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Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry

Year 2021, Volume 7, Issue 2, 57 - 66, 31.12.2021
https://doi.org/10.33904/ejfe.1031090

Abstract

Forests host diverse ecosystems that involve various habitats. There are many complex interactions between living and non-living things in most forests. It is important to conduct observations and assessments in large forestlands where short-term and long-term direct or indirect negative impacts may occur so that they are known and measured. Scientific studies have been carried out by utilizing the various data offered by today's advanced technology with satellite imagery becoming more readily available. In this study, differenced Normalized Burn Ratio (dNBR=∆NBR) and satellite images with two different resolutions were used to generate pre- and post-wildfire spatial data. An area affected by wildfire in the Mediterranean Region of Turkey was selected as the study area. Google Earth Engine (GEE) and Geographic Information System (GIS) were used to delineate areas affected by wildfire using Sentinel-2 and Landsat 8 multispectral imagery. In order to compare the differences between the two sets of imagery, burn severity levels (low, medium-low, medium-high, and highest) and the effect of water surface were considered. For the most impacted burnt lands, areas detected with Sentinel 2 and Landsat 8 are 31.90% and 32.59%, respectively. However, burn severity classes were also observed in water surface areas likely due to interactions between land cover and water reflectance. The overall results support the use of both satellite platforms and the dNBR for burn severity mapping in medium- and large-scale post-wildfire studies.

References

  • Akay, A. E., Şahin, H., 2019. Forest fire risk mapping by using GIS techniques and AHP Method: A case study in Bodrum (Turkey). Eur. J. For. Res., 5(1): 25-35.
  • Arıcak, B., Enez, K., Küçük, Ö., 2012. Determining Fire Potential by Using Satellite Images, KSU J. Engineering Sci., Special Issue: 220-225.
  • Ateşoğlu, A., 2014. Forest fire hazard identifying. mapping using satellite imagery-geographic information system and analytic hierarchy process: Bartın, Turkey. J. Environ. Prot. Ecol., 15(2): 715-725
  • Atun, R., Kalkan, K., Gürsoy, Ö., 2020. Determining the forest fire risk with Sentinel 2 images. Turkish Journal of Geosciences, 1(1): 22-26.
  • Barrow, C. J., 1993. Caring for the earth: A strategy for sustainable living, published by IUCN (World Conservation Union), UNEP (United Nations Environment Programme) and WWF (World Wide Fund for Nature). J. Int. Dev., 5(3): 352-352.
  • Bolton, D. K.,Coops, N. C., Wulder, M. A., 2015. Characterizing residual structure and forest recovery following high-severity fire in the western boreal of Canada using landsat time series and airborne LiDAR data. Remote Sens. of Environ., 163: 48-60.
  • Cavdaroglu, G.C., 2021. Google Earth Engine based approach for finding fire locations and burned areas in Muğla, Turkey. American Journal of Remote Sensing, 9(2), 72-77.
  • Çepel, N., 2002. Ekolojik Sorunlar ve Çözümleri. TÜBİTAK Bilim Kitapları, 180, 3. Basım, 2003. Ankara, s. 183. (In Turkish)
  • Chuvieco, E. (Ed.)., 2009. Earth observation of wildland fires in Mediterranean ecosystems Dordrecht, the Netherlands: Springer. pp. 129-148.
  • Çoban, H., Özdamar, S., 2014. Mapping forest fire in relation to land-cover and topographic characteristics. J. Environ. Biol., 35(1): 217-224.
  • Cocke, A. E., Fulé, P. Z., Crouse, J. E., 2005. Comparison of burn severity assessments using differenced Normalized Burn Ratio and ground data. Int. J. Wildland Fire, 14(2): 189-198.
  • Congalton, R. G., 2001. Accuracy assessment and validation of remotely sensed and other spatial information. Int. J. Wildland Fire, 10(4): 321-328.
  • Erdas., 1997. ERDAS Field Guide. Atlanta.
  • Erten, E., Kurgun, V., Musaoglu, N., 2004. Forest fire risk zone mapping from satellite imagery and GIS: a case study. In XXth Congress of the International Society for Photogrammetry and Remote Sensing, Istanbul, Turkey (pp. 222-230).
  • ESA., 2021. Specification of Sentinel 2 MSI. Online: https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/overview (Accessed: 10.10.2021)
  • Esri., 1996. Using ArcView GIS: user manual. Redlands: Environmental Systems Research Institute.
  • FAO., 2021. “Sustainable forest management” Online: https://www.fao.org/forestry/sfm/en/. (Accessed: 18.09.2021)
  • Fuller, D., 2000. Satellite remote sensing of biomass burning with optical and thermal sensors. Progress in Physical Geography, 24(4): 543-561.
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202: 18–27.
  • Gülci, N., 2021. Estimating costs of salvage logging for large-scale burned forestlands: A case study on Turkey’s Mediterranean coast. J. For. Res. 32(5): 1899-1909.
  • Gulci, S., Akay, A. E., Yuksel, K., 2016. Evaluating capabilities of using thermal imagery for detecting impacts of forest operations on residual forests. In Living Planet Symposium,2016, August. 740, p. 193.
  • Gülci, S., Gülci, N., Yüksel, K., 2019. Monitoring Water Surface Area and Land Cover Change by using Landsat Imagery for Aslantaş Dam Lake and Its Vicinity. Journal of the Institute of Science and Technology, 9(1): 100-110.
  • Keeley, J.E., 2009. Fire intensity, fire severity and burn severity: A brief review and suggested usage. Int. J. Wildland Fire, 18(1): 116–126.
  • Key, C.H., Benson, N.C., 2006. Landscape assessment: Remote sensing of severity, the Normalized Burn Ratio. In FIREMON: Fire Effects Monitoring and Inventory System; USDA Forest Service, Rocky Mountain Research Station, Fort Collins: Denver, CO, USA, pp. 305–325.
  • Konkathi, P., Shetty, A., 2021. Inter comparison of post-fire burn severity indices of Landsat-8 and Sentinel-2 imagery using Google Earth Engine. Earth Sci Inform 14, 645–653.
  • Küçük, Ö., Bilgili, E., 2006. The conveyance of fire behavior characteristics into practice by using Geographical Information Sysyems (GIS): A Case Study in Kastamonu, Kastamonu University Journal of Forestry Faculty 6(2): 262-273.
  • Miller, J. D., Thode, A. E., 2007. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sens. of Environ. 109(1): 66-80.
  • Nasi, R., Dennis, R., Meijaard, E., Applegate, G., Moore, P., 2002. Forest fire and biological diversity. UNASYLVA-FAO, 36-40.
  • Nemani, R.,Votava, P., Michaelis, A., Melton, F., Milesi, C., 2011. Collaborative super computing for global change science EOS Trans. Am. Geophys. Union. 92(13): 109-110.
  • Pan, X., Wang, Z., Gao, Y., Dang, X., & Han, Y. (2021). Detailed and automated classification of land use/land cover using machine learning algorithms in Google Earth Engine. Geocarto Int. 1-18.
  • Picotte, J. J., Cansler, C. A., Kolden, C. A., Lutz, J. A., Key, C., Benson, N. C., Robertson, K. M., 2021. Determination of burn severity models ranging from regional to national scales for the conterminous United States. Remote Sens. Environ. 263: 112569.
  • Quintano, C., Fernández-Manso, A., Fernández-Manso, O., 2018. Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity. Appl Earth Obs Geoinf. 64: 221-225.
  • Sabuncu, A., Özener, H., 2019. Detection of Burned Areas by Remote Sensing Techniques: İzmir Seferihisar Forest fire case study. Journal of Natural Hazards and Environment, 5(2), 317-326.
  • Stephens, S. L., Collins, B. M., Fettig, C. J., Finney, M. A., Hoffman, C. M., Knapp, E. E., North, M. P., Safford, H., Wayman, R. B., 2018. Drought, tree mortality, and wildfire in forests adapted to frequent fire. BioScience, 68(2): 77-88.
  • UNOOSA (The United Nations Office for Outer Space Affairs)., 2018. Step by Step: Burn Severity mapping in Google Earth Engine, Submitted by Johannes Heisig on Tue, 04/12/2018 - 13:54. Online: http://www.un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/burn-severity-earth-engine. (Accessed: 10.08.2021)
  • USDA (United States Department of Agriculture)., 2006. FIREMON: Fire Effects Monitoring and Inventory System. USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. Online: https://www.fs.fed.us/rm/pubs/ rmrs_gtr164.pdf FIREMON BR CheatSheet V4, June 2004.
  • USGS., 2021. The bands specification of Landsat 8. Online: https://www.usgs.gov/media/images/landsat-8-band-designations (Accessed: 10.10.2021)
  • Wing, M. G., Burnett, J. D., Sessions, J., 2014. Remote sensing and unmanned aerial system technology for monitoring and quantifying forest fire impacts. Int. J. Remote Sens. Appl. 4(1): 18-35.
  • Wulder, M. A., Hall, R. J., Franklin, S. E., 2005. Remote sensing and GIS in forestry. Remote sensing for GIS managers. ESRI Press, Redlands, 351-362.

Year 2021, Volume 7, Issue 2, 57 - 66, 31.12.2021
https://doi.org/10.33904/ejfe.1031090

Abstract

References

  • Akay, A. E., Şahin, H., 2019. Forest fire risk mapping by using GIS techniques and AHP Method: A case study in Bodrum (Turkey). Eur. J. For. Res., 5(1): 25-35.
  • Arıcak, B., Enez, K., Küçük, Ö., 2012. Determining Fire Potential by Using Satellite Images, KSU J. Engineering Sci., Special Issue: 220-225.
  • Ateşoğlu, A., 2014. Forest fire hazard identifying. mapping using satellite imagery-geographic information system and analytic hierarchy process: Bartın, Turkey. J. Environ. Prot. Ecol., 15(2): 715-725
  • Atun, R., Kalkan, K., Gürsoy, Ö., 2020. Determining the forest fire risk with Sentinel 2 images. Turkish Journal of Geosciences, 1(1): 22-26.
  • Barrow, C. J., 1993. Caring for the earth: A strategy for sustainable living, published by IUCN (World Conservation Union), UNEP (United Nations Environment Programme) and WWF (World Wide Fund for Nature). J. Int. Dev., 5(3): 352-352.
  • Bolton, D. K.,Coops, N. C., Wulder, M. A., 2015. Characterizing residual structure and forest recovery following high-severity fire in the western boreal of Canada using landsat time series and airborne LiDAR data. Remote Sens. of Environ., 163: 48-60.
  • Cavdaroglu, G.C., 2021. Google Earth Engine based approach for finding fire locations and burned areas in Muğla, Turkey. American Journal of Remote Sensing, 9(2), 72-77.
  • Çepel, N., 2002. Ekolojik Sorunlar ve Çözümleri. TÜBİTAK Bilim Kitapları, 180, 3. Basım, 2003. Ankara, s. 183. (In Turkish)
  • Chuvieco, E. (Ed.)., 2009. Earth observation of wildland fires in Mediterranean ecosystems Dordrecht, the Netherlands: Springer. pp. 129-148.
  • Çoban, H., Özdamar, S., 2014. Mapping forest fire in relation to land-cover and topographic characteristics. J. Environ. Biol., 35(1): 217-224.
  • Cocke, A. E., Fulé, P. Z., Crouse, J. E., 2005. Comparison of burn severity assessments using differenced Normalized Burn Ratio and ground data. Int. J. Wildland Fire, 14(2): 189-198.
  • Congalton, R. G., 2001. Accuracy assessment and validation of remotely sensed and other spatial information. Int. J. Wildland Fire, 10(4): 321-328.
  • Erdas., 1997. ERDAS Field Guide. Atlanta.
  • Erten, E., Kurgun, V., Musaoglu, N., 2004. Forest fire risk zone mapping from satellite imagery and GIS: a case study. In XXth Congress of the International Society for Photogrammetry and Remote Sensing, Istanbul, Turkey (pp. 222-230).
  • ESA., 2021. Specification of Sentinel 2 MSI. Online: https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/overview (Accessed: 10.10.2021)
  • Esri., 1996. Using ArcView GIS: user manual. Redlands: Environmental Systems Research Institute.
  • FAO., 2021. “Sustainable forest management” Online: https://www.fao.org/forestry/sfm/en/. (Accessed: 18.09.2021)
  • Fuller, D., 2000. Satellite remote sensing of biomass burning with optical and thermal sensors. Progress in Physical Geography, 24(4): 543-561.
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202: 18–27.
  • Gülci, N., 2021. Estimating costs of salvage logging for large-scale burned forestlands: A case study on Turkey’s Mediterranean coast. J. For. Res. 32(5): 1899-1909.
  • Gulci, S., Akay, A. E., Yuksel, K., 2016. Evaluating capabilities of using thermal imagery for detecting impacts of forest operations on residual forests. In Living Planet Symposium,2016, August. 740, p. 193.
  • Gülci, S., Gülci, N., Yüksel, K., 2019. Monitoring Water Surface Area and Land Cover Change by using Landsat Imagery for Aslantaş Dam Lake and Its Vicinity. Journal of the Institute of Science and Technology, 9(1): 100-110.
  • Keeley, J.E., 2009. Fire intensity, fire severity and burn severity: A brief review and suggested usage. Int. J. Wildland Fire, 18(1): 116–126.
  • Key, C.H., Benson, N.C., 2006. Landscape assessment: Remote sensing of severity, the Normalized Burn Ratio. In FIREMON: Fire Effects Monitoring and Inventory System; USDA Forest Service, Rocky Mountain Research Station, Fort Collins: Denver, CO, USA, pp. 305–325.
  • Konkathi, P., Shetty, A., 2021. Inter comparison of post-fire burn severity indices of Landsat-8 and Sentinel-2 imagery using Google Earth Engine. Earth Sci Inform 14, 645–653.
  • Küçük, Ö., Bilgili, E., 2006. The conveyance of fire behavior characteristics into practice by using Geographical Information Sysyems (GIS): A Case Study in Kastamonu, Kastamonu University Journal of Forestry Faculty 6(2): 262-273.
  • Miller, J. D., Thode, A. E., 2007. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sens. of Environ. 109(1): 66-80.
  • Nasi, R., Dennis, R., Meijaard, E., Applegate, G., Moore, P., 2002. Forest fire and biological diversity. UNASYLVA-FAO, 36-40.
  • Nemani, R.,Votava, P., Michaelis, A., Melton, F., Milesi, C., 2011. Collaborative super computing for global change science EOS Trans. Am. Geophys. Union. 92(13): 109-110.
  • Pan, X., Wang, Z., Gao, Y., Dang, X., & Han, Y. (2021). Detailed and automated classification of land use/land cover using machine learning algorithms in Google Earth Engine. Geocarto Int. 1-18.
  • Picotte, J. J., Cansler, C. A., Kolden, C. A., Lutz, J. A., Key, C., Benson, N. C., Robertson, K. M., 2021. Determination of burn severity models ranging from regional to national scales for the conterminous United States. Remote Sens. Environ. 263: 112569.
  • Quintano, C., Fernández-Manso, A., Fernández-Manso, O., 2018. Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity. Appl Earth Obs Geoinf. 64: 221-225.
  • Sabuncu, A., Özener, H., 2019. Detection of Burned Areas by Remote Sensing Techniques: İzmir Seferihisar Forest fire case study. Journal of Natural Hazards and Environment, 5(2), 317-326.
  • Stephens, S. L., Collins, B. M., Fettig, C. J., Finney, M. A., Hoffman, C. M., Knapp, E. E., North, M. P., Safford, H., Wayman, R. B., 2018. Drought, tree mortality, and wildfire in forests adapted to frequent fire. BioScience, 68(2): 77-88.
  • UNOOSA (The United Nations Office for Outer Space Affairs)., 2018. Step by Step: Burn Severity mapping in Google Earth Engine, Submitted by Johannes Heisig on Tue, 04/12/2018 - 13:54. Online: http://www.un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/burn-severity-earth-engine. (Accessed: 10.08.2021)
  • USDA (United States Department of Agriculture)., 2006. FIREMON: Fire Effects Monitoring and Inventory System. USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. Online: https://www.fs.fed.us/rm/pubs/ rmrs_gtr164.pdf FIREMON BR CheatSheet V4, June 2004.
  • USGS., 2021. The bands specification of Landsat 8. Online: https://www.usgs.gov/media/images/landsat-8-band-designations (Accessed: 10.10.2021)
  • Wing, M. G., Burnett, J. D., Sessions, J., 2014. Remote sensing and unmanned aerial system technology for monitoring and quantifying forest fire impacts. Int. J. Remote Sens. Appl. 4(1): 18-35.
  • Wulder, M. A., Hall, R. J., Franklin, S. E., 2005. Remote sensing and GIS in forestry. Remote sensing for GIS managers. ESRI Press, Redlands, 351-362.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Sercan GÜLCİ (Primary Author)
Kahramanmaraş Sürçü İmam Üniversitesi
0000-0003-3349-517X
Türkiye


Kıvanç YÜKSEL
KAHRAMANMARAS SUTCU IMAM UNIVERSITY
0000-0001-9660-5028
Türkiye


Selçuk GÜMÜŞ
KARADENIZ TECHNICAL UNIVERSITY
0000-0002-6942-160X
Türkiye


Michael WİNG
Oregon State University
0000-0002-9287-5001
United States

Thanks Google Earth Engine (GEE) and Google Inc providers were used in the analysis. We thank the U.S. Geological Survey (USGS) and European Space Agency (ESA) for providing free access to Copernicus Sentinel MSI and Landsat images for scientific research.
Publication Date December 31, 2021
Published in Issue Year 2021, Volume 7, Issue 2

Cite

Bibtex @research article { ejfe1031090, journal = {European Journal of Forest Engineering}, eissn = {2149-5637}, address = {}, publisher = {Forest Engineering and Technologies Platform}, year = {2021}, volume = {7}, number = {2}, pages = {57 - 66}, doi = {10.33904/ejfe.1031090}, title = {Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry}, key = {cite}, author = {Gülci, Sercan and Yüksel, Kıvanç and Gümüş, Selçuk and Wing, Michael} }
APA Gülci, S. , Yüksel, K. , Gümüş, S. & Wing, M. (2021). Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry . European Journal of Forest Engineering , 7 (2) , 57-66 . DOI: 10.33904/ejfe.1031090
MLA Gülci, S. , Yüksel, K. , Gümüş, S. , Wing, M. "Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry" . European Journal of Forest Engineering 7 (2021 ): 57-66 <https://dergipark.org.tr/en/pub/ejfe/issue/67032/1031090>
Chicago Gülci, S. , Yüksel, K. , Gümüş, S. , Wing, M. "Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry". European Journal of Forest Engineering 7 (2021 ): 57-66
RIS TY - JOUR T1 - Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry AU - Sercan Gülci , Kıvanç Yüksel , Selçuk Gümüş , Michael Wing Y1 - 2021 PY - 2021 N1 - doi: 10.33904/ejfe.1031090 DO - 10.33904/ejfe.1031090 T2 - European Journal of Forest Engineering JF - Journal JO - JOR SP - 57 EP - 66 VL - 7 IS - 2 SN - -2149-5637 M3 - doi: 10.33904/ejfe.1031090 UR - https://doi.org/10.33904/ejfe.1031090 Y2 - 2021 ER -
EndNote %0 European Journal of Forest Engineering Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry %A Sercan Gülci , Kıvanç Yüksel , Selçuk Gümüş , Michael Wing %T Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry %D 2021 %J European Journal of Forest Engineering %P -2149-5637 %V 7 %N 2 %R doi: 10.33904/ejfe.1031090 %U 10.33904/ejfe.1031090
ISNAD Gülci, Sercan , Yüksel, Kıvanç , Gümüş, Selçuk , Wing, Michael . "Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry". European Journal of Forest Engineering 7 / 2 (December 2021): 57-66 . https://doi.org/10.33904/ejfe.1031090
AMA Gülci S. , Yüksel K. , Gümüş S. , Wing M. Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry. Eur J Forest Eng. 2021; 7(2): 57-66.
Vancouver Gülci S. , Yüksel K. , Gümüş S. , Wing M. Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry. European Journal of Forest Engineering. 2021; 7(2): 57-66.
IEEE S. Gülci , K. Yüksel , S. Gümüş and M. Wing , "Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry", European Journal of Forest Engineering, vol. 7, no. 2, pp. 57-66, Dec. 2021, doi:10.33904/ejfe.1031090

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