Download presentation
Presentation is loading. Please wait.
1
Introduction to Remote Sensing Principles
------Using GIS-- Introduction to GIS Lecture 19: Introduction to Remote Sensing Principles By Austin Troy University of Vermont Thanks are due to Jarlath O’Neil Dunne, upon whose lecture much of this material is based, and whose graphics were used in many slides for this lecture
2
Introduction to GIS What is remote sensing Remote sensing is “the science and art of obtaining information about an object, area or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area or phenomenon under investigation.” (Lillesand and Kiefer 2000) In the case of geography, this refers to sensing of electromagnetic energy operated from airborne or spaceborne platforms. These sensors collect data on how earth surface features emit and reflect electromagnetic energy ©2005 Austin Troy
3
Why is remote important
Introduction to GIS Why is remote important Remotely sensed imagery is the original source for most of the GIS data we use RS data can be used to assess ground conditions over a very large area RS data allows us to look at changes in the environment ©2005 Austin Troy
4
Some Applications Planning and transportation Road updates
Introduction to GIS Some Applications Planning and transportation Road updates Infrastructure monitoring Growth monitoring Source: Halcon ©2005 Austin Troy
5
Some Applications Natural resource mapping Tree cover Tree conditions
Introduction to GIS Some Applications Natural resource mapping Tree cover Tree conditions Crop conditions Yield estimation Clubroot disease Source: NGIC ©2005 Austin Troy
6
Some Applications Natural resource mapping Land use change analysis
Introduction to GIS Some Applications Natural resource mapping Land use change analysis Habitat and natural communities mapping Source: TRIC ©2005 Austin Troy
7
Introduction to GIS The Physics of RS Remote sensing data are collected in the electro-magnetic radiation spectrum, principally the visible, infra-red and radio regions Passive RS systems collect data on energy that is reflected or emitted from the earth Most systems are passive, except for microwave and radar, which are active sensing mechanisms. Most RS platforms record reflectance in multiple wavelengths spectrums ©2005 Austin Troy
8
Introduction to GIS Physics of RS EM radiation exists across a range of wavelengths, referring to distance between two peaks Source: ©2005 Austin Troy
9
Introduction to GIS Physics of RS The visible spectrum constitutes a small portion, bounded by ultraviolet spectrum below and the infrared spectrum above ©2005 Austin Troy Source:
10
Physics of RS Comprises 2% of EM Spectrum Spectral Imagery
Introduction to GIS Physics of RS Wavelength (Micrometers) .01 .04 .07 1.0 3.0 5.0 14.00 um Ultra- Violet Visible Visile Near IR Shortwave IR Midwave IR Longwave IR Panchromatic Black & White Film Comprises 2% of EM Spectrum Color Film Color IR Film Spectral Imagery Source: Jarlath O’Neil-Dunne ©2005 Austin Troy
11
Introduction to GIS The Physics of RS Light can either be reflected, absorbed or transmitted on a surface, and the proportion of those three will vary at each wavelength for a given object. Reflection is emission of photons caused by excitation of the surface, due to incident radiation Reflected E = incident E - absorbed and transmitted E This relationship varies in each wavelength This is why two features may appear similar in the same wavelength band, but distinguishable in different wavelength band ©2005 Austin Troy
12
The Physics of RS White Light Reflectance Blue Green Red High Low
Introduction to GIS The Physics of RS Blue White Light Green Red High The wavelengths in which it is reflected determine the color of the object Reflectance Low 0.4mm 0.5mm 0.6mm 0.7mm Blue Green Red Source: Jarlath O’Neil-Dunne ©2005 Austin Troy
13
The Physics of RS Spectral reflectance r = E(r)l/ E(i)l
Introduction to GIS The Physics of RS Spectral reflectance r = E(r)l/ E(i)l Or the proportion of reflected to incident radiation The power of emitted photons in each wavelength depends on the surface. An RS sensor can detect spectral responses from objects in various wavelength ranges. Each class of objects has a different spectral responses across wavelength Spectral reflectance values of an object can be plotted on a graph as a function of wavelength, known as a spectral reflectance curve. ©2005 Austin Troy
14
Introduction to GIS The Physics of RS Each object feature class on the earth has a spectral reflectance curve that helps us to identify it remotely. This is why we can use RS to tell the difference between types of objects A spectral response pattern delivers much more information than a single pixel value Spectral response usually plotted as an “envelope” of values rather than a line, because the relationship varies within a range for a given class of object RS sensors only look at small portion of the x axis ©2005 Austin Troy
15
Introduction to GIS The Physics of RS A spectral reflectance curve for several very different classes of object; note how different the responses are Source :Lillesand and Kiefer Remote Sensing and Image Interpretation Wiley and Sons ©2005 Austin Troy
16
The Physics of RS Introduction to GIS Source: Jarlath O’Neil-Dunne
©2005 Austin Troy
17
Introduction to GIS The Physics of RS A Spectral reflectance curve for two classes of similar object: conifers and deciduous trees Note how visible band is similar, but near IR band is very different: means eye could not pick this up The shape of an objects curves will determine what bands we use to ID it Source :Lillesand and Kiefer 2000 ©2005 Austin Troy
18
The Physics of RS Panchromatic B&W: can’t tell deciduous from conifer
Introduction to GIS The Physics of RS Panchromatic B&W: can’t tell deciduous from conifer Infrared B&W: can clearly see deciduous because higher reflectance in those wavelengths Source :Lillesand and Kiefer 2000 ©2005 Austin Troy
19
The Physics of RS Introduction to GIS Green Reflectance
NIR Reflectance © Space Imaging © Space Imaging ©2005 Austin Troy
20
Introduction to GIS The Physics of RS RS hardware’s ability to sense in these non-visible wavelengths allow us to visualize things we normally could not perceive with the human eye, like water temperature Source :Lillesand and Kiefer 2000 ©2005 Austin Troy
21
Introduction to GIS The Physics of RS Here’s one showing suspended sediment in San Francisco Bay Source :USGS ©2005 Austin Troy
22
The Physics of RS Atmosphere has a big impact on RS imagery
Introduction to GIS The Physics of RS Atmosphere has a big impact on RS imagery Scattering of light degrades the image in shorter wavelengths, particularly the ultraviolet and blue The scattering causes “noise” which reduces contrast in these wavelengths ©2005 Austin Troy
23
Introduction to GIS The Physics of RS Many wavelengths are also absorbed by gases in the atmosphere, including CO2 and O3 Source :Lillesand and Kiefer 2000 ©2005 Austin Troy
24
So, what are RS data? RS imagery is raster data
Introduction to GIS So, what are RS data? RS imagery is raster data Each pixel has a geographic coordinate and reflectance/intensity value, or digital number (DN). The dimensions of the area represented by a single pixel defines the resolution High resolution images have small pixel size, like 1 meter square, while coarse images have large pixel size, like a square kilometer ©2005 Austin Troy
25
Introduction to GIS ©2005 Austin Troy
Source:
26
Introduction to GIS RS Data RS data also have “radiometric” resolution, which is the smallest change in reflectance value, or intensity level that can be detected by the system This, like with any raster image, is determined by the data bits The fewer pixel values, the less realistic, and the more abrupt the changes look ©2005 Austin Troy
27
Introduction to GIS Panchromatic imaging With panchromatic imaging, the sensor is a single channel detector sensitive to radiation in a broad wavelength range. Where the wavelength range coincides with the visible range, the resulting image resembles a "black-and-white" photograph. The physical quantity being measured is the apparent brightness of the targets. The spectral information or "colour" of the targets is lost. ©2005 Austin Troy
28
Multispectral Imaging
Introduction to GIS Multispectral Imaging With multispectral, or multiband data, there are several layers, or values for each pixel, each representing a different “channel” or reflectance in a wavelength spectrum Each “band” or “channel” is sensitive to radiation within a different band of wavelength, through use of different filters The sensor takes an average value for the spectral window in which it is sensing; that is, it averages the curve within that region ©2005 Austin Troy
29
Multispectral Imaging
Introduction to GIS Multispectral Imaging Grass R E F L C T A N (%) 40 Concrete 30 20 10 BLUE GREEN GREEN RED 0.8 1.3 Wavelength (micrometers) 0.4 0.5 0.6 0.7 Visible Near IR Source: Jarlath O’Neil-Dunne ©2005 Austin Troy
30
Multispectral Imaging
Introduction to GIS Multispectral Imaging These bands can be combined to make “composite” images, can be looked at separately, or can be analyzed using overlay analysis methods One band from a multispectral image would be displayed as a grayscale image, with each pixel represented by a grayscale value When three layers are combined, they can be assigned to the three color channels (red, green, blue) to make a display that appears to us in humanly visible colors, although they may represent colors outside the visible spectrum and may not coincide with the real colors ©2005 Austin Troy
31
Multispectral Imaging
Introduction to GIS Multispectral Imaging Here is an example of three bands, green, red and near infra-red displayed separately as grayscale Near-infra red green red ©2005 Austin Troy Source:
32
Multispectral Imaging
Introduction to GIS Multispectral Imaging The “best” combination of bands will depend on what the object is that’s being sensed, and what it’s spectral response curve looks like. For instance, if we go back to the conifer-deciduous example from before, we know that near IR is the key band for differentiating the two, but this won’t be so for all object types The key is to get the band that best shows contrast between two feature classes that may be indistinguishable to the human eye ©2005 Austin Troy
33
Multispectral Imaging
Introduction to GIS Multispectral Imaging True color composite: where bands are assigned to color channels in such a way that colors in the image roughly correspond with the colors in the real world. Often assigned red to red, green to green and blue to blue can result in this Another is a false color composite, which shows colors that don’t really exist in that location. An example is color infrared composite, where green band is assigned to blue display channel, red is assigned to green and Near IR is assigned to red ©2005 Austin Troy
34
Multispectral Imaging
Introduction to GIS Multispectral Imaging Composite images are usually displayed by assigning three of the bands to the red, green and blue channels and displaying them additively. This can be done in image processing software. For instance, here in AV we can assign bands to channels in the legend editor ©2005 Austin Troy
35
Multispectral Imaging
Introduction to GIS Multispectral Imaging Band Composite Output = Color Guns = Band Combination = 7 4 2 (LANDSAT) BLUE GREEN RED NEAR IR SHORT WAVE IR MID- LONGWAVE IR 1 Landsat TM Band 2 3 4 5 7 6 Source: Jarlath O’Neil-Dunne ©2005 Austin Troy
36
Multispectral Imaging
Introduction to GIS Multispectral Imaging Here are examples of simulated normal color composite (top) and simulated IR color (bottom) Other bands can be used for composites as well Source :Lillesand and Kiefer 2000 ©2005 Austin Troy
Similar presentations
