, EHASL, Colorado State University Leanne Small, EHASL, Colorado State University


Agriculture ChemicalsFertilizersPesticides

Spray drift

Drinking water

Dust

Why use satellite imagery? Traditional methods of collecting chemical exposure data don’t

work well (environmental/biological sampling, questionnaires)


Why use satellite imagery?

Cancers generally take several years to develop, therefore need to reconstruct historical exposure

Our approach: use Landsat imagery to create historical land use/crop type maps – integrate with other data (chemical use, soils, wind, etc.) to estimate exposure


#

##

0 1 Mile

0.22 - 0.240.18 - 0.220.14 - 0.180.1 - 0.140.06 - 0.10.04 - 0.060.02 - 0.040.01 - 0.020.005 - 0.010.003 - 0.0050.001 - 0.003No Data

Areas Cultivated with SorghumU.S. Census Bureau Place

# Residence with 500 Meter Buffer

N

Metric Development … Transport Modeling

(Ward et al. Environmental Health Perspectives, 2000)


Why Landsat ?

Longest running satellite sensor (1972-current)

Successful crop type mapping applications (AGRISTARS, etc.)

Appropriate spectral bands (visible, near infrared, middle infrared)

Appropriate spatial resolution (30-80 meter)

Inexpensive (compared to higher resolution data sets)


Crop Type Classification - Sheldon, NE


Case Study – Mapping Corn Chemicals used on corn

(nitrogen, atrazine) have been associated with several cancers and birth defects

From: USGS 1225, The quality of our nation’s waters

Ground-water contamination risk


Traditional classification methods are not appropriate

Only want CORN

BIG Data Sets

• Large geographical regions

• File size

~500 Mb/image

• Multi-year

30 years


Traditional classification methods are not appropriate (cont.)

Usually need ground reference data – expensive, difficult to get for historical data

Time-consuming process


Crop characteristics Corn dominates

0.0

0.2

0.4

0.6

0.8

33 32 31 30 29 28

Landsat Path Number

Hec

tare

s (m

illio

n)

corn soybeans sorghum

dry beans sugarbeets

0

20

4060

80

100

33 32 31 30 29 28

Landsat Path NumberP

ropo

rtio

n (%

)

corn soybeans sorghum

dry beans sugarbeets


Crop characteristics Large, homogeneous fields

Spectral characteristics differ from other major crops (soybeans, alfalfa, winter wheat, etc.)

Spectrally similar to deciduous trees, riparian area


Case Study – Mapping Corn

Initial method – software was developed to ….

Use existing land cover maps (NLCD) to eliminate non-row crop classes (spring grains, hay/pasture, trees, urban, wetland, etc.)

Use existing USDA acreage estimates to target specific geographic region (i.e., county) to collect training statistics

Use maximum likelihood algorithm to classify the entire image

Use the Mahalanobis distance image in combination with USDA acreage estimates to identify cut-off for “highly likely corn”, “likely corn” and “unlikely corn”


Method cont.

Use existing land cover maps (NLCD) to eliminate non-row crop classes (spring grains, hay/pasture, trees, urban, wetland, etc.)


Method cont. Use USDA acreage estimates to target specific geographic region

(i.e., county) to collect training signature

0

20

40

60

80

Corn Sorghum Soybeans All Hay WinterWheat

1000

's o

f Hec

tare

s

Hall


Method cont. Use the Mahalanobis distance image in combination with

USDA acreage estimates to identify cut-off for “highly likely corn”, “likely corn” and “unlikely corn”

Highly Likely Corn

Likely Corn

Mahalanobis distance image


MahalanobisDistanceValue

LandArea

(Hectares)

Cumulative Total(Hectares)

CumulativeTotal

(% of NASS)

ClassificationCode

1 1206.4 1206.6 2.1 12 4413.2 5619.6 9.6 13 1364.4 6984.0 11.9 1... ... … ... ...55 581.0 44107.2 75.2 156 517.7 44624.9 76.0 257 741.2 45366.1 77.3 258 141.8 45507.9 77.5 2... ... ... ... ...131 1066.3 59082.1 100.7 2132 417.2 59499.3 3... ... ... ...

1787 0.4 82893.2 3

Mahalanobis Distance Threshold


Results >80% average accuracy

Higher errors occur when …

• Spectrally similar cover types in same area (millet, sorghum)

• Image date is too early in growing season

• Non-parametric signature (clouds/haze, irrigated/non-irrigated corn)


Thank You

Susan [email protected]

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