Assessment of Forest Fire Damage Severity By Remote Sensing Techniques

Year 2023, , 151 - 158, 15.06.2023

Gamze Öncü Özşen Çorumluoğlu

https://doi.org/10.30897/ijegeo.1089014

Cited By: 5

Abstract

Forest fires are the leading natural disasters that endanger the living and lifeless environment in forest ecosystem. Every year, millions of forested areas burn out and Turkey is one of the countries most affected by the forest fires. In this study, forest fire started near Karabaglar was investigated. Using Landsat 8 satellite images, fire area was detected by using five different remote sensing indexing methods (dNDVI, dNBR, dNBRT, dBAI, RBR) were used and the fire area is mapped. Fire intensity was calculated and related map is then obtained. Thus, burn severity distribution in the region was estimated and shown as the burn severity map. As a result of the analysis, it is found that the most convenient results come from the RBR analyses with a 99% of consistency when they are compared with the burn severity data obtained in the field by the General Directorate of Forestry. The remote sensing technique has also demonstrated its ability to distinguish damage severity levels, even including undamaged sections in an entire forest fire damage zone, in a way that cannot be completely done in a field work.

Keywords

forest fire damage assessment, burn severity analyze, Remote Sensing, BAI

Uzaktan Algılama Teknikleri ile Orman Yangınının Neden Olduğu Hasarın Tayin Edilmesi

Abstract

Orman ekosistemindeki canlı ve cansız çevreyi tehlikeye sokan doğal afetlerin başında orman yangınları gelmektedir. Her yıl milyonlarca ormanlık alan yanmakta ve Türkiye orman yangınlarından en çok etkilenen ülkelerin başında gelmektedir. Bu çalışmada Karabağlar yakınlarında çıkan orman yangını incelenmiştir. Landsat 8 uydu görüntüleri kullanılarak beş farklı uzaktan algılama indeksleme yöntemi (dNDVI, dNBR, dNBRT, dBAI, RBR) kullanılarak yangın alanı tespit edilmiş ve yangın alanı haritalanmıştır. Yangın şiddeti hesaplanarak ilgili harita elde edilmiştir. Böylece bölgedeki yangın şiddet dağılımı tahmin edilmiş ve yangın şiddet haritası oluşturulmuştur. Analiz sonuçları, Orman Genel Müdürlüğü tarafından sahada elde edilen yangın şiddet verileri ile karşılaştırıldığında en uygun sonuçların %99 tutarlılık ile RBR analizlerinden geldiği tespit edilmiştir. Çalışmada, Uzaktan algılama tekniği, bir orman yangını hasar bölgesinde hasar görmemiş bölümler de dahil olmak üzere, hasar ciddiyet seviyelerinin, bir saha çalışmasında elde edilemeyecek şekilde kısa bir sürede ve yüksek bir doğrulukla belirlenebileceğini de göstermiştir.

Keywords

Orman Yangını Hasar Tespiti, Orman Yangını Zarar Analizi, Uzaktan Algılama

References

• ALLEN JL & SORBEL B. 2008. Assessing the differenced Normalized Burn Ratio’s Ability to Map Burn Severity in the Boreal Forest and Tundra Ecosystems of Alaska’s National parks. International Journal of Wildland Fire. 17: 463-475.
• CHUVIECO E, MARTIN P & PALACIOS A. 2002. Assessment of Different Spectral Indices in the Red-Near-Infrared. International Journal of Remote Sensing. Vol.23, No.23.
• COCKE AE, FULE PZ & CROUSE JE. 2005. Comparison of Burn Severity Assessments Using Differenced. International of Wildland Fire. 14: 189-198.
• COMERT R, MATCI D, EMIR H & AVDAN U. 2017. Detection of Burned Forest Areas with Object-Based Classification. Afyon Kocatepe University Journal of Science and Engineering.17:27-34.
• CORUMLUOGLU O, ASRI I & OZDEMIR E. 2015. Forest Fire Risk Analysis with Gis Support: Antalya Sample. Retrieved from ResearchGate.
• DIAZ DELGADO R, LLORET F & PONS X. 2003. Influence of Fire Severity on Plant Regeneration by Means of Remote Sensing Imagery. International of Remote Sensing.
• DOGANAY H & DOGANAY S. 2011. Forest Fires and Measures to be Taken in Turkey. Easter Geographical review. 11: 31-48. https://dergipark.org.tr/en/pub/ataunidcd/issue/2452/30960
• EARTHEXPLORER 2020, accessed 01 May 2020, <https://earthexplorer.usgs.gov>.
• EIDENSHINK J, SCHWIND K, ZHU ZL, QUAYLE B & HOWARD S. 2007. A Project for Monitoring Trends in Burn Severity. Fire Ecology Special Issue. Volume :3, No: 1.
• FILIPPONI F. 2018. BAIS2: Burned Area Index for Sentinel-2. The 2nd International Electronic Conference on Remote Sensing. 2: 364. https://www.mdpi.com/2504-3900/2/7/364
• FORESTERS’ ASSOCIATION OF TURKEY REPORT: Press Release on Izmir Forest Fire Dated 18-20/08/2019.
• GALE MG, CARY GJ, VAN DIJK AIJM & YEBRA M. 2020. Forest Fire Through the lens of remote Sensing: Review of Approaches, Challenges and Future Directions in the Remote Sensing of Biotic Determinants of Fire Behavior. Elsevier.Volume: 255. https://www.sciencedirect.com/science/article/abs/pii/S0034425720306556
• KEY CH & BENSON N. 2006. Landscape Assessment: Ground Measure of Severity, the Composite Burn Index; and Remote Sensing of Severity, the Normalized Burn Ratio. USDA Forest Service, Rocky Mountain Research Station: Ogden, UT, USA. Pp. LA 1-LA 51.
• LACOTURE DL, BROADBENT EN & CRANDALL M. 2020. Detecting Vegetation Recovery After Fire in a Fire- Freguented Habitat Using Normalized Difference Vegetation Index. Forests. 11, 749. https://www.mdpi.com/1999-4907/11/7/749
• LI Z, FRASER R, JIN J, ABUELGASIM AA ET AL. 2003. Evaluation of algorithms for fire detection and mapping across North America from satellite. Journal Of Geophysical Research. Volume:108, No: D2, 4076.
• L3HARRIS GEOSPATIAL 2020, Burn indices background, accessed 04 May 2020, <https://www.l3harrisgeospatial.com/docs/backgroundburnindices.html>.
• MARTIN P, GOMEZ I & CHUVIECO E. 2005. Performance of a Burned-Area Index (BAIM) for Mapping Mediterranean Burned Scars from MoDIS Data. Proceedings of the 5th International Workshop on Remote Sensing and GIS Applications to Forest Fire Management: Fire Effects Assessment: 193-197.
• MURPHY KA, REYNOLDS JH & KOLTUN JM. 2008. Evaluating the Ability of the Differenced Normalized Burn Ratio to Predict Ecologically Significant Burn Severity in Alaska Boreal Forests. International Journal of Wildland Fire. 17: 490-499.
• PARKS AS, DILLON GK & MILLER C. 2014. A New Metric for Quantifying Burn Severity: The Relativized. Remote Sensing. 6: 1827-1844.
• ROZARIO PF, MAURAPPERUMA BD & WANG Y. 2018. Remote Sensing Approach to Detect BurnSeverity Risk Zones in Palo Verde Natioanal Park, Costa Rica. Remote Sensing. 10: 1427. https://www.mdpi.com/2072-4292/10/9/1427/htm
• SABUNCU A & OZENER H. 2019. Detection of Burned Areas by Remote Sensing Techniques: Izmir Seferihisar Forest fire case study. Journal of Natural Hazards and Environment. 5:317-326
• SHIMABUKURO YE, DUTRA AC, ARAI E ET AL. 2020. Mapping Burned Areas of Mato Grosso State Brazilian Amazon Using Multisensor Datasets. Remote Sensing. 12: 3827. https://www.mdpi.com/2072-4292/12/22/3827?type=check_update&version=3
• UN-SPIDER 2020, Normalized burn ratio, accessed 04 May 2020, <http://www.un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio>.
• USGS, 2020, What are the band designations for the Landsat satellites?, accessed 01 May 2020, <https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products>.
• VERAVERBEKE S, LHERMITTE S, VERSTRAETEN WW & GOOSSENS R. 2010. The Temporal Dimension of Differenced Normalized Burn Ratio (dNBR) Fire/Burn Severity Studies: The Case of the Large 2007 Peloponnese Wildfires in Greece. Elsevier. 114: 2548-2563.
• VIANA-SOTO A, AGUADO I & MARTINEZ S. 2017. Assessment of Post Fire Vegetation Recovery Using Fire Severity and Geographic Data in the Mediterranean Region (Spain). Envirenments. 4,90.