When we say “current” and “future” we don’t mean exactly that. All of the methods we’ll talk about are currently in use, but we didn’t want to say “good” and “bad” or “older” and “newer”. We believe that the satellites in the “future” categoryare better for wildfire use and although they’re not perfect, are more likely to be improved in the future than the“current” methods. So for lack of better terminology we used “current” and “future”.

Why?

-Why we need remote sensing and what it can be used for, for wildfires.

  1. An increase in urbanization and human populations living in the wildland-urban interface combined with an increase in recent fire size and severity has resulted in millions of dollars in damage to property and loss of life.
  2. Using images for environmental planning can reduce fire risks for people and homes and they help land managers prioritize areas for fire mitigation and hazardous fuel reduction.
  3. Land managers need cost effective methods for mapping and characterizing fire fuels quickly and accurately.
  4. It’s also quicker and can get images more frequently than using planes and/or field observations. Remote sensing decreases the cost and time needed to map fuels, update fuel maps (especially in areas that see frequent change due to construction, logging, etc), and characterize fuels in models.
  5. Satellites provide a larger coverage area, they are more flexible with areas that want to be looked at and frequent, and the data is easily manipulated. The longest time in-between images is only 16 days with is a lot shorter than when depending on planes or humans.
  6. Satellites increase the accuracy of fuel mapping by using a “vegetation triplet” which incorporates three layers of the canopy including potential vegetation type, cover type, and structural stage, all of which are hard and time consuming to categorize without satellites.
  7. However, even with all of satellites’ benefits, it’s important to know that no singe sensor technology has been proven capable of quantifying all stratas of fuel sources.

Current Methods

  1. Aerial photography with the use of planes.
  2. Field measurements and mapping with the use of humans.
  3. Passive remote sensing. It’s important to note that one of our “future” instruments (Landsat) is a passive sensor, and although it’s good enough to put into our “future” category, it still has a lot of limitations.
  4. Medium resolution which uses satellites but has trouble penetrating the canopy.

Aerial Photography

  1. Although the images are high resolution they have a limited number of wavelength bands (generally only visible images) and limited coverage.
  2. Taking planes up are not only time consuming but they’re also weather dependant, leading to times when pictures are needed but it’s not safe to fly the plane.
  3. It costs more to maintain an airplane than a satellite.
  4. Since visible pictures can only see the surface of the canopy, not only is assessing the canopy subjective to whoever is evaluating it, but as is the understory since little, if any, of it can be seen.

Problems in the Field

  1. Not only is the field obs time consuming itself, but also itdoesn’tallow for frequent obs. To accurately assess fire conditions, land managers need to sample a wide range of topography and vegetation in a time frame that allows for comparison for data sets. This means that obs would need to be taken way before a fire, right before a fire breaks out, during the fire, right after the fire, and some time after the fire. This isn’t possible with manual labor alone.
  2. Visual assessment is very impractical for very large and/or remote areas. And although you can take obs of the perimeter, you can’tget to the middle of the area to take obs during a fire. And on average, visual assessment underestimates fuel loads.
  3. It’s costly both in monetary costs and in data gaps.
  4. Accurate assessment requires frequent updates and this is a method that just can’t keep up.

Passive Sensors

-Passive sensor: sensors that use external energy sources to “observe” an object. For example, sunlight.

-Active sensor: sensors that rely on their own sources of radiation to “illuminate” objects so that the energy reflected and returned to the sensor may be measured. For example, radar.

  1. Effectiveness depends on extensive baseline fuel data. Passive sensors do not easily detect understory fuels since the sunlight just reflects off the top of the canopy.
  2. Passive sensors are good for specific measurements, but may be too specific for forest characterization. For example, Landsat, which we’ll focus on a little later, has bandwidths that are sensitive to surface brightness temperature. It’s selected to maximize sensitive wavelengths of green vegetation.

Medium Resolution

  1. These types of satellites rely on reflectance from thecanopy. This makes it difficult to determine understory components.
  2. The reflectance observed by these satellites is not necessarilyrelated to vegetation height, which is a critical variable to distinguish fuel types.
  3. Field sampling is necessary to effectively assess fuel loads. So jointly with field obs this method can be used to semi-assess fuel load, but certainly not on its own.

LIDAR

  1. Stands for light detection and ranging. This satellite uses a laser to transmit light pulses and a receiver with sensitive detectors to measure backscattered light.
  2. The strength of the pulse can penetrate forest canopies and can record forest structural information.
  3. LIDAR increases the accuracy in fuel characterization, the number of fuel attributes measured, and reduces map costs.
  4. LIDAR estimates the understory height, crown bulk density, crown cover, foliage biomass, canopy height profiles, crown volume, elevation, and tree and canopy structure.
  5. We’re able to get detailed spatial info on forest attributes relevant to fire behavior that may be used in spatial fire behavior models. It removes falsely identified tree crowns and identifies surface canopy types. This is potentially the most accurate measurement of forest structure and fuel characteristics.

-Thepicture is a three dimensional image of canopy structure with pulsed LIDAR. Taken from

Landsat

  1. A multispectral satellite that uses both visible and mid-infrared bandwidths. 0.4-0.7 microns is best for measuring the sunlight absorbed by the canopy.
  2. This is a passive instrument, which has a problempenetrating the forest canopy that limits its ability to map surface fuels. Although the images that you do get are very high resolution.
  3. Some of the surface and canopy characteristics it’s able to measure are percent canopy cover, canopy height, tree biomass, and tree volume.
  4. It’s able to characterize images into vegetation categories and assign fuel models/types to each category. An example of this is the leaf area index, which maps layers of leaves per unit of ground area.
  5. A problem with this instrument besides its inability to see the ground is that when put into models, it has to be recalibrated for each different type of vegetation.

-This is an infrared image where the pink is a recent burn scar (not seen in this image but darker red indicated a still burning fire), dark green being areas of older forest, light green is area of recent regrowth, and blue being clouds and/or smoke. Image taken from

AVHRR

  1. Stands for advance very high-resolutionradiometer.
  2. Originally a meteorology satellite used for cloud cover and sea surface temperatures.
  3. This satellite uses two different bandwidths to make different measurements; visible to detect smoke plumes and thermal infrared for hot spots. The thermal infrared is best at night since the land has cooled down so the temperature difference the land and hot spots are more noticeable. The thermal infrared bands are between 3.6 and 12 micrometers but with a very coarse resolution.
  4. Global data has been collected for 20 years with this instrument at no cost. This has allowed for long term modeling, which is especially good for remote and isolated areas where it’s hard for people to get to.
  5. The problem this satellite does have is that it’s restricted to a limited number of reflectance and thermal bands due to a large pixel size.

-The first picture is a visible image where the origin of the smoke plumes and where the smoke is blowing can be clearly seen. The second picture is the thermal infrared image where the fire hotspots can be seen glowing the brightest.

ASTER

  1. Stands for advanced spaceborn thermal emission and reflection radiometer. It’s a sensor aboard NASA’s Terra Platform and is multispectral with both a visible and near infrared telescope. It’s spatial resolution consists of 0.5-0.6 microns (green), 0.63-0.69 (red), and 0.76-0.86 (near infrared). It has 14 different colors which allows for wavelengths not visible to the human eye to be seen.
  2. This instrument takes measurements of cover type, structural stage, and potential vegetation type. This “vegetation triplet” is then entered into models to model fuel layers and to estimate percent canopy closure, crown bulk density, and surface fuels. In addition to this, vegetation indices such as normalized vegetation index, the simple ratio, and the green-red ratio index have been calculated from ASTER images. It has been confirmed through field obs that the model run with both the normalized and green-red vegetation indexes is the best and most accurate invegetation modeling.
  3. Canopy models combining the previously mentioned indexes is also shown to be the best model fit for canopy closure and bulk density. Fuel characteristics mapped using ASTER images matches local expert knowledge. Since it’s so accurate in areas that are already known about by humans, it’s safe to assume that it would be accurate in mapping areas that humans can’t reach.
  4. Fuel mapping and vegetation mapping is typically validated by field mapping, proving ASTER images and quantitatively accurate.

-the image is a composite of a number of land surface characteristics and ultimately shows land cover change, taken from

Hyperspectral

  1. Hyperspectral satellites show biophysical and chemical info directly related to quality of forest fire fuels; including fuel type, moisture, biomass, fuel condition, vegetation structure/dynamics, vegetation biochemical composition, stress detection, and canopy species identification.
  2. It’s also able to map dominant vegetation types. The only problem with this is since it isn’t able to see every part of the plant’s trunk; it treats the entire trunk as the reflectance that is does observe.
  3. Images are reclassified to standard fuel models, which provide species specific fuel info. Species mapping includes the ability for the images to differentiate between leaves and bark.
  4. Images help classify and map vegetation with data given, but is not always relevant since some plants within the same species may present different fire propagation rates due to fuel load and/or vertical changes.
  5. The biggest problem with this imagery is its limited spatial coverage. It’s best if used with other data sources. Because of its narrow view, it should be targeted towards high-risk areas. It can also be used to improve analysis of broadband data such as Landsat and to predict areas likely to burn or have uncharacteristic effects.

-This image is a map of total chlorophyll content generated from the AISA Eagle sensor, taken from

Conclusions

  1. The use of “future” remote sensing for forest fires is a big improvement from “current” methods.
  2. Even though it’s better, all of the “future” methods have their shortcomings. They should be used in conjunction not only with themselves, but with “current” methods as well, especially field obs.
  3. Although satellites aren’t cheap, over they long term they are more cost efficient than the use of planes and field obs, not only in money but in timespent as well.
  4. Satellites allow for frequent updates and hard to reach areas.
  5. All forest fires are different, both in fuels, burn patterns, how they start, how they end, etc. It’s important when using this information to not generalize it to every fire, but to possible learn of underlying links and processes.