Title: Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity
Journal: Remote sensing of Environment, 2004, Vol. 92, 397-408
Authors: Jan W. Van Wagtendonk, Ralph R. Root and Carl H. Key
Reviewed by: Alpana Khairom
@00592236
"... we can't cut our way out of the problem. We can't burn our way out. We can't simply suppress. And we can't walk away. But maybe, we can cut a little, burn a little, suppress a little, and sometimes just back off from a fire that we can't hope to stop. The process will take decades, not years, but taken together, a lot of small steps could make a big difference." (Stephen Pyne)
1

Abstract:
Wildfire is one of the most significant forms of natural disturbance, impacting a wide range of ecosystems ranging from boreal forests to Mediterranean shrub lands and tropical rainforest. Each year, fire burns between 6 and 14 million hectares of forest. The resulting loss and degradation of forested land is roughly equal to that caused by destructive logging and conversion to agriculture combined. The use of remote sensing to evaluate forest fires proved to be very useful as it could be used to reduce the risk and minimize damage. This article deals with the data comparison of a burned area collected from Airborne Visible and Infrared Spectrometer (AVIRIS) and Landsat Enhanced
Thematic Mapper Plus (ETM+) using differenced Normalized Burn Ratio (dNBR). Data from two AVIRIS channels and ETM+ were collected from hoover fire, which occurred in Yosemite National Park, California. Data was collected during pre-fire and post-fire, and they were compared for the severity of four plots (high, moderate, low and unburned areas). Results showed that post-fire reflectance decreased than the pre-fire for the channels 47 and 60 when compared to channels 148 and 210, in the case of high burned where as in the case of unburned areas both the post- and pre-fire reflectance showed the same curve.
Background:
Disturbance by wildfire is common in the Californian regions. About 17% of the federally owned lands available for public use have been designated as wilderness, to be managed such that their “natural conditions” are preserved (Wilderness Act, Public Law
88-577). In recent years, wild land fires and altered fire disturbance regimes have become a significant management and science problem affecting ecosystems and wild land/urban interface across the United States (Wagtendonk et al., 2004). It is necessary to understand the role of forest fires in the environment and to elaborate prevention measures on those areas in which fire recurrence is high. Hence we need to consider the extent of mapping and impacts of the forest fires. High fire frequency leads to changes in vegetation structure and may result in the loss of soil capacity to regenerate vegetative cover. The need for a consistent, broad scale approach to mapping fires globally has led to several recent initiatives to detect and characterize fire using satellite remote sensing systems
(Arino et al., 2001; Barbosa et al., 1999; Eva & Lambin, 1998; Fraser et al., 2000;
Gregoire et al., 2003; Justice et al., 2002; Li et al., 2000; Roy et al., 2002; Stroppiana et al., 2000; Sukhinin, 2003).
Several studies have investigated the ability of satellite imagery to produce accurate burned area estimates (Caetano et al., 1994; Chuvieco & Congalton, 1988;
Milne, 1986; Mitri & Gitas, 2002; Pereira, 1992; Pereira et al., 1997; Pereira & Setzer,
1993; Siljestrom & Moreno, 1995; Tanaka et al., 1983). Some of the studies were done by the following methods Multispectral Scanner (MSS), Thematic Mapper ™ and
Enhanced Thematic Mapper Plus (ETM+) using Normalized Difference Vegetation Index
(NDVI). Most research concerning the use of remote sensing techniques for forest fire applications have focused on detecting active fires, mainly using middle-infrared images
(Ahern et al., 2001; Martyn et al., 1999). Fire risk and behavior depend heavily on the fuel properties such as the quantity of biomass, partitioning of living and dead biomass,
2

moisture content, and the vertical and horizontal structure of the canopy (Chuvieco et al.,
2002; Lynham et al., 2002). Fire risk and behavior depend heavily on the fuel properties such as the quantity of biomass, partitioning of living and dead biomass, moisture content, and the vertical and horizontal. The spectral reflectance of a plant canopy is known to be primarily a function of the foliage optical properties, the canopy structure, the understory and soil background reflectance, the illumination conditions, and finally, the viewing geometry (Chen et al., 2000; Goel, 1988).
Low spatial resolution NOAA-AVHRR imagery has been successively used for the mapping of large forest fires on a regional scale (Martin et al., 2002; Pereira, 1999;
Pereira et al., 1999). In particular, the thermal channels of AVHRR play a significant role in separating the burned areas from other land cover classes (Martin et al., 2002). In order to plan, monitor and implement fuels management programs, improved methods of mapping fuels need to be developed. Landsat ETM+ imagery and AVIRIS hyperspectral data show promise for characterizing the condition of fuels and changes in those conditions over time. AVIRIS first measured spectral images in 1987 and was the first imaging spectrometer to measure the solar reflected spectrum from 400 nm to 2500 nm
(Green et al., 1998). AVIRIS measures upwelling radiance through 224 contiguous spectral channels at 10 nm intervals across the spectrum. These radiance spectra are measured as images of 11 km width and up to 800 km length with 20 m spatial resolution
(Green et al., 1998).
The objective of this study was:
- To validate the data collected from Landsat ETM+ with that taken from
AVIRIS.
- Whether data collected by AVIRIS was more accurate than from ETM+.
Study area:
Yosemite National Park (Fig. 1) is located in the central Sierra Nevada of
California. Designated a World Heritage Site in 1984, Yosemite is internationally recognized for its spectacular granite cliffs, waterfalls, clear streams, giant sequoia groves, and biological diversity
1
. The 750,000-acre, 1,200 square-mile park contains thousands of lakes and ponds, 1600 miles of streams, 800 miles of hiking trails, and 350 miles of roads. Two federally designated wild and scenic rivers, the Merced and
Tuolumne, begin within Yosemite’s borders and flow west into California's Central
Valley. Yosemite is one of the largest and least-fragmented habitat blocks in the Sierra
Nevada, and it supports a diversity of plants and wildlife. The park has an elevation range from 2,000 to 13,123 feet and contains five major vegetation zones: chaparral/oak woodland, lower montane, upper montane, subalpine and alpine. Of California’s 7,000 plant species, about 50% occur in the Sierra Nevada and more than 20% within
Yosemite. There is suitable habitat or documented records for more than 160 rare plants in the park, with rare local geologic formations and unique soils characterizing the restricted ranges many of these plants occupy. Weather can change rapidly during the year. Winters are relatively mild, with most rainfall in the months of January, February and March. However, clouds can form during the summer and produce thunderstorms.
Its –11ºC during the month of January and 34ºC maximum during the month of July.
3

Fig. 1. Arrow pointing Hoover fire, 2001 in Yosemite National Park, California, USA
( http://www.thedesktop.com/scenic/park/park_maps.html
)
The Sierra Nevada has a high incidence of lightning fires. Over the past 30 years in Yosemite National Park, lightning has stated an average of 55 fires, or 83 fires per million burnable acres each year (NPS 1990). Yosemite’s fire management program is designed to protect life, property, and cultural resources, while ensuring the continuation of natural processes. People have lived in Yosemite Valley for at least 4000 years.
Traditional burning practices were stopped by early pioneers and fire suppression was official policy until the 1970’s. Since the early 1970’s, National Park Service fire policy has been to allow fire to play a more natural role in park ecosystems. The Hoover fire was ignited by lightning on July 26, 2001 in the Illilouette Creek drainage approximately five miles southeast of glacier point.
Data and methods:
Data was collected from pre- and post-fire acquisitions on separate dates. The
Normalized Burn Ratio (NBR) was calculated and differenced between pre- and post-fire
Landsat ETM+ scenes. From a random sample of pixels inside the burns, TM bands 4 and 7 exhibited the greatest reflectance change in response to fire. A ratio of those bands, discriminating for burn effects, known to be as the NBR from their transformed reflectance (R) values:
NBR
ETM+
= (R
4
-R
7
) / (R
4
+R
7
)
The dNBR was then calculated as follows: dNBR
ETM+
= NBR pre
- NBR post
4

All calculations were done with floating point math and results were scaled by 1000 to store positive and negative values. EROS USGS developed dNBR data from pre- and post-fire Landsat TM images. The Composite Burn Index (CBI) is designed to define burn severity ecologically, and measure ground effects, which collectively provide a signal, detected at moderate resolution by the Landsat Thematic Mapper. 63 CBI plots were sampled inside the burn perimeter and guided by dNBR data, four severities were classified (high, moderate, low and unburned). And they were rated by criteria, which correspond to identified burn levels along gradient from unburned to extremely burned conditions.
Fig 2. Severity is a measure along that gradient of change, and the principal focus of this project (USGS).
High severity can be identified by the combustion of litter, duff and small logs and also mortality of small sized and medium sized trees. Moderate severity is shown by the mortality of small trees and the consumption of the crowns of medium and large trees.
Low severity can be identified by burning of fine fuels and some understory trees.
5

Unburned plots have accumulations of woody and litter fuels, thick understory trees and low live branches. AVIRIS is an airborne imaging spectrometer that acquires 224 spectral channels between 350 and 2500 nm at a nominal sampling interval of 10 nm with a ground-instantaneous field of view (GIFOV) of 20 m and a swath of 12 km when flown no the ER2 at 20 km altitude (Green et al., 1998). The AVIRIS data was acquired on
August 17, 2001, before the firebreak and later on July 22, 2002, by two different flights.
The two flights provided the opportunity to compare burn severity assessments between data collected by the ETM+ sensor and by AVIRIS sensor (Wagtendonk, 2003). It was flown on the NASA ER-2 at an elevation of 16.7 km above sea level. Using ACORN algorithm, data were calibrated to reflectance and corrected for atmospheric moisture.
NBR was calculated using the channels, which were found to be the most prominent, i.e.
47 and 210:
NBR
AVIRIS
= (R
47
-R
210
) / (R
47
+R
210
)
The dNBR was then calculated as follows: dNBR
AVIRIS
= NBR pre
- NBR post
Reflectance was observed from pre- to post-fire for the classified areas at different
AVIRIS channels.
Results and Discussion:
It has been observed that both Landsat and AVIRIS showed similar patterns.
Channels 47 and 210 to detect the burn severity were selected after going through the spectral responses. In fig 3, graph was plotted between AVIRIS reflectance and wavelength (nm). Channel 47 and 60, at 788 nm and 913 nm respectively, showed decrease in reflectance for post-fire indicating that there was complete loss in the vegetation. For channel 148 and 210, post-fire reflectance increased than the pre-fire. A similar pattern was observed in the case of moderate burn severity (fig. 4). The low burn severity showed an increase of post-fire and pre-fire reflectance for the channels (fig. 5).
In the case of unburned areas (fig. 6), the reflectance looks the same in both the years.
The results when compared to the results of Landsat TM and ETM+ sensors showed a similarity. The wavelengths selected in AVIRIS occurred within the wavelengths of
Landsat TM bandwidth used. The severity was validated by comparing the CBI taking dNBR as a function for both Landsat and AVIRIS. The r
2
was found to almost same for both dNBR and CBI (fig. 7).
6

Fig 3: Pre- and post-fire AVIRIS spectra-high severity area. Channels 47 and 60 at wavelengths 788 and 913 nm showed the greatest negative response to fire, while channels 148 and 210 showed the greatest positive response.
Fig 4: Pre- and post-fire AVIRIS spectra-moderate severity area. The moderate severity showed similar pattern of reflectance as that of high severity.
7

Fig 5: Pre- and post-fire spectra-low severity area. The low severity area showed a similar pattern as that of moderate severity area, but a decrease in absorption by chlorophyll was observed in the 550-700 nm range
Fig 6: Pre- and post-fire spectra-unburned area. Similar patterns were observed for both the years.
8

Fig. 7: Regression of the composite Burn Index as a function of the differenced
Normalized Burn Ratio based on ETM+ data and AVIRIS data. The r 2 was 0.894 and 0.853 respectively, that was slightly different but the maximum CBI was similar.
Regression for Landsat ETM+ was slightly more when compared to that of
AVIRIS. Unburned plots did not show much difference in the spectral reflectance as nothing much was burned (fig. 6). ETM+ bands 4 and 7 detected the burn severity and the AVIRIS channels fell within or very close to the ETM+ sensor bandwidths. Higher
AVIRIS channels produced better correlations of burn severity. Greater amplitude of response and correlation to field-rated burn severity led to better contrast, broader range of severity levels and sharper delineation of burn perimeters. Seasonal differences however were influenced by phenology and not strictly by date
2
.
Conclusions:
Overall, similar burn severity detection capabilities exist between AVIRIS and
Landsat ETM+ and change detection approach is informative. AVIRIS has the ability to retrieve surface reflectance in a variable atmosphere and improved classification of vegetation. Unfortunately use of AVIRIS sensor is slightly complex and has high cost of data acquisition. For further research it is always necessary that past AVIRIS data has to be looked into and also new pre- and post-fire datasets should be considered to fulfill an objective. This analysis of AVIRIS data showed that the advances in remote sensing resulted in tool applicable to study many parts of Yosemite National Park.
Acknowledgments:
The authors thank the Joint Fire Science program from funding their study. They also thanked the members of National Park Service in Yosemite National Park, namely,
Kara Paintner and Mark Grupe, for field data collection and database construction; Ian
McCubbin of the National Atmospheric and Space Agency for arranging the AVIRIS flights; Joe Boardman of Analytical Imaging and Geophysics for georectifying the images; and Susan Ustin and Carlos Ramirez of the University of California, Davis, for assisting with the analysis of the AVIRIS data.
9

References:

1.
http://www.nps.gov/yose/pphtml/nature.html

2.
http://www.nrmsc.usgs.gov/research/ndbr.htm

Ahern, F. J., J. G. Goldammer and C. O. Justice (Eds.), 2001. Global and regional vegetation fire monitoring from space: Planning a coordinated international effort.
The Hague, The Netherlands: SPB Academic Publishing.

Arino, O., N. Simon, P. Piccolini & J. M. Rosaz, 2001. The ERS-2 ATSR-2
World Fire Atlas and the ERS-2 ATRS-2 World Burnt Surface ATLAS projects.
Proc. 8th ISPRS Conference on Physical Measurements and Signatures in Remote
Sensing, AUSSOIS.

Barbosa, P. M., J. M. Gregoire & J. M. C. Pereira, 1999. An algorithm for extracting burned areas from time series of AVHRR GAC data applied at a continental scale. Remote Sensing of Environment, 69: 253– 263.

Chen, J. M., X. Li, T. Nilson and A. Strahler, 2000. Recent advances in geometrical optical modelling and its applications. Remote Sensing Reviews, 18:
227– 2262.

Chuvieco, E., D. Riano, I. Aguado and D. Cocero, 2002. Estimation of fuel moisture content from multitemporal analysis of Landsat thematic mapper reflectance data: Applications in fire danger assessment. International Journal of
Remote Sensing, 23(11): 2145– 2162. Sensing Reviews, 4, 1– 212.

Eva, H. and E. F. Lambin, 1998. Burnt area mapping in Central Africa using
ATSR data. International Journal of Remote Sensing, 19: 3473–3497.

Fraser, R. H., Z. Li and J. Cihlar, 2000. Hotspot and NDVI differencing synergy
(HANDS): A new technique for burned area mapping over boreal forest. Remote
Sensing of Environment, 74: 362–376.

Goel, N. S., 1988. Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data. Remote photosynthetic biomembranes. Methods of Enzymology, 148: 350–382.

Green, R. O., M. L. Eastwood, C. M. Sarture, T. G. Chrien, M. Aronsson, B. J.
Chippendale, J. A. Faust, B. E. Pavri, C. J. Chovit, M. Solis et al., 1998. Imaging
Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS).
Remote Sensing of Environment, 65 (3): 227-248.

Gregoire, J. M., K. Tansey and J. M. N. Silva, 2003. The GBA2000 initiative:
Developing a global burned area database from SPOTVEGETATION imagery.
International Journal of Remote Sensing, 24: 1369–1376.

Justice, C. O., L. Giglio, S. Korontzi, J. Owens, J. T. Morisette, D. Roy et al.,
2002. The MODIS fire products. Remote Sensing of Environment, 83: 244–262.

Li, Z., S. Nadon and J. Cihlar, 2000. Satellite-based detection of Canadian boreal forest fires: Development and application of the algorithm. International Journal of Remote Sensing, 21: 3057–3069.

Lynham, T. J., C. W. Dull and A. Singh, 2002. A report by the Wildland fire hazard team, committee on earth observation satellites (CEOS) disaster management group (DMSG). Toronto, Canada: IGARSS.

Martin, P. M., Diaz Delgado, R., Chuvieco, E., & Ventura, G. (2002). Burned land Mapping using NOAA-AVHRR and TERRA-MODIS. In D. X. Viegas
10

(Ed.), Forest fire research and wildland fire safety. pp. 227–244. Rotterdam7
Millpress.

Martyn, M. P., P. Ceccato, S. Flasse and I. Downey, 1999. Fire detection and fire growth monitoring using satellite data. In E. Chuvieco (Ed.), Remote sensing of large wildfires in the European Mediterranean Basin pp. 101–122. Berlin:
Springer-Verlag.

Pereira, J. M., A. C. L. Sa, A. M. O. Sousa, M. P. Martyn and E. Chuvieco, 1999.
Regional-scale burnt area mapping in Southern Europe using NOAA-AVHRR 1 km data. In E. Chuvieco (Ed.), Remote sensing of large wildfires in the European
Mediterranean Basin. pp. 139–155. Berlin7 Springer-Verlag.

Pereira, J. M. C., 1999. A comparative evaluation of NOAA-AVHRR vegetation indices for burned surface detection and mapping. IEEE Transactions on
Geoscience and Remote Sensing, 37: 217– 226.

Roy, D. P., P. E. Lewis and C. O. Justice, 2002. Burned area mapping using multi-temporal moderate spatial resolution data—A bi-directional reflectance model-based expectation approach. Remote Sensing of Environment, 83: 263–
286.

Stroppiana, D., S. Pinnock and J. M. Gregoire, 2000. The global fire product:
Daily fire occurrence from April 1992 to December 1993 derived from NOAA

AVHRR data. International Journal of Remote Sensing, 21: 1279–1288.
Sukhinin, A. I. 2003. Russian Federation Fire 2002 Special: Part III. The 2002 fire season in the Asian part of the Russian federation. International Forest Fire News,
28.
 USGS National Park Service-U. S. Geological Survey. National Burn Severity
Mapping Project, 2003. ( http://burnseverity.cr.usgs.gov/overview/index.asp
)

Wagtendonk, J. W. V., Root, R. and C. H. Key, 2003. Comparison of AVIRIS and Lnadsat ETM+ detection capabilities for burn severity. Remote sensing of
Environment, 92:397-408.

Wagtendonk, J. W. V., Z. Zhu and E. L. Lile, 2004. Prefire risk assessment and fuels mapping. In . Livingston, R. K., (ed) Third U. S. Geological Survey
Wildland. Fire-Science Workshop. USGS Scientific Investigations Report 2004-
2005. pp. 49-52.

Wagtendonk, J. W. V., Key, C. and Root, R., 2004. The use of thematic mapper and AVIRIS data to map fuel characteristic classes in western ecosystems.
11