Passive vs. Active Remote SensingSolar RadiationSolar Electromagnetic RadiationSatellite Imagery - 4 ResolutionsTemporal ResolutionSpectral ResolutionRadiometric ResolutionImage Pre-ProcessingImage EnhancementsSpatial EnhancementsSpectral EnhancementsClassificationAirborne Remote SensingAir Photo ScaleOrthophotographsLandsat Platforms and their SensorsLandsat Orbits‘Wiskbroom’ SensorsSPOT Characteristics‘Pushbroom’ SensorsIkonosQuickbirdGeostationary OrbitAVHRR CharacteristicsAVHRR BandsMODIS CharacteristicsImprovement Over Old Global DEMsNexrad Doppler Weather RADARCONUS Hourly Nexrad RainfallProbability – Some DefinitionsProbability – An Example, Part IIIProbability Mass FunctionsContinuous Random VariablesProbability Density FunctionsProbability Density FunctionsProbability RulesMutually Exclusive & Collectively ExhaustiveProbability RulesSampling ConceptsRandom Spatial SamplingData PortrayalScales of MeasurementNominal DataNominal DataNominal DataBuilding a HistogramFrequencies & DistributionsSimple Descriptive StatisticsSimple Descriptive Summary MeasuresSimple Descriptive StatisticsMeasures of Central TendencyMeasures of Central Tendency - MeanMeasures of Dispersion - RangeMeasures of Dispersion – Variance, Standard Deviation, Z-scoresMeasures of Dispersion – Variance, Standard Deviation, Z-scoresMeasures of Dispersion – Variance, Standard Deviation, Z-scoresDiscrete Probability DistributionsThe Uniform DistributionThe Binomial DistributionThe Poisson DistributionThe Poisson DistributionThe Normal DistributionLST DistributionThe Normal DistributionLST Z-ScoresDavid Tenenbaum – GEOG 070 – UNC-CH Spring 2005Passive vs. Active Remote SensingPassive sensors receive solar energy reflected by the Earth’s surface (2), along with energy emitted by the atmosphere (1), surface (3) and sub-surface (4)Active sensors receive energy reflected from the Earth’s surface that originally came from an emitter other than the Sunhttp://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/chapter3/chapter3_1_e.htmlDavid Tenenbaum – GEOG 070 – UNC-CH Spring 2005Electromagnetic radiation energy: Wave-particle dualityparticleWavelength (λ)• EMR energy moves at the speed of light (c): c = f λ• f = frequency: The number of waves passing through a point within a unit time (usually expressed per second)• Energy carried by a photon: ε = h f [h=Planck constant (6.626×10-34Js)]• The shorter the wavelength, the higher the frequency, and the more energy a photon carries. Therefore, short wave ultraviolet solar radiation is very destructive (sunburns)Solar RadiationDavid Tenenbaum – GEOG 070 – UNC-CH Spring 2005Atmospheric windowsSolar Electromagnetic Radiation•The sun emits EMR across a broad spectrum of wavelengths:But the atmosphere blocks much of the energy before it reaches the surfaceDavid Tenenbaum – GEOG 070 – UNC-CH Spring 2005Satellite Imagery - 4 Resolutions• Satellite imagery can be described by four resolutions:– Spatial resolution: area on ground represented by each pixel, e.g.• Landsat Thematic Mapper - 30m• Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolutions Imaging Spectrometer (MODIS) - 1km• SPOT - 10m panchromatic /20m multispectral• IKONOS - 1m panchromatic /4m multispectral– Temporal resolution: how often a satellite obtains imagery of a particular area– Spectral resolution: specific wavelength intervals in the electromagnetic spectrum captured by each sensor (bands)– Radiometric Resolution: number of possible data values reportable by each sensor (how many bits)David Tenenbaum – GEOG 070 – UNC-CH Spring 2005IKONOS panchromatic image of Sydney Olympic Park - 1mSpatial ResolutionDavid Tenenbaum – GEOG 070 – UNC-CH Spring 2005Temporal Resolution• Number of days between overhead passes - satellite orbit– Landsat - 16 days– AVHRR & MODIS - daily– IKONOS - 1 to 3 daysDavid Tenenbaum – GEOG 070 – UNC-CH Spring 2005Spectral Resolution• Number, spacing and width of sampled wavelength bands (Landsat: 7 bands, AVIRIS: 224 bands!)• Multispectral vs. Panchromatic• Higher resolution results in more precision in the representation of spectral signaturesDavid Tenenbaum – GEOG 070 – UNC-CH Spring 2005Radiometric Resolution• Number of possible data values reported by the sensor, which determines how many levels of brightness it can distinguish• Range is expressed as 2npower– 8-bit radiometric resolution has 28values, or 256 values - range is 0-255 (e.g. Landsat TM data)– 16-bit resolution has 216 values, or 65,536 values- range is 0-65535 (e.g. MODIS data)• The value in each pixel is called the– Digital Number (DN)– Brightness Value (BV)David Tenenbaum – GEOG 070 – UNC-CH Spring 2005Image Pre-Processing• Radiometric Corrections– changing the image data BVs to correct for errors or distortions from a variety of sources:• atmospheric effects• sensor errors• Geometric Corrections– changing the geometric/spatial properties of the image data so that we can accurately project the image, a.k.a.• image rectification• rubber sheetingDavid Tenenbaum – GEOG 070 – UNC-CH Spring 2005Image Enhancements• Image enhancements are designed to improve the usefulness of image data for various applications:– Contrast Enhancement - maximizes the performance of the image for visual display– Spatial Enhancements - increases or decreases the level of spatial detail in the image– Spectral Enhancements - makes use of the spectral characteristics of different physical features to highlight specific featuresDavid Tenenbaum – GEOG 070 – UNC-CH Spring 2005Spatial Enhancements• Filters - used to emphasize or de-emphasize spatial information– Low-pass filter -emphasize large area changes and de-emphasize local detail– High-pass filter -emphasize local detail and de-emphasize large area changesDavid Tenenbaum – GEOG 070 – UNC-CH Spring 2005Spectral Enhancements• Often involve taking ratios or other mathematical combinations of multiple input bands to produce a derived index of some sort, e.g.:• Normalized Difference Vegetation Index (NDVI)– Designed to contrast heavily-vegetated areas with areas containing little vegetation, by taking advantage of vegetation’s strong absorption of red and reflection of near infrared:– NDVI = (NIR-R) / (NIR + R)• Other examples: Surface temperature (Ts) from IR bands, TVDI from NDVI and TsDavid