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Frontiers of Earth Science

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Front. Earth Sci.    2020, Vol. 14 Issue (2) : 446-461    https://doi.org/10.1007/s11707-019-0786-4
RESEARCH ARTICLE
Spatial pattern analysis of post-fire damages in the Menderes District of Turkey
Emre ÇOLAK(), Filiz SUNAR
Civil Engineering Faculty, Istanbul Technical University, Istanbul 80626, Turkey
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Abstract

Forest fires, whether caused naturally or by human activity can have disastrous effects on the environment. Turkey, located in the Mediterranean climate zone, experiences hundreds of forest fires every year. Over the past two decades, these fires have destroyed approximately 308000 ha of forest area, threatening the sustainability of its ecosystem. This study analyzes the forest fire that occurred in the Menderes region of Izmir on July 1, 2017, by using pre- and post-fire Sentinel 2 (10 m and 20 m) and Landsat 8 (30 m) satellite images, MODIS and VIIRS fire radiative power (FRP) data (1000 m and 375 m, respectively), and reference data obtained from a field study. Hence, image processing techniques integrated with the Geographic Information System (GIS) database were applied to a satellite image data set to monitor, analyze, and map the effects of the forest fire. The results show that the land surface temperature (LST) of the burned forest area increased from 1 to 11°C. A high correlation (R= 0.81) between LST and burn severity was also determined. The burned areas were calculated using two different classification methods, and their accuracy was compared with the reference data. According to the accuracy assessment, the Sentinel (10 m) image classification gave the best result (96.43% for Maximum Likelihood, and 99.56% for Support Vector Machine). The relationship between topographical/forest parameters, burn severity and disturbance index was evaluated for spatial pattern distribution. According to the results, the areas having canopy closure between 71%–100% and slope above 35% had the highest burn incidence. As a final step, a spatial correlation analysis was performed to evaluate the effectiveness of MODIS and VIIRS FRP data in the post-fire analysis. A high correlation was found between FRP-slope, and FRP-burn severity (0.96 and 0.88, respectively).
Keywords remote sensing      GIS      spectral indices      disturbance index      land surface temperature      burn severity     
Corresponding Author(s): Emre ÇOLAK   
Just Accepted Date: 04 November 2019   Online First Date: 05 December 2019    Issue Date: 21 July 2020
 Cite this article:   
Emre ÇOLAK,Filiz SUNAR. Spatial pattern analysis of post-fire damages in the Menderes District of Turkey[J]. Front. Earth Sci., 2020, 14(2): 446-461.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-019-0786-4
https://academic.hep.com.cn/fesci/EN/Y2020/V14/I2/446
Fig.1  Study area. (a) Satellite image, (b) tree species- Calabrian Pine (Pinus brutia), (c) elevation map.
Fig.2  Air temperature and relative humidity of the Menderes District on July 1, 2017 (Meteorological Service 2018).
Parameters Optimum values Average values in the Menderes District
Temperature >25°C 35°C
Wind speed 3 – 8.8 m/s 7 m/s
Relative humidity <40% 30%
Tab.1  Meteorological parameters affecting a forest fire in Turkey (Akkaş et al., 2006)
Fig.3  Flow chart of the study.
Variables Classes Fire risk ratings
Slope
(weight= 5)		 0%–5% Low
5%–15% Moderate-Low
15%–35% Moderate-High
>35% High
Vegetation Species (weight= 10) Oak and Coppice Low
Degraded areas Moderate-Low
Beech and Fir Moderate-High
Black pine and Calabrian pine High
Canopy Closure (weight= 5) <11% Low
11%–40% Moderate-Low
41%–70% Moderate-High
71%> High
Aspect
(weight= 3)		 0–23 N Low
23–68 NE Moderate-Low
68–113 E Moderate-Low
113–158 SE Moderate-High
158–203 S High
203–248 SW High
248–293 W Moderate-High
293–338 NW Moderate-High
338–360 N Low
Tab.2  Fire risk variables and classes (Sivrikaya et al., 2014)
Spectral index Formulation References
Normalized Difference Vegetation Index (NDVI) NDVI=(NIR−R) (NIR+R) Tucker, 1979
Burn Area Index (BAI) BAI=1(0 .1-Red)2+(0 .06− NIR)2 Schepers et al., 2014
Normalized Burn Ratio (NBR) NBR=(NIR−SWIR)(NIR+SWIR) Key and Benson, 2005
Normalized Burn Ratio-Thermal (NBRT) NBRT=(NIR− SWIR(TIR1000 ))( NIR+SWIR( TIR1000 )) Holden et al., 2005
Tab.3  Spectral Indices used in this study
dNBR Burn severity
<-0.25 High post-fire regrowth
-0.25 – -0.1 Low post-fire regrowth
-0.1 – +0.1 Unburned
0.1 – 0.27 Low-severity
0.27 – 0.44 Moderate-low severity
0.44 – 0.66 Moderate-high severity
>0.66 High-severity
Tab.4  dNBR severity category definitions (©USGS)
Stage Formulation Explanation
i Lλ =ML× Qcal+AL Lλ = spectral radiance (W/(m2× sr × μm))
ML = band-specific multiplicative rescaling factor from the metadata
AL = band-specific additive rescaling factor from the metadata
Qcal = L1 pixel value as digital number
ii T =K2ln( K 1Lλ+1) T = brightness temperature (K)
K1= band-specific thermal conversion constant from the metadata
K2= band-specific thermal conversion constant from the metadata
iii N DVI=NIR−RedNIR+ Red N DVI={ NDVI<0.2,??=0.97;NDV I>0.5,??= 0.99;0.2≤NDVI≤0.5,??=?v×Pv+ ?s×(1−Pv)+d?;
εv: vegetation emissivity;
εs: soil emissivity;
Pv: proportion of vegetation;
dε: comprise the impact of geometrical distribution of the natural surfaces and also the internal reflections:
d ?=(1−?s)× (1− Pv)×F× ?v,
F: geometrical factor ranging from 0 to 1, contingent on the geometrical distribution of the surface, that is typical 0.55
P v=(NDVI− NDVI min NDVImax+NDVImin )2
iv L ST= T1+(λ×T ρ)×ln(?) λ = the central band wavelength of emitted energy
ρ = h×c/σ (1.438 × 10-2 m × K)
h = the Planck constant (6.626 × 10−34 J×s)
c = speed of light (2.99792 × 108 m/s)
σ = the Boltzmann constant (1.38 × 10−23 J/K)
Tab.5  The stages of LST calculation
Explanation Formulation
Determination of normalized
tasseled cap indices		 Bn=(B−Bμ)/Bσ
Gn=(G−Gμ)/Gσ
Wn=(W−Wμ)/Wσ
Bµ, Gµ, and Wµ: mean Tasseled Cap Brightness, Greenness and Wetness
Bσ, Gσ, and Wσ: corresponding standard deviations
Rn, Gn and Wn: normalized Brightness, Greenness and Wetness
Calculation of DI DI=Bn−( Gn+Wn)
Tab.6  DI calculation
Fig.4  Fire Risk map (a) and Spectral indices (b) NBR, (c) NDVI.
Fire Risk /ha
Low Moderate Low Moderate High High Total
38 329 556 63 986
Tab.7  Quantitative analysis of the fire risk in the burned area
Fig.5  Burn severity map (a) short-term vegetation survival monitoring, (b) NDVI map (01.11.2018), (c) Google Earth view (23.08.2018).
Fig.6  LST maps of burned area produced from Landsat images dated (a) 30.06.2017 and (b) 16.07.2017, (c) dLST map, (d) Correlation between dLST and dNBR.
Satellite Maximum Likelihood Support Vector Machine
Overall
Accuracy /%		 Kappa
Coefficient		 Overall
Accuracy /%		 Kappa
Coefficient
Sentinel 2 (10 m) 96.43 0.96 99.56 0.99
Sentinel 2 (20 m) 82.22 0.81 97.86 0.97
Landsat 8 (30 m) 80.30 0.79 97.52 0.96
Tab.8  Accuracy assessment of ML and SVM classification results (Appendix A)
Fig.7  Sentinel 2 (10 m) post-fire thematic maps. (a) Maximum Likelihood, (b) Support Vector Machine.
Fig.8  GIS maps of Menderes region. (a) Canopy closure, (b) Slope.
Burn Severity /ha Canopy Closure /% Total area Slope /% Total
area
<11 11-40 41-70 71-100 Σ /ha 0-5 5-15 15-35 >35 Σ /ha
Low 6 18 298 468 790 1 4 337 448 790
Moderate low 6 7 80 50 143 1 1 54 87 143
Moderate high 0 1 24 28 53 0 0 4 49 53
Σ /ha 12 26 402 546 986 2 5 395 584 986
Tab.9  The spatial pattern analysis of canopy closure and slope with burn severity
DI (ha) Canopy Closure (%) Total area Slope (%) Total area
<11 11-40 41-70 71-100 Σ (ha) 0-5 5-15 15-35 >35 Σ (ha)
Low 0 2 143 396 541 1 0 225 315 541
Moderate low 0 10 169 124 303 0 5 118 180 303
Moderate high 12 6 67 51 136 0 69 63 4 136
High 1 1 3 1 6 0 0 1 5 6
Σ (ha) 13 19 382 572 986 1 74 407 504 986
Tab.10  The spatial pattern analysis of canopy closure and slope with disturbance (DI)
Fig.9  The FRP data distribution on the (a) Slope map, (b) dNBR map. Correlation analysis between (c) FRP – slope, (d) FRP – dNBR.
Truth
Burned forest area Forest Bare soil Mine Lake Agriculture Total
Predicted Burned forest area 49 0 1 0 0 0 50
Forest 0 50 0 0 0 0 50
Bare soil 1 0 49 0 0 0 50
Mine 0 0 0 50 0 0 50
Lake 0 0 0 0 50 0 50
Agriculture 0 0 0 0 0 50 50
Total 50 50 50 50 50 50 300
  SVM – SENTINEL – 10 m:
Truth
Burned forest area Forest Bare soil Mine Lake Agriculture Total
Predicted Burned forest area 49 0 1 0 0 0 50
Forest 0 50 0 0 0 2 52
Bare soil 1 0 49 0 0 1 51
Mine 0 0 2 48 0 0 50
Lake 1 0 0 0 48 0 49
Agriculture 0 1 0 0 0 47 48
Total 51 51 52 48 48 50 300
  SVM – SENTINEL – 20 m:
Truth
Burned forest area Forest Bare soil Mine Lake Agriculture Total
Predicted Burned forest area 49 0 1 0 0 0 50
Forest 0 49 0 0 0 3 52
Bare soil 1 0 49 0 0 1 51
Mine 0 0 2 48 0 0 50
Lake 0 0 0 0 49 0 49
Agriculture 0 1 0 0 0 47 48
Total 50 50 52 48 49 51 300
  SVM – LANDSAT – 30 m:
Truth
Burned forest area Forest Bare soil Mine Lake Agriculture Total
Predicted Burned forest area 48 0 0 0 0 0 48
Forest 0 49 0 0 0 3 52
Bare soil 3 0 50 0 0 0 53
Mine 0 0 2 47 0 0 49
Lake 0 0 0 0 50 0 50
Agriculture 0 2 0 0 0 46 48
Total 51 51 52 47 50 49 300
  ML – SENTINEL – 10 m:
Truth
Burned forest area Forest Bare soil Mine Lake Agriculture Total
Predicted Burned forest area 41 4 0 0 5 0 50
Forest 0 41 0 0 4 5 50
Bare soil 5 0 41 5 0 0 51
Mine 3 0 5 42 0 0 50
Lake 0 0 2 1 42 5 50
Agriculture 1 5 1 0 0 42 49
Total 50 50 49 48 51 52 300
  ML – SENTINEL – 20 m:
Truth
Burned forest area Forest Bare soil Mine Lake Agriculture Total
Predicted Burned forest area 40 0 6 0 0 0 46
Forest 0 41 0 0 3 5 49
Bare soil 5 0 40 5 0 0 50
Mine 0 7 5 40 0 0 52
Lake 1 6 0 0 41 6 54
Agriculture 0 5 0 0 4 40 49
Total 46 59 51 45 48 51 300
  ML – LANDSAT – 30 m:
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