Hyperspectral remote sensing ('Image spectroscopy')

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

Remote sensing, promising tool of the future Mária Szomolányi Ritvayné – Gabriella Frombach VITUKI CONSULT MOKKA Conference, June
CHRIS (Compact High Resolution Imaging Spectrometer) sira group sira electro-optics Dr Mike Cutter EO & Technology Business Manager.
Hyperspectral Image Acquisition and Analysis PECORA 15 Workshop 7 Airborne Remote Sensing: A Fast-track Approach to NEPA Streamlining for Transportation.
Optical Imaging and Field Spectroscopy: CLPX 2002 and 2003 Thomas H. Painter.
AMwww.Remote-Sensing.info Ch.2 Remote Sensing Data Collection
August 5 – 7, 2008NASA Habitats Workshop Optical Properties and Quantitative Remote Sensing of Kelp Forest and Seagrass Habitats Richard C. Zimmerman -
Remote Sensing Hyperspectral Imaging AUTO3160 – Optics Staffan Järn.
Remote Sensing of Mining. Remote Sensing Imagery Identification of mining operations – map the extent, changes over time Identification of tailings, overburden.
Multispectral Remote Sensing Systems
Hyperspectral Imagery
Detector Configurations Used for Panchromatic, Multispectral and Hyperspectral Remote Sensing Jensen, 2000.
Remote Sensing What is Remote Sensing? What is Remote Sensing? Sample Images Sample Images What do you need for it to work? What do you need for it to.
Remote Sensing What can we do with it?. The early years.
Introduction, Satellite Imaging. Platforms Used to Acquire Remote Sensing Data Aircraft Low, medium & high altitude Higher level of spatial detail Satellite.
January 20, 2006 Geog 258: Maps and GIS
Remote sensing is up! Inventory & monitoring Inventory – To describe the current status of forest Landcover / landuse classification Forest structure /
Remote Sensing Part 1.
Meteorological satellites – National Oceanographic and Atmospheric Administration (NOAA)-Polar Orbiting Environmental Satellite (POES) Orbital characteristics.
Integration of sensors for photogrammetry and remote sensing 8 th semester, MS 2005.
Modern Remote Sensing: Imagery, Capabilities, Possibilities Paul F. Hopkins Workshop on Advanced Technologies.
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.
An Overview of Remote Sensing and Image Processing by Miles Logsdon with thanks to Robin Weeks and Frank Westerlund.
Published in Remote Sensing of the Environment in May 2008.
Introduction to Digital Data and Imagery
Carolyn J. Merry NCRST-Flows The Ohio State University.
Course: Introduction to RS & DIP
Remote Sensing Hyperspectral Remote Sensing. 1. Hyperspectral Remote Sensing ► Collects image data in many narrow contiguous spectral bands through the.
Introduction to Remote Sensing. Outline What is remote sensing? The electromagnetic spectrum (EMS) The four resolutions Image Classification Incorporation.
Characterizing non-pigment canopy biochemistry from imaging spectrometer data for studying ecosystem processes Gregory P. Asner, Mary E. Martin, Scott.
Spectral Characteristics
Remote Sensing with Multispectral Scanners. Multispectral scanners First developed in early 1970’s Why use? Concept: Gather data from very specific wavelengths.
Hyperspectral remote sensing (Imaging Spectroscopy)
Resolution A sensor's various resolutions are very important characteristics. These resolution categories include: spatial spectral temporal radiometric.
Center for Remote Sensing and Spatial Analysis, Rutgers University Remote Sensing: Digital Image Analysis.
Slide #1 Emerging Remote Sensing Data, Systems, and Tools to Support PEM Applications for Resource Management Olaf Niemann Department of Geography University.
Christine Urbanowicz Prepared for NC Climate Fellows Workshop June 21, 2011.
1 Exploiting Multisensor Spectral Data to Improve Crop Residue Cover Estimates for Management of Agricultural Water Quality Magda S. Galloza 1, Melba M.
1 Suborbital Science Program Airborne Remote Sensing of the SF Bay NASA Ames Research Center University of California Santa Cruz Airborne Science & Technology.
Estimating Water Optical Properties, Water Depth and Bottom Albedo Using High Resolution Satellite Imagery for Coastal Habitat Mapping S. C. Liew #, P.
Remote Sensing Introduction to light and color. What is remote sensing? Introduction to satellite imagery. 5 resolutions of satellite imagery. Satellite.
What is an image? What is an image and which image bands are “best” for visual interpretation?
A Study on the Effect of Spectral Signature Enhancement in Hyperspectral Image Unmixing UNDERGRADUATE RESEARCH Student: Ms. Enid Marie Alvira-Concepción.
Mirza Muhammad Waqar HYPERSPECTRAL REMOTE SENSING - SENSORS 1 Contact:
USGS - California Fire Response -Hyperspectral Remote Sensing
Hyperspectral remote sensing
Hyperspectral Remote Sensing Ruiliang Pu Center for Assessment and Monitoring of Forest and Environmental Resources Department of Environmental Science,
Data Models, Pixels, and Satellite Bands. Understand the differences between raster and vector data. What are digital numbers (DNs) and what do they.
HYPERSPECTRAL IMAGING. nemo.nrl.navy.mil NEMO satellite COIS sensor instrument Navy Earth Map Observer Characterization of world littoral regions demonstrate.
Sub pixelclassification
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 Imagery Types and Sources GIS Management and Implementation GISC 6383 October 27, 2005 Neil K. Basu, Janice M. Jett, Stephen F. Meigs Jr.,
Landsat Satellite Data. 1 LSOS (1-ha) 9 Intensive Study Areas (1km x 1km) 3 Meso-cell Study Areas (25km x 25km) 1 Small Regional Study Area (1.5 o x 2.5.
Reading assignments for chapter 6 Pages – – – –
Remote Sensing Section Global soil monitoring and mineral mapping from emerging remote sensing technologies Sabine Chabrillat and the group “hyperspectral.
Using vegetation indices (NDVI) to study vegetation
AVIRIS By: Evan, Mike, Ana Belen ER-2 Twin Otter.
Mapping of Coastal Wetlands via Hyperspectral AVIRIS Data
Hyperspectral Sensing – Imaging Spectroscopy
FIGURE Different sensor types
Hyperspectral Remote Sensing
Remote Sensing What is Remote Sensing? Sample Images
This week’s earth observatory: false colour image
Satellite Sensors – Historical Perspectives
Introductory Digital Image Processing
Worldview II Launched October 8, 2009 Altitude: 770 km
Summary - end of term Lab Monday: 4 April return of assignments Lecture exam: 5 April, projects by end of term: April 6 Lecture evaluation.
Hyperspectral Terminology
Remote Sensing Landscape Changes Before and After King Fire 2014
Hyperspectral Remote Sensing
Presentation transcript:

Hyperspectral remote sensing ('Image spectroscopy') multispectral systems contain ~5-10 bands (70-400 nm wide = 0.07-0.40 μm) e.g. Landsat TM (below) Hyperspectral consists of 100- 200+ channels from 0.38 - 2.5μm (5-10nm each) Bands are contiguous and high spectral resolution

Some airborne hyperspectral systems Sensor     Wavelength (nm)     Band width (nm)     # bands AVIRIS      400-2500      10      224   TRWIS III      367-2328     5.9      335 HYDICE      400-2400      10      210 CASI  (1500)    400- 900      1.8      288 OKSI AVS      400-1000      10      61 ESSI Probe-1 400-2450 15 128

Quantifying structural physical habitat attributes using LIDAR and hyperspectral imagery <- LiDAR DEM IR image and 10 class -> ISODATA classification

Spectral signatures: Landsat TM v hyperspectral http://www.ccrs.nrcan.gc.ca/hyperspectral/isst_e.php

http://www. murraystate http://www.murraystate.edu/qacd/cos/marc/projects/nasa98/veg_library/lblspec.gif

CASI (Compact Airborne Spectrographic Imager) - ITRES, Calgary –   www.itres.com 1989 CASI 1 400-900 0.5-10m pixels, 12 bit data (0-4095) 2002 CASI 2, 3   400- 1050   14 bit data (0-16383) SASI-600  950-2450 100 bands x 15nm MASI-600 3-5 μm 64 bands 1m TASI-600  8-11.5 μm    32 bands TABI -1800 8-12 μm 160 bands 320 pixels x 3metre (12 bit data) MASI-600 flight line, 1m resolution. Displayed 3 bands: r: 3571 nm g: 3952nm b: 4778nm

APPLICATIONS: wetland and coastal vegetation mineral composition agricultural crops forest structure soil types   Some important absorption feature wavelengths for agriculture: Absorption Feature     Wavelengths (nm) Water      970, 1180, 1450 Chlorophyll      480, 660 Cellulose     1730, 2100, 2300 N absorption    764, 1640, 2100 Lignin      1730, 2300 Carbonates     1750 - 2250 Value of Principal Components Transformation in classification

Satellite borne hyperspectral systems Hyperion:  launched on Earth Observing 1 (EO-1), Dec 2000; 50km behind Landsat7; http://gers.uprm.edu/geol4048/pdfs/14_hyperspectral.pdf

Venice by CHRIS (Compact High Resolution Imaging Spectrometer) on PROBA (2001) CHRIS provides 19 bands in the VNIR range (400 - 1050 nm) at 17 m. Each nominal image forms a square of 13 km x 13 km. CHRIS can be reconfigured to provide 63 spectral bands at a spatial resolution of 34 m and can provide up to 150 channels. Launch: http://www.esa.int/SPECIALS/Proba/index.html

The Niau atoll, in the central South Pacific Ocean, acquired by ESA’s Proba satellite on 6 October 2005 with its Compact High Resolution Imaging Spectrometer (CHRIS). Niau is one of nearly 80 coral reef atolls that forms the Tuamotu Archipelago in French Polynesia. Gallery: http://earth.esa.int/cgi-bin/satimgsql.pl?search=&sat=12