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?