Remote Sensing and Unmanned Aerial System Technology for Monitoring and Forest Fire Impacts

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International Journal of Remote Sensing Applications Volume 4 Issue 1, March 2014 doi: 10.14355/ijrsa.2014.0401.02

Remote Sensing and Unmanned Aerial System Technology for Monitoring and Quantifying Forest Fire Impacts Michael G. Wing 1, *, Jonathan D. Burnett1, John Sessions1 Forest Engineering, Resources, and Management, Oregon State University, Peavy Hall 204, Corvallis, OR 97331, USA 1

1,

*michael.wing@oregonstate.edu; 1jonathan.burnett@oregonstate.edu; 1john.sessions@oregonstate.edu

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 realtime technology applications for mapping burn impacts and wildfire detection and monitoring. A review of traditional remote sensing applications are 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 real-time 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. Millions of hectares of forest 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 is 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) are instrumental 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

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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 are 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 real-time remote sensing techniques for monitoring elements related to monitoring fire ignition and behavior are discussed and followed by a brief investigation on potential applications 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 has been Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) imagery with a ground

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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 hosts 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

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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 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 19

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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

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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 2-12 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.

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

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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 ProbeI 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

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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 moderateresolution 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 subgroup 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 mapping tool, this potential may not always be achievable.

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.

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 21

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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 forest landscapes and briefly examines Landsat TM applications for classifying fire severity.

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.

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 22

advantage of sensor bandwidth combinations in quantifying burn presence and severity. Often, the results of burn indices are compared against groundbased 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 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

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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 satellite-derived 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

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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 preand post-fire 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 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.

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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 24

atmospheric variables. Fire detection via coarseresolution remote sensing include NOAA AVHRR (detected with a single 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

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a defined area. This approach also allows for twodimensional 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 coarse-scale 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 detected by 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.

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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 25

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potentially be automated but weather conditions and other objects could potentially lead to false positive results, thus hindering system reliability. Other lightbased 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 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 26

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.

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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 crosstalk between spectral bands that may have contaminated the SWIR spectrum and affected the satellites ability to pick up 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.

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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 treeshrub, 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 rapid and reliable classification ofLiDAR 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, 27

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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 multispectral 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 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.

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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 nearreal 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.

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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 geo-rectification 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 georectification. 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 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 MQ9 and equipped it with the AMS. The aircraft was also

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equipped with automatic collision avoidance sensors and a satellite transceiver for over-the-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 real-time (NRT) images that were used for hotspot detection and postfire 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 CombustionAtmospheric 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 39kg 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 30

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 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

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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 WiFi 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

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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) and provided realtime 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

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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 32

and OrbView3 provide 4 m multispectral and 1 m Panchromatic coverage. Quickbird provides 2.4 m multispectral and 60cm 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 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.

Delayed response 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

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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, 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 enhanced future applications of remote sensing for wildfire management.

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Barrado, C., R. Messeguer, J. Lopez, E. Pastor, E. Santamaria, and P. Royo. Wildfire Monitoring Using a Mixed Airground Mobile Network. IEEE Pervasive Computing 2010, 9 (4): 24 –32.

Bobbe, T., M. V. Finco, B. Quale, K. Lannon, R. Sohlberg, and A. Parsons. Field measurements for the training and validation of burn severity maps from Spaceborne, remotely sensed imagery. U.S.D.A. Forest Service, Salt Lake City, UT, 2003, Report No. RSAC-43-TIP. 14 p.

Christiansen, S. Drones over Alaska, Anchorage Press, August 19, 2009. Last accessed Nov 11, 2012 at: http://www. anchoragepress.com/news/drones-over-alaska/article_ 18cd878a-503a-511f-b4c9-eb5cca8cd03.html.

Diaz-Delgado, R., Lloret, F., Pons., X. Statistical analysis of fire frequency models for Catalonia (NE Spain, 1975-1998) based on fire scar maps from Landsat MSS data.

International Journal of Wildland Fire 2004, 13:89-99.

Diaz-Delgado, R., Lloret, F., Pons, X. Influence of fire severity on plant regeneration by means of remote sensing imagery. International Journal of Remote Sensing 2003, 24(8):1751-1763.

ACKNOWLEDGMENT

Epting, J., Verbyla, D. Landscape-level interactions of prefire

We gratefully acknowledge support from the U.S. National Institute for Food and Agriculture.

vegetation, burn severity, and postfire vegetation over a 16-year period in interior Alaska. Canadian Journal of Forest Resources 2005, 35:1367-1377.

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