Hi, I’m Zack Holden. Today’s presentation is on remote sensing techniques for measuring post-fire effects.

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Todays lecture will begin with some of the fundamental physical principles behind remote sensing techniques. We will discuss some of the sensors that are now available, and how characteristics of those sensors may influence their ability to detect post-fire effects. We will then focus on the Landsat sensor as well as some of the common methods that use landsat for mapping burn severity and post-fire effects. We will conclude by discussing a new method, char fraction mapping, that has recently been developed for measuring fire effects.

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We have already talked about some common field measures of burn severity. However, Satellite imagery has become a fundamental tool for measuring and mapping active fires and post-fire effects. Most fires are simply too large to characterize on the ground. Some fires occur in steep terrain or in wilderness areas without road access. An aerial view is critical for even a basic assessment of damage caused by a wildfire. In addition, there are post-fire characteristics that we can measure from the air that would be virtually impossible to measure from the ground For example we may want to measure the total area burned by a wildfireground. For example, we may want to measure the total area burned by a wildfire. Or perhaps we are interested in the size of or patterns of severely burned patches, that combined with information about topography could be used to predict where post-fire erosion is likely to occur. Again, it becomes almost impossible to make these kinds of measurements when fires become large.

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Satellite imagery has become a commonly used tool in both fire science and management, and maps of burned areas derived from satellite images are commonly used in the field by managers. During or immediately after large destructive wildfires, teams of specialists called BAER teams may be called in to mitigate post-fire damage. BAER teams are typically made up of hydrologists, engineers and biologists. Using satellite imagery, they identify severely burned areas. They then attempt to prevent or stabilize post-fire erosion by a number of different methods that depend on the post-fire conditions Rapid access to aerial imagery is critical forthat depend on the post-fire conditions. Rapid access to aerial imagery is critical for this kind of work.

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A variety of post-fire effects are now commonly mapped using remotely sensed data. Several satellite sensors including Landsat and MODIS are commonly used to map the area burned by wildfires. We can also measure crown damage and overstory vegetation loss as well as vegetation recovery. More recently, methods have been developed for mapping the post fire burn severity.

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Government land management agencies including the National Park Service and the Forest Service now commonly use burn severity assessments from satellite imagery to measure and monitor the effects of fires on public lands. Most large National Parks now use Landsat satellite imagery to delineate area burned and to map post-fire effects. A nationally funded project called the Monitoring Trends in Burn Severity Project is now underway to map all large wildfire events across the United States during the last 20 years using Landsat imagery. Most resource managers and fire management officers won’t be required to process and analyzemanagers and fire management officers won t be required to process and analyze data from satellite imagery. This imagery is usually provided by the USGS. However, satellite imagery has some important strengths and limitations. It is important for users of these data to understand where satellite-derived burn severity data come from as well as the strengths and limitations of these data.

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We will briefly review some basicWe will briefly review some basicprinciples behind remote sensing.‘Remote sensing is the science ofobtaining information about an objectfrom measurements made at a distancefrom measurements made at a distancefrom the object. A camera is an example ofa type of remote sensing. Remote Sensingof the Earth

Light energy from the sun interacts withthe surface of the earth. Some of thatenergy is absorbed some is reflected

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energy is absorbed, some is reflectedback into space, and some is absorbedand re-emitted at different wavelengths.Satellite sensors record the radiationreflected back from the earth’s surface

Data fromdozens of airborne and space-borne sensors are available, some of them at little or no cost. To understand the characteristics and limitations of these satellite sensors, we must understand basic principles of electromagnetic radiation. Light travels in the form of waves. We see light in the visible wavelengths of the electromagnetic spectrum. These wavelengths are also referred to as Photosynthetically Active Radiation or PAR because this is the light energy used by plants for photosynthesis). Although we cannot see radiation beyond the visible wavelengths of the electromagnetic spectrum We can build instruments (e gwavelengths of the electromagnetic spectrum, We can build instruments (e.g. satellite sensors) that can detect and measure light that occurs outside the visible range. X rays, microwaves and radio waves are all forms of electromagnetic radiation that we use in our daily lives.

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We can describe electromagnetic radiation in terms of wavelength and frequency. Frequency simply means the number of waves that pass per unit of time. Wavelength is the distance between two successive waves. Wavelength and frequency are intuitively related to each other. Higher frequency radiation has smaller wavelengths. Longer wavelength radiation has lower frequency. Characteristics of electromagnetic radiation, in turn, determine the amount of energy contained within a quantity of electromagnetic radiation. High frequency, short wavelength radiation contains more energy than longer wavelength radiationshort wavelength radiation contains more energy than longer wavelength radiation. As an example, think about the types of radiation that are harmful to people. Radiation from the sun contains small amounts of ultra-violet radiation containing so much energy that our skin is unable to filter all of it. X-rays and deadly Gamma radiation are also forms of high frequency radiation that contain enough energy to be damaging to plant and animal tissues. This relationship between energy and frequency will become important when we look at the characteristics of different sensors available for remotely sensing land surfaces.

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The curve you see here is called a spectral response function, with wavelength of light on the x axis and intensity on the y axis. The pattern of this curve tells us where light is being absorbed and where it is being reflected. This spectral response curve is for a plant. The basic principal that you need to understand is that light which strikes an object can either be absorbed or reflected. Plants appear green to the human eye because they reflect most light in the green wavelengths of the electromagnetic spectrum. Changes in the color or structure of vegetation under the same light conditions would produce a slightly different spectral response in certainsame light conditions would produce a slightly different spectral response in certain regions of the electromagnetic spectrum. It is by measuring the differential absorbtion and reflection of different wavelengths of light that we infer things about the surface of the earth.

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Live green plants absorb solar radiation in the photosynthetically active radiation (PAR) spectral region, which they use as a source of energy to drive photosynthesis. Leaf cells have also evolved to scatter (i.e., reflect and transmit) solar radiation in the near-infrared spectral region. This portion of the electromagnetic spectrum carries approximately half of the total incoming solar energy; more radiation than a plant can effectively deal with. Absorbtion in this portion of the EM spectrum would result in overheating and damage to plant tissue. Hence, live green plants tend to reflect NIR radiation and absorb red wavelength rediationtend to reflect NIR radiation and absorb red wavelength rediation.

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In a burned forest canopy, incoming radiation from the sun will interact differently with the earths surface depending on the characteristics of the vegetation and the ground. Looking at this picture, your eyes will tell you than more radiation in the green portion of the electromagnetic spectrum is being reflected in the unburned area to the right, while more brown is being reflected by the scorched trees at the center of the picture. Recall also that Light is also being reflected and absorbed in areas of the electromagnetic spectrum that can’t be seen with the human eye.

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The utility of different sensors for measuring things on the ground depends on the characteristics of that sensor. These characteristics are determined by basically 3 things: The optical properties of the sensor (what wavelengths of light is the sensor sensitive to) the distance at which the satellite orbits the earth, and the instantaneous field of view of the sensor, or pixel size. If a satellite orbits near the earth, it will have higher resolution (smaller pixels) but will take longer to return to the same place on the earth. The distance the satellite orbits the earth and its instantaneous field of view or pixel size determine the wavelength of light that the sensor canfield of view or pixel size, determine the wavelength of light that the sensor can detect. Recall from our discussion of the electromagnetic spectrum that electromagnetic radiation at longer wavelengths contains less energy than high frequency, shortwavelength radiation. This means that sensor with high spatial resolution and small pixel sizes generally aren’t sensitive to longer wavelength radiation. The important point to remember is that there are always tradeoffs between pixel size, spectral resolution and satellite revisit time.

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There are literally hundreds of satellites orbiting the earth, many of which provide data that are available to the public. This slide shows the same burned area in New Mexico as seen by 4 different satellite sensors. The Quickbird satellite is a privately owned sensor with 2.4 meter pixel sizes, the highest commercially available spatial resolution. These data are quite expensive. The ASTER sensor is housed aboard the NASA TERRA satellite. With 15 meter pixel sizes, It has slightly higher spatial resolution that Landsat. The MODIS sensor is also housed aboard the NASA TERRA satellite MODIS data are freely available but with 250 meter pixel sizesTERRA satellite. MODIS data are freely available, but with 250 meter pixel sizes have coarse resolution compared with other sensors that are commonly used to map burn severity.

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Again, these images of a burned area in northern Idaho demonstrate some of the tradeoffs between spatial and spectral resolution. You can see with the naked eye that Quickbird’s higher resolution provides greater detail of features on the ground that Landsat data. The resulting classifications of these two images, taken of the same place at the same time provide remarkably different outputs. Although you may never process or analyze remotely sensed data, be aware that a variety of tools are available to land managers. Also be aware that although remote sensing may sometimes be sold as the solution to many problems many of these data types aresometimes be sold as the solution to many problems, many of these data types are still being evaluated in for utility in specific applications.

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The Landsat family of sensors are most commonly used for remotely assessing post-fire effects. Therefore, we will now focus on this sensor and some of the techniques employed using Landsat data for detecting post-fire effects. The first Landsat sensor was launched in 1972. An updated sensor called Landsat TM was launched in 1982. This sensor provides most of the data that we now use to map historical and current fires. Landsat data have 6 reflectance bands. Three of these bands are in the visible wavelength of the electromagnetic spectrum. The remaining 3 bands are in the Near-infrared and middle infrared portions of the electromagnetic spectrumNear-infrared and middle infrared portions of the electromagnetic spectrum. Landsat has 30 meter spatial resolution and a single scene is 180 x 180 kilometers squared.

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One of the most common methods of analyzing Landsat data is to calculate spectral indices. Indices are dimensionless mathematical calculations using 2 or more measurements taken from the same location. Spectral indices derived from imagery are simply the ratios of 2 or more bands that optimize change in some phenomenon of interest.

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One of the best known and most commonly used spectral indices in the Normalized Difference Vegetation Index or NDVI. The NDVI takes advantage of the differential absorbtion of red wavelength radiation and reflectance of near-infrared wavelength radiation exhibited by healthy green vegetation. This figure shows spectral response curves for vegetation with different amounts of healthy green vegetation measured by the Leaf Area Index. With increasing leaf area index values, more near infrared radiation is reflected and more re radiation is absorbed. Changes in the amount of green vegetation will change the amount of light reflected in each of thesegreen vegetation will change the amount of light reflected in each of these wavelengths. So for example, removal of green biomass from an area will increase the amount of reflected red wavelength radiation and decrease the amount of reflected near-infrared radation. By calculating the ratio of nearinfrared to red radiation, we optimize the effective change in vegetation for a given pixel or area. An increase in the amount of biomass at a site should result in a higher NDVI value, while removing vegetation should decrease the NDVI value.

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The NDVI can be used to detect fire caused changes in vegetation cover. However, a much more widely used index for inferring post-fire effects is called the Normalized Burn ratio, or NBR. As with the NDVI, removal of vegetation by fire results in a reduction in the amount of near-infrared radiation that is reflected. The Landsat band 7 is sensitive to soil properties. When fire removes aboveground vegetation or exposes mineral soil, more radiation in this region of the electromagnetic spectrum is reflected. The NBR is a measure of change in these two bands that has been shown to be strongly correlated with post-fire changebands that has been shown to be strongly correlated with post-fire change.

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A commonly used application of the normalized burn ratio is called the dNBR. The dNBR is obtained by subtracting the post-fire NBR from the NBR calculated from a pre-fire image. This calculates the amount of post-fire change relative to the pre-fire conditions.

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The dNBR is a good measure of current post-fire canopy condition. Several studies have demonstrated strong statistical relationships between the common field measures and corresponding dNBR values. The dNBR is most effective 1 year after a fire.

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The dNBR is often used in association with what has become a common field measure of burn severity called the Composite Burn Index of CBI. However, several studies have show a strongly non-linear asymptotic relationship between the CBI and dNBR. The reason for this relationship is not entirely clear. However, ideally, we would like our field measures of severity to be directly and linearly related to what we are inferring from satellite imagery. This suggests that there may be alternative methods with which we can remotely evaluate burn severity.

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One recently developed method is a modified version of the dNBR called the Relative Differenced normalized burn ratio, or RdNBR. The relative dNBR appears to correct for a couple of problems in the dNBR. The figure shown here demonstrates one of these problems. In the top figure, a moderately dense canopy forest with an initial dNBR value of 400 experiences a severe stand replacing fire. The resulting dNBR value is 375. In the second figure, a dense canopy forest with a pre-fire NBR value of 800 experiences only a moderately severe fire, resulting in a dNBR value of 400 Although the effects of the fire in the moderately dense forestdNBR value of 400. Although the effects of the fire in the moderately dense forest are clearly more severe, we see that it is theoretically possible for the two areas to have the same post-fire severity value as measured by the dNBR. The relative dNBR corrects for this effect by dividing the overall dNBR value by the initial pre-fire NBR. Thus the resulting post-fire severity value takes into account the pre-fire stand condtions.

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As we see from this figure, the relative dNBR also appears to improve the relationship between field and sensor data by reducing the heteroskedasticity in the data. It also appears to linearize the relationship between the CBI and the corresponding satellite measure.

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The dNBR is most often used 1 year post-fire. However, information about post-fire effects is often needed immediately. BAER teams use NBR maps created days or weeks after a fire to select sites for rehabilitation and post-fire erosion. While the dNBR and relative dNBR are good predictors of current canopy condition, they are not necessarily good predictors of 1 year post-fire response.

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Alistair Smith and colleagues recently began testing a new method of remotely sensing post-fire effects called char fraction mapping. The data presented in the next few slides are from the 2000 Jasper fire that burned in the black hills of South Dakota.

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After a fire, several things are common to almost all fires across vegetation types. The ground surface is made up of either live green vegetation, dead brown vegetation and black char. Exposed mineral soil is also present, but we’ll ignore this for now because it varies a lot across the landscape. You or I could go out to almost any fire and measure the amount of of each of these components covering the ground after a fire. It turns out that using a remote sensing technique called spectral linear unmixing, we can break individual pixels down into these component fractions as wellfractions as well.

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How well is the immediate post-fire char fraction mapping method able to predict 1 year post-fire measures compared to the DNBR?

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As we have seen, the DNBR is a good measure of post-fire canopy condition, and both do a decent job of predicting post-fire tree cover.

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Interestingly, the char fraction method is actually able to predict bole scorch fairly accurately, where the DNBR could not do this at all.

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Similarly, the char fraction method is able to predict 1 year post-fire litter organic weight, a measure of sub canopy ecosystem condition post fire. These results, while preliminary, highlight the potential for this method to actually predict some post-fire measures of ecosystem condition.

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