Determination of Forest Burn Scar and Burn Severity from Free Satellite Images: a Comparative Evaluation of Spectral Indices and Machine Learning Classifiers

Yıl 2021, Cilt: 8 Sayı: 4, 488 - 497, 15.12.2021

Nooshin Mashhadi Ugur Alganci

https://doi.org/10.30897/ijegeo.879669

Cited By: 5

Öz

Remote sensing data indicates a considerable ability to map post-forest fire destructed areas and burned severity. In this research, the ability of spectral indices, which are difference Normalized Burned Ratio (dNBR), relative differenced Normalized Burn Ratio (RdNBR), Relativized Burn Ratio (RBR), and difference Normalized Vegetation Index (dNDVI), in mapping burn severity was investigated. The research was conducted with free access moderate to high-resolution Landsat 8 and Sentinel 2 satellite images for two forest fires cases that occurred in Izmir and Antalya provinces of Turkey. Performance of the burn severity maps from different indices were validated by use of NASA Firms active fires dataset. The results confirmed that, RdNBR showed more precise results than the other indices with an accuracy of (89%, 93%) and (84%, 79%) for Landsat 8 and Sentinel 2 satellites over Izmir and Antalya respectively. Moreover, in this research, the ability of machine learning classifiers, which are Support Vector Machine (SVM) and Random Forest (RF), in mapping burned areas were evaluated. According to the accuracy metrics that are user’s accuracy, producer's accuracy and Kappa coefficient, we concluded that both classifiers indicate reliable and accurate detection for both regions.

Anahtar Kelimeler

forest fire, burn scar, burn severity, landsat 8, sentinel 2

Kaynakça

• Bar S, Parida BR, Pandey AC. (2020). Landsat-8 and Sentinel-2 based Forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya. Remote Sens Appl Soc Environ., 18, 100324.
• Breiman L. (2001). Random forests. Machine Learning, 45, 5–32.
• Chuvieco E, Martín MP, Palacios A. (2002). Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. Int J Remote Sens., 23(23), 5103–5110.
• Collins L, Griffioen P, Newell G, Mellor A. (2018). The utility of Random Forests for wildfire severity mapping. Remote Sens Environ., 216, 374–384.
• Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783–2792.
• Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, et al. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sens Environ., 120, 25–36.
• Evangelides C, Nobajas A. (2020). Red-Edge Normalised Difference Vegetation Index (NDVI705) from Sentinel-2 imagery to assess post-fire regeneration. Remote Sens Appl Soc Environ., 17, 100283.
• Fernández-García V, Santamarta M, Fernández-Manso A, Quintano C, Marcos E, Calvo L. (2018). Burn severity metrics in fire-prone pine ecosystems along a climatic gradient using Landsat imagery. Remote Sens Environ., 206, 205–217.
• Fraser RH, Li Z, Cihlar J (2000). Hotspot and NDVI differencing synergy (HANDS): a new technique for burned area mapping over boreal forest. Remote Sens Environ., 74, 362–376.
• Foody, G (2010). Assessing the accuracy of land cover change with imperfect ground reference data. Remote Sens Environ., 114, 2271-2285.
• García-Llamas P, Suárez-Seoane S, Fernández-Guisuraga JM, Fernández-García V, Fernández-Manso A, Quintano C, Taboada A, Marcos E, Calvo L. (2019). Evaluation and comparison of Landsat 8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems. Int J Appl Earth Obs Geoinf., 80, 137–144.
• Gibson R, Danaher T, Hehir W, Collins L. (2020). A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest. Remote Sens Environ., 240, 111702.
• Herrando S, Brotons L. (2002). Forest bird diversity in Mediterranean areas affected by wildfires: A multi-scale approach. Ecography, 25(2), 161–172.
• Herrando S, Brotons L, Llacuna S. (2003). Does fire increase the spatial heterogeneity of bird communities in Mediterranean landscapes? Ibis (Lond 1859), 145(2), 307–317.
• Huang C, Davis L, Townshend J (2002). An assessment of support vector machines for land cover classification. Int J. Rem. Sens., 23, 725-749
• Huang H, Roy DP, Boschetti L, Zhang HK, Yan L, Kumar SS, Gomez-Dans J, Li J. (2016). Separability analysis of Sentinel-2A Multi-Spectral Instrument (MSI) data for burned area discrimination. Remote Sens, 8(10), 873.
• Kesgin Atak B, Ersoy Tonyalıoglu E. (2020). Evaluating spectral indices for estimating burned areas in the case of Izmir / Turkey. Eurasian J For Sci. 8(1), 63–73.
• Key C.H., and Benson, N.C. (2005). Landscape Assessment: Remote Sensing of Severity, the Normalized Burn Ratio and Ground Measure of Severity, the Composite Burn Index. FIREMON Fire Eff Monit Invent Syst USDA For Serv Rocky Mt Res.:Station 164.
• Konkathi P, Shetty A. (2019). Assessment of Burn Severity using Different Fire Indices: A Case Study of Bandipur National Park. In Proc. IEEE Recent Adv Geosci Remote Sens Technol Stand Appl TENGARSS 2019, 151–154.
• Laris PS. (2005). Spatiotemporal problems with detecting and mapping mosaic fire regimes with coarse-resolution satellite data in savanna environments. Remote Sens Environ., 99(4), 412–424.
• Louis J, Debaecker V, Pflug B, Main-Knorn M, Bieniarz J, Mueller-Wilm U, Cadau E, Gascon F. (2016). Sentinel-2 SEN2COR: L2A processor for users. Eur Sp Agency, (Special Publ ESA SP. SP-740(May):9–13.
• Lutes DC, Keane RE, Caratti JF, Key CH, Benson NC, Sutherland S, Gangi LJ. (2006). FIREMON: Fire effects monitoring and inventory system. Gen. Tech. Rep. USDA Forest Service RMRS-GTR-164-CD. (June):1–55.
• Mallinis G, Mitsopoulos I, Chrysafi I. (2018). Evaluating and comparing sentinel 2A and landsat-8 operational land imager (OLI) spectral indices for estimating fire severity in a mediterranean pine ecosystem of Greece. GIScience Remote Sens., 55(1), 1–18.
• Miller JD, Thode AE. (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sens Environ., 109(1), 66–80.
• Miller JD, Knapp EE, Key CH, Skinner CN, Isbell CJ, Creasy RM, Sherlock JW (2009) Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sens Environ, 113, 645–656
• Mouillot F, Schultz MG, Yue C, Cadule P, Tansey K, Ciais P, Chuvieco E. (2014). Ten years of global burned area products from spaceborne remote sensing-A review: Analysis of user needs and recommendations for future developments. Int J Appl Earth Obs Geoinf, 26(1), 64–79.
• Navarro G, Caballero I, Silva G, Parra PC, Vázquez Á, Caldeira R. (2017). Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery. Int J Appl Earth Obs Geoinf., 58, 97–106.
• Parks SA, Dillon GK, Miller C. (2014). A new metric for quantifying burn severity: The relativized burn ratio. Remote Sens., 6(3), 1827–1844.
• Rahman S, Chang HC, Magill C, Tomkins K, Hehir W. (2019). Spatio-Temporal Assessment of Fire Severity and Vegetation Recovery Utilising Sentinel-2 Imagery in New South Wales, Australia. Int Geosci Remote Sens Symp., 9960–9963.
• Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J Photogramm Remote Sens., 67(1), 93–104.
• Roteta, E., Bastarrika, A., Padilla, M., Storm, T., Chuvieco, E., (2019). Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for subSaharan Africa. Remote Sens. Environ. 222, 1–17
• Roy DP, Wulder MA, Loveland TR, C.E. W, Allen RG, Anderson MC, Helder D, Irons JR, Johnson DM, Kennedy R, et al. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sens Environ., 145, 154–172.
• RTGDF. (2019). Forestry statistics, Republic of Turkey General Directorate of Forestry. Available online: https://www.ogm.gov.tr/tr/ormanlarimiz/resmi-istatistikler
• Scholes RJ, Kendall J, Justice CO. (1996). The quantity of biomass burned in southern Africa. J Geophys Res Atmos., 101(19), 23667–23676.
• Seidl R, Thom D, Kautz M, Martin-Benito D, Peltoniemi M, Vacchiano G, Wild J, Ascoli D, Petr M, Honkaniemi J, et al. (2017). Forest disturbances under climate change. Nat Clim Chang., 7(6), 395–402.
• Sertel E, Alganci, U. (2016). Comparison of pixel and object-based classification for burned area mapping using SPOT-6 images. Geomatics, Natural Hazards and Risk, 7 (4), 1198-1206.
• Shoko C, Mutanga O. (2017). Seasonal discrimination of C3 and C4 grasses functional types: An evaluation of the prospects of varying spectral configurations of new generation sensors. Int J Appl Earth Obs Geoinf., 62, 47–55.
• Silva JMN, Sá ACL, Pereira JMC. (2005). Comparison of burned area estimates derived from SPOT-VEGETATION and Landsat ETM+ data in Africa: Influence of spatial pattern and vegetation type. Remote Sens Environ., 96(2), 188–201.
• Sothe C, Almeida CMd, Liesenberg V, Schimalski MB. (2017). Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil. Remote Sens, 9(8), 838.
• Soverel NO, Perrakis DDB, Coops NC. (2010). Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada. Remote Sens Environ., 114(9), 1896–1909.
• Tran, BN, Tanase, MA, Bennett, LT, Aponte, C (2018). Evaluation of Spectral Indices for Assessing Fire Severity in Australian Temperate Forests. Remote Sens., 10, 1680.
• van der Werff H, van der Meer F. (2016). Sentinel-2A MSI and Landsat 8 OLI provide data continuity for geological remote sensing. Remote Sens., 8(11), 883.
• Zuhlke et al. 2015. SNAP (sentinel application platform) and the ESA sentinel 3 toolbox. [place unknown].