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Evaluating Landsat ETM+ emissivityenhanced spectral indices for
burn severity discrimination in
Mediterranean forest ecosystems
a
A. Fernández-Manso & C. Quintano
b
a
Agrarian Science and Engineering Department, University of
León, Ponferrada, Spain
b
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Electronic Technology Department, Sustainable Forest
Management Research Institute, University of Valladolid-INIA,
Valladolid, Spain
Published online: 02 Apr 2015.
To cite this article: A. Fernández-Manso & C. Quintano (2015) Evaluating Landsat ETM+ emissivityenhanced spectral indices for burn severity discrimination in Mediterranean forest ecosystems,
Remote Sensing Letters, 6:4, 302-310, DOI: 10.1080/2150704X.2015.1029093
To link to this article: http://dx.doi.org/10.1080/2150704X.2015.1029093
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Remote Sensing Letters, 2015
Vol. 6, No. 4, 302–310, http://dx.doi.org/10.1080/2150704X.2015.1029093
Evaluating Landsat ETM+ emissivity-enhanced spectral indices for
burn severity discrimination in Mediterranean forest ecosystems
A. Fernández-Mansoa* and C. Quintanob
a
Agrarian Science and Engineering Department, University of León, Ponferrada, Spain; bElectronic
Technology Department, Sustainable Forest Management Research Institute, University of
Valladolid-INIA, Valladolid, Spain
Downloaded by [Carmen Quintano] at 13:18 06 April 2015
(Received 14 January 2015; accepted 9 March 2015)
Fires are a yearly recurring phenomenon in Mediterranean forest ecosystems. Accurate
classification of burn severity is fundamental for the rehabilitation planning of affected
areas. This work shows how conventional remote sensing methods for burn severity
assessment may be improved by using land surface emissivity (LSE) to enhance
standard spectral indices. We considered a large wildfire in August 2012 in north
western Spain. The composite burn index (CBI) was measured in 111 field plots and
grouped into three burn severity levels. Evaluation of the relationship between Landsat
7 Enhanced Thematic Mapper LSE-enhanced spectral indices and CBI was performed
by correlation analysis, regression models, and one-way analysis of variance. The
result was a 16.22% overall improvement in adjusted coefficient of determination over
the standard spectral indices. Our results demonstrate the potential of LSE for improving mapping of burn severity. Future research, however, is needed to evaluate the
performance of the proposed spectral indices in other fire regimes and ecosystems.
1. Introduction
Fires are one of the main causes of environmental alteration in Mediterranean forest
ecosystems. Accurate knowledge of both the extent of burned areas and the burn severity
is essential for fire management, planning and monitoring restoration (Brewer et al. 2005).
The composite burn index (CBI) developed by Key and Benson (2006) has became a
standard field assessment method for assessing burn severity. Regarding remote sensing,
methods based on spectral indices are widely used because of their computational
simplicity and straightforward application (Harris, Veraverbeke, and Hook 2011). In this
context, both the normalized difference vegetation index (NDVI) and the normalized burn
ratio (NBR) are frequently used (Key and Benson 2006; Fang and Yang 2014; Loboda
et al. 2013; Nortona et al. 2009).
Veraverbeke, Harris, and Hook (2011) and Harris, Veraverbeke, and Hook (2011),
however, have shown the potential of land surface emissivity (LSE)-enhanced spectral
indices in fire mapping applications. Veraverbeke, Harris, and Hook (2011) demonstrated
that these indices improved burned area estimations; the separability index of emissivityenhanced indices was higher than the separability index of NBR in the four fires
considered in their study. Harris, Veraverbeke, and Hook (2011) used the emissivityenhanced indices to discriminate burn severity in Southern California. In their study, an
*Corresponding author. Email: alfonso.manso@unileon.es
© 2015 Taylor & Francis
Remote Sensing Letters
303
ordinal regression used the burn severity field classes as dependent variable and different
spectral index values as independent variables. Its goodness-of-fit was estimated by the
deviance D, which was used to compare the performance of the different spectral indices
as predictor variables for the burn severity field classes. The emissivity-enhanced indices
obtained a deviance D and correlations with field data of severity similar to the NBR.
The aim of this study is to evaluate the potential of LSE-enhanced spectral indices for burn
severity class discrimination in Mediterranean forest ecosystems. This is the first LSE-based
study that assesses burn damage in Mediterranean ecosystems from Landsat Enhanced
Thematic Mapper (ETM+) data and the first that relates LSE-enhanced spectral indices to CBI.
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2. Materials
We selected the ‘Castrocontrigo’ fire, occurred in August 2012 in north-western Spain, as
our study area (Figure 1). The fire started on August 19 and was contained on August 21,
burning 117.75 km2 according to the government of the Autonomous Community of
Castilla y León. The Third Spanish National Forest Inventory shows that within the fire
affected area, roughly 73% was covered by Pinus pinaster Ait., 3% by Pinus nigra Arm.,
2% by Pinus sylvestris L., 7% by Quercus ilex L., 5% by Quercus pyrenaica Willd, and
10% by shrubs (Erica australis L., Calluna vulgaris (L.) Hull, Chamaespartium tridentatum (L.) P.E. Gibbs, Halimium alyssoides Lam, and Genista florida L.). It is a natural
forest dominated by a unique species.
CBI was measured in a total of 111 plots 9–12 weeks after the wildfire (see Key and
Benson [2006] for a complete description). The 30-m-diameter circular ground plots were
located in areas of homogeneous forest structure (both vertical and horizontal) and with
similar burn severity, and the number of plots evaluated in each burn severity class was
selected according to the proportional surface included in each class, taking into account
an initial burn severity map made by the Ecology Department of the University of León.
Figure 1.
(left).
Location of study area. Normalized burn ratio (NBR) is represented in the zoomed area
304
A. Fernández-Manso and C. Quintano
We used 27 unburned plots, 8 low severity plots, 29 moderate severity plots, and 47 high
severity plots. Following Miller and Thode (2007), we chose to place the thresholds
halfway between the CBI values as general guides for low, moderate, and high categories:
unburned between 0.00 and 0.09, low severity between 0.10 and 1.24, moderate severity
between 1.25 and 2.24, and high severity between 2.25 and 3.00.
We used a post-fire Landsat-7 ETM+ scene (path/row 203/31), acquired on 6
September 2012, downloaded from the US Geological Survey (USGS). Fortunately, the
forest fire was located in the middle of the scene where there is very little duplication or
data loss, and the Scan Line Corrector (SLC) failure has no impact on the radiometric
performance with the valid pixels (Chander, Markham, and Helder 2009). We did not
locate any field plot in the affected area by the SLC failure.
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3. Methods
First, the Landsat 7 ETM+ scene was preprocessed. A subset of the image covering the
forest fire was selected (latitude/longitude coordinates: upper left corner, 42°20ʹ38.27”N/
6°16ʹ49.51”W; and lower right corner 42°13ʹ59.97’N/6°8ʹ28, 40”W). The subset image
was topographically normalized using the C-correction algorithm (Teillet, Guindon, and
Goodenough 1982), and the reflective bands were scaled to surface reflectance by using
the image-based cosine of the solar transmittance (COST) method (Chavez 1996).
Second, LSE was computed using the semi-empirical NDVI-based method from
Sobrino and Raissouni (2000) and Sobrino et al. (2008) that estimates LSE from the
red band reflectivity (ρred) and the proportion of vegetation cover (Pv). Li et al. (2013) in
their review of LSE extraction methods affirmed that the NDVI-based method has two
main advantages: its simplicity and that it takes cavity effects of emissivities into account.
As limitations they mentioned: it requires a priori knowledge of the emissivities of soil
and vegetation, it needs NDVI thresholds for soil (NDVIs) and vegetation (NDVIv), as
well as accurate estimation of Pv, and it displays discontinuities.
When NDVI < NDVIs (soil pixels), the relationship between LSE and ρred is assumed to
be linear and the coefficients can be determined from laboratory measurements of the soil
spectra. When NDVIs < NDVI < NDVIv (mixed pixels composed of soil and vegetation),
the mean cavity effect can be expressed as a linear function of Pv (Sobrino and Raissouni
2000). When NDVI > NDVIv (vegetation pixels), LSE is approximated by a constant value.
For the linear coefficients, we considered the values proposed by Sobrino et al. (2008)
that are also used in the pre-processing of the Landsat data within the framework of the
Spanish Remote Sensing Program (PNT) (see Equation (1)).
LSE ¼ 0:979 0:035ρred
LSE ¼ 0:979 þ 0:004Pv
LSE ¼ 0:990
NDVI < NDVIS
NDVIS NDVI NDVIV
NDVIV NDVI
(1)
Values where NDVIv = 0.5 and NDVIs = 0.2 were proposed by Sobrino and Raissouni
(2000) to apply the method in global conditions. Pv can be derived from the NDVI
(Sobrino and Raissouni 2000) as showed in Equation (2). To obtain consistent values of
Pv, it must be set to zero for soil pixels and set to one for vegetation pixels.
Pv ¼
NDVI NDVIS
NDVIV NDVIS
2
(2)
Remote Sensing Letters
305
We obtained an LSE image whose field plot values ranged from 0.974 for high
severity level plots to 0.990 for vegetated unburned plots, values that are consistent
with the surface characteristics.
Third, spectral indices for burn severity level discrimination were computed.
Specifically, we used the LSE-enhanced versions of NBR initially proposed by
Veraverbeke, Harris, and Hook (2011) (in our study, respectively, represented by
ENBRv1 and ENBRv2). We designed analogously the LSE-enhanced versions of NDVI
(respectively, ENDVIv1 and ENDVIv2) (Equations (3)–(6)). NDVI and NBR were
considered a reference to evaluate the performance of the LSE-enhanced versions.
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ENBRv1 ¼
ρNIR ρSWIR
ðLSEÞ
ρNIR þ ρSWIR
(3)
ρNIR ρSWIR þ LSE
ρNIR þ ρSWIR þ LSE
(4)
ρNIR ρred
ðLSEÞ
ρNIR þ ρred
(5)
ρNIR ρred þ LSE
ρNIR þ ρred þ LSE
(6)
ENBRv2 ¼
ENDVIv1 ¼
ENDVIv2 ¼
where ρNIR and ρSWIR represent reflectance in the near infrared (NIR) and the short
wave infrared (SWIR) band, respectively.
Finally, after applying a mean 3 × 3 filter to the considered spectral indices, the digital
values for the field plots surveyed were extracted for analysis. Pearson correlation analysis
and regression models were used (Loboda et al. 2013; Miller and Thode 2007). The
performance of the different tested spectral indices was evaluated using the adjusted
coefficient of determination (R2adj). Linear and nonlinear regression models with the
spectral indices as the independent variable and CBI as the response showed negligible
differences in the R2adj value. For this reason, we chose the linear model to express the
relationship between the LSE-enhanced spectral indices and CBI. ANOVA was also used.
Fisher’s least significant difference (LSD) test was used to determine significantly different sample means and how many severity levels can be discriminated by each spectral
index. Box plots of the spectral index values from the field plots grouped by burn severity
level illustrated minimum and maximum values, of the quartiles (Q1, 25%, lower edge of
the box; Q2, 50%; and Q3, 75%, top edge of the box), median (Q2, central line of the
box), and the atypical values and distribution symmetry, allowing for the visual identification of potential confusions between the burn severity levels.
4. Results
The correlations of the two reference indices (NDVI and NBR) with CBI were similar.
The correlation between CBI and ENBRv1 and ENBRv2 were also similar (R2adj = 71%),
but markedly greater than the other indices (Table 1). The inclusion of LSE to enhance the
NBR index resulted in an increase of approximately 16% in the R2adj (71.27% vs.
61.32%). We did not observe any improvement by including LSE with NDVI, that is,
NDVI and ENDVIv1 performed similarly and ENDVIv2 decreased by 17%.
306
A. Fernández-Manso and C. Quintano
Table 1. Linear regression models between ground measured CBI and the tested spectral indices,
and Fisher’s least significant difference test for the spectral indices and burn severity levels.
Spectral indices
NDVI
NBR
ENDVIv1 ENDVIv2
Linear regression models (CBI = a × (spectral index) + b)
Intercept
2.8558
1.4077
2.8468
Slope
−5.5844
−3.30823 −5.64047
62.89
61.32
62.81
R2adj (%)
Standard error
0.7022
0.7169
0.7030
Mean absolute error
0.5362
0.5645
0.5376
−8.0780
−12.2782
51.77
0.8006
0.6128
ENBRv1
ENBRv2
1.4891
−9.4819
−3.52157 −14.0028
71.14
71.27
0.6109
0.6094
0.4930
0.4931
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Note: R2adj (%): the values of R2adj have been adjusted according to the number of degrees of freedom.
Table 2.
Burn
severity
levels
Unburned
Low
Moderate
High
Fisher’s least significant difference test for the spectral indices and burn severity levels.
NDVI
NBR
ENDVIv1
ENDVIv2
ENBRv1
ENBRv2
μ
HG
μ
HG
μ
HG
μ
HG
μ
HG
μ
HG
0.438
0.145
0.119
0.121
a
b
b
b
0.289
−0.159
−0.225
−0.251
a
b
b
b
0.432
0.141
0.116
0.119
a
b
b
b
−0.711
−0.802
−0.833
−0.832
a
b
b
b
0.286
−0.155
−0.220
−0.245
a
b
c
c
−0.718
−0.814
−0.845
−0.843
a
b
c
c
Note: μ: mean value; HG: Homogeneous Groups, each letter in the HG column indicates a group for which there
is no significant difference between the means.
The results of the one-way ANOVA (Table 2) showed significant differences (p-value < 0.05)
between the mean unburned values and the rest of the burn severity levels for every spectral
index tested. Therefore, all spectral indices should allow us to discriminate burned from
unburned areas. ENBRv1 and ENBRv2 demonstrated significant difference between low
severity and the combined moderate and high severity levels at the 95% confidence level.
Therefore, it was possible to differentiate two levels of burn severity (low and moderate-high)
when using the LSE-enhanced NBR indices. The boxplots of Figure 2 allow us to observe this
fact graphically. They represent the spectral index values from the field plots grouped by burn
severity level, highlighting the 25% and 75% quartiles and the median.
Figure 3 displays NBR, ENBRv1, and ENBRv2 in a zoom of the study area. The three
indices allow us to visually discriminate between burned and unburned areas. Though
from Figure 3 NBR and ENBRv1 are quite similar, from Table 2, we could observe that
ENBRv1 and ENBR v2 performed comparably (distinguishing three classes: unburned,
low, and moderate-high burn severity), whereas NBR allowed us to distinguish only two
classes (burned and unburned).
5. Discussion
The linear regression models between ENBRv1/ENBRv2 and the field measured CBI had
the highest correlation value (R2adj = 71%). Harris, Veraverbeke, and Hook (2011), in the
first and only study relating such indices to burn severity, also found that ENBRv1 and
ENBRv2 had an adequate performance in discriminating burn severity, ranking 3 and 4
out of 19 spectral indices (according to the deviance obtained from ordinal logistic
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Remote Sensing Letters
307
Figure 2. Box plots of the spectral index values from the field plots grouped by burn severity level
(H: high, 47 plots; M: moderate, 29 plots; L: low, 8 plots; and U: unburned, 27 plots) for the
normalized difference vegetation index (NDVI, upper left), emissivity-enhanced NDVI versión 1
(ENDVIv1, upper centre), emissivity-enhanced NDVI versión 2 (ENDVIv2, upper right), normalized burn ratio (NBR, lower left), emissivity-enhanced NBR versión 1 (ENBRv1, lower centre), and
emissivity-enhanced NBR version 2 (ENBRv2, lower centre).
Figure 3. Left, normalized burned ratio, NBR; centre, LSE-enhanced NBR version 1, ENBRv1;
right, LSE-enhanced NBR version 2, ENBRv2 (for the location of the study area, ‘Castrocontigo’
wildfire, see Figure 1).
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A. Fernández-Manso and C. Quintano
regression). These R2adj values are similar to R2adj values of regression models between
spectral indices and CBI reported by other studies. Murphy, Reynolds, and Koltun (2008)
found a maximum R2adj of 64% in the assessment of differenced normalized burn ratio
(dNBR) in Alaskan boreal forests. In the same ecosystem, Hall et al. (2008) showed an R2
of 76% using NBR. Miller et al. (2009) obtained a maximum R2 of 68% when considering
a relative version of dNBR (RdNBR) in Californian mountains.
LSE-enhanced versions of NBR enabled us to distinguish, with statistical significance,
two burn severity levels (low and moderate-high) instead of the three levels initially
proposed. Differentiating only two different levels of burn severity has been strongly
influenced by the characteristics of the wildfire. It was a convection fire where the high
severity level was predominant. Other remote sensing-based studies (e.g. Cocke, Fulé, and
Crouse 2005; Tanese, de la Riva, and Pérez-Cabello 2011; Miller and Thode 2007) also
reported discrimination of only two severity levels. Miller and Thode (2007) stated that
there is always confusion in the unburned, low, and moderate categories because it is
difficult to see under tree canopies using passive sensors. However, minimizing classification errors for the high severity class will prove beneficial to land managers because it
allows identification of more areas that are severely burned. Furthermore, given that
perhaps the most common reason for studying burn severity is to target areas for recovery
(Cocke, Fulé, and Crouse 2005), differentiating the higher severity levels from the rest of
the burn severity levels may provide enough information for forest managers.
Both LSE-enhanced versions of the NBR had similar performance and improved NBR
for burn severity discrimination. LSE is an inherent surface characteristic, and as such it is
independent on the incoming solar radiation (Veraverbeke, Harris, and Hook 2011).
However, as Hulley, Hook, and Baldridge (2010) pointed out, LSE is dependent on
vegetation cover and type, surface roughness, and soil moisture. For this reason,
Veraverbeke, Harris, and Hook (2011) highlighted that gradual post-fire recovery changes
and seasonal variations may influence the post-fire temporal acquisition window in which
the LSE component complements the NIR and SWIR reflectance layers. Unfortunately,
we did not have the information to determine the optimum post-fire period to best
discriminate levels of burn severity.
6. Conclusion
We analysed the performance of LSE-enhanced spectral indices to assess burn severity in
Mediterranean forest ecosystems. Correlation analysis, regression models, and one-way
ANOVA between ground measured CBI and LSE-enhanced NBR indices showed that the
enhanced indices ENBRv1 and ENBRv2 could differentiate accurately two levels of burn
severity in the forest fire, whereas NBR could only distinguish between burned and
unburned areas. These results demonstrate the potential of LSE for assessing burn severity
in Mediterranean ecosystems. Forest managers could benefit from the use of the LSEenhanced NBR for burn severity assessment as an effective tool to define post-fire
management strategies. The performance of the proposed spectral indices should be,
however, evaluated in other fire regimes and vegetation types.
Acknowledgements
The authors thank the Autonomous Government of León for sharing their information about the
forest fires. We appreciate the comments and suggestions from three anonymous reviewers who
significantly improved the quality of the manuscript.
Remote Sensing Letters
309
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
The research work was financially supported by the Spanish Ministry of Economy and
Competitiveness, and the European Regional Development Fund (ERDF) in the frame of the
GESFIRE project ‘Multi-scale tools for the post-fire management of fire-prone ecosystems in the
context of global change’ (AGL2013-48189-C2-1-R).
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