Data collectionA review of remote sensing

Road map

Data Collection

Remote sensingIntroductionConceptsSpectral signaturesResolutions: spectral, spatial, temporalDigital image processing (classification)Other systems

Remote sensing and GIS

Data Collection

One of most expensive GIS activitiesMany diverse sourcesTwo broad types of collection

Data capture (direct collection)Data transfer

Two broad capture methodsPrimary (direct measurement)Secondary (indirect derivation)

Data Collection Techniques

Raster Vector

Primary Digital remote sensing images(Image classification)

GPS measurements

Digital aerial photographs(Photogrammetry)

Survey measurements(COGO)

Secondary Scanned maps or photos

On-screen digitizing of maps

DEMs from contours

Toponymy data sets from atlases

Surveying

Primary Data CaptureCapture specifically for GIS useRaster – remote sensing

e.g. Landsat, SPOT and IKONOS satellites, and aerial photography (previously analog and vector, but increasingly digital and raster)

Resolution is key considerationSpatialSpectralTemporal(Radiometric)

Remote Sensing“is the measurement or acquisition of information of some property of an object or phenomena by a recording device that is not in physical or intimate contact with the object or phenomena under study”Both a science (math and physics underlie the technology) and an art (image interpretation is not an immutable process)

Who has done remote sensing?

Remote SensingIncludes UAV, aircraft, spacecraft and satellite-based systemsProducts can be analog (e.g., photos) or digital imagesRemotely sensed images need to be interpreted to yield thematic information (roads, crop lands, etc.)

Yellow = Pine leadingOrange = Fir leadingLight Green = Pine and Fd typeDark Green = Fd, Spruce, Pine typePurple = Fd, Spruce, Aspen minor pine

Remote sensing applications

MappingMonitoringModelling

Global, continental, landscape, local

(Scale is always an issue)

Aerial photography

Important for updating large scale topographic maps (e.g., new roads, urban areas)Stereo-effect: pairs of images that are displaced produce a 3-D effectAllows for measuring elevationMuch greater precision than satellite images

Advantages of Satellite R. S.

Synoptic coverage (study of inter-relations)Repetitive (enables monitoring of change)Multispectral imaging (beyond the visible region)Survey of inaccessible terrainCan also provide stereo coverage

Satellite-based systemsData recorded for pixels (picture elements)Size on-ground of a pixel varies from <1m to 60m or more for commercial systems; up to 1km for many environmental monitoring systemsImages are sent back from satellite as very large raster data sets

MODIS image

Concepts: Sensor typesActive or passive sensorsPassive: sensors measure the amount of energy reflected (or emitted) from the earth’s surfaceActive: sensor emits radiation in the direction of the target, it then detects and measures the radiation that is reflected or backscattered from the target.

Concepts (physics)Energy sources and radiation principles (e.g., Stefan-Boltzmann law, Wien’s displacement law)Different sensors measure different parts of the electromagnetic spectrum

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Electromagnetic spectrumvisible lightnear infraredm

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TV and radio

blue redUV near

infrared

110-110-210-310-410-510-6 10410310210 107106105

wavelength (µm) wavelength (µm)

0.4 0.5 0.6 0.7 µm

Electromagnetic RadiationThe glue that holds it all together

Note that different disciplines reverse the order (placing Gamma Rays at the far left).

A simple remote sensing systemEarth’s Surface

Sourceof energy

Sensing system

Object-EMR Interactions

EMR can be:

1: scattered2: reflected3: absorbed / emitted4: transmitted

by an object

EMR EMR EMR

EMR

EMR

A complete remote sensing ‘system’

A: Source of EMR

B: Electromagnetic radiation (EMR)

C: Object of interest

D: Sensor

E: Transmission to receiver

F: Data products

G: Results of analyses

A

Atmospheric windows

Primary blockers: Ozone, water vapour, CO2

TheoreticalEMR

emissions

Spectral reflectance curves

Road map

Data Collection

Remote sensingIntroductionConceptsSpectral signaturesResolutions: spectral, spatial, temporal, radiometricDigital image processing (classification)Other systems

Remote sensing and GIS

Satellite-based sensors

Landsat, SPOT, IKONOS, GeoEye, …. also: Russian, Indian, Japanese, Chinese, European, and Canadian satellitesThey often differ in their spectralresolutions:

Panchromatic or multispectral orhyperspectral resolution.

What are the different types of spectral resolutions?

Spectral resolution

Multispectral vs Hyperspectral sensors

# and widthof bands

determine howclosely the spectralreflectance curve matches ‘reality’

bands

Spectral andRadiometric resolutions

# and widthof bands

determine howclosely the spectralreflectance curve matches ‘reality’

(shades of gray)

MODIS imagery (500 m) showing Myanmar before and

after being hit by a cyclone.

Spatial resolution ~f ( spectral region )

ASTER imagery15 m VNIR30 m SWIR90 m TIR

Fire scars north ofLos Angeles

Spatial resolution is typically tied tothe extent of the area you wish to study.

from IKONOS-11 m resolution

Source: http://gis.washington.edu/cfr250/lessons/remote_sensing/

Panchromatic image of the Jefferson memorial

Spatial Resolutionpixel resolutionInteractive tools:

Pan sharpening

Temporal resolution

Geostationary Orbit: The satellite appears stationary with respect to the Earth's surface.

Earth observation satellites usually follow sun synchronous orbits. A sun synchronous orbit is a near-polar orbit whose altitude is such that the satellite will always pass over a location at a given latitude at the same local solar time. In this way, the same solar illumination condition (except for seasonal variation) can be achieved for the imagesof a given location taken by the satellite.

Satellite orbits

Road map

Data Collection

Remote sensingIntroductionConceptsSpectral signaturesResolutions: spectral, spatial, temporalDigital image processing (classification)Other systems

Remote sensing and GIS

Digital image processing

Digital satellite data usually need considerable processingRegistration and atmospheric correctionAnalysis: - measurement- classification- estimation

The result of atmospheric correctionfor a pixel from a grass field.

Beforecorrection

Aftercorrection

The issue: we have spectral signaturesderived from ground-based analyses,

but the satellite data is ‘top of the atmosphere’data that is not necessarily equivalent.

Image display

True-color composite image(3, 2, 1)

Near Infrared Composite (4,3,2)

Shortwave Infrared Composite (7,4,3 or 7,4,2)

Vegetation in the NIR band is highly reflective due to chlorophyll, and an NIR composite vividly shows vegetation in various shades of red.

Water appears dark, almost black, due to the absorption of energy in the visible red and NIR bands.

Reflectance in the SWIR region is due primarily to moisture content. SWIR bands are especially suited for camouflage detection, change detection,

disturbed soils, soil type, and vegetation stress.

Interactive tool: band combinations

Measurement

TemperatureVegetation biomass- Normalized Difference Vegetation Index (NDVI)ElevationCrop conditionUrbanized area

High(much vegetation)

(little vegetation)Low

Classification

Identify and map areas with similar characteristicsAssign meaningful categories such as land-use or land-cover classes to pixel values Need “training areas” (ground-truth)Statistical approaches

Unsupervised vs supervised classes

Supervised Classification

ESRI Image Classification

LODGEPOLE PINELEADING

DOUGLAS FIR LEADING

MIXED / OTHERS

Yellow = Pine leadingOrange = Fir leadingLight Green = Pine and Fd typeDark Green = Fd, Spruce, Pine typePurple = Fd, Spruce, Aspen minor pine

Classification

ClassificationReflectance varies with time of dayOften large uncertainty in classification- pixels may contain several classes

Mixed pixelsDespite sophisticated image processing systems, good classification depends upon lots of experience (part art, part science)

Effect of sun angle

Estimation

Objective is to estimate total amounts of a quantity, or areas under cultivation for an administrative or management area Examples: crop areas, forest resources, drought monitoring

JULY 2001 JULY 2002

LANDSAT THEMATIC MAPPER (SOURCE: CCRS 2002)

Road map

Data Collection

Remote sensingIntroductionConceptsSpectral signaturesResolutions: spectral, spatial, temporalDigital image processing (classification)Other systems

Remote sensing and GIS

Other systemsMeteorological satellites

e.g., Advanced Very High Resolution Radiometercoarser resolution but higher frequency and larger areas covereddesigned for meteorology but used for many other purposes (e.g., NDVI) (News of the latest GOES satellite)

Radar remote sensing (e.g., microwave)advantages in areas where cloud cover is frequent (e.g., tropical areas close to the equator)difficult to interpret

http://www.geog.ucsb.edu/~jeff/wallpaper2/page.html

Other systemsAerial video

visible lightusing off-the-shelf video cameras and post-processing systemscheap, rapid data collection for monitoring and data capture (http://www.draganfly.com)

Light Detection and Ranging

Forest canopy (1st return)

Using many rapid small bursts of laser light, an aircraft-borne apparatus records reflection

from multiple sources.

Ground surface (last return)

LIDAR

Archaeology

Oceanography

Socioeconomic applications

Urban studies: e.g., delineation of newly urbanized areas (e.g., Quito, Manila)

Demography: e.g., mapping of villages for population estimation (e.g., Sudan, W-Africa) with Landsat (rooftop surveys)

Defense Meteorological Satellite Program’s (DMSP) nighttime visible light emissions

Human health and epidemiology: e.g., identify wetlands—sources of malaria mosquitoes

Archaeology and anthropology

DMSP data

Japan South-East Asia

(water areas masked)

Road map

Data Collection

Remote sensingIntroductionConceptsSpectral signaturesResolutions: spectral, spatial, temporalDigital image processing (classification)Other systems

Remote sensing and GIS

Remote sensing and GISRemotely sensed data is an important data source (currency, frequency)Large scale: e.g., “cities revealed”Medium scale: framework data, urban/non-urban, crop conditions, etc.Small scale: NDVI, global land cover data sets

Remote sensing and GISRequires considerable processing to achieve high accuracy productsImage rectification and registration with GIS data sets introduces uncertainty when working with raster systems (note: resampling types)Image interpretation guided by GIS data

The remote sensing process

Visual

Digital

Reference data

Air photos

Digital data

Maps

Statistics

GIS datasets

User

DecisionMaker

Data products

Interpretation Informationproducts

Targetaudience

A summary ofspatial and temporal

resolutions associatedwith remote sensing

systems.

See your text for a full-sized illustration

Summary

Data Collection (expensive)

Remote sensingIntroductionConcepts (A/P, EMR)Spectral signaturesResolutions: spectral, spatial, temporal, radiometricDigital image processing (classification)Other systems

Remote sensing and GIS