Lecture for landsat

Editor's Notes

• #3: Landsat was conceived by a NASA study group in 1967, inspired by the Gemini and Apollo space missions. 3 p517 USGS and the USDA were also interested in helping shape the design of the Landsat system. 3 p517 http://earthobservatory.nasa.gov/Newsroom/NewImages/images.php3?img_id=10815 http://earthasart.gsfc.nasa.gov/garden.html
• #4: The first Landsat was launched in 1972, and from 1972 to 1982, the Landsat mission was sustained as a NASA experimental activity. The Landsat 7 mission Terrestrial research and applications for the 21st century In 1982, began a period of commercial and operational missions, before returning to government management in 1992, with both experimental and operational activities.
• #6: The RBV system on Landsats 1 and 2 consisted of three television-like cameras aimed to view the same 185 km-by-185 km area as the multispectral scanner (MSS) sensor. The RBV system did not contain film. The images were exposed by a shutter device and stored on a photosensitive surface within each camera. This surface is then scanned in raster form by an internal electron beam to produce a video signal. The RBV system instantaneously imaged an entire scene, had greater inherent cartographic fidelity than imagery acquired by the Landsat MSS sensor, and contained a reseau grid in the image to facilitate geometric correction of the imagery. This resulted in an array of tick marks that were precisely placed in each image. The RBV system on Landsat 1 produced only 1690 scenes between July 23 and August 5, 1972, when a tape recording switching problem forced a system shutdown. The RBV system on Landsat 2 was operated primarily for engineering evaluation purposes and only occasional RBV imagery was obtained, primarily for cartographic uses in remote areas. These images are no longer available.
• #7: Landsats 1 through 3 operated in a near-polar orbit at an altitude of 920 km with an 18-day repeat coverage cycle. These satellites circled the Earth every 103 minutes, completing 14 orbits a day. Eighteen days and 251 overlapping orbits were required to provide nearly complete coverage of the Earth's surface with 185 km wide image swaths. The amount of swath overlap or sidelap varies from 14 percent at the Equator to a maximum of approximately 85 percent at 81 degrees north or south latitude. Landsat satellites 1 through 3 carried return beam vidicon (RBV) cameras and the MSS sensor. The RBV cameras did not achieve the popularity of the MSS sensor. The MSS sensor scanned the Earth's surface from west to east as the satellite moved in its descending (north-to-south) orbit over the sunlit side of the Earth. Six detectors for each spectral band provided six scan lines on each active scan. The combination of scanning geometry, satellite orbit, and Earth rotation produced the global coverage necessary for studying land surface change. The resolution of the MSS sensor was approximately 80 m with radiometric coverage in four spectral bands from the visible green to the near-infrared (IR) wavelengths. Only the MSS sensor on Landsat 3 had a fifth band in the thermal-IR wavelength. Landsats 4 and 5 carried both the MSS and the TM sensors; however, routine collection of MSS data was terminated in late 1992. The satellites orbited at an altitude of 705 km and provide a 16-day, 233-orbit cycle with a swath overlap that varies from 7 percent at the Equator to nearly 84 percent at 81 degrees north or south latitude. These satellites were also designed and operated to collect data over a 185 km swath. The MSS sensors flown aboard Landsats 4 and 5 were identical to the ones that were carried on Landsats 1 and 2.
• #9: The MSS and TM sensors primarily detected reflected radiation from the Earth's surface in the visible and IR wavelengths, but the TM sensor provides more radiometric information than the MSS sensor. The wavelength range for the TM sensor is from the visible (blue), through the mid-IR, into the thermal-IR portion of the electromagnetic spectrum. Sixteen detectors for the visible and mid-IR wavelength bands in the TM sensor provide 16 scan lines on each active scan. Four detectors for the thermal-IR band provide four scan lines on each active scan. The TM sensor has a spatial resolution of 30 meters for the visible, near-IR, and mid-IR wavelengths and a spatial resolution of 120 meters for the thermal-IR band. Freden, S.C. & Gorden, F., Jr. (1983). Landsat Satellites. In Manual of Remote Sensing (ed R.N. Colwell), Vol. 1, pp. 517-570. American Society of Photogrammetry, Falls Church, Virginia . Sun-synchronous, repetitive orbit of 16 days with an equatorial altitude of 703km, a 9:45am local sun time overpass, and a 30x30m IFOV. p548-9 233 orbits per cycle. p565 To get the necessary signal strength so that an acceptable signal-to-noise detector performance can be achieved, the sweep rate of the scanning mirror had to be slowed down from 13.67 cycles per second to 6.999 cycles per second. Also, it scans in both directions – west to east and east to west. Landsats 1-5 have been in Sun-synchronous orbits with equatorial crossing times ranging from 8:30 a.m. for Landsat 1 to approximately 9:45 a.m. for Landsat 5. A Landsat-4 or -5 TM scene has an instantaneous field of view (IFOV) of 30 meters by 30 meters (900 square meters) in bands 1 through 5 and band 7, and an IFOV of 120 meters by 120 meters (14,400 square meters) on the ground in band 6.
• #10: The Enhanced Thematic Mapper Plus (ETM+) instrument is a fixed "whisk-broom", eight-band, multispectral scanning radiometer capable of providing high-resolution imaging information of the Earth's surface. It detects spectrally-filtered radiation in VNIR, SWIR, LWIR and panchromatic bands from the sun-lit Earth in a 183 km wide swath when orbiting at an altitude of 705 km. The primary new features on Landsat 7 are a panchromatic band with 15 m spatial resolution, an on-board full aperture solar calibrator, 5% absolute radiometric calibration and a thermal IR channel with a four-fold improvement in spatial resolution over TM. Landsat 7 collects data in accordance with the World Wide Reference System 2, which has catalogued the world's land mass into 57,784 scenes, each 183 km wide by 170 km long. The ETM+ produces approximately 3.8 gigabits of data for each scene. An ETM+ scene has an Instantaneous Field Of View (IFOV) of 30 meters in bands 1-5 and 7 while band 6 has an IFOV of 60 meters on the ground and the band 8 an IFOV of 15 meters. Please visit the L7 Science Data Users Handbook for a detailed description of ETM+ spatial characteristics . GSFC, NASA
• #14: The Worldwide Reference System (WRS) is a global notation system for Landsat data. It enables a user to inquire about satellite imagery over any portion of the world by specifying a nominal scene center designated by PATH and ROW numbers. The WRS has proven valuable for the cataloging, referencing, and day-to-day use of imagery transmitted from the Landsat sensors.
• #38: Broad categories of surfaces have very different reflections as seen on this plot These differences are very obvious when one looks at the reflectance curves from three different surface Here we have reflectance curves from 3 common surfaces Soil – Generally has high reflectance which increases gradually, but stays fairly constant in the IR Water – very low reflectance in all  regions Vegetation –complex, low in some, high in others - low less than .6 um, high in near IR, and then drops in Middle IR The differences in reflectance provide the basis for discrimination between these surfaces For example, what would it take for us to tell the difference between these three surfaces – could probably easily do it with two channels, say one at 0.5 um, and a second at 1.0 Make chart of differences in reflectance on board in two wavelength regions
• #40: IF you were actually an EM wavelength of light going into a leaf, this is what you would see at the molecular level You have a bunch of cells, that have intercellular air spaces, and a bunch of chloroplast cells that absorb visible light
• #41: Going back to basic biology, it is a set of plant pigments in the chloroplasts that absorb EM radiation – These pigments are converting EM radiation into sugars through the process of photosynthesis There are several different plant pigments, each that absorb EM radiation in different wavelength regions
• #43: We all know that the color of leaves change in the fall This is because chlorophyll begins to die, and therefore the reflectance of the leaf changes
• #45: Two plant or tree types might have very similar levels of chlorophyll and moisture, but still have different reflectances The microscopic structure of foliage can influence absorption and scattering, and hence influence reflection For example, needles are much different than leaves, they have much higher levels of cellulose that absorbs EM energy, especially Near IR EM energy Hence pine trees have different spectral signatures than deciduous trees, as seen in this graph
• #46: GO OUTSIDE AND LOOK AT A TREE When you are talking about reflectance of vegetation, you have to realize that we are dealing with a process that occurs at microscopic levels Therefore, you really have to look at reflectance at the cellular structure of the leaf that is doing the reflecting There are three major components of the leaf that influence reflection Chlorophyll Water content IF you take these two away, then you are dealing with the cellular structure of the leaf itself, e.g., the leaf structures that comprise the leaf Next, you need to realize that a single leaf not only reflects energy, but it absorbs and transmits energy Thus, you really have to think of a vegetation canopy as a multiple layer of different surfaces that reflect, absorb, and transmit energy Also, this leaves on vegetation change throughout time A very complex surface
• #47: Transition to next plot Here we have the same tree species that was monitored under different leaf moisture conditions Note that there are distinct variations in the spectral reflectance as a function of leaf moisture Reflectance from vegetated surface is a complex process What I want to do is to discuss the sources of variations in plant reflectance GO TO NEXT SLIDE
• #48: Here is one way to view the three processes controlling reflection from a plant surface Leaf pigments dominate reflectance in the visible Cell structure dominates in the near IR Water absorption dominates in the shortwave IR
• #52: Different forest types will have different seasonal profiles because they have different levels of green biomass present For example, we see that black and white spruce forests, which have lower reflectance in the Near IR, also have lower NDVI signatures during the summer time In this case, you can use the seasonal profile of NDVI to discriminate between spruce and aspen forests
• #53: You can also use seasonal NDVI profiles to analyze differences in vegetation condition between years In this case, we see that NDVI in 1992 was lower than in 1991 Note the green up in the spring of 1992 was later 1992 had a late spring, and overall cooler temperatures because of the eruption of Mount Pinatubo
• #54: First example is our study region in Alaska
• #55: Our field studies and analysis of landsat imagery showed that the heavy burned area had a higher reflectance in bands 4 (IR) and 7 (SWIR) than the light burned areas This is because of two things Mineral soil versus organic soil (organic soil has lower reflectance) No shadows in severe burned resulted in higher radiance We were able to use the spectral information in a supervised classification approach to map burn severity
• #61: Image Classification – it is a very broad area of research in remote sensing Perhaps one of the most intensive areas of research in the field over the past 30 years In this lecture, I want to briefly review some of the basic approaches to image classification – because in many cases, what you are going to see is an image product that is based on a classification approach
• #68: Supervised classification typically uses something called training sets or areas WRITE ON BOARD Training areas Specified by the analyst to represent the land cover categories of interest Used to compile a numerical “interpretation key” that describes the spectral attributes of the areas of interest Each pixel in the scene is compared to the training sets, and then assigned to one of the categories
• #73: Most land cover products from AVHRR time series data are derived using decision tree classifiers