This article was downloaded by: [Carmen Quintano] On: 06 April 2015, At: 13:18 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Remote Sensing Letters Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/trsl20 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 Click for updates 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 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Downloaded by [Carmen Quintano] at 13:18 06 April 2015 Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions 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: [email protected] © 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. Downloaded by [Carmen Quintano] at 13:18 06 April 2015 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. Downloaded by [Carmen Quintano] at 13:18 06 April 2015 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. Downloaded by [Carmen Quintano] at 13:18 06 April 2015 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 Downloaded by [Carmen Quintano] at 13:18 06 April 2015 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 Downloaded by [Carmen Quintano] at 13:18 06 April 2015 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). Downloaded by [Carmen Quintano] at 13:18 06 April 2015 308 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). Downloaded by [Carmen Quintano] at 13:18 06 April 2015 References Brewer, C. K., J. C. Winne, R. L. Redmond, D. W. Opitz, and M. V. Mangrich. 2005. “Classifying and Mapping Wildfire Severity: A Comparison of Methods.” Photogrammetric Engineering and Remote Sensing 71: 1311−1320. doi:10.14358/PERS.71.11.1311. 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