Presentation on theme: "Review for Introduction to Remote Sensing: Science Concepts and Technology Ann Johnson Associate Director Funded by National Science Foundation."— Presentation transcript:
Review for Introduction to Remote Sensing: Science Concepts and Technology Ann Johnson Associate Director Funded by National Science Foundation Advanced Technological Education program [DUE # Author’s opinions are not necessarily shared by NSF “Empowering Colleges: Expanding the Geospatial Workforce”
What is Remote Sensing and how is it used? Passive and Active Remote Sensing Electromagnetic Spectrum and sensor wavelength and their “band numbers” Resolutions – Temporal, Spatial, Spectral and Radiometric Composite images: Pixels, Brightness and Digital Numbers Pixels and its Remote Sensing Signature graphic Finding and using data – Landsat focused Lidar – what is it and how can it be used Resources to learn more
USGS Definition Acquiring information about a natural feature or phenomenon, such as the Earth’s surface, without actually being in contact with it. Sensor can be ground based, aerial or satellite.
Not just a pretty picture! How it can be used! Land Use Change Climate Disasters ▫Floods, fires, volcanoes, earthquakes Forestry Agriculture Many more!
Factors to consider when you use remote sensing data to understand or solve a geospatial problem Scale – or Resolution ▫Where is the study location? ▫How large is the study are? ▫What is the “size” of features under study? ▫Is this a one time event or over multiple times over days, months or years? Access to needed resources: ▫Data and its cost? ▫Hardware and software and skills to use them Why is study important? Important for “real- world use by industry or government - ROI
Use “sensors” to detect and acquire the “information” about features The human eye as a sensor and brain as processor!
Two Types of Remote Sensing Sensors Active – Energy source is “provided” ▫Lidar – Light Detection and Ranging using pulsed laser beam from one wavelength) ▫SAR – Synthetic Aperture Radar – pulses of radio wavelengths Passive – Sun as the “energy source” ▫Landsat ▫MODIS ▫Aster
Two Types of Remote Sensing Sensors Active – Energy source is “provided” ▫Lidar – Light Detection and Ranging using pulsed laser beam (of varying wavelengths) ▫SAR – Synthetic Aperture Radar – pulses of radio wavelengths Passive – Sun as the “energy source” ▫Landsat ▫MODIS ▫Aster What about our eyes – Active or Passive?
Graphic From: Natural Resources Canada Fundamentals of Remote Sensing Tutorial Need: energy source, sensor(s), target, collection method, processing method and a distribution method photos/satellite-imagery-products/educational-resources/9309
Electromagnetic Spectrum NASA Movie Can download a NASA book on the Tour of the Electromagnetic Spectrum
crest One Wavelength
“Resolution” Spectral –wavelengths of spectrum collected by sensors Spatial – size of area on the ground covered by one pixel (grid size) which can affect size of image footprint Temporal – how often data (image) is acquired for a location Radiometric – the sensitivity of sensor to collect very slight differences in emitted or reflected energy
Landsat Sensors Collect data in specific Wavelengths or “Bands” of Electromagnetic Spectrum Band 1: m (Blue) Band 2: m (Green) Band 3: m (Red) Band 4: m (Near infrared) Band 5: m (Mid-Infrared) Band 6: m (Thermal infrared) Band 7: m (Mid-infrared) Our Eyes Landsat 7 Spectral Resolutions
SAR; radar Lidar; nm (some visible and some infrared) Multispectral: nm (some visible and some infrared) Lidar Multispectral
Spatial Resolution Comparison – Scale High spatial resolution: ▫Meter to sub meter pixels ▫Small objects can be identified ▫Small area for each image footprint Moderate spatial resolution ▫Generally 30 meter pixels (Landsat) ▫Object identification generally greater than 30 meters ▫Moderate area image footprint Low spatial resolution ▫1 KM or larger pixels (MODIS) ▫Objects smaller than 1 KM not observable ▫Very large footprint
Look at Examples of Different types of Imagery and compare their “footprints” – logon to link below: nh.org/tool.php?content_id=144
Temporal Resolution How often data is collected of the same location ▫Only once ▫Daily – or multiple times a day ▫“Frequently” – every so many days Landsat missions ▫Once every 16 days – but.... Must be clear (or have a percent cloud coverage) Must be “important” (U.S. and outside U.S.)
Landsat Image – Orbits (Path and Rows) View Orbits video
Why focus on Landsat Data? Cost Access Archive Tools and other resources
Atmosphere “blocks” some wavelengths: sensors collect wavelength data in specific regions (bands or channels) of the spectrum Lidar
Gray shading: Wavelength Regions with good transmission Lidar
What Does “data” look like? Landsat 7 Spectral Bands and “gray scale” values of each band data set Landsat 7 - Band data comes in as rasters with grayscale values 0 to 255 Landsat 8 – more than 4,000 scaled to 55,000 gray values
Radiometric Resolution Ability of a Sensor to discriminate very small differences in reflected or emitted energy Pixel Brightness – White to Black in shades of Gray for one band Digital Number: the numeric values of its Brightness Landsat 5 and 7 are 8 bit for 256 gray levels (0 to 255) Landsat 8 is 12 Bit for 4,096 gray levels (scaled to 55,000) A B C
Creating Visualizations: Composites Brightness values (DN) from three Bands are combined and colored on a computer monitor by designating which of the 3 bands will be coded as Red, Blue or Green
Landsat 7 Natural or True Color Bands 3, 2, 1 False Color Band 5, 4, 3 Pseudo Color Bands 7, 5, 3
Selecting three different bands as Red, Green or Blue creates different images of the same location Note: Band numbers for Landsat 5 and 7 are different than for Landsat 8
Resource for Viewing Natural and False Color Composites on USGS Website xamples.php Go to this site and use the swipe to see the difference using different bands for images from four regions of the U.S.
Change Matters Website See imagery/viewer
Identifying and Classifying Features Visual investigate using composites Using “band algebra” with data from bands ▫Normalized Difference Vegetation Index (NDVI) uses Near Infra Red and Red bands Classification using spectral data from multiple bands for one pixel creating a “spectral signature”
Spectral Signatures From Different Surfaces in an Image
NDVI Leaf NDVI –Image Analysis and “Greeness” Using NIR and Red Bands
Land Cover Change – and “Greeness” - NDVI
Classification Using Software Unsupervised Classification ▫User tells Software how many “classes” to group the image data into and software “gathers like values” into “classes” with similar spectral values ▫User then labels the classes into land use types and may combine classes
Unsupervised Classification Natural Color Composite of San Fernando Valley, CA Data clustered by software and colored to match Land Use types (i.e. blue = water, green = vegetation, etc.)
Supervised Classification ▫User identifies pixels that are different types of feature (soil, urban, vegetation, etc) and creates a file with spectral information that can be used by software. ▫Software uses spectral value file of the different features and classifies pixels based on the specified land cover types.
So many satellites! Resources: Satellite Viewer al&satellite=14484 EarthNow! Landsat Image Viewer ▫Real time view as data is collected showing current path of satellite ▫http://earthnow.usgs.gov/earthnow_app.h tml?sessionId=fdbe7bc eda2c68d 1e603ed
Finding Data: https://lpdaac.usgs.gov/data_access https://lpdaac.usgs.gov/data_access/glovis Go to GloVIS and Try Path 41 and Row 36
Lidar – Active Remote Sensing NOAA Lidar Tutorial:
Thank You! Much of the material for this Presentation was developed by iGETT-Remote Sensing grant from the National Science Foundation (DUE ) More Exercises: iGETT.delmar.edu Concept Modules on YouTube Channel at iGETT Remote Sensing Education Ann Johnson