Presentation on theme: "Ann Johnson Associate Director"— Presentation transcript:
1 Ann Johnson Associate Director firstname.lastname@example.org Review for Introduction to Remote Sensing: Science Concepts and Technology“Empowering Colleges:Expanding the GeospatialWorkforce”Funded by National Science Foundation Advanced Technological Education program [DUE # Author’s opinions are not necessarily shared by NSF
2 What is Remote Sensing and how is it used? Passive and Active Remote SensingElectromagnetic Spectrum and sensor wavelengthand their “band numbers”Resolutions – Temporal, Spatial, Spectral andRadiometricComposite images: Pixels, Brightness and DigitalNumbersPixels and its Remote Sensing Signature graphicFinding and using data – Landsat focusedLidar – what is it and how can it be usedResources to learn moreBut How can that be done and what resources existed to help?
3 USGS DefinitionAcquiring 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.
4 Not just a pretty picture! How it can be used! Land Use ChangeClimateDisastersFloods, fires, volcanoes, earthquakesForestryAgricultureMany more!
5 Factors to consider when you use remote sensing data to understand or solve a geospatial problem Scale – or ResolutionWhere 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 themWhy is study important? Important for “real- world use by industry or government - ROI
6 Use “sensors” to detect and acquire the “information” about features The human eye asa sensor and brainas processor!
7 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 wavelengthsPassive – Sun as the “energy source”LandsatMODISAster
8 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 wavelengthsPassive – Sun as the “energy source”LandsatMODISAsterWhat about our eyes – Active or Passive?
9 Graphic From: Natural Resources Canada Fundamentals of Remote Sensing Tutorial Need:energy source,sensor(s),target,collection method,processing method anda distribution method
10 Electromagnetic Spectrum NASA MovieCan download a NASA book on the Tour of the Electromagnetic Spectrum
12 “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 footprintTemporal – how often data (image) isacquired for a locationRadiometric – the sensitivity of sensor to collect very slight differences in emitted or reflected energy
13 Spectral ResolutionsLandsat Sensors Collect data in specific Wavelengths or “Bands” of Electromagnetic Spectrum321In fact, the Landsat mission has been around since The sensors (located on the satellites platforms) on different missions should be understood, but for our lab we are going to be looking at the sensors that collect data in these 7 bands of the electromagnetic spectrum. Our “eyes” are sensors that collect data in three bands of the spectrum that correspond to Bands 1, 2 and 3. For most of us, our eyes and brain assigns the values and produces colored images using a mix of Blue, Green and Red.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 EyesLandsat 7
14 Spectral Resolutions SAR; radar Lidar; nm (some visible and some infrared)Multispectral: nm (some visible and some infrared)LidarMultispectral
15 Spatial Resolution Comparison – Scale High spatial resolution:Meter to sub meter pixelsSmall objects can be identifiedSmall area for each image footprintModerate spatial resolutionGenerally 30 meter pixels (Landsat)Object identification generally greater than 30 metersModerate area image footprintLow spatial resolution1 KM or larger pixels (MODIS)Objects smaller than 1 KM not observableVery large footprint
16 Look at Examples of Different types of Imagery and compare their “footprints” – logon to link below:
17 Temporal Resolution How often data is collected of the same location Only onceDaily – or multiple times a day“Frequently” – every so many daysLandsat missionsOnce every 16 days – butMust be clear (or have a percent cloud coverage)Must be “important” (U.S. and outside U.S.)
18 Landsat Image – Orbits (Path and Rows) View Orbits video
19 Why focus on Landsat Data? CostAccessArchiveTools and other resources
20 Atmosphere “blocks” some wavelengths: sensors collect wavelength data in specific regions (bands or channels) of the spectrumLidar
21 Gray shading: Wavelength Regions with good transmission Lidar
22 What Does “data” look like What Does “data” look like? Landsat 7 Spectral Bands and “gray scale” values of each band data setLandsat 7 - Band data comes in as rasters with grayscale values 0 to 255Landsat 8 – more than 4,000 scaled to 55,000 gray values
23 Radiometric Resolution Ability of a Sensor to discriminate very small differences in reflected or emitted energyPixel Brightness – White to Black in shades of Gray for one bandDigital Number: the numeric values of its BrightnessLandsat 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)ABC
24 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
25 Landsat 7. Natural or True Color. Bands 3, 2, 1. False Color Landsat 7 Natural or True Color Bands 3, 2, 1 False Color Band 5, 4, Pseudo Color Bands 7, 5, 3
26 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
27 Resource for Viewing Natural and False Color Composites on USGS Website xamples.phpGo to this site and use the swipe to see the difference using different bands for images from four regions of the U.S.
29 Identifying and Classifying Features Visual investigate using compositesUsing “band algebra” with data from bandsNormalized Difference Vegetation Index (NDVI) uses Near Infra Red and Red bandsClassification using spectral data from multiple bands for one pixel creating a “spectral signature”
30 Spectral Signatures From Different Surfaces in an Image
31 NDVI –Image Analysis and “Greeness” Using NIR and Red Bands NDVI LeafUsing the Image Analysis Window, you can easily use theNDVI algorithm by highlighting band 3 and 4, then click on theNDVI button that looks like a green maple leaf in the Processingpart of the Window. The resulting image is added to the map, displayingthe vegetation in shades of green and non-vegetationFrom yellow and orange to red.
33 Classification Using Software Unsupervised ClassificationUser tells Software how many “classes” to group the image data into and software “gathers like values” into “classes” with similar spectral valuesUser then labels the classes into land use types and may combine classes
34 Unsupervised Classification Natural Color Composite of San Fernando Valley, CAData clustered by software and colored to match Land Use types (i.e. blue = water, green = vegetation, etc.)
35 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.
36 So many satellites! Resources: Satellite Vieweral&satellite=14484EarthNow! Landsat Image ViewerReal time view as data is collected showing current path of satellitetml?sessionId=fdbe7bc eda2c68d 1e603ed
37 Finding Data:. https://lpdaac. usgs. gov/data_access. https://lpdaac Go to GloVIS and Try Path 41 and Row 36
39 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.eduConcept Modules on YouTube Channel atiGETT Remote Sensing EducationAnn Johnson
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