Chapter 4. Remote Sensing Information Process
Chapter 4. Remote Sensing Information Process
Remote sensing can provide fundamental Remote sensing can provide fundamental biophysical informationbiophysical information, including , including x,yx,y location,location, z z elevation or depth, biomass, elevation or depth, biomass, temperature, and moisture content. temperature, and moisture content.
IntroductionIntroduction
Remote sensing–derived information is now Remote sensing–derived information is now critical to the successful modeling of critical to the successful modeling of numerous numerous naturalnatural (e.g., drought) and (e.g., drought) and culturalcultural (e.g., land-use conversion at the (e.g., land-use conversion at the urban fringe; population estimation) urban fringe; population estimation) processes.processes.
The remote sensing The remote sensing data-collectiondata-collection and and analysisanalysis proceduresprocedures used for applications in used for applications in resources and environment are often resources and environment are often implemented in a systematic fashion implemented in a systematic fashion referred to as the referred to as the remote sensing processremote sensing process. .
Information process in remote sensingInformation process in remote sensing
Radiant energy is collected by remote sensors and processed using analog and/or digital image processing techniques to extract information.
Such information is usually valuable only when used in conjunction with other information in a well-conceived application.
Data collectionData collection
In situ measurement Collateral data Remote sensing
In situ measurementField
• GPS• Biomass• spectroradiometer
Laboratory• Reflectance• Leaf area index (LAI)
Collateral dataDEMsSoilPopulation density
Remote sensingPassive
• Frame photography• Scanners (multispectral, hyperspectral)• Linear and area arrays (multispectral,
hyperspectral)Active
• Microwave (RADAR)• Laser (LIDAR)
The amount of electromagnetic radiance, The amount of electromagnetic radiance, LL (watts m (watts m-2-2 sr sr-1-1; watts ; watts per meter squared per steradian) recorded within the IFOV of per meter squared per steradian) recorded within the IFOV of an optical remote sensing system (e.g., a picture element in a an optical remote sensing system (e.g., a picture element in a digital image) is a function of:digital image) is a function of:
where, where,
= wavelength (spectral response measured in various bands = wavelength (spectral response measured in various bands or at specific frequencies). or at specific frequencies).
The amount of electromagnetic radiance, The amount of electromagnetic radiance, LL (watts m (watts m-2-2 sr sr-1-1; watts ; watts per meter squared per steradian) recorded within the IFOV of per meter squared per steradian) recorded within the IFOV of an optical remote sensing system (e.g., a picture element in a an optical remote sensing system (e.g., a picture element in a digital image) is a function of:digital image) is a function of:
where, where,
= wavelength (spectral response measured in various bands = wavelength (spectral response measured in various bands or at specific frequencies). or at specific frequencies).
,,,,, ,, PtsfL zyx
Remote Sensing Data CollectionRemote Sensing Data CollectionRemote Sensing Data CollectionRemote Sensing Data Collection
sx,y,z = x, y, z location of the picture element and its size (x, y)
t = temporal information, i.e., when and how often the information was acquired
= set of angles that describe the geometric relationships among the radiation source (e.g., the Sun), the terrain target of interest (e.g., a corn field), and the remote sensing system
P = polarization of back-scattered energy recorded by the sensor
= radiometric resolution (precision) at which the data (e.g., reflected, emitted, or back-scattered radiation) are recorded by the remote sensing system.
sx,y,z = x, y, z location of the picture element and its size (x, y)
t = temporal information, i.e., when and how often the information was acquired
= set of angles that describe the geometric relationships among the radiation source (e.g., the Sun), the terrain target of interest (e.g., a corn field), and the remote sensing system
P = polarization of back-scattered energy recorded by the sensor
= radiometric resolution (precision) at which the data (e.g., reflected, emitted, or back-scattered radiation) are recorded by the remote sensing system.
Remote Sensor ResolutionRemote Sensor ResolutionRemote Sensor ResolutionRemote Sensor Resolution
• • SpatialSpatial - the size of the field-of-view, e.g. 10 x - the size of the field-of-view, e.g. 10 x 10 m.10 m.
• • SpectralSpectral - the number and size of spectral - the number and size of spectral regions the sensor records data in, e.g. blue, green, regions the sensor records data in, e.g. blue, green, red, near-infrared thermal infrared, microwave red, near-infrared thermal infrared, microwave (radar).(radar).
• • TemporalTemporal - how often the sensor acquires data, - how often the sensor acquires data, e.g. every 30 days.e.g. every 30 days. • • RadiometricRadiometric - the sensitivity of detectors to small - the sensitivity of detectors to small differences in electromagnetic energy.differences in electromagnetic energy.
• • SpatialSpatial - the size of the field-of-view, e.g. 10 x - the size of the field-of-view, e.g. 10 x 10 m.10 m.
• • SpectralSpectral - the number and size of spectral - the number and size of spectral regions the sensor records data in, e.g. blue, green, regions the sensor records data in, e.g. blue, green, red, near-infrared thermal infrared, microwave red, near-infrared thermal infrared, microwave (radar).(radar).
• • TemporalTemporal - how often the sensor acquires data, - how often the sensor acquires data, e.g. every 30 days.e.g. every 30 days. • • RadiometricRadiometric - the sensitivity of detectors to small - the sensitivity of detectors to small differences in electromagnetic energy.differences in electromagnetic energy.
10 m10 m
BB GG RR NIRNIR
JanJan1515
FebFeb 1515
10 m10 m
Phenological Cycle of Hard Red Winter Wheat in the Great PlainsPhenological Cycle of Hard Red Winter Wheat in the Great PlainsPhenological Cycle of Hard Red Winter Wheat in the Great PlainsPhenological Cycle of Hard Red Winter Wheat in the Great Plains
JULJUNMAY AUGAPRMARFEBJANDECNOVOCTSEP
crop establishment
10 14
greening up heading mature
14 14 21 13 425 7 9 5 21 29 34 28 108 days 50
26
Sow Tillering
Emergence
Dormancy Growth resumes
Heading Boot
Dead ripe
Hard doughSoft dough
Harvest
Jointing
Maximum Coverage
Winter Wheat Phenology
snow cover
JULJUNMAY AUGAPRMARFEBJANDECNOVOCTSEP
crop establishment
10 14
greening up heading mature
14 14 21 13 425 7 9 5 21 29 34 28 108 days 50
26
Sow Tillering
Emergence
Dormancy Growth resumes
Heading Boot
Dead ripe
Hard doughSoft dough
Harvest
Jointing
Maximum Coverage
Winter Wheat Phenology
snow cover
Phenological Cycles Phenological Cycles of San Joaquin and of San Joaquin and Imperial Valley, Imperial Valley, California Crops and California Crops and Landsat MSS Landsat MSS Images of One Field Images of One Field During A Growing During A Growing SeasonSeason
Phenological Cycles Phenological Cycles of San Joaquin and of San Joaquin and Imperial Valley, Imperial Valley, California Crops and California Crops and Landsat MSS Landsat MSS Images of One Field Images of One Field During A Growing During A Growing SeasonSeason
Data-to-information conversionData-to-information conversion
Analog (visual) image processing
Using the elements of photo-interpretation Digital image processing
Digital image processing Pre-processing Enhancement Digital photogrammetry Thematic mapping Hyperspectral analysis Change detection Modeling Scientific geovisualization
Pre-processing Radiometric correction Geometric correction
Thematic mappingParametric
• Maximum likelihood classifierNon-parametric
• Artificial neural networksNon-metric
• Expert systems• Decision-tree classifiers• Machine learning
ModelingSpatial modeling using GIS dataScene modeling based on physics of
energy/matter interactions
Information presentationInformation presentation
Analog and digital outputAnalog and digital output GraphsGraphs StatisticsStatistics MetadataMetadata Accuracy assessmentAccuracy assessment
Image/map ouputImage/map ouput OrthoimagesOrthoimages Thematic mapsThematic maps GIS databasesGIS databases AnimationsAnimations SimulationsSimulations
Landsat Landsat Thematic Thematic
Mapper Imagery Mapper Imagery of the Imperial of the Imperial
Valley, Valley, California California
Obtained on Obtained on December 10, December 10,
19821982
Landsat Landsat Thematic Thematic
Mapper Imagery Mapper Imagery of the Imperial of the Imperial
Valley, Valley, California California
Obtained on Obtained on December 10, December 10,
19821982
Band 1 (blue; 0.45 – 0.52 m) Band 2 (green; 0.52 – 0.60 m) Band 3 (red; 0.63 – 0.69 m)
Band 4 (near-infrared; 0.76 – 0.90 m) Band 5 (mid-infrared; 1.55 – 1.75 m) Band 7 (mid-infrared; 2.08 – 2.35 m)
Band 6 (thermal infrared; 10.4 – 12.5 m)
Sugarbeets
Alfalfa
Cotton
Fallow
feed lot
fl
Ground Reference
Landsat Thematic Mapper Imagery of
Imperial Valley, California, December 10, 1982
Band 1 (blue; 0.45 – 0.52 m) Band 2 (green; 0.52 – 0.60 m) Band 3 (red; 0.63 – 0.69 m)
Band 4 (near-infrared; 0.76 – 0.90 m) Band 5 (mid-infrared; 1.55 – 1.75 m) Band 7 (mid-infrared; 2.08 – 2.35 m)
Band 6 (thermal infrared; 10.4 – 12.5 m)
Sugarbeets
Alfalfa
Cotton
Fallow
feed lot
fl
Ground Reference
Landsat Thematic Mapper Imagery of
Imperial Valley, California, December 10, 1982
Landsat Thematic Landsat Thematic Mapper Color Mapper Color
Composites and Composites and Classification Map of a Classification Map of a Portion of the Imperial Portion of the Imperial
Valley, CaliforniaValley, California
Landsat Thematic Landsat Thematic Mapper Color Mapper Color
Composites and Composites and Classification Map of a Classification Map of a Portion of the Imperial Portion of the Imperial
Valley, CaliforniaValley, California
Integrated analysis using RS and GISIntegrated analysis using RS and GIS
Geospatial data sourcesGeospatial data sources Improved DIPImproved DIP This merging creates a synergy in which the GIS This merging creates a synergy in which the GIS
improves the ability to extract information from improves the ability to extract information from remotely sensed data, and this in turn keeps the remotely sensed data, and this in turn keeps the GIS up-to-date with actual environmental GIS up-to-date with actual environmental conditions. conditions.
SummarySummary
The EMR reflected, emitted, or back-scattered The EMR reflected, emitted, or back-scattered from an object or geographic area is used as a from an object or geographic area is used as a surrogatesurrogate for the actual property under for the actual property under investigation.investigation.
Metadata, processing lineage, and the accuracy of Metadata, processing lineage, and the accuracy of the information are provided along with the information are provided along with information productsinformation products
Integration of remote sensing and GIS is the key Integration of remote sensing and GIS is the key
QuestionsQuestions
1. Can we formulate remote sensing of land use and 1. Can we formulate remote sensing of land use and land cover change quantitatively? land cover change quantitatively?
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