Review for Introduction to Remote Sensing: Science Concepts and Technology Ann Johnson Associate Director ann@baremt.com Funded by National Science Foundation Advanced Technological Education program [DUE #1304591. 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 1
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! 2
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 realworld use by industry or government - ROI Use sensors to detect and acquire the information about features The human eye as a senor and brain as processor! 3
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 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? 4
Graphic From: Natural Resources Canada Fundamentals of Remote Sensing Tutorial Need: energy source, sensor(s), target, collection method, processing method and a distribution method http://www.nrcan.gc.ca/earth-sciences/geomatics/satellite-imagery-airphotos/satellite-imagery-products/educational-resources/9309 Electromagnetic Spectrum NASA Movie http://missionscience.nasa.gov/ems/index.html Can download a NASA book on the Tour of the Electromagnetic Spectrum http://missionscience.nasa.gov/ems/tourofems_booklet_web.pdf 5
One Wavelength crest crest Resolution Spectral wavelengths of spectrum collected by sensors Spatial size of area on the ground by one pixel & 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 6
Spectral Resolutions Landsat Sensors Collect data in specific Wavelengths or Bands of Electromagnetic Spectrum 6 7 5 4 3 2 1 Landsat 7 Our Eyes Band 1: 0.45-0.52 m (Blue) Band 2: 0.52-0.60 m (Green) Band 3: 0.63-0.69 m (Red) Band 4: 0.76-0.90 m (Near infrared) Band 5: 1.55-1.75 m (Mid-Infrared) Band 6: 10.4-12.5 m (Thermal infrared) Band 7: 2.08-2.35 m (Mid-infrared) Spectral Resolutions SAR; radar Lidar; 600-1000 nm (some visible and some infrared) Multispectral: 450-2300 nm (some visible and some infrared) Lidar Multispectral 7
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: http://biodiversityinformatics.am nh.org/tool.php?content_id=144 8
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 9
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 10
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 11
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 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 12
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 13
Resource for Viewing Natural and False Color Composites on USGS Website http://landsat.usgs.gov/ldcm_image_e 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 Matter Website See handout and investigate website for different locations and dates 14
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 15
NDVI Image Analysis and Greeness Using NIR and Red Bands NDVI Leaf Land Cover Change and Greeness - NDVI 16
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.) 17
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 http://science.nasa.gov/isat/?group=visu 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=fdbe7bc05944802eda2c68d 1e603ed8462919 18
Finding Data: https://lpdaac.usgs.gov/data_access https://lpdaac.usgs.gov/data_access/glovis http://earthexplorer.usgs.gov/ http://glovis.usgs.gov/ Go to GloVIS and Try Path 41 and Row 36 Lidar Active Remote Sensing NOAA Lidar Tutorial: http://www.csc.noaa.gov/digitalcoast/_/pdf/lidar101.pdf 19
Thank You! Much of the material for this Presentation was developed by igett-remote Sensing grant from the National Science Foundation (DUE 1205069) More Exercises: igett.delmar.edu Concept Modules on YouTube Channel at igett Remote Sensing Education Ann Johnson ann@baremt.com ajohnson0847@kctcs.edu 20