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34 Cards in this Set
- Front
- Back
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Electromagnetic radiation |
- the information recorded by remote sensing - visible light is just on form |
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Wave and particle models I - EMR can be described in terms of a wave model,where the energy can be defined as having a specificrange of wavelengths or frequencies |
Wave and particle models I - EMR can also be described in terms of a quantummodel comprised of photons, or particles of light |
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Wave and particle models I These are not independent: - Wavelength (λ) is determined by the number oftimes the charged particle is accelerated - Frequency (v) depends on the number ofaccelerations per second |
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Wave and particle models • The wave and quantum models can be related to each other by rearranging the formulae: 𝑸 = h*c / λ |
Wave and particle models • The energy of a quantum is inverselyproportional to is wavelengthThe longer the wavelength, the lower theenergy |
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Electromagnetic radiation Three main ways it is emitted: - as a broad spectrum - as a spectral band - as an individual wavelength |
Electromagnetic radiation Sun is most obvious source |
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Electromagnetic radiation All bodies with a temperature above absolute zero will emit energy. |
Electromagnetic radiation Terrestrial objects are also sources of EMR |
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Electromagnetic radiation Each radiation source will emit acharacteristic array of waves: - this is called the spectral signature ofthe body |
Electromagnetic radiation How much energy is emitted by an object is determined, among other factors, by its surface temperature: Stefan-Boltzmann law |
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Stefan-Boltzmann law: |
Energy emitted and temperature |
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Wien’s displacement law I |
Wavelength |
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Energy interactions • All radiation is effected by, and interacts with, the atmosphere and surface features • In the atmosphere:- scattering, absorption •With surface features:- absorption, transmission and reflectance |
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Atmosphere: Scattering |
• Describes the diffusion of radiation by particles in theatmosphere, varies dependent on conditions |
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1. Rayleigh scatter |
(e.g. air particles; particle diameter < radiation λ) - blue skies and sunrise/sunsets |
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2. Mie scatter |
(e.g. dust; particle diameter ~= radiation λ) |
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3. Non-selective scatter |
(e.g. water droplets; particle diameter > radiation λ) |
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Surface features: |
• Proportions of energy transmitted, reflectedand absorbed varies for different features |
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• This allows different features to be identified on imagery |
• This also varies at different wavelengths - Two different features indistinguishable in onewavelength band, but very different in another |
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Reflectance We are mainly concerned with the reflectance properties of Earthsurface materials and vegetation, termed spectral reflectance |
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Digital image classification: • Applies automated methods of featureidentification from remote sensing data |
Digital image classification: • Assigns pixels to a category or class |
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Digital image classification: • Each class represents a characteristicmaterial/landcover |
Digital image classification: • Produce thematic maps |
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Digital image classification: • Based on the spectral pattern of each pixel - the set of radiance measurements foreach wavelength band recorded withinmultispectral data |
Digital image classification: • Different features produce differentcombinations of these, allowing them to bedifferentiated |
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In general, image classification is… |
The classification of individual pixelsbased on their spectral properties |
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The grouping of pixels with similar spectral properties into ‘classes’ |
The classification of each ‘class’ as aland cover type based on their spectralcharacteristics |
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NDVI Normalised Difference VegetationIndex |
Certain band combinations are sensitiveindicators of the presence and condition ofvegetation |
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Specifically, these tend to be red (visiblelight) and near infrafed (NIR) |
• When these bands are arranged in certainmathematical combinations, they can beused to assess vegetation presence andcondition based on the spectral contrast |
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The normalised difference vegetation indexis one such combination: t |
NDVI= (NIR-Red)/(NIR+Red) = (Band 4 - Band 3)/(Band + Band 3) |
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NDVI analysis: |
creates a new image based on the reflectance characteristics of vegetation in the original image |
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In your examples, it creates a BLACK andWHITE image, where: - stronger, white colours are dense vegetationcover |
- darker areas are patchy vegetation or bareground |
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Calculations of NDVI for a given pixelalways result in a number that rangesfrom minus one (-1) to plus one (+1): |
• No green leaves (e.g. no vegetation) gives a value close to zero. |
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• Close to +1 (0.8 - 0.9) indicates the highest possible density ofgreen leaves (white). |
• Negative values of NDVI (values approaching -1) correspond towater |
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• values close to zero (-0.1 to 0.1) generally correspondto barren areas of rock, sand, or snow (black or grey). |
• positive values represent shrub and grassland(approximately 0.2 to 0.4), while high values indicate temperateand tropical rainforests (values approaching 1). |
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Change detection: |
• Determining change from multi-temporaldatasets, e.g. landcover - short-term phenomena, e.g. flooding - long-term phenomena, e.g. desertification |
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• Typically applied post-image classification |
• Requires accurate spatial registration of twoimages – within ¼ or ½ a pixel |
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Image difference |
works out difference by subtracting one from the other detects brightness in pixels from images |
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Highlight change |
Assess amount of change between images classifies and colours each pixel |
