Uzaktan algılama verileri ile orman yangınlarında yanma şiddetinin ve hava kirleticilerinin izlenmesi: 2021 yazı Mersin orman yangınları örneği

Yıl 2022, Cilt: 12 Sayı: 2, 487 - 497, 15.04.2022

Muzaffer Can İban Ezgi Şahin

https://doi.org/10.17714/gumusfenbil.1008242

Cited By: 4

Öz

Hava kirleticilerini, madde zehirliliğini, arazi örtüsü değişimini ve yanma şiddetini izlemek için uzaktan algılama verilerinden faydalanan çalışmalar yapılmaktadır. Bu çalışmanın amacı, Mersin İli’nde 28 Temmuz – 3 Ağustos 2021 tarihleri arasında gerçekleşen orman yangınları sonucunda gözlemlenen arazi örtüsü ve atmosfer verilerini analiz etmektir. Yangın öncesi (20-27 Temmuz), yangın (28 Temmuz-3 Ağustos) ve yangın sonrası (4-10 Ağustos) dönemleri için çeşitli açık erişimli uzaktan algılama verileri (MODIS, Sentinel 2A ve Sentinel 5P) kullanılan bu çalışmanın bulguları iki kategoride belirtilebilir. İlk kategori, etkilenen bölgenin arazi yüzey sıcaklığı ve yanık şiddeti haritalarının hazırlanmasını ve bunların arasındaki ilişkinin karşılaştırılmasını içermektedir. İkinci kategori ise, kirletici gazların (karbon monoksit, formaldehit, sülfür dioksit ve ozon) düşey yoğunluk haritalarını içeren atmosferik çıktılardır. Bu analizlerin nicel sonuçları, yüksek şiddetli yanma alanlarının 16.536 hektara karşılık geldiğini ve maksimum düşey molekül yoğunluğunun karbon monoksit için 0.071 mol/m2, formaldehit için 0.0043 mol/m2, kükürt dioksit için 0.00049 mol/m2 ve ozon için 0.137 mol/m2'ye ulaştığını göstermektedir. Yanma şiddetinin, arazi yüzey sıcaklıkları ile yüksek oranda ilişkili olduğu saptanmıştır. Orman yangını sırasında ve sonrasında atmosferdeki kirletici seviyelerinin yükseldiğini tespit eden bu çalışma, kentsel alanların yakınında hava kirleticilerinde önemli bir artış olduğuna dair herhangi bir kanıt bulamamıştır. Ancak, ozon gazı yoğunluğunun orman yangınından sonra, il genelindeki yüksek nitrojen oksit seviyesine bağlı olarak önemli ölçüde yükseliş gösterdiği gözlemlenmiştir.

Anahtar Kelimeler

Hava kirleticileri, Yanma şiddeti endeksi, Arazi yüzeyi sıcaklığı, Sentinel-5P, Orman yangınları

Monitoring burn severity and air pollutants in wildfire events using remote sensing data: the case of Mersin wildfires in summer 2021

Öz

Remotely sensed data have been used to investigate air pollutants and matter toxicity, land cover changes and burn severity. The goal of this study is to analyze land and atmospheric data for wildfire events that occurred in Mersin Province between July 28 and August 3, 2021. We used a variety of open-access remotely sensed data sets (MODIS, Sentinel 2A and Sentinel 5P TROPOMI) from the pre-fire (20-27 July), fire (28 July – 3 August), and post-fire period (4-10 August). This comprehensive study's findings can be divided into two categories. The first group is the land cover output, which includes maps of the affected region's land surface temperature and burn severity, as well as comparisons of the two. The atmospheric output, which consists of trace gas column density maps (for carbon monoxide, formaldehyde, sulphur dioxide and ozone), is the second group. The quantitative results of these analyses indicate that high severity areas correspond to 16.536 hectares, and the maximum column number density reached to 0.071 mol/m2 for carbon monoxide, 0.0043 mol/m2 for formaldehyde, 0.00049 mol/m2 for sulphure dioxide and 0.137 mol/m2 for ozone. The burn severity is found to be highly correlated with land surface temperatures. Pollutant levels in the atmosphere were found to be rising during and after the wildfire. There has not been any evidence of a significant increase in air pollutants near urban areas. However, ozone concentrations rose significantly after the wildfire because the province's nitrogen oxide levels were high enough to produce ozone.

Anahtar Kelimeler

Air pollutants, Burn severity index, Land surface temperature, Sentinel-5P, Wildfires

Kaynakça

• Alvarado, L. M. A., Richter, A., Vrekoussis, M., Hilboll, A., Kalisz Hedegaard, A. B., Schneising, O., & Burrows, J. P. (2020). Unexpected long-range transport of glyoxal and formaldehyde observed from the Copernicus Sentinel-5 Precursor satellite during the 2018 Canadian wildfires. Atmospheric Chemistry and Physics, 20(4), 2057–2072. https://doi.org/10.5194/acp-20-2057-2020
• Aryal, R., Kafley, D., Beecham, S., & Morawska, L. (2018). Air Quality in the Sydney Metropolitan Region during the 2013 Blue Mountains Wildfire. Aerosol and Air Quality Research, 18(9), 2420–2432. https://doi.org/10.4209/aaqr.2017.10.0427
• Bao, Y., Chen, S., Liu, Q., Xiao, Q. & Cao, C., (2011). Land surface temperature and emissivity retrieval by integrating MODIS data onboard Terra and Aqua satellites. International Journal of Remote Sensing, 32(5), 1449–1469. https://doi.org/10.1080/01431160903559754
• Cansler, C. A., & McKenzie, D. (2012). How robust are burn severity ındices when applied in a new region? Evaluation of alternate field-based and remote-sensing methods. Remote Sensing, 4(2), 456–483. https://doi.org/10.3390/rs4020456
• Chuvieco, E., & Congalton, R. G. (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment, 29(2), 147–159. https://doi.org/10.1016/0034-4257(89)90023-0
• Cocke, A. E., Fulé, P. Z., & Crouse, J. E. (2005). Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data. International Journal of Wildland Fire, 14(2), 189. https://doi.org/10.1071/WF04010
• Çolak, E., & Sunar, F. (2020). Spatial pattern analysis of post-fire damages in the Menderes District of Turkey. Frontiers of Earth Science, 14(2), 446–461. https://doi.org/10.1007/s11707-019-0786-4
• Díaz-Delgado, R., Lloret, F., & Pons, X. (2003). Influence of fire severity on plant regeneration by means of remote sensing imagery. International Journal of Remote Sensing, 24(8), 1751–1763. https://doi.org/10.1080/01431160210144732
• Dindaroglu, T., Babur, E., Yakupoglu, T., Rodrigo-Comino, J., & Cerdà, A. (2021). Evaluation of geomorphometric characteristics and soil properties after a wildfire using Sentinel-2 MSI imagery for future fire-safe forest. Fire Safety Journal, 122, 103318. https://doi.org/10.1016/j.firesaf.2021.103318
• Ebrahimy, H., Aghighi, H., Azadbakht, M., Amani, M., Mahdavi, S., & Matkan, A. A. (2021). Downscaling MODIS land surface temperature product using an adaptive random forest regression method and Google Earth Engine for a 19-years spatiotemporal trend analysis over Iran. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 2103–2112. https://doi.org/10.1109/JSTARS.2021.3051422
• Eva, H., & Lambin, E. F. (2000). Fires and land-cover change in the tropics:a remote sensing analysis at the landscape scale. Journal of Biogeography, 27(3), 765–776. https://doi.org/10.1046/j.1365-2699.2000.00441.x
• Fassnacht, F. E., Schmidt-Riese, E., Kattenborn, T., & Hernández, J. (2021). Explaining Sentinel 2-based dNBR and RdNBR variability with reference data from the bird’s eye (UAS) perspective. International Journal of Applied Earth Observation and Geoinformation, 95, 102262. https://doi.org/10.1016/j.jag.2020.102262
• Fernández, C., Vega, J. A., Fonturbel, T., Pérez-Gorostiaga, P., Jiménez, E., & Madrigal, J. (2007). Effects of wildfire, salvage logging and slash treatments on soil degradation. Land Degradation & Development, 18(6), 591–607. https://doi.org/10.1002/ldr.797
• Fu, A. S. (2022). Risky cities: The physical and fiscal nature of disaster capitalism. Rutgers University Press. Imhoff, M. L., Zhang, P., Wolfe, R. E., & Bounoua, L. (2010). Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment, 114(3), 504–513. https://doi.org/10.1016/j.rse.2009.10.008
• Kaplan, G., & Yigit Avdan, Z. (2020). Space-borne air pollution observation from Sentinel-5p TROPOMI: relationship between pollutants, geographical and demographic data. International Journal of Engineering and Geosciences, 5(3), 130-137. https://doi.org/10.26833/ijeg.644089
• 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. https://doi.org/10.1071/WF07049
• Key, C. H., & Benson, N. C. (2006). Landscape assessment (LA) sampling and analysis methods. Langmann, B., Duncan, B., Textor, C., Trentmann, J., & van der Werf, G. R. (2009). Vegetation fire emissions and their impact on air pollution and climate. Atmospheric Environment, 43(1), 107–116. https://doi.org/10.1016/j.atmosenv.2008.09.047
• Lindenmayer, D. B., Kooyman, R. M., Taylor, C., Ward, M., & Watson, J. E. M. (2020). Recent Australian wildfires made worse by logging and associated forest management. Nature Ecology & Evolution, 4(7), 898–900. https://doi.org/10.1038/s41559-020-1195-5
• Liu, X., Huey, L. G., Yokelson, R. J., Selimovic, V., Simpson, I. J., Müller, M., Jimenez, J. L., Campuzano‐Jost, P., Beyersdorf, A. J., Blake, D. R., Butterfield, Z., Choi, Y., Crounse, J. D., Day, D. A., Diskin, G. S., Dubey, M. K., Fortner, E., Hanisco, T. F., Hu, W., … Wolfe, G. M. (2017). Airborne measurements of western U.S. wildfire emissions: Comparison with prescribed burning and air quality implications. Journal of Geophysical Research: Atmospheres, 122(11), 6108–6129. https://doi.org/10.1002/2016JD026315
• Llorens, R., Sobrino, J. A., Fernández, C., Fernández-Alonso, J. M., & Vega, J. A. (2021). A methodology to estimate forest fires burned areas and burn severity degrees using Sentinel-2 data. Application to the October 2017 fires in the Iberian Peninsula. International Journal of Applied Earth Observation and Geoinformation, 95, 102243. https://doi.org/10.1016/j.jag.2020.102243
• Magro, C., Nunes, L., Gonçalves, O., Neng, N., Nogueira, J., Rego, F., & Vieira, P. (2021). Atmospheric Trends of CO and CH4 from Extreme Wildfires in Portugal Using Sentinel-5P TROPOMI Level-2 Data. Fire, 4(2), 25. https://doi.org/10.3390/fire4020025
• McClure, C. D., & Jaffe, D. A. (2018). Investigation of high ozone events due to wildfire smoke in an urban area. Atmospheric Environment, 194, 146–157. https://doi.org/10.1016/j.atmosenv.2018.09.021
• Na, K., & Cocker, D. R. (2008). Fine organic particle, formaldehyde, acetaldehyde concentrations under and after the influence of fire activity in the atmosphere of Riverside, California. Environmental Research, 108(1), 7–14. https://doi.org/10.1016/j.envres.2008.04.004
• Nasery, S., & Kalkan, K. (2020). Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey. Turkish Journal of Geosciences, 1(2), 72–77.
• National Toxicology Program. (2010). Final report on carcinogens background document for formaldehyde. Report on Carcinogens Background Document for Formaldehyde. Access: http://www.ncbi.nlm.nih.gov/pubmed/20737003
• Omaye, S. T. (2002). Metabolic modulation of carbon monoxide toxicity. Toxicology, 180(2), 139–150. https://doi.org/10.1016/S0300-483X(02)00387-6
• Sabuncu, A., & Özener, H. (2019). Uzaktan Algılama Teknikleri ile Yanmış Alanların Tespiti: İzmir Seferihisar Orman Yangını Örneği. Doğal Afetler ve Çevre Dergisi, 5(2), 317–326. https://doi.org/10.21324/dacd.511688
• Salman, T., Zia, U.-H., Ayesha, M., Usman, M., & Waseem, A. (2021). Assessment of air quality during worst wildfires in Turkey. Natural Hazards. https://doi.org/https://doi.org/10.21203/rs.3.rs-903604/v1
• Savenets, M., Osadchyi, V., Oreshchenko, A., & Pysarenko, L. (2020). Air quality changes in Ukraine during the April 2020 wildfire event. Geographica Pannonica, 24(4), 271–284. https://doi.org/10.5937/gp24-27436
• Schneising, O., Buchwitz, M., Reuter, M., Bovensmann, H., & Burrows, J. P. (2020). Severe Californian wildfires in November 2018 observed from space: the carbon monoxide perspective. Atmospheric Chemistry and Physics, 20(6), 3317–3332. https://doi.org/10.5194/acp-20-3317-2020
• Tonbul, H., Kavzoglu, T., & Kaya, S. (2016). Assessment of fire severity and post-fire regeneration based on topographical features using multitemporal LANDSAT imagery: A case study in Mersin, Turkey. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B8, 763–769. https://doi.org/10.5194/isprsarchives-XLI-B8-763-2016
• TROPOMI. (2021). Level 2 Products. Access: http://www.tropomi.eu/data-products/level-2-products Wan, Z., Zhang, Y., Zhang, Q., & Li, Z.-L. (2004). Quality assessment and validation of the MODIS global land surface temperature. International Journal of Remote Sensing, 25(1), 261–274. https://doi.org/10.1080/0143116031000116417
• Wang, M., He, G., Zhang, Z., Wang, G., Wang, Z., Yin, R., Cui, S., Wu, Z., & Cao, X. (2019). A radiance-based split-window algorithm for land surface temperature retrieval: Theory and application to MODIS data. International Journal of Applied Earth Observation and Geoinformation, 76, 204–217. https://doi.org/10.1016/j.jag.2018.11.015
• Watson, G. L., Telesca, D., Reid, C. E., Pfister, G. G., & Jerrett, M. (2019). Machine learning models accurately predict ozone exposure during wildfire events. Environmental Pollution, 254, 112792. https://doi.org/10.1016/j.envpol.2019.06.088
• Weber, J.-N., Kaufholdt, D., Minner-Meinen, R., Bloem, E., Shahid, A., Rennenberg, H., & Hänsch, R. (2021). Impact of wildfires on SO2 detoxification mechanisms in leaves of oak and beech trees. Environmental Pollution, 272, 116389. https://doi.org/10.1016/j.envpol.2020.116389
• Wolfe, R. E., Nishihama, M., Fleig, A. J., Kuyper, J. A., Roy, D. P., Storey, J. C., & Patt, F. S. (2002). Achieving sub-pixel geolocation accuracy in support of MODIS land science. Remote Sensing of Environment, 83(1–2), 31–49. https://doi.org/10.1016/S0034-4257(02)00085-8
• Wu, J., M Winer, A., & Delfino, R J. (2006). Exposure assessment of particulate matter air pollution before, during, and after the 2003 Southern California wildfires. Atmospheric Environment, 40(18), 3333–3348. https://doi.org/10.1016/j.atmosenv.2006.01.056
• Zhang, Y., & Cheng, X. (2017). Satellite observation based thermal anomalies detection for 2016 Menyuan MS6.4 earthquake. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1930–1933. https://doi.org/10.1109/IGARSS.2017.8127356