Ge111A Remote Sensing and GIS Lecture

Ge111A Remote Sensing and GIS Lecture Remote Sensing - many different geophysical data sets. We concentrate on the following: Imagery (optical and rad...
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Ge111A Remote Sensing and GIS Lecture Remote Sensing - many different geophysical data sets. We concentrate on the following: Imagery (optical and radar) Topography Geographical Information Systems (GIS) – a way to organize the imagery as well as point, line, and shapefile data; useful for cataloguing and searching regional data bases Note: •Positions and ΔPositions (GPS @ end of quarter) •For more info on RS, there is a class: Introduction to the Physics of Remote Sensing (EE/Ge 157 abc)

Why use GIS in a field geophysics class? Understand what is in the field as best you can before you go there: • Terrain & topography • Geology • Roads • Access • Geomorphic features (faults, mountain ranges, etc) Add your own data and locations to the map (locations of survey points and/or lines) Easily produce base maps showing where different surveys were conducted during the class activities

Equations relating wavelength, frequency, and speed: λ=c/f


If the wave travels at the speed of light, c c=0.3 m/ns = 3x108 m/s. A wave with a frequency of 1015 Hz has a wavelength, λ, of 3 x 10-7 m, which is 300 nm or 0.3 μm – in the ultraviolet part of the spectrum.

Thought questions: 1)

What happens to the wave if it travels in a medium with speed less than the speed of light?


Can you find the mistake in the graph on this page?

Measurements conducted from: •Satellites •Aircraft •Handheld sensors

Character of imagery is based on the reflectance and backscatter characteristics of the surface, f(λ) Different materials have different spectral behavior (rocks of different kinds, water, vegetation…) Both material type + physical state of material (grain size, weathering) are important

Ways you could correct for atmospheric absorption •Make atmospheric observations simultaneous with the remote sensing (hard to get usually) • Use an atmospheric model of absorption based on other dates or locations •Make surface spectrometer measurements for calibration, during the survey or during similar season and time as original survey •Don’t use bands in the spectral area of max. absorption

Atmospheric absorption

Spectra of common vegetation +

Spectra of common rocks/minerals

Sensitive to: energy states of electrons in outer shells of transition metals (visible wavelengths) Twisting, rotation, vibrations of bonds in compounds (3-14 micron region)

From Hunt (1977) spectral locations of absorption signals for different minerals and rocks

Critical questions to ask when using imagery 1. 2. 3. 4.

Spatial resolution (pixel size) Image extent (General rule: target is always on the boundary) Wavelengths $$$$ Common systems

Platform Aster Landsat 4,5,7 SPOT** Ikonos*** Planes/Helicopter

Pixel (m) 15/30 15/30 5/10 1/4 O(10cm)

+ Quickbird… * ** *** ****

A variety of cheaper combos exist French Military Camera + height above ground

Extent (km) 60 180 60 10 10****

Cost ($) Free $400+* O(1000) O(1000) ----

Landsat: Only 7 spectral bands, not very useful for discerning material types But because of large image spatial extent and reasonable resolution, good for overview

ASTER (14 bands) Instrument Bands Spatial resolution Swath width Cross track pointing Quantization (bits)

VNIR 1-3 15m 60km ±318km(± 24°) 8

SWIR TIR 4-9 10-14 30m 90m 60km 60km ±116km(± 8.6°) ±116km(± 8.6°)) 8 12

Note: Band 3 has nadir and backward telescopes for stereo pairs from a single orbit.

Example: Aster band combination Saline Valley Assign different λ bands or combination of bands to RGB to form color image Thermal infrared bands 13, 12 and 10 as RGB Variations in: quartz content appear as more or less red; carbonate rocks are green mafic volcanic rocks are purple

Hyperspectral Imagery Multiple bands (images) each at different wavelengths e.g. AVIRIS - 224 bands

Large data volumes!

What is the advantage of hyperspectral images? Much narrower wavelength bands – easier to see smaller features in the absorption spectrum.

At radar wavelengths, the atmosphere is transparent Frequencies and Wavelength of the IEEE Radar Band designation Band L S C X Ku K Ka

Frequency (GHz) 1-2 2-4 4-8 8-12 12-18 18-27 27-40

Wavelength (cm) 30-15 15-7.5 7.5-3.75 3.75-2.50 2.5-1.67 1.67-1.11 1.11-0.075

Aircraft: Shown here: AIRSAR Measures topography, ocean currents

SAR/InSAR Platforms

Satellites: Repeat pass Fly over once, repeat days-years later •Images •Measures deformation and topography

Space shuttle: Shuttle Radar Topography Mission (SRTM)

From: H. Zebker

Both from: JPL

Radar is active imaging Natural image coordinates are in units of time: along track & line-of-sight (LOS) range ¾foreshortening ¾layover ¾shadows

Imaging radar is side looking (why?)

Achieve resolution by clever combination of consecutive radar images: Synthetic Aperture Radar (SAR)

Topography (DEM, DTM, DTED, topo, height,…) Methods •Land surveys (now GPS or total station) •Radar altimeter •Air or space borne laser - point or swath mapping altimeter •Stereo imagery (air photos, also now satellite) •Radar interferometry a.k.a. InSAR (plane, shuttle, satellite) •Optical interferometry a.k.a. LIDAR Practical availability •U.S.: 10-30 m/px (USGS, SRTM) on the net 0.5-15 m (Airborne InSAR, optical, laser swath) - e.g., TOPSAR •Foreign: 90 m/px (SRTM 60S-60N), 30-60 m/px by begging (classified) 900 m/px open access •Make your own (InSAR, optical) 10-20 m/px

Practical Concerns with Imagery and DEMs 1. 2.




Continuity of adjacent images Reference mapping information • Origin • Georeferencing – how many tie points are needed? • Datum (WGS84, NAD27, NAD83) • Projections… ‰ UTM - eastings and northings (m) ‰ Geographic - longitude and latitude (deg) File format • # px in x and y coordinates • How to store multiple bands (BIL, BIP) • Precision (bytes/band/pixel) - always in binary Software (raster + vector) • ESRI - ArcGIS • ERDAS - Imagine • Matlab/IDL (ENVI software) • GIS permits easy use of data bases and geographical logic Imaging combinations • Shaded relief (intensity) + color (something else) • Use Google Earth for simple tasks

The next few images are from Jane Dmochowski’s PhD thesis (Caltech Seismo Lab, 2005) Isla San Luis is an active volcanic island in the Gulf of California (Mexico) The imagery is Modis-Aster Simulator (MASTER) airborne data, with about a 4 m pixel size. It was collected with a very lowflying small airplane. The MASTER sensor has 50 spectral bands from visible to thermal infrared (TIR).

LIDAR images of San Andreas fault – from P4 project (high resolution topography) – can see through the trees

LIDAR – “light detection and ranging” works at optical frequencies

Cajon Pass I-15 Fault Crossing

Another example of LIDAR data for topography along the San Andreas fault

Ge111a GIS project • • • • • •

Topo map (USGS) SPOT image ASTER bands 1-3 Landsat-Thematic Mapper bands 1-3 DEM made from TOPSAR Geographic features (roads, drainages)

Homework – due Thurs April 19th 1. Construct a basemap(s) of the greater Queen Valley region. Annotate your map with any geologically interesting features (faults, major alluvial fans, place names etc.) and include scale bars and geographic reference (ticks or something) as well as legends for any colors or symbols that you use. Print out your map to turn in, but save the file so you can use it later on in the class. 2.

Make a perspective image of the Queen Valley fault using Google Earth or similar product (based on aerial photographs and an unknown DEM). Turn this in with your HW.

3. Write a paragraph comparing the strengths and weaknesses of the different data types you have available in the GIS project (DEM, shaded relief DEM, Aster, SPOT). Discuss the different types of natural and man made features that are detectable. Don’t forget the railroad grade and the aqueduct. The GIS Lab is available to you all the time. For workstation use, students doing classwork have priority over those doing research. There will be two sessions on Tuesday in the GIS lab: one at 9 and one at 10.

Tuesday 4/10 GIS lab sessions 309 North Mudd 9-10 a.m.

10-11 a.m.

Alisic Bowers Ortega Phillips Craig

Dow Savage Watson Peek? Tikoo?

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