national mapping division eros data center u. s. geological survey u.s. geological survey earth...
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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
National Mapping Division EROS Data Center U. S. Geological Survey
USGS EROS DATA CENTERLand Remote Sensing from Space:
Acquisition to Applications
Earth Observation Satellites
USGS National Archive Challenge
Data 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!
National Mapping Division EROS Data Center U. S. Geological Survey
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
National Mapping Division EROS Data Center U. S. Geological Survey
USGS EDC Data Holdings
Landsat Satellite Images 1972-present > 18 million frames Global coverage 15-80 meter
Landsat 5 MSS
National Mapping Division EROS Data Center U. S. Geological Survey
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
National Mapping Division EROS Data Center U. S. Geological Survey
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)
National Mapping Division EROS Data Center U. S. Geological Survey
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
National Mapping Division EROS Data Center U. S. Geological Survey
#
##
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)
National Mapping Division EROS Data Center U. S. Geological Survey
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)
National Mapping Division EROS Data Center U. S. Geological Survey
Crop Type Classification - Sheldon, NE
National Mapping Division EROS Data Center U. S. Geological Survey
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
National Mapping Division EROS Data Center U. S. Geological Survey
Traditional classification methods are not appropriate
Only want CORN
BIG Data Sets
• Large geographical regions
• File size
~500 Mb/image
• Multi-year
30 years
National Mapping Division EROS Data Center U. S. Geological Survey
Traditional classification methods are not appropriate (cont.)
Usually need ground reference data – expensive, difficult to get for historical data
Time-consuming process
National Mapping Division EROS Data Center U. S. Geological Survey
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
40
60
80
100
33 32 31 30 29 28
Landsat Path NumberP
ropo
rtio
n (%
)
corn soybeans sorghum
dry beans sugarbeets
National Mapping Division EROS Data Center U. S. Geological Survey
Crop characteristics Large, homogeneous fields
Spectral characteristics differ from other major crops (soybeans, alfalfa, winter wheat, etc.)
Spectrally similar to deciduous trees, riparian area
National Mapping Division EROS Data Center U. S. Geological Survey
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”
National Mapping Division EROS Data Center U. S. Geological Survey
Method cont.
Use existing land cover maps (NLCD) to eliminate non-row crop classes (spring grains, hay/pasture, trees, urban, wetland, etc.)
National Mapping Division EROS Data Center U. S. Geological Survey
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
10
00
's o
f H
ec
tare
s
Hall
National Mapping Division EROS Data Center U. S. Geological Survey
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
National Mapping Division EROS Data Center U. S. Geological Survey
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
National Mapping Division EROS Data Center U. S. Geological Survey
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)
National Mapping Division EROS Data Center U. S. Geological Survey
Thank You
Susan Maxwell