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

Transcript of Remote Sensing and Internet Data Sources Unit 3: Module 12, Lecture 1 – Satellites and Aerial...

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