Assessing fire severity in Turkey’s forest ecosystems using spectral indices from satellite images

doi:10.1007/s11676-023-01620-7

摘要/Abstract

Abstract:

Fire severity classifications determine fire damage and regeneration potential in post-fire areas for effective implementation of restoration applications. Since fire damage varies according to vegetation and fire characteristics, regional assessment of fire severity is crucial. The objectives of this study were: (1) to test the performance of different satellite imagery and spectral indices, and two field—measured severity indices, CBI (Composite Burn Index) and GeoCBI (Geometrically structured Composite Burn Index) to assess fire severity; (2) to calculate classification thresholds for spectral indices that performed best in the study areas; and (3) to generate fire severity maps that could be used to determine the ecological impact of forest fires. Five large fires in Pinus brutia (Turkish pine) and Pinus nigra subsp. pallasiana var. pallasiana (Anatolian black pine)—dominated forests during 2020 and 2021 were selected as study sites. The results show that GeoCBI provided more reliable estimates of field—measured fire severity than CBI. While Sentinel-2 and Landsat-8/OLI images performed similarly well, MODIS performed poorly. Fire severity classification thresholds were determined for Sentinel-2 based RdNBR, dNBR, dSAVI, dNDVI, and dNDMI and Landsat-8/OLI based dNBR, dNDVI, and dSAVI. Among several spectral indices, the highest accuracy for fire severity classification was found for Sentinel-2 based RdNBR (72.1%) and Landsat-8/OLI based dNBR (69.2%). The results can be used to assess and map fire severity in forest ecosystems similar to those in this study.

Key words: Remote sensing, Forest fire, Fire severity, Spectral indices, Composite burn index

Coşkun Okan Güney, Ahmet Mert, Serkan Gülsoy. [J]. 林业研究(英文版), 2023, 34(6): 1747-1761.

Coşkun Okan Güney, Ahmet Mert, Serkan Gülsoy. Assessing fire severity in Turkey’s forest ecosystems using spectral indices from satellite images[J]. JOURNAL OF FORESTRY RESEARCH, 2023, 34(6): 1747-1761.

参考文献 75

1	Alleaume S, Hely C, Le Roux J, Korontzi S, Swap RJ, Shugart HH, Justice CO. Using MODIS to evaluate heterogeneity of biomass burning in southern African savannahs: a case study in Etosha. Int J Remote Sens, 2005, 26(19): 4219-4237, DOI
2	Allen JL, Sorbel B. Assessing the differenced normalized burn ratio’s ability to map burn severity in the boreal forest and tundra ecosystems of Alaska’s national parks. Int J Wildland Fire, 2008, 17(4): 463-475, DOI
3	Amatulli G, Camia A, San-Miguel-Ayanz J. Estimating future burned areas under changing climate in the EU-Mediterranean countries. Sci Total Environ, 2013, 450: 209-222, DOI
4	Ariza A, Rey JS, de Miguel SM. Comparison of maximum likelihood estimators and regression models for burn severity mapping in Mediterranean forests using Landsat TM and ETM+ data. Rev Cartogr, 2019, DOI
5	Arnett JTTR, Coops NC, Daniels LD, Falls RW. Detecting forest damage after a low-severity fire using remote sensing at multiple scales. Int J Appl Earth Obs Geoinf, 2015, 35: 239-246, DOI
6	Ba R, Song W, Lo S, Xie Z (2020) Spectral characteristic analysis of burned area based on MODIS Data. In: Wu GY, Tsai KC, Chow WK (eds) The proceedings of 11th Asia-Oceania symposium on fire science and technology. AOSFST 2018. Springer, Singapore, pp 391–404. https://doi.org/10.1007/978-981-32-9139-3_29
7	Baysal İ, Bilgili E, Bașkent EZ. Orman yanginlari ve orman amenajman planlari. Kast Üniv Orman Fak Derg, 2016, 16(1): 169-180, DOI
8	Beltrán-Marcos D, Suárez-Seoane S, Fernández-Guisuraga JM, Fernández-García V, Pinto R, García-Llamas P, Calvo L. Mapping soil burn severity at very high spatial resolution from unmanned aerial vehicles. Forests, 2021, 12(2): 179, DOI
9	Borini Alves D, Montorio Llovería R, Pérez-Cabello F, Vlassova L. Fusing Landsat and MODIS data to retrieve multispectral information from fire-affected areas over tropical savannah environments in the Brazilian Amazon. Int J Remote Sens, 2018, 39(22): 7919-7941, DOI
10	Cansler CA, McKenzie D. How robust are burn severity indices when applied in a new region? Evaluation of alternate field based and remote rensing methods. Remote Sens, 2012, 4(2): 456-483, DOI
11	Chen X, Vogelmann JE, Rollins M, Ohlen D, Key CH, Yang L, Huang C, Shi H. Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest. Int J Remote Sens, 2011, 32(23): 7905-7927, DOI
12	Choubin B, Solaimani K, Roshan MH, Malekian A. Watershed classification by remote sensing indices: a fuzzy c-means clustering approach. J Mt Sci, 2017, 14(10): 2053-2063, DOI
13	Chuvieco E, Martin MP, Palacios A. Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. Int J Remote Sens, 2002, 23(23): 5103-5110, DOI
14	Chuvieco E, Mouillot F, van der Werf GR, San Miguel J, Tanasse M, Koutsias N, García M, Yebra M, Padilla M, Gitas I. Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sens Environ, 2019, 225: 45-64, DOI
15	Cochrane MA, Ryan KC. Cochrane MA. Fire and fire ecology: Concepts and principles. Tropical Fire Ecology, 2009 Chichester Springer 25-62, DOI
16	De Santis A, Chuvieco E. GeoCBI: a modified version of the composite burn index for the initial assessment of the short-term burn severity from remotely sensed data. Remote Sens Environ, 2009, 113(3): 554-562, DOI
17	Epting J, Verbyla D, Sorbel B. Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sens Environ, 2005, 96(3–4): 328-339, DOI
18	Escuin S, Navarro R, Fernandez P. Fire severity assessment by using NBR (normalized burn ratio) and NDVI (normalized difference vegetation index) derived from LANDSAT TM/ETM images. Int J Remote Sens, 2008, 29(4): 1053-1073, DOI
19	Fernández-García V, Santamarta M, Fernández-Manso A, Quintano C, Marcos E, Calvo L. Burn severity metrics in fire-prone pine ecosystems along a climatic gradient using Landsat imagery. Remote Sens Environ, 2018, 206: 205-217, DOI
20	Fernández-Manso A, Fernández-Manso O, Quintano C. SENTİNEL-2A red-edge spectral indices suitability for discriminating burn severity. Int J Appl Earth Obs Geoinf, 2016, 50: 170-175, DOI
21	Fornacca D, Ren G, Xiao W. Evaluating the best spectral indices for the detection of burn scars at several post-fire dates in a mountainous region of Northwest Yunnan, China. Remote Sens, 2018, 10(8): 1196, DOI
22	French NH, Kasischke ES, Hall RJ, Murphy KA, Verbyla DL, Hoy EE, Allen JL. Using Landsat data to assess fire and burn severity in the North American boreal forest region: an overview and summary of results. Int J Wildland Fire, 2008, 17(4): 443-462, DOI
23	Gao B-C. NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ, 1996, 58(3): 257-266, DOI
24	García-Llamas P, Suárez-Seoane S, Fernández-Guisuraga JM, Fernández-García V, Fernández-Manso A, Quintano C, Taboada A, Marcos E, Calvo L. 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, 2019, 80: 137-144, DOI
25	Gascon F, Ramoino F (2017, April). Sentinel-2 data exploitation with ESA's sentinel-2 toolbox. In: EGU general assembly conference abstracts, p 19548
26	Hoscilo A, Tansey KJ, Page SE. Post-fire vegetation response as a proxy to quantify the magnitude of burn severity in tropical peatland. Int J Remote Sens, 2013, 34(2): 412-433, DOI
27	Hoy EE, French NHF, Turetsky MR, Trigg SN, Kasischke ES. Evaluating the potential of Landsat TM/ETM+ imagery for assessing fire severity in Alaskan black spruce forests. Int J Wildland Fire, 2008, 17(4): 500-514, DOI
28	Huang H, Roy D, Boschetti L, Zhang H, Yan L, Kumar S, Gomez-Dans J, Li J. Separability analysis of Sentinel-2A multi-spectral instrument (MSI) data for burned area discrimination. Remote Sens, 2016, 8(10): 873, DOI
29	Hudak AT, Morgan P, Bobbitt MJ, Smith AMS, Lewis SA, Lentile LB, Robichaud PR, Clark JT, McKinley RA. The relationship of multispectral satellite imagery to immediate fire effects. Fire Ecol, 2007, 3(1): 64-90, DOI
30	Huete AR. A soil-adjusted vegetation index (SAVI). Remote Sens Environ, 1988, 25(3): 295-309, DOI
31	Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ, 2002, 83(1–2): 195-213, DOI
32	Ilori CO, Pahlevan N, Knudby A. Analyzing performances of different atmospheric correction techniques for Landsat 8: application for coastal remote sensing. Remote Sens, 2019, 11(4): 469, DOI
33	Itten KI, Meyer P. Geometric and radiometric correction of TM data of mountainous forested areas. IEEE Trans Geosci Remote Sens, 1993, 31(4): 764-770, DOI
34	Kasischke ES, Bruhwiler LP. Emissions of carbon dioxide, carbon monoxide, and methane from boreal forest fires in 1998. J Geophys Res Atmos, 2002, DOI
35	Kaufman YJ, Sendra C. Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery. Int J Remote Sens, 1988, 9(8): 1357-1381, DOI
36	Keeley JE. Fire intensity, fire severity and burn severity: a brief review and suggested usage. Int J Wildland Fire, 2009, 18(1): 116-126, DOI
37	Key CH. Ecological and sampling constraints on defining landscape fire severity. Fire Ecol, 2006, 2(2): 34-59, DOI
38	Key CH, Benson NC (2006) Landscape assessment (LA). In: Lutes DC (ed) FIREMON: fire effects monitoring and inventory system. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, pp LA-1–55. https://doi.org/10.2737/RMRS-GTR-164
39	Korhonen L, Packalen P, Rautiainen M. Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index. Remote Sens Environ, 2017, 195: 259-274, DOI
40	Lasaponara R, Lanorte A, Pignatti S. Multiscale fuel type mapping in fragmented ecosystems: preliminary results from hyperspectral MIVIS and multispectral Landsat TM data. Int J Remote Sens, 2006, 27(3): 587-593, DOI
41	Lewis SA, Lentile LB, Hudak AT, Robichaud PR, Morgan P, Bobbitt MJ. Mapping ground cover using hyperspectral remote sensing after the 2003 Simi and Old wildfires in Southern California. Fire Ecol, 2007, 3(1): 109-128, DOI
42	Liu Z, Yang J, Dwomoh F. Mapping recent burned patches in Siberian larch forest using Landsat and MODIS data. Eur J Remote Sens, 2016, 49(1): 861-887, DOI
43	Lunetta RS, Elvidge C (1999) Remote sensing change detection: environmental monitoring methods and applications. CRC Press, Florida, pp 318
44	Luo H, Wu J. An assessment of the suitability of Sentinel-2 data for identifying burn severity in areas of low vegetation. J Indian Soc Remote Sens, 2022, DOI
45	Lutes DC, Keane RE, Caratti J F, Key CH, Benson NC, Sutherland S, Gangi LJ (2006) FIREMON: fire effects monitoring and inventory system (Gen. Tech. Rep. RMRS-GTR-164). FS Department of Agriculture, Rocky Mountain Research Station, Fort Collins, USA. https://doi.org/10.2737/RMRS-GTR-164
46	Mallinis G, Mitsopoulos I, Chrysafi I. 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. Gisci Remote Sens, 2018, 55(1): 1-18, DOI
47	Matthew MW, Adler-Golden SM, Berk A, Felde G, Anderson GP, Gorodetzky D, Paswaters S, Shippert M (2002) Atmospheric correction of spectral imagery: evaluation of the FLAASH algorithm with AVIRIS data. In: Applied imagery pattern recognition workshop, pp 157–163
48	McCarley TR, Smith AMS, Kolden CA, Kreitler J. Evaluating the Mid-Infrared Bi-spectral Index for improved assessment of low-severity fire effects in a conifer forest. Int J Wildland Fire, 2018, 27(6): 407, DOI
49	Michaletz ST, Johnson EA. How forest fires kill trees: a review of the fundamental biophysical processes. Scand J for Res, 2007, 22(6): 500-515, DOI
50	Miller JD, Quayle B. Calibration and validation of immediate post-fire satellite-derived data to three severity metrics. Fire Ecol, 2015, 11(2): 12-30, DOI
51	Miller JD, Thode AE. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta normalized burn ratio (dNBR). Remote Sens Environ, 2007, 109(1): 66-80, DOI
52	Miller JD, Knapp EE, Key CH, Skinner CN, Isbell CJ, Creasy RM, Sherlock JW. 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, 2009, 113(3): 645-656, DOI
53	Papageorgiou K, Hadjimitsis DG, Agapiou A, Themistocleous K, Koutsias N, Chrysoulakis N (2012) Spectral signatures of Pinus brutia post fire regeneration in Paphos forest, using ground spectroradiometers. In: Conference: 32nd EARSeL symposium, pp 223–230
54	Parker BM, Lewis T, Srivastava SK. Estimation and evaluation of multi-decadal fire severity patterns using Landsat sensors. Remote Sens Environ, 2015, 170: 340-349, DOI
55	Parks SA, Dillon GK, Miller C. A new metric for quantifying burn severity: the relativized burn ratio. Remote Sens, 2014, 6(3): 1827-1844, DOI
56	Pinty B, Verstraete M. GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio, 1992, 101(1): 15-20, DOI
57	Pleniou M, Koutsias N. Sensitivity of spectral reflectance values to different burn and vegetation ratios: a multi-scale approach applied in a fire affected area. ISPRS J Photogramm Remote Sens, 2013, 79: 199-210, DOI
58	Pletsch MAJS, Penha TV, Junior CHLS, Morelli F (2019) Combination of spectral indices for burned area detection in the Brazilian Amazonia. In: XIX Brazilian symposium on remote sensing, pp 1248–1251
59	Rogan J, Franklin J. Mapping wildfire burn severity in southern California forests and shrublands using enhanced thematic mapper imagery. Geocarto Int, 2001, 16(4): 91-106, DOI
60	Saulino L, Rita A, Migliozzi A, Maffei C, Allevato E, Garonna AP, Saracino A. Detecting burn severity across Mediterranean forest types by coupling medium-spatial resolution satellite imagery and field data. Remote Sens, 2020, 12(4): 741, DOI
61	Smith A, Drake N, Wooster M, Hudak A, Holden Z, Gibbons C. Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS. Int J Remote Sens, 2007, 28(12): 2753-2775, DOI
62	Song X-P, Huang W, Hansen MC, Potapov P. An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping. Sci Remote Sens, 2021, DOI
63	Soverel NO, Perrakis DDB, Coops NC. Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada. Remote Sens Environ, 2010, 114(9): 1896-1909, DOI
64	Teillet PM. Image correction for radiometric effects in remote sensing. Int J Remote Sens, 1986, 7(12): 1637-1651, DOI
65	Trigg S, Flasse S. An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah. Int J Remote Sens, 2001, 22(13): 2641-2647, DOI
66	Tucker CJ. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ, 1979, 8(2): 127-150, DOI
67	Turner MG, Romme WH. Landscape dynamics in crown fire ecosystems. Landsc Ecol, 1994, 9(1): 59-77, DOI
68	Valor T, González-Olabarria JR, Piqué M, Casals P. The effects of burning season and severity on the mortality over time of Pinus nigra spp. salzmannii (Dunal) Franco and P. sylvestris L. For Ecol Manag, 2017, 406: 172-183, DOI
69	Veraverbeke S, Lhermitte S, Verstraeten WW, Goossens R. Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with Landsat Thematic Mapper. Int J Remote Sens, 2011, 32(12): 3521-3537, DOI
70	White JD, Ryan KC, Key CC, Running SW. Remote sensing of forest fire severity and vegetation recovery. Int J Wildland Fire, 1996, 6(3): 125-136, DOI
71	Whitman E, Batllori E, Parisien MA, Miller C, Coop JD, Krawchuk MA, Chong GW, Haire SL. The climate space of fire regimes in north-western North America. J Biogeogr, 2015, 42(9): 1736-1749, DOI
72	Wilson EH, Sader SA. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sens Environ, 2002, 80(3): 385-396, DOI
73	Wu Z, Middleton B, Hetzler R, Vogel J, Dye D. Vegetation burn severity mapping using Landsat-8 and WorldView-2. Photogramm Eng Remote Sens, 2015, 81(2): 143-154, DOI
74	Ye H, Chen C, Yang C. Atmospheric correction of Landsat-8/OLI imagery in turbid estuarine waters: a case study for the Pearl River estuary. IEEE J Sel Top Appl Earth Observations Remote Sens, 2016, 10(1): 252-261, DOI
75	Zhu Z, Key C, Ohlen D, Benson N (2006) Evaluate sensitivities of burn severity mapping algorithms for different ecosystems and fire histories in the United States. Final Report to the Joint Fire Science Program (JFSP Project No. 01-1-4-12). USGS, Sioux Falls, USA. https://www.frames.gov/catalog/391