remote sensing and internet data sources unit 3: module 12, lecture 1 – satellites and aerial...
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Remote Sensing and Internet Data Sources
Unit 3: Module 12, Lecture 1 – Satellites and Aerial Photography
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Sources of spatial and environmental data
Remotely sensed data (raster data) Airphoto Satellite
Digital data repositories - (Module 14) On-line Electronic media
GPS data (point data) - (Module 16) Input of hard-copy data – (Module 16)
Digitizing (vector data) Scanning (raster data)
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Sources of data: remote imagery
Satellite imagery Digital imagery Numerous satellites with
different levels of resolution SeaWIFS SPOT LANDSAT AVHRR MODIS
MODIS image of Hurricane Isobel off US East Coast, September 17, 2003
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SeaWIFS image of California FiresOct 26, 2003
SeaWIFS 1 km res Daily NASA
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QuickBird image of Grand Prix Fire, CAOctober 27, 2003
60 cm resolution natural color image
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QuickBird image of Grand Prix Fire, CAOctober 27, 2003 – detail view
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GOES Weather Satellite
Geostationary orbit 36,000 km above earth
East and West satellites provide complete coverage
High frequency (up to 15 min intervals) Visible Infrared Water vapor
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Resolution in Satellite imagery
Satellite sensors vary in the different types of resolution Spatial resolution = pixel size Spectral resolution = # of bands, band width Radiometric resolution = data intensity in band Temporal resolution = frequency of sampling
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Pixel resolution
1 km AVHRR classification of forest land Relatively coarse Broad picture of
landscape Regional
assessment
30 m LANDSAT classification of forest and land use Much finer detail Local assessment
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Spectral Resolution: Number of bands
“Bands” are regions of the electromagnetic spectrum sampled by the sensor Visible light (RGB) Near and far infrared Other frequencies
More bands = more information to classify land features Multispectral Hyperspectral – very
fine divisions of the spectrum
Landsat MSS 4 bands
Landsat TM 7 bands
Quickbird 4 bands
Hyperspectral 30-256+ bands
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Landsat Thematic Mapper bands
Band Spectral range Use
1 Blue Bathymetric mapping/deciduous-coniferous veg
2 Green Peak vegetation – plant vigor
3 Red Vegetation slopes
4 Near IR Biomass content/ shorelines
5 Mid IR Moisture content of soil and vegetation
6 Thermal IR Thermal mapping/ soil moisture
7 Short wave IR Hydrothermally altered rocks
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Image classification
Remote sensing satellites and aircraft-borne sensors simply record information on spectral reflectance
The science of “Image Classification” makes these volumes of information useful
Goal – develop a relationship between the “spectral signature” and a classification of the landscape Coarse: forest, ag, urban Fine: aspen forest, corn, high-density residential
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Differences in “spectral signatures” are used to classify land features
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Common classified satellite images
Classification Satellite/sensor
Pixel resolution
USFS Forest Land cover AVHRR 1 km
Coastwatch Sea Surface Temp
MODIS/Aqua
1 km
National Land Cover Dataset (NLCD)
Landsat 30 m
NOAA C-CAP Land use change
Landsat 30 m
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Sources of data: remote imagery
Aerial photography and imagery
Film technology Oblique Vertical
Black and White Color Infrared - common
in agriculture and forestry applications
Usually interpreted as map polygons (vector format)
B & W photo
Color IR
Photointerpreted
Oblique photo
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Sources of data: remote imagery
Aerial photography and imagery
Digital imagery Images from non-
photographic sensors
Usually classified by computer algorithms
Multispectral or hyperspectral available
AISA
hyperspectral
sensor
Hyperspectral crop circles
courtesy CALMIT labs, NE
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Hyperspectral data
A large number of spectral bands (30-100s)
Capable of discriminating very fine differences in color (reflectance)
Used to map aquatic veg, Chlorophyll content, turbidity, many other attributes
Hyperspectral image of Kingsbury Creek – image acquired by Nebraska Space Grant for WOW
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Common aerial photography: DOQs
USGS Digital Orthophoto Quad
Natl’ Aerial Photography Program (NAPP) Cloud-free 20000 ft altitude B&W or CIR Each photo 5.5 x 5.5 mi Began in 1987 5-7 yr photography cycle
Big files! Med resolution – 40 Mb High res. – 117 Mb-1.3 Gb
Color-infrared NAPP photo San Diego, CA
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Common aerial photography: FSA DOQs
Farm Services Administration (FSA) Color Orthophotos 1 m resolution natural
color imagery Summer – leaf on Available in quarter
quads Available as unclassified
imagery, but very good resolution
FSA photo – 1:7,000 scakeHouston Co, MN
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Sources of data: scanned imagery
Scanning and rectification (raster data) Hard copies of airphotos or other images can be
scanned at high resolution (600-800 dpi) These typically need to be georectified to use
with other spatial layers (correct for camera lens abberations, plane tilt, etc)Control points (known locations on ground) are used to georectify image
ImageWarp or other software used to “stretch” image to fit control points
Image can then be used as a backdrop for other spatial data layers, or for classification
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Summary
Classified data from satellites are useful for land use planning, but the efforts involved in classification mean these are updated relatively infrequently (years)
Real-time satellite data (AVHRR, SeaWIFS, GOES) are typically unclassified, but can be interpreted visually with relatively little effort
Aerial photographs provide high resolution coverage (meter to submeter), and many on-line sources of recent photography exist