Carolyn J. Merry NCRST-Flows The Ohio State University.

Slides:



Advertisements
Similar presentations
Remote Sensing. Readings: and lecture notes Figures to Examine: to Examine the Image from IKONOS, and compare it with the others.
Advertisements

Resurs-P. Capabilities. Standard products. A. Peshkun The 14 th International Scientific and Technical Conference “From imagery to map: digital photogrammetric.
WorldView-1 By Michael Jones. Abstract  The WorldView-1 satellite is one part of what is to be a three satellite constellation. The group includes QuickBird,
AMwww.Remote-Sensing.info Ch.2 Remote Sensing Data Collection
Some Basic Concepts of Remote Sensing
Resolution.
Spencer Anderson Brent Fogleman Daryl Vonhagel.  Objectives:  C-band (w = 3.8 – 7.5 cm) & X-band (w = 2.4 – 3.8 cm) IFSAR to acquire topographic data.
Remote Sensing II Sensors Konari, Iran Image taken 2/2/2000
Multispectral Remote Sensing Systems
Integration of sensors for photogrammetry and remote sensing 8 th semester, MS 2005.
Remote Sensing of Our Environment Using Satellite Digital Images to Analyze the Earth’s Surface.
Detector Configurations Used for Panchromatic, Multispectral and Hyperspectral Remote Sensing Jensen, 2000.
Introduction, Satellite Imaging. Platforms Used to Acquire Remote Sensing Data Aircraft Low, medium & high altitude Higher level of spatial detail Satellite.
History and Features of Landsat 7 By: Andy Vogelsberg Photo of Landsat 7 taken from tures/litho/landsat/land.jpg.
ÖNCEL AVCIKADİR ÖNCEL AVCIKADİR LANDSAT OBSERVATİON SATELITE SYSTEM Landsat satellites have been collecting images of the Earth's surface for.
Satellite Thermal Remote Sensing of Boiling Springs Lake Jeff Pedelty NASA Goddard Space Flight Center Goddard Center for Astrobiology.
Remote Sensing Part 1.
Meteorological satellites – National Oceanographic and Atmospheric Administration (NOAA)-Polar Orbiting Environmental Satellite (POES) Orbital characteristics.
The image characteristics are usually referred to as:
Principals of Remote Sensing
Geosynchronous Orbit A satellite in geosynchronous orbit circles the earth once each day. The time it takes for a satellite to orbit the earth is called.
More Remote Sensing Today- - announcements - Review of few concepts - Measurements from imagery - Satellites and Scanners.
Lecture 21: Major Types of Satellite Imagery By Austin Troy University of Vermont Using GIS-- Introduction to GIS.
Course: Introduction to RS & DIP
©2008 Austin Troy Lecture 21: Major Types of Satellite Imagery By Austin Troy and Weiqi Zhou University of Vermont Using GIS-- Introduction to GIS.
Photogrammetry and Multispectral Remote Sensing Lecture 3 September 8, 2004.
Copyright © 2003 Leica Geosystems GIS & Mapping, LLC Turning Imagery into Information Suzie Noble, Product Specialist Leica Geosystems Denver, CO.
OC3522Summer 2001 OC Remote Sensing of the Atmosphere and Ocean - Summer 2001 Land/Ice Surface & Applications.
Geography 1010 Remote Sensing. Outline Last Lecture –Electromagnetic energy. –Spectral Signatures. Today’s Lecture –Spectral Signatures. –Satellite Remote.
Remote Sensing Theory & Background GEOG370 Instructor: Christine Erlien.
Remote Sensing Applications Supporting Regional Transportation Database Development CLEM 2001 August 6, 2001 Santa Barbara, CA Chris Chiesa,
Pollution Monitoring  Defense / Intelligence Planning  Yield Forecasting  Pesticide Applications Transportation Planning  Delivery Routing  Watershed.
Introduction to Remote Sensing. Outline What is remote sensing? The electromagnetic spectrum (EMS) The four resolutions Image Classification Incorporation.
Geography 372 Fall 2003November 4, Remote Sensing of the Land Surface: High Spatial Resolution Michael D. King & Compton J. Tucker Outline  Land.
Remote Sensing of Urban Landscape Lecture 11 November 10, 2004.
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
Remotely Sensed Data EMP 580 Fall 2015 Dr. Jim Graham Materials from Sara Hanna.
ASPRS Annual Conference 2005, Baltimore, March Utilizing Multi-Resolution Image data vs. Pansharpened Image data for Change Detection V. Vijayaraj,
Remote Sensing with Multispectral Scanners. Multispectral scanners First developed in early 1970’s Why use? Concept: Gather data from very specific wavelengths.
The role of remote sensing in Climate Change Mitigation and Adaptation.
Resolution A sensor's various resolutions are very important characteristics. These resolution categories include: spatial spectral temporal radiometric.
Remote Sensing Platforms. Remote Sensing Platforms - Introduction Allow observer and/or sensor to be above the target/phenomena of interest Two primary.
Resolution Resolution. Landsat ETM+ image Learning Objectives Be able to name and define the four types of data resolution. Be able to calculate the.
Terra Launched December 18, 1999
Remote Sensing SPOT and Other Moderate Resolution Satellite Systems
CHARACTERISTICS OF OPTICAL SENSORS Course: Introduction to RS & DIP Mirza Muhammad Waqar Contact: EXT:2257 RG610.
USA Select Briefing to The Federal Geographic Data Committee November 6, 2001 Glenn Geoghegan SPOT Image Corporation Reston, VA
Land Observation Satellites Dr. M. M. Yagoub URL :
Geosynchronous Orbit A satellite in geosynchronous orbit circles the earth once each day. The time it takes for a satellite to orbit the earth is called.
REMOTE SENSING DATA Markus Törmä Institute of Photogrammetry and Remote Sensing Helsinki University of Technology
Remote Sensing Imagery Types and Sources GIS Management and Implementation GISC 6383 October 27, 2005 Neil K. Basu, Janice M. Jett, Stephen F. Meigs Jr.,
Reading assignments for chapter 6 Pages – – – –
Orbits and Sensors Multispectral Sensors. Satellite Orbits Orbital parameters can be tuned to produce particular, useful orbits Geostationary Sun synchronous.
PLATFORMS & SENSORS Platform:
Remote Sensing.
Hyperspectral Sensing – Imaging Spectroscopy
Satellite Image Pixel Size vs Mapping Scale
Basic Concepts of Remote Sensing
LANDSAT OBSERVATİON SATELITE SYSTEM
ERT 247 SENSOR & PLATFORM.
From balloon, launched by students in Massachusetts, September 2, 2009
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
Satellite Sensors – Historical Perspectives
CNES’s SPOT 5 (Satellite Pour l’Observation de la Terre)
-1 Sensor: Satellite: Panchromatic – 0.41m x 0.41m res. (450–800 nm)
IKONOS ~Derived from the Greek term eikōn, meaning image~
EMP 580 Fall 2015 Dr. Jim Graham Materials from Sara Hanna
High Resolution Sensors – QuickBird
Worldview II Launched October 8, 2009 Altitude: 770 km
Worldview-1 The DigitalGlobe constellation of very high resolution
Presentation transcript:

Carolyn J. Merry NCRST-Flows The Ohio State University

Available sensors Current satellite sensors Wavelength region – spectral resolution Area of coverage Spatial resolution Costs of imagery Projected uses in transportation flow applications

Satellite systems Landsat-7 – Enhanced Thematic Mapper Plus (ETM+) SPOT-4, -5 – High Resolution Visible (HRV) Ikonos-2 Quickbird-2

Landsat-7 satellite 705-km altitude 16-day repeat cycle 185-km swath width Descending node at 10: min. Whisk-broom scanner Radiometric resolution: 2 8 (256 levels)

Landsat MSS sensor – 920 km altitude Landsat-1 (23 Jul Jan 78) Landsat-2 (22 Jan Feb 82) Landsat-3 (5 Mar Mar 83) Landsat MSS, TM, ETM+ – 705 km altitude Landsat-4 (16 July 82 - present) Landsat-5 (1 Mar 84 - present) Landsat-6 (5 Oct 93 – lost shortly after launch) Landsat-7 (15 Apr 99 - present) Landsat data availability

Landsat-7 imagery 15-m pan ( µm) 6-band multispectral (XS) – 30-m µm (blue), µm (green), (red) (near IR), µm (mid IR), µm (mid IR) µm (thermal IR – 60 m) Image data ~$600/scene (185 by 185 km) Web site:

ETM+ Band  m ETM+ Band  m ETM+ Band  m ETM+ Band  m ETM+ Band  m ETM+ Band  m ETM+ Band  m ETM+ PAN Band  m Landsat-7 spectral bands

Landsat-7 spatial resolution False Color Composite (4,3,2) True Color Composite (3,2,1) 15M PAN Band

DISP, 1962 Landsat 5, 1984 Landsat 5, 1994 Landsat 5, 1989 Landsat 7, 1999 Historical archive

Information content Highway centerline – principally interstates & wider roads General utility line mapping & routing Land use mapping for corridor studies – USGS level I, II Monitoring urban heat island effect

SPOT-1, -2, -3 satellites 822-km altitude 98° inclination – sun- synchronous orbit 101-minute orbit, 26-day repeat cycle, allows 1-2 day revisit 2 HRVs; CCDs – 6000 detectors; 60-km swath

French SPOT satellites SPOT-1 (22 Feb 86 – 31 Dec 90) SPOT-2 (22 Jan 90 – present) SPOT-3 (26 Sep 93 – 14 Nov 97) SPOT-4 (24 Mar 98 – present) SPOT-5 (3 May 02 – present) Web site:

SPOT-1, -2, -3 satellites 10-m pan ( µm) 3-band multispectral (XS) – 20-m µm (green), (red), (near IR) Off-nadir viewing capability (+27°) Allows us to acquire stereo pairs Image data ~$2000/scene for level 1B data (60 km by 60 km, up to 80 km by 60 km for off-nadir scenes)

SPOT-4 satellite 2 HRVIR Instruments – 10, 20 m resolution Pan – µm XS – green ( m); red ( µm); near IR ( µm); mid IR ( µm)

SPOT-5 satellite 3 May 2002 launch; 830-km orbit HRG (High Resolution Geometry) – 12,000 CCD array 2.5 m & 5 m pan (instead of 10 m) 10 m 3-band XS (instead of 20 m); 20 m for mid IR In-line stereo – fore, aft, nadir 10 m planimetric accuracy; 5 m elevation accuracy – ~1:50,000 map scale

SPOT-2 spectral bands SPOT Band 1,  m SPOT Band 2,  mSPOT Band 3,  m SPOT, Color Composite (3,2,1)

SPOT 10-m pan SPOT-2 spatial bands SPOT Selection, 10-m pan mosaic of Ohio

Information content Highway centerline – interstates, roads with better road definition General utility line mapping & routing Land use mapping for corridor studies – USGS level I, II

Hyperspectral data – EO-1 satellite Hyperion sensor 242 spectral bands ( µm) – 30 m resolution 7.5 by 100 km area Research imagery – NASA investigators

Example Hyperion data True Color Composite, (29, 23, 16) Image cube

Information content Highway centerline – principally interstates, & wider roads General utility line mapping & routing Land use mapping for corridor studies – USGS level I, II Material type of roads – asphalt, concrete

Commercial companies Space Imaging, Inc. – Thornton, CO EarthWatch, Inc. – Longmont, CO

Space Imaging – Ikonos-2 Launched 24 Sept 99 Sun-synchronous, polar-orbiting 680 km altitude, 98.1° inclination ±26° off-nadir collection, 1-day revisit 10:30 local time 11 x 11 km scene size 11-bit data

Space Imaging – Ikonos-2 1-m pan – µm 1:24,000 scale (12.2 m w/o ground control) 1:2,400 scale (2 m with ground control) 4-m multispectral sensor µm (blue) µm (green) µm (red) µm (near IR) $97-$211/sq mi for 1-m pan or 4-m XS, depending on orthorectification process Ikonos model data products – DEMs 15-m & 30-m postings (7 m vertical accuracy)

Ikonos Band  m Ikonos Band  mIkonos Band  m Ikonos Band  m Ikonos spectral bands

False Color Composite (4,2,1) 1M Pan Image Ikonos sensor resolution True Color Composite (4,2,1)

Multi-resolution data merging Landsat-7 ETM+ 30m & Ikonos 4m XS Ikonos 1m Pan & 4m XS

Ikonos 1-m panchromatic image of highway segment Enlargement High resolution image of highways

Entire image histogram Pavement pixels only Original image Image histogram

Ikonos 1-m pan –Tucson, AZ Background image Road extracted

PCA 3PCA 4 Principal components analysis

EarthWatch, Inc. – QuickBird-2 18 October 2001 launch 450 km altitude; off-nadir viewing (+25°) 0.61-m pan ( µm) 2.44-m multispectral µm (blue) µm (green) µm (red) µm (near IR) 16.5-km image swath 11-bit data

Denver, CO – 17 July cm natural color pan-sharpened Paris, France – Champs-Elysées 27 Mar 02 – 61-cm pan Source: QuicKBird-2 sample image

Dresden, Germany – 2.4 m natural color XS 22 Aug 02 recent flooding Yokohama, Japan – 9 Mar cm natural color pan-sharpened Source: QuicKBird-2 sample image

Information content Transportation infrastructure - detailed road centerline with road width Detect vehicles on roads for traffic count studies Disaster emergency response – pre- & post-imagery, damaged housing stock, damaged transportation, damaged utilities & services Building & property infrastructure – building perimeter, area, height & cadastral information (property lines) Land use/land cover for corridor studies – USGS level I, II, III, IV

Conclusions Satellite data are available Spatial resolution is detailed enough to map highway networks & detect vehicles Methods are being developed to incorporate imagery results into transportation flow methodologies