بررسی قابلیت و حساسیت سنجی شاخص‌های طیفی ماهواره‌ای در پهنه‌بندی شدت آتش‌سوزی مناطق جنگلی (مطالعۀ موردی: جنگل‌کاری عرب داغ–گلستان)

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد علوم و مهندسی جنگل- مدیریت جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران.

2 استاد گروه جنگلداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران.

3 استادیار علوم و مهندسی جنگل، دانشکدۀ کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران.

چکیده

تهیۀ نقشۀ دقیق شدت آتش‌سوزی برای مدیریت ریسک آتش در اکوسیستم‌های جنگلی حائز اهمیت است. شاخص‌های طیفی از سنجنده‌های نوری به‌عنوان یکی از باندهای قابل ‌قبول برای طبقه‌بندی و نشان دادن تفاوت طیفی طبقات مختلف پوشش گیاهی شناخته ‌شده است. در این تحقیق قابلیت مجموعه‌ای از شاخص‌های استخراج‌شده از تصاویر ماهواره‌های Sentinel-2 و Landsat-8 با اندازۀ تفکیک مکانی مختلف برای تهیۀ نقشۀ دقیق شدت آتش‌سوزی با استفاده از الگوریتم جنگل تصادفی در منطقۀ دچار آتش‌سوزی سال 1397 جنگلکاری‌های عرب‌داغ استان گلستان بررسی شد. بعد از پیش‌پردازش‌های لازم، شاخص‌های تک و دوزمانۀ مناسب از تصاویر سنجنده‌های تحت بررسی ایجاد شد. مقادیر شاخص بهینه برای باندها در فضای دوبعدی قبل و بعد از آتش‌سوزی برای بررسی حساسیت این باندها به تغییرات اتفاق‌افتاده درون طبقات آتش‌سوزی محاسبه شد. بهترین نتیجه مربوط به باندهای NIR-SWIR2 با مقدار شاخص بهینۀ 77/0 برای سنجندۀ Sentinel-2 و 68/0 برای سنجندۀ Landsat8-OLI به‌دست آمد. براساس مقادیر شاخص بهینه، بهترین شاخص‌ها انتخاب شد و مقادیر این شاخص‌ها پس از آتش‌سوزی و همچنین شاخص‌های دوزمانه (قبل و بعد آتش‌سوزی) استخراج شدند. نقشۀ واقعیت زمینی نمونه‌ای طبقات شدت آتش‌سوزی با استفاده از روش نمونه‌گیری انتخابی با بازدید میدانی از طبقات شدت دچار آتش‌سوزی‌ در منطقه تهیه شد. طبقه‌بندی با شاخص‌های مختلف با الگوریتم جنگل تصادفی انجام گرفت و نتایج با نقشۀ واقعیت زمینی نمونه‌ای ارزیابی شد. بهترین نتیجه با تلفیق شاخص‌ها از همۀ باندهای استخراج‌شده از سنجندۀ Landsat8-OLI به روش شاخص دوزمانه با ضریب کاپای 96/0 به‌دست آمد.

کلیدواژه‌ها


عنوان مقاله [English]

Ability and Sensitivity Study of Spectral Indices for Wildfire Severity Mapping (Case Study: Arabdagh-Golestan Reforestations)

نویسندگان [English]

  • Mhd.Wathek Alhaj Khalaf 1
  • Shaban Shataee 2
  • Roghaye Jahdi 3
1 M.Sc. Student, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, I.R. Iran
2 Prof., of Forestry, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, I.R. Iran
3 Assist. Prof., of Forest Sciences, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, I.R. Iran
چکیده [English]

Fire severity mapping is very important for managing the fires in forest ecosystems. The extraction of spectral indices from optical sensors is recognized as one of the most effective bands for the classification of vegetation classes. In this study, the ability and sensitivity of some spectral indices extracted from Sentinel-2 and Landsat 8-OLI images with different spatial resolutions have been investigated for fire severity mapping using the Random Forest algorithm in a burned area located in the reforested area of Arabdagh, Golestan province. After necessary preprocessing on the bands, the appropriate mono and bi-temporal spectral vegetation indices were created. The optimal index values for bands in the bi-spectral spaces pre/post-fire were calculated to evaluate the sensitivity of bands to the changes occurring within the fire classes. The best results were obtained for the NIR-SWIR2 bands with an optimal index value of 0.77 for Sentinel-2 and 0.67 for Landsat8-OLI. The best indices were selected based on values of optimality index. The values of these indices were calculated after the fire as well as the differential (pre/post-fire) ones. The ground truth of fire severity classes map was prepared by a selective sampling method through field surveying. The classification was done with different indices by random forest (RF) algorithm and the results were assessed by the ground truth points. The result showed that the best results were obtained for a combination of many differential indices from all bi-bands of Landsat 8-OLI with kappa coefficient (0.96).

کلیدواژه‌ها [English]

  • Bi-Spectral indices
  • Optimality
  • random forest algorithm
  • Satellite images
  • wildfire severity
[1]. Van der Werf, G.R., Randerson, J.T., Giglio, L., Collatz, G.J., Kasibhatla, P.S., and Arellano Jr, A.F. (2006). Interannual variability in global biomass burning emissions from 1997 to 2004. Atmospheric Chemistry and Physics, 6 (11): 3423-3441.

[2]. Cardil, A., and Molina, D. (2015). Factors causing victims of wildland fires in Spain (1980–2010). Human and Ecological Risk Assessment: An International Journal, 21(1): 67-80.

[3]. Sugihara, N.G., Van Wagtendonk, J.W., Fites-Kaufman, J., Shaffer, K.E., and Thode, A.E. (2006). Fire in California's Ecosystems. University of California Press.

[4]. Hessburg, P.F., Agee, J.K., and Franklin, J.F. (2005). Dry forests and wildland fires of the inland Northwest USA: contrasting the landscape ecology of the pre-settlement and modern eras. Forest Ecology and Management, 211(1-2): 117-139.

[5]. Kasischke, E.S., and Stocks, B.J. (2012). Fire, Climate Change, and Carbon Cycling in the Boreal Forest. Springer-Verlag, New York, Inc.

[6]. Escuin, S., Navarro, R., and Fernandez, P. (2008). Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images. International Journal of Remote Sensing, 29(4): 1053-1073.

[7]. DeBano, L.F., Neary, D.G., and Ffolliott, P.F. (1998). Fire Effects on Ecosystems. John Wiley & Sons, USA.

[8]. Brewer, C.K., Winne, J.C., Redmond, R.L., Opitz, D.W., and Mangrich, M.V. (2005). Classifying and mapping wildfire severity. Photogrammetric Engineering & Remote Sensing, 71(11): 1311-1320.

[9]. García, M.L., and Caselles, V. (1991). Mapping burns and natural reforestation using Thematic Mapper data. Geocarto International, 6(1): 31-37.

[10]. Parks, S., Dillon, G., and Miller, C. (2014). A new metric for quantifying burn severity: the relativized burn ratio. Remote Sensing, 6(3): 1827-1844.

[11]. Keeley, J.E. (2009). Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire, 18(1): 116-126.

[12]. Macdonald, S.E. (2007) Effects of partial post-fire salvage harvesting on vegetation communities in the boreal mixedwood forest region of northeastern Alberta, Canada. Forest Ecology and Management, 239(1-3): 21-31.

[13]. Johnstone, J., and Chapin, F. (2006). Fire interval effects on successional trajectory in boreal forests of northwest Canada. Ecosystems, 9(2): 268-277.

[14]. Chuvieco, E. (2012) Remote sensing of large wildfires: in the European Mediterranean Basin. Springer Science & Business Media.

[15]. Key, C., and Benson, N. (2005). Landscape assessment: remote sensing of severity, the normalized burn ratio and ground measure of severity, the composite burn index. FIREMON: Fire effects monitoring and inventory system Ogden, Utah: USDA Forest Service, Rocky Mountain Res. Station.

[16]. Roy, D.P., Boschetti, L., and Trigg, S.N. (2006) Remote sensing of fire severity: assessing the performance of the normalized burn ratio. IEEE Geoscience and Remote Sensing Letters, 3(1): 112-116.

[17]. Veraverbeke, S., Verstraeten, W.W., Lhermitte, S., and Goossens, R. (2010). Evaluating Landsat Thematic Mapper spectral indices for estimating burn severity of the 2007 Peloponnese wildfires in Greece. International Journal of Wildland Fire, 19(5): 558-569.

[18]. Stroppiana, D., Bordogna, G., Carrara, P., Boschetti, M., Boschetti, L., and Brivio, P. (2012). A method for extracting burned areas from Landsat TM/ETM+ images by soft aggregation of multiple Spectral Indices and a region growing algorithm. ISPRS Journal of Photogrammetry and Remote Sensing, 69: 88-102.

[19]. Warner, T.A., Skowronski, N.S., and Gallagher, M.R. (2017). High spatial resolution burn severity mapping of the New Jersey Pine Barrens with WorldView-3 near-infrared and shortwave infrared imagery. International Journal of Remote Sensing, 38(2): 598-616.

[20]. Filipponi, F. (2018). BAIS2: Burned Area Index for Sentinel-2, 2nd International Electronic Conference on Remote Sensing, 22 March–5 April, 2018.

[21]. Tran, B., Tanase, M., Bennett, L., and Aponte, C. (2018). Evaluation of spectral indices for assessing fire severity in Australian temperate forests. Remote Sensing, 10(11): 1680.

[22]. Lasaponara, R., and Tucci, B. (2019). Identification of Burned Areas and Severity Using SAR Sentinel-1. IEEE Geoscience and Remote Sensing Letters, 16(6): 917-921.

[23]. Lotan, J.E. (1985). Proceedings - Symposium and Workshop on Wilderness Fire; November 15, 1983; Missoula, Montana. General Technical Report. INT-GTR-182. USDA Forest Service. 434 p.

[24]. Ryan, K.C. (2002). Dynamic interactions between forest structure and fire behavior in boreal ecosystems. Silva Fennica, 36(1): 13-39.

[25]. Turner, M.G., Hargrove, W.W., Gardner, R.H., and Romme, W.H. (1994). Effects of fire on landscape heterogeneity in Yellowstone National Park, Wyoming. Journal of Vegetation Science, 5(5): 731-742.

[26]. Morgan, P., Keane, R.E., Dillon, G.K., Jain, T.B., Hudak, A.T., Karau, E.C., Sikkink, P. G., Holden, Z. A., and Strand, E. K. (2014). Challenges of assessing fire and burn severity using field measures, remote sensing and modelling. International Journal of Wildland Fire, 23(8): 1045-1060.

[27]. Van Wagtendonk, J.W., Root, R.R., and Key, C. H. (2004). Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Remote Sensing of Environment, 92(3): 397-408.

[28]. Pepe, M., and Parente, C. (2018). Burned area recognition by change detection analysis using images derived from Sentinel-2 satellite: The case study of Sorrento Peninsula, Italy. Journal of Applied Engineering Science, 16(2): 225-232.

[29]. Gao, B. C. (1996). NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257-266

[30]. Smith, A.M., Wooster, M.J., Drake, N.A., Dipotso, F.M., Falkowski, M.J., and Hudak, A.T.(2005).Testing the potential of multi-spectral remote sensing for retrospectively estimating fire severity in African Savannahs. Remote Sensing of Environment, 97 (1), 92-115.

[31]. Rouse Jr, J.W., Haas, R., Schell, J., and Deering, D. (1974). Monitoring vegetation systems in the Great Plains with ERTS. Remote Sensingcenter, Texas A&M hivemity, Colfegp Station, Texas.

[32]. Chuvieco, E., Martin, M.P., and Palacios, A. (2002). Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. International Journal of Remote Sensing, 23(23): 5103-5110.

[33]. Pinty, B., and Verstraete, M. (1992). GEMI: a non-linear index to monitor global vegetation from satellites. Vegetation, 101(1): 15-20.

[34]. Bannari, A., Asalhi, H. and Teillet, P.M. (2002). Transformed difference vegetation index (TDVI) for vegetation cover mapping. Geoscience and Remote Sensing Symposium, 2002. IGARSS '02, 5: 3053-3055.

[35]. Gitelson, A.A., and Merzlyak, M.N. (1998). Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research, 22(5): 689-692.

[36]. Sripada, R.P., Heiniger, R.W., White, J.G., and Meijer, A.D. (2006). Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy Journal, 98(4): 968-977.

[37]. McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432.

[38]. Guide, P. (2017). Landsat Surface Reflectance-Derived Spectral Indices; 3.6 Version. Department of the Interior US Geological Survey (USGS): Reston, VA, USA.

[39]. Trigg, S., and Flasse, S. (2001). An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah. International Journal of Remote Sensing, 22(13): 2641-2647.

[40]. Gray, A., Abbena, E., and Salamon, S. (2006). Modern Differential Geometry of Curves and Surfaces with Mathematica. Chapman and Hall/CRC; 3 Edition. 1016 pages.

[41]. Zacharski, R. (2015). A Programmer's Guide to Data Mining: The Ancient Art of the Numerati. Available: www.guidetodatamining.com

[42]. Lowe, B., and Kulkarni, A. (2015). Multispectral image analysis using random forest. International Journal on Soft Computing, 6(1): 1-1

[43]. Breiman, L. (2001). Random forests. Machine Learning, 45(1): 5-32.

[44]. Congalton, R.G., and Green, K. (2002). Assessing the accuracy of remotely sensed data: principles and practices. CRC press.

[45]. Jenness, J., and Wynne, J.J. (2005). Cohen's Kappa and classification table metrics 2.0: An ArcView 3. x extension for accuracy assessment of spatially explicit models. Open-File Report OF 2005-1363. Flagstaff, AZ: US Geological Survey, Southwest Biological Science Center. 86 p.

[46]. Schepers, L., Haest, B., Veraverbeke, S., Spanhove, T., Vanden Borre, J., and Goossens, R. (2014). Burned area detection and burn severity assessment of a heathland fire in Belgium using airborne imaging spectroscopy (APEX). Remote Sensing, 6 (3):1803-1826.

[47]. Mallinis, G., Mitsopoulos, I., and 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 Sensing, 55 (1): 1-18.

[48]. Epting, J., Verbyla, D., and Sorbel, B. (2005). Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sensing of Environment, 96 (3-4): 328-339.

[49]. Veraverbeke, S., Lhermitte, S., Verstraeten, W.W., and Goossens, R. (2011). Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with Landsat Thematic Mapper. International Journal of Remote Sensing, 32 (12): 3521-3537.

[50]. Athanasakis, G., Psomiadis, E., and Chatziantoniou, A. (2017). High-resolution Earth observation data and spatial analysis for burn severity evaluation and post-fire effects assessment in the Island of Chios, Greece. International Society for Optics and Photonics, 104281P.

[51]. Tanase, M., de la Riva, J., and Pérez-Cabello, F. (2011). Estimating burn severity at the regional level using optically based indices. Canadian Journal of Forest Research, 41(4):863-872.