Remote Sensing and Unmanned Aerial System
Technology for Monitoring and Quantifying
Forest Fire Impacts
Michael G. Wing 1,*, Jonathan Burnett1, John Sessions1
Forest Engineering, Resources, and Management, Oregon State University, Peavy Hall 204, Corvallis, OR 97331, USA
E-Mail addresses: michael.wing@oregonstate.edu (Michael G. Wing); jonathan.burnett @oregonstate.edu (Jonathan
Burnett); john.sessions@oregonstate.edu (John Sessions);
1
* Author to whom correspondence should be addressed; Tel.: +1-541-737-4009; Fax: +1-541-737-4316
Abstract
Fire is a regular occurrence throughout the world’s forested
landscapes and affects millions of hectares annually. A variety
of remote sensing applications have been developed to
quantify wildfire impacts in forests with varying success.
Remote sensing technology applications for wildfires have
typically involved quantifying burn severity, fuel levels, and
forest resource recovery following burn. Wildfire remote
sensing applications have recently included active or real-time
technology applications for mapping burn impacts and
wildfire detection and monitoring. A review of traditional
remote sensing applications was presented in this paper to
quantify fire impacts, tracking vegetation recovery,
establishing fuel conditions, and fire detection and monitoring
in forested landscapes; then recent examples of active and realtime remote sensing techniques were examined to monitor fire
ignition and behavior, followed by an assessment of potential
future applications. The application of unmanned aerial
systems (UAS’s) with both spectral and thermal sensors may
hold great promise for future remote sensing applications
related to forest fires.
Keywords
Wildfire, UAS, remote sensing
Introduction
Fire is a regular occurrence in forested landscapes
throughout the world. Worldwide, millions of hectares
burn annually in fire-adapted ecosystems in large areas
of Africa south of the equator, central Asia, southern
South America, Australia, and many areas of the boreal
forest in Russia and Canada [1]. In the U.S., it has been
estimated that 74,126 wildfires affected over 2 million ha
of land in 2011 [2]. Institutions such as the Global Fire
Monitoring Center (University of Freiburg) have been
instrument in bringing the world’s fire situation to the
attention of a global audience. The Global Burned Area
Product derived from a satellite remote sensing system
(SPOT Vegetation) was perhaps the first important step
towards obtaining prototype baseline data on the extent
of global wildland fires [3].
Researchers have investigated a variety of remote
sensing applications to quantify wildfire impacts in
forests with varying degrees of success. Traditional
wildfire remote sensing applications have considered
how best to quantify burn severity, potential fuel levels,
and the rate of forest resource recovery following burn.
More recently, wildfire remote sensing applications
have considered active or real-time technology
applications that include implications for not only
mapping burn impacts, but also detecting wildfires
shortly after ignition and tracking the rate of wildfire
spread across a landscape. A review of traditional
remote sensing applications was presented in this paper to
quantify fire impacts, tracking vegetation recovery,
establishing fuel conditions, and fire detection and
monitoring in forested landscapes. Our review includes
previous research results that have been published
within the last ten years, as well as previous results that
were frequently cited or that provided unique or novel
examples of remote sensing applications to forest fire
research. In addition, recent examples of active and realtime remote sensing techniques for monitoring elements
related to monitoring fire ignition and behavior were
discussed followed by investigation on the applications
1
and potential of unmanned aerial systems (UAS’s) for
remote sensing activities related to forest fire
management.
Quantifying Forest Fire Impacts
The quantification of forest fire impacts involves
assessing the extent and severity of burn across a
landscape. A common satellite platform for this work is
Landsat Thematic Mapper (TM) or Enhanced Thematic
Mapper Plus (ETM+) imagery with a ground resolution
of 30 m for its primary spectral bands. Landsat TM
sensors capture seven spectral bands and one thermal
band, with a 16-day repeat cycle. Another lesser used
sensor for remote sensing of forest fires is the Landsat
Multispectral Scanner (MSS) with a ground resolution of
79 m. MSS imagery is no longer acquired by the Landsat
Program.
Two other sensors that have been applied by multiple
forest fire studies include the Moderate Resolution
Imaging Spectroradiometer (MODIS) and Système Pour
l'Observation de la Terre (SPOT). MODIS is a
multidisciplinary instrument designed to measure
biological and physical processes on a global basis of
every one to two days. MODIS acquires data in 36 bands
distributed across the visible to thermal infrared regions
of the electromagnetic spectrum with spatial resolutions
depending on band from 250 m to 1 km. Two NASA
satellites, Aqua and Terra, are equipped with MODIS
instruments. SPOT is managed by the French Space
Agency (CNES) and currently hosted by three satellites.
The latest SPOT sensors (SPOT 5) have ground
resolutions that span 2.5 to 20 m and include five
spectral bands. SPOT has a return period of 26 days.
Researchers have developed a number of indices for
these purposes including the Normalized Burn Ratio
(NBR) and differenced NBR (dNBR) that make use of
multiple sensor bandwidths to evaluate burn impacts.
The Normalized Difference Vegetation Index (NDVI)
has also been used to measure forest vegetation
response to fire and makes use of multiple sensor bands.
Many previous studies have also endeavored to evaluate
burn impacts on-the-ground and have developed
inventory methods to establish impacts that can be
related to results derived from remote sensing
classifications. A prominent method for ground
inventorying is the Composite Burn Index (CBI). The
CBI divides a burned landscape according to burn
severity, typically described as low, moderate, or high.
Salvador et al. [4] used Landsat MSS data to map fire
scars within the Catalonia region located northeast of
Spain. Landsat data from 1975 to 1993 were used to
calculate NDVI values throughout a 32,100 km2 study
area. NDVI values were analyzed using two approaches
to determine areas that had been burned and to assess
whether burn areas were subjected to more than one fire
episode. Local records of fire occurrence were used to
quantify whether fire scar identification was accurate.
The two different fire scar analysis approaches resulted
in a 53% and 44% accurate identification of fires that
were greater than or equal to 30 ha. Omission errors
were 24% and 9% for fires greater than or equal to 30 ha.
As the size of fires increased, the percent of accurate
identifications increased. The fire scar identification
methodology was adopted by another study [5] that also
mapped fire scars within the same region. Fire
frequency models were used to simulate the observed
fire frequency and calculate a fire rotation period.
Sunar [6] investigated the use of remote sensing in
assessing post fire damage in Turkey. Landsat TM and
other ancillary data were combined to develop a
computerized decision support system for forest fire and
fuels management. Spectral profile analysis was used
with the satellite images to identify unique features on
the landscape. Vegetation indices (ratio between two
values from independent spectral bands) and
classification were also used. The vegetative quality
present after a fire is much poorer than before the fire;
the spectral properties of these nutritionally deficient
areas are very distinctive and significantly different
compared to normal vegetation. Because of this
property, Sunar was able to use spectral reflectance
properties to identify damaged areas and classify
degrees of damage to these areas as well using a
correlation between fire severity and plant health. A fire
information system combined with remote imagery
could provide significant assistance in quantifying fire
impacts.
Bobbe et al. [7] collected field data for an accuracy
assessment of Burned Area Reflectance Classification
(BARC) products used to create burn severity maps.
Field data were collected from seven wildfires located in
California, Colorado, Oregon, and Utah. A BARC was
2
performed for all wildfire databases using Landsat TM
data. Field data collection was used to provide a burn
severity classification for BARC product comparison.
Field plots were categorized according to burn severity:
unburned, low, moderate, and high. Classification
accuracy assessments were performed for both single
pixels and for field plots that were at least 40 acres in
size. Accuracy results were similar for both single pixel
and 40 acre minimum size. Overall, accuracies were
higher for the unburned and high severity categories.
Low and moderate severity burn areas were not as
accurately classified, with low severity burns being the
least accurate classification category. The reason for this
low accuracy was attributed to field methods whereby a
field plot that showed any sign of burn influence was
scored as having at least a low severity burn influence.
The 30 m pixel size of the Landsat TM data would likely
assign an unburned category to areas that were only
minimally burned.
A dNBR produced by both hyperspectral Airborne
Visible and Infrared Imaging Spectrometer (AVIRIS)
and Landsat ETM+ sensors was examined for ability to
measure burn severity by van Wagtendonk et al [8].
AVIRIS has a nominal spatial resolution of 17 m. Preand post-fire data from a study area in Yosemite
National Park (California, U.S.A.) was used in the
analysis. In addition to imagery, field crews collected
data from plots within the burn area and classified burn
severity as high, moderate, or low severity. A dNBR was
created from both sensors and demonstrated
comparable patterns of fire. The AVIRIS and Landsat
ETM+ sensors were judged to be effective at capturing
differences in fire severity across a landscape.
Correlation with the ground plot samples was 89% for
Landsat ETM+ and 85% for AVIRIS. Although the two
sensors were both judged to be useful to quantify burn
severity, the finer spatial resolution of AVIRIS was
apparent during data viewing. Although not rigorously
examined, the authors believed that the improved
spatial resolution of AVIRIS yielded potential for
improved results through improved classification
algorithms or classification techniques.
Epting and Verbyla [9] investigated the use of satellite
imagery to monitor and categorize vegetative response.
Photos taken before a 1986 fire in interior Alaska were
digitized into the computer and used as a baseline.
Successive images collected from Landsat TM and ETM+
were then used to classify areas based on spectral
reflectance. The data collected was analyzed to
determine the amount of vegetative change and
development using NDVI. With the use of Landsat
images and aerial photos, the relationship between burn
severity index and pre- and post-fire vegetative
response was studied. Results indicated that pre-fire
vegetation had a large influence on burn severity, with
coniferous forests having a much higher fire severity
than deciduous. It was also suspected that broadleaf
forests may possibly provide a barrier to fire spreading
due to the patterns seen on the aerial photos. Epting et
al. (2005) [9] were able to use the satellite data to classify
pixels into burn severity classes and track changes over
time.
Key [10] addressed several variables that affect remote
sensing capabilities with regard to fire: spatial
resolution, sampling time of year, and time since the
fire. A NBR index and its correlation to fire variables
were examined. Studies were done on fires throughout
the US that were previously analyzed and classified
using Landsat TM data. Spatial sensitivity of the remote
sensing was divided into intra- (Alpha) and inter-(Beta)
site variation. Landsat TM sensors have an approximate
30 m ground resolution, and the author explained that
while this can show general effects, many individual
ecological effects cannot be determined at such a scale.
With 30 m resolution, differences between burn sites (i.e.
a transition point from a ground fire to a crown fire) can
be lost and specific boundaries overlooked. Seasonal
timing also affects the ability of remote sensing to
accurately detect and classify fires; if the surrounding
vegetation is very green (spring) it is easy to
discriminate edges. Conversely, if the data is collected
after the fire season when many plants’ leaves are
changing color, it can be difficult to delineate
boundaries. Key [10] also discusses how the time since
the fire burned can affect the data collected. Waiting 212 months after the fire is out to collect post-vegetation
data is judged to be the most effective because it can
capture survivorship and delayed mortality more
effectively than data that is collected sooner. Remote
sensing as related to fire is affected spatially, temporally,
and radiometrically. By examining these three variables,
the optimal time period for data collection can be chosen
with greater confidence.
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Lentile et al. [11] applied remotely sensed data to
characterize burn severity and examine landscape
patterns across different forest landscapes. Fire areas
within California, Montana, and Alaska were used as
study locations. Landsat TM images were taken one year
before each fire and one year after each fire were used to
characterize landscape patterns in the fire mosaic. A
NBR was calculated from the images and was the
primary measure of burn severity. After creating
landscape maps of the fires, the researchers found that
the percentage of land in each burn severity class (high,
medium, and low) varied greatly between sites. For the
fires studied in the continental United States, only about
5% of forested land was classified as high burn severity.
Approximately 30% was classified as unburned, 20% as
low severity, and 40% as medium severity (values are
estimated from tables provided in the manuscript).
Alaska was the only exception to this general trend, with
over 50% of burned area classified as high severity.
Patch size was also highly varied, with patches
appearing to be largest in unburned and low severity
areas and smallest in high severity burn sites. Results
from Alaska appeared to also differ from general trends
with the largest patches located in high severity burn
areas. Post-burn remote sensing supported the
investigation of fire effects on a landscape level and the
identification of landscape patterns.
Lewis et al. [12] applied airborne hyperspectral data to
classify burn severity in the California chaparral and the
corresponding effects on erosion mitigation efforts.
Spectra of remaining vegetation (alive and dead) were
collected extensively using an ASD Pro-FR field
spectroradiometer after the fires, along with the Probe-I
whisk broom sensor from ESSI. The ASD Pro-FR
collected spectral data about soil, rock, and green nonphotosynthetic vegetation using a bare tip foreoptic
pointed at the target material with a sampling interval of
1.4-2 nm over a wavelength range of 350-2500 nm. The
Probe-I sensor collected hyperspectral imagery of
reflected EM energy over five flight lines that each
corresponded to a 512 pixel swath with a 4.2 m Ground
Instantaneous Field of View. Landsat 5 TM data were
collected to compare NBR values with hyperspectral
image results.
Both the Landsat data and the
hyperspectral data were then compared to field
measured cover estimates to determine how accurately
the remotely sensed data classified cover type. Lewis et
al. (2007) [12] found that green vegetation and charred
vegetation were highly correlated with ground data, and
therefore provided the most reliable cover estimates.
Robichaud et al. [13] processed hyperspectral imagery
and field data to measure and compare burn severity
assessment methods within an area in Colorado, U.S.A.
An airborne hyperspectral sensor collected imagery at a
5 m resolution which was later processed using a partial
spectral unmixing algorithm. Results indicated that the
unmixed hyperspectral image classification, in
comparison to Landsat-derived burn ratio information,
resulted in an enhanced ability to detect ash, soil, and
both green and burnt vegetation. These characteristics
provide utility in quantifying fire severity and
watershed response. The authors identified several
hindrances to working with hyperspectral imagery
including timely image capture, comparatively large
image files, the need for consistent flight paths, and
canopy cover.
French et al. [14] reviewed 41 studies that considered
satellite remote sensing applications for assessing fire
severity in North American boreal forest zones. The
studies included Landsat TM and MSS, MODIS, and
SPOT imagery spanning spatial resolutions from 20 to
500 m. Many of the studies (26) used Landsat-derived
NBR values to map burn severity while others
additional
indices
that
involved
multi-sensor
applications, different sensor band ratios, or other
surface measures of fire impact. Average classification
accuracy was 73% for studies that applied NBR ratios
but variation ranged from 50 to 95%. Classification
accuracy varied according to sensor ground resolution
with finer resolution sensors outperforming others.
Landscape influences, such as topographic shadowing,
also affected classification accuracy. Analysis of
previous studies also indicated that moderate-resolution
sensors were more sensitive to aboveground vegetation
rather than landscape surface conditions as reflected by
soil or soil cover characteristics. French et al. [14] also
analyzed a sub-group of the 41 studies in which the CBI
was utilized to evaluate NBR approaches. Although
there is generally a correlation between the CBI and
NBR for measuring burn severity, correlations vary
widely depending on time, location, vegetation strata,
and NBR index approach. These uneven results suggest
that while there is potential for NBR as a burn severity
4
mapping tool, this potential may not always be
achievable.
forest landscapes and briefly examines Landsat TM
applications for classifying fire severity.
Mitri and Gitas [15] applied IKONOS imagery to
quantify burn severity within Greece. Burn severity was
classified with the IKONOS imagery according to three
impact levels: heavy, moderate, and light burn. Field
data collection gathered on-the-ground inventory
information for an independent assessment of burn
severity for comparative purposes. An overall image
classification accuracy of 83% was reported with the best
accuracy occurring within the heavily burned category,
followed by the moderate and light burn areas. The
primary limitation of IKONOS for assessing burn
severity was thought to be an inability to penetrate
dense canopy. Classification errors between the
moderate and light burn areas were suspected to be a
result of tree crown shadowing and spectral limitations
from IKONOS sensors. The authors speculated that
IKONOS could be combined with RADAR or LiDAR to
improve future burn severity mapping.
Wulder et al. [17] examined Landsat TM/ETM+ imagery
and LiDAR for detecting changes in forest composition
following fire within a boreal forest in Alberta, Canada.
Pre- and post-fire Landsat and LiDAR data were
analyzed to determine whether burn impacts could be
accurately assessed. Whereas the Landsat data were
examined for ability to capture post-fire change in
horizontally distributed forest structure, the LiDAR data
were utilized to assess vertical structure for fire impacts.
Several burn ratio indices were calculated from the
Landsat data and all indices were found to produce
similar utility for describing post-fire landscapes. LiDAR
data were collected along transects within the study area
and examined for ability to detect forest structural
changes related to fire. Fire influences were observable
in measures of vegetation fill, crown closure, and
canopy height that were derived from the LiDAR data.
LiDAR was judged to be potentially useful as a means of
describing post-fire impacts and structural changes to
forest conditions.
Keeley [16] addressed the usage of the terms fire
intensity and fire severity. Fire intensity is described as
“the physical combustion process of energy releasing
from organic matter.” In forestry, the term ‘intensity’ is
sometimes used inappropriately and has been construed
into a new term, fireline intensity. This is the rate of heat
transfer per unit length of a fireline. Fire severity is a
term that was developed to account for ecological
repercussions and affects of fire intensity. Several
indices have been developed to classify fire severity,
with many indices using categorical titles such as
unburned (heat has no direct effect on plants), scorched
(plants lose leaves from radiant heat), light (surface litter
consumed), moderate/severe (understory and some
canopy trees consumed), and deep burning/crown fire
(canopy trees killed and organic soil layer consumed).
All of the indices provide some indication of fuels
consumed and organic matter lost in the soil. With
Landsat remote sensing applications, many researchers
have been able to relate the NDVI and burn severity
estimates. One specific example [16] was in southern
California in 2003, where Landsat TM was found to be
accurate in classifying fire severity when compared to
data collected on the ground. Others have attempted to
relate fire severity to ecosystem response variables with
NBRs with little success. Keeley [16] provides
clarification and guidance for terms related to burned
Many of the remote sensing studies that have appeared
over the last ten years have concentrated on quantifying
forest impacts with space-based sensors. Typically these
studies have employed Landsat TM imagery but other
imagery, such as MSS, MODIS, Spot, AVIRIS, IKONOS,
and LiDAR have also been applied. A number of burn
indices, such as the NBR, dNBR, and other derivatives
have been developed which take advantage of sensor
bandwidth combinations in quantifying burn presence
and severity. Often, the results of burn indices are
compared against ground-based inventories such as the
CBI in order to assess effectiveness. The results of these
comparisons have been encouraging in some studies
and have proven less advantageous in others. One
synthesis article [14] had determined that although NBR
ratios taken from a number of studies had overall
positive classification success rates for burn severity, the
range of accurate classification was large and varied
from 50 to 95%. Whether these classification success
rates are acceptable will likely vary project-by-project.
Researchers have generally found the relatively coarse
resolution (30 m2) of Landsat TM to be a hindrance in
some cases in detecting the imprints of smaller and of
less severe fire impacts in some instances. The relatively
5
fine scale of LiDAR imagery [17] has shown promise for
quantifying forest structural changes but not much
research has considered LiDAR applications in burned
forest landscapes. Some studies have found potential
advantages in classification flexibility with increased
resolution or multispectral imagery ([8] [13]). Areas that
have experienced low burn severity or extreme burn
severity are sometimes more accurately classified in
remotely sensed imagery ([7] [12] [15]) due to their
spectrally contrast with areas of moderate burn severity.
The effectiveness of remotely sensed data can also
depend on seasonality [10] if vegetation has passed
through the primary growing season and is in decline,
differences from burned vegetation may be less
apparent. This finding might encourage the use of
remote sensing imagery that is captured during times of
vegetative vigor and growth.
Post-fire Recovery and Regeneration
A multitude of inter-connected resources are impacted
by forest fires and include soils, hydrology, and
vegetation, among others. Following fire and depending
on the severity of the fire, forested resources are likely to
begin to recover at varying rates. A primary concern for
many land managers following wildfire is the rate at
which forests will regenerate and the types of
regenerated vegetation that will become dominant
across a landscape. Remote sensing applications for
post-fire recovery have attempted to couple assessments
of fire severity or fire impact with observed vegetation
regeneration. In some cases, regeneration rates have
been compared with rates that have been observed by
previous studies.
Fraser and Li [18] investigated the accuracy and utility
of SPOT VEGETATION (VGT) to measure burned area,
post-fire regeneration age, and forest biomass in boreal
Canada. This sensor is comparable to AVHRR and
includes a short wave infrared channel. It is the SWIR
reflectance that the authors were most interested in, as
preliminary studies have shown that this channel is
highly effective at differentiating between burned and
unburned areas. Because of the specific reflectance of
VGT, it accurately distinguished between burned and
non-burned areas better than the conventional method
of using NDVI. It was also found that stand age up to
thirty years could be determined by the sensor. This
cutoff point is due to the reflectance of regeneration
becoming less distinct over time as the canopy becomes
denser and leaf area increases, which causes the foliar
moisture to increase and the SWIR reflectance values to
decrease. Overall, VGT was effective in differentiating
between burned areas and was highly effective in
measuring post-fire regeneration age. It is hoped that
data collected with VGT can be used to supplement
ground-based data and provide information relating to
forest fire extent and post-fire regeneration for remote
forest areas.
Riano et al. [19] used the spectral mixture analysis
(SMA) properties of AVIRIS (Airborne Visible/Infrared
Imaging Spectrometer) to analyze post fire vegetation
recovery in the Santa Monica Mountains of California.
Scenes from AVIRIS at a 30 m resolution were acquired
both pre- and post-fire and validated against a regional
vegetation map to ensure the accuracy of the pre-fire
images. Data from AVIRIS was also atmospherically and
geometrically corrected. The authors also established
several control plots to utilize as indices of what the
normal vegetation would look like. It was shown that
remote sensing (specifically the use of AVIRIS and
NDVI) can be extremely useful in examining post-fire
vegetation recovery provided that the data is compared
with other methods, such as on the ground control sites
and historical fire maps. The data comparison can help
ensure accuracy and correct classification of satellitederived vegetation layers.
Diaz-Delgado et al. [20] examined the effects of fire
severity on plant regeneration through the use of
ground based data and images collected from Landsat
TM imagery in the northeast province of Barcelona,
Spain. A reference map was developed via field data
and then supplemented with remote sensing images.
Eight images taken from the Landsat data over
successive periods were used to monitor post fire
vegetation and develop maps of fire severity and
vegetation response. Identifying “damage” values on
the images for each pixel allowed for the creation of a
map with fire severity gradients. “Damage” was
calculated by utilizing the fact that NDVI values are
known to drop after a fire. It was calculated as the value
found after subtracting NDVI values from pre- and postfire images. These maps were then used to look for
correlations between plant and soil fire damage and
regenerating areas. A significant relationship was found
between the ground fire damage classes developed on
6
the images and vegetative recovery. While this method
of monitoring post fire vegetative response in relation to
fire severity was found to be extremely effective in the
authors’ study, more research is needed before this
application can be applied as a predictive measure.
Lentile et al. [21] analyzed a large 2000 fire in the Black
Hills of South Dakota to examine how fire severity is
related to patch structure, fire-scars, and tree
regeneration. Burn severity was classified as low
(surface), moderate (mixed surface and torching), and
high (stand replacing). A burn severity map was
produced in 2003 with Landsat 7 TM imagery at a
spatial resolution of 30 m [22]. The authors studied
spatial relationships between fire patches using spectral
reflectance values to classify the burn severities, and also
conducted field studies to collect burn information. Field
plots were situated according to the burn severity map
and a fire patch database. The authors concluded that
patch size and tree regeneration were significantly
different between fire severity classes and that very few
remaining live trees were not affected by the fire in some
way. Remotely sensed data enhanced ground based data
collection methods by stratifying the fire mosaic more
accurately.
MODIS and Landsat ETM data were applied to estimate
burn severity and fuel treatment effects in Arizona,
U.S.A [23]. Landcover databases included not only
burned areas that had been subjected to prior fuel
treatments but also an unburned reference site. The
Landsat ETM data were effective at identifying burn
severity and produced results that were consistent with
previous research that found that fuel treatments
reduced fire severity in ponderosa pine forests. In
addition, roads, watershed drainages, and topography
appeared to be correlated with fire severity patterns. The
MODIS data was at a spatial resolution of 250 m and a
temporal spacing of 16 days. The temporal spacing was
used to examine pre- and post-fire NDVI values over a
seven-year period. Areas with fuel treatments appeared
to experience less probability for high severity fires in
contrast to other burned areas. Vegetation recovery and
succession rates also appeared to be correlated with fuel
treatment occurrence. Areas that were classified as
having higher burn severity levels were found to have
higher vegetation recovery rates. While the MODIS 16
day temporal pattern was judged to be adequate for
examining vegetation response trends, a more
frequently temporal pattern was considered potentially
capable of producing additional insight.
Remote sensing of post-fire recovery and regeneration
rates is an important application for monitoring the
types and rate of vegetation change in a burned forest
landscape with significant implications for future forest
conditions in the decades following fire. Although there
have been applications of Landsat TM and other coarse
resolution imagery for monitoring post-fire conditions
[20] [21] [23], higher resolution imagery likely holds
more promise for quantifying stand age and other
vegetative growth characteristics [18]. One large
hindrance to monitoring recovery rates is the required
multi-temporal component so that vegetation condition
can be assessed over multiple time periods following a
fire event. Without two or more subsequent
observations following fire, little can be discerned
regarding vegetation recovery. Given the cost of
accessing and classifying imagery, the cost of post-fire
monitoring can be a significant obstacle for many
organizations.
Fire Detection and Monitoring
Fire detection and monitoring can involve various
activities. Detection activities can seek to identify
historical evidence of forest fire occurrences that might
be evidenced by vegetative regrowth or fire scars on the
landscape. Detection can also imply that it is the
identification of a fire outbreak that is of interest. Being
able to identify that a forest fire is occurring has
traditionally been accomplished by posting fire
observers in prominent locations where they are able to
view a large portion of the landscape. More recent
approaches have examined whether remote sensing
technologies such as LiDAR might be able to identify
smoke plumes and alert fire officials that an area might
necessitate closer inspection.
Fuller [24] addressed different methods of satellite fire
detection, whether it be active fires or old fire scars.
Fires are detected both thermally and/or optically,
depending upon the satellite capabilities. Some thermal
detection is unreliable due to saturation, and straight
visual identification through image interpretation is
often difficult due to cloud cover and other atmospheric
variables. Fire detection via coarse-resolution remote
sensing include NOAA AVHRR (detected with a single
7
threshold value), DMSP-OLS (with a spatial resolution
of 0.56-2.07km), GOES-8 (highly sensitive to light
sources and passing over large swaths of land with a
spatial resolution of 1km on visible channels and 4km
on infrared channels), and MODIS (using temperature
thresholds to distinguish fires in smoldering and
flaming stages with a resolution varying between 250 m,
500 m, and 1 km). Fire scars from fires already burnt
can be detected with Landsat TM and SPOT XS
(resolution of 10 m) through spectral analysis (no
assessment of accuracy or reliability was given). NDVI
is another method that is commonly used to identify fire
scars. The authors also discuss how remote sensing can
improve the ability to calculate and evaluate gaseous
emissions and aerosols.
Isaev et al. [25] examined whether remote sensing could
significantly improve the fire monitoring system
currently in effect in Russia. The study took place in
east Siberia. The authors used images from civilian
satellites, national security systems, and aerial photos to
categorize and assess forest fire damage. They also
investigated post-fire recovery and fire detection. Postfire damage was assessed using an NDVI derived from
SPOT and RESURS-O/MSU-E satellite data with a 30 m
resolution. Aerial photos were used as references for
satellite data and compared to ensure the accuracy of
satellite imagery classification.
The authors also
calculated carbon emissions based on a database
developed from forest stocking and structure in pre- and
post-fire conditions derived from aerial photographs.
Establishing this baseline data helps support research
into greenhouse gas emissions and human contributions
to atmospheric pollution. Remote sensing techniques
were found to be accurate in delineating forest fire types
and intensities. The combination of both civilian remote
sensing systems and declassified national security
system images, which have higher resolutions, has
strong potential to aid in the detection and assessment
of forest fires.
Utkin et al. [26] explored LiDAR applications for
locating smoke plumes in Portugal and discussed
several potential benefits of these applications. Smoke
plume location techniques could provide benefits by
locating remote fires and those that are out of the line of
sight of lookouts. By detecting smoke plumes instead of
actual flames, LiDAR location techniques could help
locate a fire in an initial development stage. To do this,
automatic LiDAR scans must be set within a defined
area. This approach also allows for two-dimensional
maps to be created that show the horizontal extent of
smoke particles in the atmosphere.
The authors
acknowledge that significant work is yet need to so that
certain conditions do not inadvertently initiate LiDAR
scans. Things such as fog, trees, birds, and high-tension
cables can cause false alarms because of the intense
backscatter they produce. Atmospheric phenomena can
also cause false alarms. The authors concluded that
LiDAR could be productively applied for smoke plume
detection with certain modifications so that accurate
results are achieved.
Muller et al. [27] investigated particles in the
troposphere over Germany that were identified as
having come from forest fires in Canada and Siberia.
Dual wavelength Raman LiDAR was used. For this
experiment, Raman LiDAR used laser pulses generated
at 355, 532, and 1064 nm to develop profiles of particle
volume to calculate particle backscatter coefficients with
the use of nitrogen vibrational Raman signals. The
backscatter coefficients of the data collected were then
examined. These backscatter values had increased over
the time period of about a year, and researchers were
able to attribute the increased particle matter in the
atmosphere to residual particle release from forest fires.
To determine where the particles came from, a
backward dispersion model called FLEXPART was
initiated that calculated trajectories of the particles.
Muller et al. (2005) [27] were also able to distinguish
between human-related emissions particles and biomass
burnt particles by the size of the particle. With LiDAR,
the authors were able to detect change in an atmospheric
condition (increased amounts of particles causing haze)
and to trace this change back to its source (intense forest
fires).
Lentile et al. [28] provided a comprehensive list of
current remote sensing satellites and techniques
available for fire detection and monitoring. These space
and airborne sensors are particularly important for
wildfire data collection due to the remote nature of
many forest fires and difficult accessibility. Both coarsescale and high-resolution imagery are available, and
both can be used for varying purposes related to
quantifying fire. The authors discuss passive and active
remote sensors, both of which can be either aerial- or
satellite-based. Optical and thermal energy can be
8
detected by sensors as well as sensors using thermal
energy to estimate radiation from a fire as it burns.
Methods are also available to assess cover/surface
change over a given area and to map perimeters of
burned areas through the characteristics of spatial
heterogeneity. Also addressed are future issues of
remote sensing of fires, including the integration of field
work with remote sensing and perfecting analysis at
spatial and temporal scales. This paper provides a
comprehensive overview of current abilities of remote
sensing in active fire detection and monitoring post-fire
effects.
Wang et al. [29] reviewed the capacity and accuracy of
satellite-derived indices to detect forest fires in Georgia
USA and Greece. Indices investigated were NMDI
(normalized multi-band drought index), NDWI
(normalized difference water index), and NBR. NMDI
uses two liquid water absorption bands while NDWI
only uses one. NBR was used to discriminate burn
affects by using NIR (near infrared) and SWIR (short
wave infrared) sensors. The ratio between these sensor
wavelengths reacts uniquely to burn effects and is able
to provide a good indicator of burn severity. Overall,
NMDI was superior at recognizing and detecting forest
fires though the combined use of SWIR and NIR sensors.
NMDI only gives direct information on fire size and
location, but some information regarding fire intensity
can be inferred from the pixel values. This method was
found to be reliable in several locations and judged not
to be site specific. Therefore, it is applicable to many
different areas to detect active fires, is not dependent
upon vegetation type, is manner, and has the potential
to become a large-scale forest fire location technique.
NBR and NDWI had little success locating fire hot spots,
although NBR did slightly better than NDWI.
Fire detection and monitoring have been investigated
using a variety of remote sensing approaches.
Previously, observers were placed in prominent
landscape positions and asked to keep an eye open for
fire-related conditions. Remote sensing techniques have
sought to use thermal bandwidths for heat detection and
light source sensitive bandwidths to detect fire flames
[24] within active fires. These approaches place an
emphasis on rapid access to imagery and the ability to
classify imagery with confidence within an abbreviated
period if the results are to be of use. Although a number
of remote sensing databases may be available within a
specific region [25], the integration of these databases
may be challenging in a timely manner. Indices have
also been developed that seek to detect forest fires [29].
Smoke plumes have also been examined for their ability
to be detected by laser-based devices [26] which could
return a shorter-than-expected range should a plume
reflect the light energy. These systems could potentially
be automated but weather conditions and other objects
could potentially lead to false positive results, thus
hindering system reliability. Other light-based sensors
have been applied [27] to detect biomass burnt particles
in the atmosphere with the inference being that forest
fires contributed the burnt particles.
Forest Fire Fuel and Risk Mapping
Forest fire fuel mapping is an attempt to describe the
potential for fire across a landscape. Variables in this
approach can simply focus on vegetation and canopy
structure, or can involve topography, weather
conditions, and other landscape components. A limiting
factor in this area appears to be the ability to accurately
and precisely capture the spatial dimensions of
landscape factors, particularly in regards to vegetation
moisture and weather conditions. Nonetheless, some
previous research results have reported fuel assessments
that have been at least moderately correlated with
ground surveys of fuel conditions.
Riano et al. [30] described the development of largescale spatial and temporal fuel maps to aid in natural
resource planning and management. The study was
done in Cabaneros National Park in central Spain.
Landsat TM and digital elevation models were used. An
exhaustive field study was undertaken to use as a
comparison to the satellite derived data. Landsat TM
images were chosen because of their relatively good
spatial and spectral resolution for large land areas.
Areas on the images were then classified into fuel types
and cover types by using a maximum-likelihood
algorithm. Classification was also done by using
vegetation height, density, and spectral response. By
using both spring and fall images, the authors were able
to greatly improve the accuracy of the fuel maps by
taking into account differing plant phenology at
different times of year (i.e. seeing underground
vegetation in a deciduous forest in the fall).
Atmospheric disturbances were adjusted for using a
9
correction factor to reduce the external affects on
vegetative reflectance. The authors were also able to
verify the accuracy of the map to about 83% by
comparing vegetation boundaries identified on the
ground to the satellite-derived vegetation map.
Erten et al. [31] used Landsat TM images taken before
and after a forest fire in Turkey in combination with GIS
to map boundaries and estimate vegetation loss. A
model for predicting forest fire risk was also developed
that integrated satellite data. The spectral characteristics
of the classified land cover categories were linked to
unique information class values to develop a map of the
area. Different categories (slope, distance to roads,
vegetation types, etc) were weighted differently to
determine fire risk in specific areas. After finishing the
project, the authors concluded that remote sensing is a
useful tool to determine fire risk and behavior and could
support forest management activities related to fire.
Rollins et al. [32] addressed the need for spatially
explicit, comprehensive fire regime information by
presenting a method to use remote sensing, field
sampling, and modeling efforts to create maps of
predictive landscape fuel maps. Extensive field
sampling was done in northwestern Montana, along
with the creation of GIS map layers to develop an
accurate appraisal of vegetative structure and several
types of gradients (spectral, physiographic, ecological,
etc). Landsat TM 5 images were used to collect spectral
data as spatial predictors for fuel mapping. These
predictor variables were raw reflectance, spectral
transformations, and ancillary parameters. Landsat data
was then used to delineate “landscape polygons” that
were used to simulate weather and biogeochemical
variables. Overall classification accuracy of the data was
fairly low (50-80%), but the authors believed their
methods could be improved upon to enhance future
fuels and fire mapping efforts. Remote sensing was
judged to be very useful for creating gradients across
large areas. In addition, the combination of remote
sensing techniques with field sampling, simulation
modeling, and gradient analysis allowed for an efficient
initial fuel mapping process.
Andersen et al. [33] examined the ability of LiDAR to
accurately record multiple tree attributes (crown height,
crown density, etc) in relation to canopy fuel loading.
Data was collected with a helicopter mounted SAAB
TopEye LiDAR system and compared to data collected
on the ground. Although there were some discrepancies
in the data comparison, the authors concluded that the
LiDAR system performed capably in measuring stand
attributes as related to forest fuels.
LiDAR data
collection was found to be much faster than traditional
on-the-ground methods and could be quickly
downloaded into GIS or some other fire modeling
system, such as FLAMMAP of FARSITE to create
landscape maps of canopy fuels over the landscape.
Canopy fuel load data collected via LIDAR was
determined to be potentially more accurate and could
also better predict forest fire spread and intensity. This
study was done in the U.S. Pacific Northwest in the state
of Washington.
In searching for a cost effective way to map and
characterize forest fuels, Falkowski et al. [34]
investigated the accuracy of ASTER (advanced
spaceborne thermal emission and reflection radiometer),
a sensor located on NASA’s Terra platform. ASTER is
capable of 15 m resolution data. Traditionally, forest
fuels mapped via aerial methods have been attributed
with upper crown vegetation types and models. With
ASTER, the authors were able to look at various land
attributes in higher spectral resolution and combine
them with gradient mapping to develop data compatible
with FLAMMAP and FARSITE, two prominent fire
models. The data collected by ASTER was found to have
accuracies at least as good as traditional methods when
measuring surface and crown fuels. This collection
method is potentially more affordable and effective than
typical remote sensing in mapping vegetation and forest
fuels.
Toomey and Vierling [35] examined the ability of remote
sensors to classify foliar moisture content and
subsequent effects on forest fire potential. Their study
site was in the Black Hill of South Dakota. Recently,
laboratories have found a correlation between short
wave infrared reflectance (SWIR) and foliar moisture
content in conifers. Toomey and Vierling [35] utilized
this correlation to develop SWIR moisture indices.
Three Landsat TM images and two ASTER images were
georectified and used in the study. It was found that
Landsat TM performed better when estimating moisture
content than ASTER; perhaps due to cross-talk between
spectral bands that may have contaminated the SWIR
spectrum and affected the satellites ability to pick up
10
foliar moisture. It is hoped that foliar water content can
be measured in conjunction with other fuels data to help
assess fire danger and fuel loading.
Srinivasan and Narasimhan [36] estimated the KeetchByram Drought Index (KBDI) in real time (daily) using
GIS and remote sensing technologies. The KBDI is a
widely used drought/fire index that indicates the
amount of moisture in duff and upper soil layers. To
calculate the KBDI, daily precipitation data was
collected using NEXRAD, a Doppler radar, and
combined with AVHRR data from the NOAA series of
satellites (NOAA-14 and NOAA-15) to estimate land
surface temperatures and weather station data to
provide long-term maximum air temperature. The use
of the AVHRR data reportedly permits calculation of the
KBDI at the 1 km x 1 km level. The daily KBDI is used
to allocate burn days as well as station fire suppression
personnel.
Mbow et al. [37] used the Kauth and Thomas Tasseled
Cap Transformation (TCAP) approach on Landsat-TM
images for the purpose of assessing risk of intensive fire
propagation in savanna ecosystems. TCAP transforms
six reflectance bands from Landsat TM data into three
indices representing Brightness, Wetness, and
Greenness. The TCAP Wetness and brightness indices
were combined into an algorithm for fire risk level
discrimination. At the beginning of the management
fire period, FARSITE was used to determine areas that
would potentially burn in tree-shrub, woodland, and
shrub savannas. The simulated burns matched very
closely to the actual burns during the subsequent fire
season.
Describing fuel loadings across a landscape is essential
to identifying locations where forest fire potential is
high. This activity has significant implications for
human welfare and safety where development contacts
or merges into the forest environment. Landsat TM
imagery has been used prominently for fuel mapping
and fire risk assessment in previous studies ([30] [31]
[32] [35] [37]). Imagery at both finer ([33] [34]) and
coarser [36] resolution has also been applied for forest
fire fuel quantification.
Identifying risk may involve moving beyond classifying
potential fuels to include other information sources such
as slope and other landscape features [31]. High
resolution LiDAR has been used to record individual
tree dimensions across a landscape [33] so that highly
precise measurements of canopy conditions could be
assessed for fuel potential. This method has significant
potential for increased efficiency over ground-based
methods that lead to individual tree metrics. Fire
modeling programs could make use of this increased
resolution canopy attribute information in order to
model potential fire spread from specific ignition sites.
Challenges remain, however, for rapidly and reliably
classification of LiDAR imagery across large landscapes
to derive tree attributes expediently. A need exists to
develop approaches and algorithms that are capable of
pulling individual tree metrics efficiently from LiDAR
data.
Real-time and Near Real-time Forest Fire
Applications
Real-time and near real-time technology applications
seek to make information available shortly after the
information is recorded. Data of interest include fire
detection, hot spot location, fire perimeter mapping, fire
temperature, and fire intensity. A variety of methods
including stationary, on-ground sensors, manned and
unmanned aircraft platforms and satellites have been
used. These range from monitoring systems with remote
camera capabilities that can record still or video images
of fire conditions including smoke plumes and fire
movement or use weather sensing instrumentation that
is sensitive to smoke particles in the atmosphere or
unusual temperature changes to various platforms for
multi-spectral imaging. Regardless of approach, these
technological advancements can allow forest fire
monitoring and suppression efforts an increased ability
to detect and respond to fire events.
The earliest fire detection methods using manned fire
towers and human patrols relied upon visual
identification of fire starts. The manned fire tower was
probably the most common symbol of fire protection.
Visibility was limited to a radius of 10 to 25 km and at
one time more than 5000 towers were in use in the
United States. Beginning in the 1940’s, fixed tower
detection has been supplemented with aerial detection
from manned planes and by the 1960’s airborne infrared
scanners had been introduced. Originally only used for
initial detection, infrared sensors are now used to detect,
monitor, and direct fire suppression and mop-up
operations [38]. A combination of high resolution digital
11
thermal imaging and LiDAR mapping systems that take
advantage of real-time differential GPS are now
available as fire mapping systems that provide orthorectified imagery mosaics within an hour of landing.
Hard copy, soft copy, and downloads to GPS-enabled
hand-held computers can be used to locate recently
detected hot spots.
Interest in unmanned fixed towers increased in the
1990’s. Originally developed in South Africa in 1993, the
use of tower-mounted video cameras linked to remote
monitors and camera control permits wildfire detection
without direct human supervision. The semi-automated
systems use motion and scene-change detection
algorithms to detect smoke and then alert an operator
[39]. The camera rotates each three minutes. The alerts
provide a time-date stamp for later fire investigation as
well as integrating directly into the GIS system. The
operator can then take direct control of the camera to
follow suppression operations, identify spot fires, and
continually update fire operations control on the fire
situation. Existing communication towers can be used
which provide better visibility than the shorter, manned
towers. Other possible applications include monitoring
for landslides in steep terrain, arson and illegal logging.
Camera mounted detection is receiving increasing
attention in North America. The National Center for
Landscape Fire Analysis (NCLFA) created a remote fire
monitoring system for a portion of the Lolo National
Forest in Montana [40]. The monitoring system includes
a camera coupled with a weather monitoring device that
transmits imagery and data in real-time through the
WWW. The imagery and data are designed to allow
forest staff to monitor conditions within an area in
anticipation of wildfire. Similar camera systems have
been installed near fires in other remote forest areas
within the western U.S. Cameras are powered by solar
energy and have viewing areas that can be remotely
controlled [41]. The remote control capability allows
operators to monitor fire progress and status including
flare-up and re-ignition. These capabilities enable fire
managers to make better informed decisions regarding
the allocation of firefighting crews and other resource
allocations. In addition, fire imagery is sometimes
shared with the public and can act as an information
source. This capability has reduced the workload in
responding to phone contacts regarding fire status.
Emergence of Unmanned Aerial Systems
By 1990, the usefulness of high elevation aircraft in
monitoring large fires and multiple fires in North
America had been demonstrated. Aircraft, as opposed
to satellites, have the ability to survey as needed without
fixed trajectories, and can loiter at fires with flight
altitude adjustable to satisfy resolution and coverage
requirements. By 2000, the potential for collecting,
transmitting, and converting thermal infrared data from
aircraft into a GIS format in near-real time had been
demonstrated. However, manned flights have
limitations in terms of mission duration, mission safety,
and cost. In the late 1990’s, NASA and the US Forest
Service (USFS) began studying the application of
unmanned (sometimes termed uninhabited) aerial
vehicles (UAV) to support fire mitigation efforts [42].
UAV’s, also known as unmanned aircraft systems
(UAS), have the ability to operate in areas that are
dangerous for manned observation and the ability to
operate for flight periods of 24 hours or more (Figure 1).
A civil industry has begun to emerge around UAS
technology. Due to relative newness of the industry and
the heavy restrictions on flights, relatively few wildfire
application studies have been published. However,
research and development efforts for UAS applications
to wildland fire management are ongoing.
12
Figure 1. The Prioria Maveric UAS platform.
The United States (US) federal government, specifically
the USFS, stands at the forefront of wildfire UAS
application development. In 2001, Ambrosia et al. [43]
reported the results of a USFS and NASA funded
experiment using an ALTUS II UAS to collect thermal
imagery over a controlled burn. The ALTUS II was
based on the Predator military aircraft, had a wingspan
of approximately 17 m, a length of approximately 7 m,
and was capable of high altitude, long endurance flights.
The UAS contained a thermal scanner with a 2.5 m pixel
resolution at 944 m AGL. Near-real-time imagery georectification
capabilities
and
a
satellite-based
communication system for transmitting and accessing
data provided actionable images to the Incident
Command Center within 10 minutes. The system was
tested in 2001 on a controlled burn in southern
California. Successes included an enhanced ability,
through the satellite-based communication system, of
delivering image data to ground locations and improved
accuracies of image geo-rectification. These successes
bolstered the integration of collected imagery into
existing GIS databases in support of fire management
activities. Barriers to implementing UAS technology
included communication antenna that limited flight
patterns, the lack of an on-board differential-GPS (dGPS)
to provide improved altitude positions over barometric
pressure instrumentation, and limits on the transmission
of information used for imagery geo-rectification. This
mission served as the precursor to the more advanced
demonstrations of the Ikhana experiments.
A major advantage of high endurance UAS’s over
manned aircraft is the lack of crew rest cycles. In a UAS
ground station personnel can be relieved without
disrupting flight operations, however, in manned
aircraft range and data resolution are affected by ~10
hour limitation of flight crews. Most UAS are also
lighter than equivalent manned aircraft and have greater
fuel economy. To demonstrate the value of this
13
capability NASA and the USFS experimented with a
civilianized MQ-9 UAS. In 2006 they revealed the Altair
which is an experimental UAS owned by NASA based
on a modified Predator B (MQ-9) platform, more
commonly known as the Reaper [44]. The Altair flew
one fire mission in which it demonstrated the ability to
detect fire hotspots at the Esperanza fire in California
using the autonomous modular sensor (AMS) for
wildfire detection. AMS is a 16-channel infrared scanner
with onboard dGPS and georectification software
capable of near real-time imagery dissemination.
Shortly after the success of this demonstration, a USFS
and NASA joint venture acquired the Ikhana, which has
a 20 m wingspan MQ-9 and equipped it with the AMS.
The aircraft was also equipped with automatic collision
avoidance sensors and a satellite transceiver for overthe-horizon communication capable of providing
imagery to the Incident Command Center via the
internet. From 2006 – 2009 the UAS piloted AMS sensor
conducted 35 flights on 60 fires with 207 hours of flight
time. These missions produced hundreds of near realtime (NRT) images that were used for hotspot detection
and post-fire damage assessments [45]. One major
success was the detection of an unseen fire front
occluded by heavy smoke, which resulted in the
evacuation of 10,000 at risk residents [44; 45].
Following the successes of the Ikhana platform there
have been increased efforts from a consortium of
government agencies to test a variety of platforms in
various wildland fire monitoring roles. In 2009, The
University of Alaska-Fairbanks (UA-F) was issued an
emergency COA by the FAA allowing the use of the
Insitu ScanEagle UAS to be flown in support of wildfire
monitoring and mapping at the Crazy Mountain fire in
Alaska. This is significant because UA-F was the first
non-government entity afforded this opportunity by the
FAA [46]. In 2011 three UAS platforms, a Prioria
Robotics Maveric, USFS/USGS Raven A, and USAF G2R
UAS were flown at Eglin Air Force Base as part of the
Prescribed Fire Combustion-Atmospheric Dynamics
Research Experiments (RxCADRE) exercise [47]. Testing
revealed the effectiveness of small UAS for providing
real-time surveillance while carrying research payloads
such as those used to measure in-situ convective
dynamics, burn consumption, and radiant heat release.
More recently, the USFS has begun to organize their
efforts for UAS integration. In mid-2012, the USFS
formed the UAS Advisory committee [48]. This
committee acts to identify niche applications and seeks
to use UAS to augment, not replace, manned aerial
platforms.
One specific strategic vision of this
committee is a dual approach to UAS integration where
a large, high altitude UAS is supported by small low
endurance UAS. As part of this effort the USFS tested a
SIERRA GP-SAR which is a moderate altitude smaller
UAS (relative to the Ikhana). The purpose was to collect
actionable imagery on topography and fuel structure
with ground penetrating synthetic aperture radar (SAR)
[49]. The same year they tested the AAAI RS-16 which is
a small catapult launched 39 kg UAS equipped with
electro-optical and infrared sensors. The platform has
14-hour endurance demonstrated the capability to
provide real-time imagery for fire perimeter tracking
and hot spot detection [50]. They have also
experimented with the capabilities of small low
elevation UAS’s such as the joint USFS/USGS sponsored
Raven A mentioned previously.
Outside of U.S. government sponsored experimentation,
there have been significant gains both nationally and
internationally. Ollero et al. [51] tested the concept of
autonomous formation flying under the COMETS
project. Their objective was to conduct real-time
coordination and control of multiple heterogeneous
unmanned aerial vehicles. Testing occurred in May 2003
and 2004 in Lousã and Castanheira de Pera, near
Coimbra, Portugal with two helicopters (Marvi and
Heliv) and an airship (Karma). UAS’s were equipped
with dGPS and cameras that were capable of varying
capability and temperature sensors. The autonomous
onboard flight computers were loaded with a mission
planning system (MPS) which is a decision architecture
and perception system designed to integrate the three
UAS’s and provide each platform instructions based on
sensor feedback. During the field experiment, the
COMETS system the UAS provided imagery of the fire
from different positions and successfully demonstrated
the use of automated flight path control using the MPS.
The authors noted some concerns with flight path
deviations from the instruction set but ascribed these to
high winds.
Merino et al. [52] further examined the feasibility of
using a formation of heterogeneous aerial vehicles
coordinated by a central station for fire monitoring. This
was a follow-on experiment to the COMET program
14
with the intent of demonstrating the much improved
Central Perception and Central Decision systems. These
systems were developed to coordinate each UAS for task
planning and allocation. Similar to the COMETS
program, the UAS formation uses two autonomous
helicopters and an autonomous blimp. The system was
field tested on a large prescribed burn in Serra da
Gestosa Portugal. With the provision of initial activation
from an operator, the Central Decision system
automatically instructed one helicopter to patrol an area
and identify possible fires. Upon recognition of fire, the
second helicopter responded and the two aircraft
worked in tandem to precisely map the location and
extent of the fire. Once the full extent has been
identified, the blimp receives instructions to begin a
detailed mapping mission. Successes of the experiment
included automatic fire detection based on spectral
signature intensity, automatic length and area
measurements using photogrammetric techniques, the
ability to map the evolution of the fire front using an
infrared camera and the capability of autonomous UAS
coordination. At the conclusion of the experiment, the
authors expressed the need for longer endurance UAS’s
in real-world fire missions while impressing the value of
having multiple UAS’s on the scene.
Kiefer et al. [53] tested the use of a UAS as a sensor
platform to improve models estimating water emission
from wildfires. Previous methods used towers to
measure water emission and heat but data was of
limited value due to the strategic placement necessary to
avoid extreme temperatures. The goal of the study was
to directly measure plume temperature and humidity
properties to understand and quantify water vapor
release in wildfires. Two remote controlled (RC) aircraft
were equipped with 90-gram radiosondes and flown on
a 40.5 hectare prescribed burn in San Jose, CA during
2008. Two flights were conducted and the flight paths
planned in such a way that that aircraft would cross the
center of the fire plume over burn plots. The airplanes
were able to measure temperature flux and water vapor
mixing ratio. During the second flight turbulence
increased with the arrival of a “sea-breeze” but the
aircraft was able to continue its mission without any
interruption. In addition to sensing temperature and
water vapor mixing, the aircraft was able to conduct
soundings to create a vertical profile of the smoke plume
and compare it to ambient conditions outside the plume.
The experiment demonstrated the capability to fly a low
cost, low risk asset into the smoke plume in order to take
measurements necessary for modeling convection
dynamics for fire behavior prediction.
In 2010, Barado et al. [54] proposed a communication
system that used tethered balloons equipped with Wi-Fi
repeaters to create an adhoc network in a remote
location to facilitate data transfer between firefighters, a
UAS, and a control station. In the simulation the UAS
flew continuous temperature mapping missions for hot
spot detection. The resulting images were transmitted to
fire fighters equipped with a Personal Electronic Device
(PED) that displays their relative position to hot spot.
Findings included that repeaters need to have a very
high transmission range of 4 – 8 km and that the
network could be overwhelmed during image transfers.
The ability to link all firefighters with the UAS is a
tremendous boon giving the proposal some merit
because situational awareness is paramount to survival
in a wildfire.
In 2006, Restas [55] reported the findings of Szendro Fire
Department’s experiment using a UAS helicopter to
conduct air reconnaissance of two forest fires at
Aggtelek National Park in Hungary. They determined
that a relatively low altitude flight (~500 m AGL) for 2 –
3 minutes would yield enough information to allow an
incident commander to make a decision. This improves
efficiency and response time because the direction of the
spreading front can be identified and the magnitude of
the fire can be estimated more objectively, which allows
the incident commander to better gauge the resources
needed for a response. Additional conclusions were that
a very large fire would be better served with manned
aircraft and that infrared cameras provided much more
information than either panchromatic or electro-optical
sensors.
In July 2010, The Association for Unmanned Vehicle
Systems International (AUVSI) hosted a Fire Fighting
Tabletop Exercise (FF-TTX) in July 2010 [56]. This event
was purposed with identifying logical requirements for
UAS and unmanned ground vehicle (UGV) integration
in the wildland firefighting arena. Two UAS, the Prioria
Robitics Maveric and the Lockheed Martin Stalker, were
flown to survey a small grass fire in Utah as a concept
operation for wildland fire monitoring. The Maveric
flew with a shortwave thermal infrared sensor (SWIR)
15
and provided real-time monitoring of fire intensity by
loitering in designated areas. The Stalker is a larger UAS
with a 2.9 m wingspan that exhibited similar capabilities
only with a higher resolution pan and tilt IR camera. The
exercise substantiated the value of UAS for wildfire
monitoring while demonstrating the need for more
integrated planning with wildland firefighting entities
Doubtlessly, there will be many innovative UAS
applications for wildfire management as technology
develops. One current example involves automated
delivery of flame retardant to a targeted area. In Spain, a
platform known as FLAMINGO (Forest fLAMes
INtelliGent Outputter) is currently being tested. This
system drops multiple flame-retardant filled bombs
from an aircraft flying at ~3048 m. The bombs are
individually self-guided to an operator-defined point of
impact. Immediately before impact, the guidance system
separates using a parachute for controlled descent so it
can be recovered [57].
UAS technology has progressed substantially over the
past decade. Most platforms can provide real-time fire
monitoring in addition to more specialized capabilities.
Some platforms specialize in collecting high resolution
georectified thermal and electro-optical imagery that is
accessible via the internet within minutes. Other systems
can respond to on-ground conditions to coordinate and
task multiple platforms to characterize and monitor the
evolving fire front. Large systems like the Ikhana have
the benefit of endurance and complex high resolution
sensors but are cost prohibitive, logistically difficult to
deploy and not widely available. Small systems carry
less complex sensor packages and have reduced
endurance (~1 hour) but are easier to task and in some
cases reduce financial risk by providing low-cost
sensors. Overall, the wildfire monitoring potential of
UAS equipped with NRT feedback to the ICC offer a
synergistic advantage when used to augment typical
aerial remote sensing operations, including reduced risk
to human life and the ability to deliver geo-rectified
landscape imagery in near-real-time to locations
throughout the world. However, widespread UAS
integration in the wildland firefighting arena will
require additional policy-level planning to address
issues such as airspace deconfliction, platform selection
and operator training [56].
Conclusion: Potential Future Applications of Remote
Sensing for Forest Fires
The field of remote sensing has seen dramatic
improvements in sensor technology, data processing
techniques, and data processing power as illustrated by
published research over the period 2000-2010. The
usefulness of remote sensing for quantifying fire
impacts, post-fire recovery planning, wildfire detection
and tactical suppression support will certainly increase.
A number of useful burn severity measures have been
derived from remote sensing including NDVI, dNDVI,
NBR, and dNBR. Errors in classification remain and
result in over and underestimates of burn severity
compared to CBI field measurements. Opportunities to
develop relative indexes to improve classification and
ecological interpretation have been suggested [48].
Errors due to drought as well as temporal issues relating
to rate of regrowth can degrade interpretations.
Opportunities to reduce existing uncertainties between
pre- and post-fire effects using different remote sensing
measures might be addressed by introducing
supplemental climate data, wildfire severity, and
latitude to the analysis.
Several authors cited limitations with 30 m resolution in
discriminating among fire effects ([4], [7], [10], [14]).
Remote sensing availability at the sub-meter level is now
available. The availability of additional bands may also
help in discriminating fire effects. IKONOS and
OrbView3 provide 4 m multispectral and 1 m
Panchromatic coverage. Quickbird provides 2.4 m
multispectral and 60 cm Panchromatic coverage.
Another opportunity that may improve classification of
fire effects is hyperspectral imagery. The Airborne
Visible Infrared Imaging Spectrometer (AVIRIS) has 224
spectral bands between 0.4-2.45 um compared to six
bands for Landsat and four bands for SPOT. Platt and
Goetz [49] reported limited improvement is
classification of landscapes in the urban fringe using
AVIRIS. Mitri and Gita [15] suggested combining
RADAR or LiDAR to improve future burn severity
mapping. Both have the capability of penetrating clouds
and canopy and can be used either day or night. Several
satellites with RADAR and LiDAR capabilities are in
orbit but currently concentrate primarily on ocean, ice
sheet and atmospheric measurement missions. Given
the higher intensity of pulse returns with airborne
16
RADAR and LiDAR, it is likely that aircraft will
continue to be the platform for fire applications.
Forest fire fuel mapping has been an active area for the
purpose of protecting lives, assets, forest values. A
limiting factor in this area appears to be the ability to
accurately and precisely capture the spatial dimensions
of landscape factors, particularly in regards to
vegetation
moisture
and
weather
conditions.
Opportunities exist to combine evolving remote sensing
technologies for measuring biomass, soil moisture, foliar
moisture, with advances in weather forecasting. Sensor
technologies are evolving so that finer scale remote
sensing of forest resources is possible through increased
band availability and resolution. Individual tree
measurements taken from across a landscape may have
potential for determining burn severity and forest
regeneration with greater reliability. Wing et al. [50]
examined the potential of the near infrared capabilities
of LiDAR for determining whether individual trees were
impacted by fire.
Disaster management relies heavily on intelligence that
can be assembled in a reliable and timely manner. Delay
in respose to wildfire has serious consequences for both
humans and resources. As sensors and their potential
applications continue to evolve, enhanced spectral and
thermal imaging sensors will permit more critical data
to be derived on fire characteristics with greater
precision.
These
advancements
will
require
improvements in data communications telemetry, so
that faster uploading and downloading capabilities are
supported for communications between sensor
platforms and those who will apply the remotely sensed
information. Faster computing capabilities will also be
needed so that rapid geo-rectification of data can occur
and enhance data integration and map analysis within
GIS systems.
The use of remote sensing to support real-time fire
tactics is in its infancy. While satellites offer valuable
long term monitoring, and UASs have greater potential
to provide enhanced flexibility for positioning and
repeated data collection. As UASs are integrated into
mainstream airspace activities, availability will increase
and costs are likely to decrease. One can foresee the
coupling of near-real-time LiDAR capabilities with UAS
platforms for more precise measurement of resources
impacted by fire. This technology combination may
make LiDAR technology available to a greater number
of organizations, and further its potential for use by
forest managers.
The wildfire management community is in the process
of a great technological leap forward with remote
sensing being a central part. UAS platforms may
provide a pathway to enhance future applications of
remote sensing for wildfire management.
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