Notes
Outline
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What is Remote Sensing?
There are many different ways to define remote sensing. Perhaps one of the better explains remote sensing as
“ the science (and art) of acquiring information about the Earth's surface without actually being in contact with it. This is done by sensing and recording reflected or emitted energy and processing, analyzing, and applying that information to help understand a problem".
A Few Examples
Environmental Assessment
Geologic Mapping
Land Cover Mapping and Change Detection
What Makes Remote Sensing Work?
The Elements of Remote Sensing
Elements (continued)
Electromagnetic Radiation
Wavelength and Frequency
What that means……..
The Electromagnetic Spectrum
The Visible Spectrum
The Infrared Region
The Microwave Region
Effects of the Atmosphere
Rayleigh Scattering
Mie Scattering
Absorption
Atmospheric Windows
Where are the Windows?
Sensing in the Window
Radiation-Target Interaction
Reflectance Properties
Illustrative Examples
Target Properties
Target Properties
Sensing Systems
Passive  Sensors
Active Sensors
Orbital Characteristics
Geostationary Orbits
Near-Polar Orbits
Near-Polar Characteristics
Related Concepts
Earth Observation Platforms
Land Observation Satellites
Overview of Select Satellites
Land Observation Satellites
Land Observation Satellites/Sensors : LANDSAT
Sensor Characteristics
Multi-Spectral Scanning
Across-track Scanning
Along-track scanners
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Image Composition
Image Format
Spectral Signatures
Resolution – Image (Spatial)
Resolution - Spectral
Image Processing Fundamentals
Preprocessing
Common Problems - Noise
Noise Examples
Geometric Correction
Correction/Resampling Algorithms
Resampling Methods
Image Enhancement
Spatial Filtering
Resolution- Radiometric
Resolution - Temporal
Temporal Considerations
Image Processing
Image Transformations
Image Ratio-ing
Principal Components Analysis
Image Characteristics
Image Classification
Making Data Useful
Data to Information
Supervised Classification
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Looking for Numerical Patterns
Forming Groups
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Unsupervised Classification
Soft Image Classification
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Neural Network Models
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Ground Truth Testing
Sensing in the Temporal Domain
The Need for Monitoring
Why Monitoring ?
The Need for Useful Indicators
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Vegetation Indicies
Much effort has gone into the development of vegetation indices – defined as dimensionless radiometric measures that function as indicators of relative abundance and activity of green vegetation.
Although there are more than 20 vegetation indices in use, a “good” index has four important properties
What The Tell Us?
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Index Qualities
Maximize sensitivity to plant biophysical parameters
Normalize or model external effects
Normalize internal effects such as canopy background
Be coupled to some specific measurable biophysical parameters such as biomass
Landscape Metrics
Although vegetation indices are useful for monitoring the condition and health of vegetated pixels, they do not provide much information about the larger site and surrounding characteristics.
Landscape ecology principles have been developed that increasingly incorporate remote sensor data and this has resulted in the introduction of numerous landscape metrics
Selected Landscape Indicators
Selected Structure Metrics
Dominance
Identifies the extent to which the landscape is dominated by a single land cover type.
The metric 0 < D<1 is computed as:
D = 1 - [ ∑ (-Pk * lnPk) / ln (n) ]
Where 0<Pk <1 is the proportion of land cover type (k) and (n) is the total number of land cover types present
Contagion
Fractal Dimension
Indicates the extent of human reshaping of the landscape. This dimension is found by regressing the log of patch perimeter against the log of the patch area for each patch in the landscape
Monitoring Change
Successful remote sensing change detection requires careful attention to both the remote sensor system and environmental characteristics
Data should be collected with the same spatial resolution, look angle and spectral/radiometric resolution
Next, certain environmental variables need to be understood…atmospheric conditions, soil moisture conditions, phenological cycle characteristics
Change Detection
Change Sequence
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Digital Change Detection Techniques
Post Classification Comparision
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Direct multi-Date Classification
Image Differencing
Image Regression
Image Ratioing
Vegetation Index Differencing
Other Methods
Advances  in
Remote Sensing
Hyperspectral Remote Sensing
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Image Cube
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The OhioView Project
Getting the Data – The OhioView Archive
Browsing the Archive
Downloading the Image
Saving The Image to Disk