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Spatio-Temporal Analysis of Carbon Storage in Urban Areas After Wildfires: The Case of Marmaris Fire

Yıl 2024, , 43 - 53, 27.06.2024
https://doi.org/10.33904/ejfe.1467509

Öz

Cities and urban areas are the primary source of CO2 worldwide by using around 70% of global energy and emitting more than 71% of CO2. Urban vegetation, referring to all trees and shrubs, are important components of urban environments. They provide many ecosystem services to human beings both directly and indirectly. Especially, they play a key role in reducing carbon emissions in urban areas by storing and capturing the carbon. However, recently, an increase in the number and intensity of wildfires that occur within urban areas has been observed. It resulted in losing stored carbon, releasing GHG to the atmosphere. Hence, quantifying above-ground carbon stored by urban trees and its distribution is essential to better understanding urban vegetation's role in urban environments and to better urban vegetation management. This study aimed to examine how forest fire affects the amount and distribution of stored carbon in the urban environment for the case of the Marmaris fire in the Summer of 2021 in Türkiye. For the study, urban forest carbon storage maps were generated before and after the Marmaris forest fire using remote sensing-based methodology with freely available remote sensing (RS) data. The results indicated that using the existing methodology could be rapid and cost-effective in monitoring the carbon storage change after an anthropogenic and natural disaster. However, for precise and reliable estimation of total carbon storage and the change in total urban carbon storage, the methodology needs to be developed at a local scale using field sampling along with RS data.

Kaynakça

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Yıl 2024, , 43 - 53, 27.06.2024
https://doi.org/10.33904/ejfe.1467509

Öz

Kaynakça

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  • Adaktylou, N., Stratoulias, D., Landenberger, R. 2020. Wildfire risk assessment based on geospatial open data: Application on Chios, Greece. ISPRS International Journal of Geo-Information, 9(9): 516.
  • Aicardi, I., Garbarino, M., Lingua, A., Lingua, E., Marzano, R., Piras, M., 2016. Monitoring post-fire forest recovery using multi-temporal Digital Surface Models generated from different platforms. EARSeL eProceedings, (15): 1–8.
  • Akay, A. E., and Şahin, H. 2019. Forest fire risk mapping by using GIS techniques and AHP method: A case study in Bodrum (Turkey). European Journal of Forest Engineering, 5(1): 25-35.
  • Akyürek, Ö. 2022. Monitoring of combustion related air pollutants occurring after forest fires with remote sensing images: a case study Turkey and Greece fires. Environmental Engineering & Management Journal (EEMJ), 21(8).
  • Al-Bilbisi, H. 2019. Spatial monitoring of urban expansion using satellite remote sensing images: A case study of Amman City, Jordan. Sustainability, 11(8): 2260.
  • Arslan, R. 2021. Marmaris Tarihi. ISBN: 978-625-8007-64-0 Iktisadi Yayın Evi, Ankara, Türkiye. Baccini, A.G.S.J., Goetz, S.J., Walker, W.S., Laporte, N.T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P.S.A., Dubayah, R., Friedl, M.A., Samanta, S. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature climate change, 2(3): 182-185.
  • Berland, A. 2012. Long-term urbanization effects on tree canopy cover along an urban–rural gradient. Urban Ecosystems, 15(3): 721-738.
  • 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 Sensing of Environment. 163: 48–60.
  • Bone, C., Dragicevic, S., Roberts, A. 2005. Integrating high resolution remote sensing, GIS and fuzzy set theory for identifying susceptibility areas of forest insect infestations. International Journal of Remote Sensing, 26(21): 4809-4828.
  • Boydak, M., Dirik, H., Çalikoğlu, M. 2006. Biology and Silviculture of Turkish Red Pine (Pinus brutia Ten.). Ankara, Turkey. Ogem-Vak. 253 p.
  • Chander, G., Markham, B.L., Helder, D.L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5): 893–903. http://dx.doi.org/10.1016/j.rse.2009.01.007
  • Chen, B. and Jin, Y. 2022. Spatial patterns and drivers for wildfire ignitions in California. Environmental Research Letters, 17(5), p.055004.
  • Chen, L., Zhao, S., Han, W., Li, Y. 2012. Building detection in an urban area using lidar data and QuickBird imagery. International Journal of Remote Sensing, 33(16): 5135-5148.
  • Chuvieco, E., Congalton, R.G. 1989. Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment, 29: 147–159.
  • Climate Action Reserve, 2014. Urban Forest Management Project Protocol version 1.0. Climate Action Reserve, Los Angeles, CA 2014, 44 p.
  • Collins, L., Griffioen, P., Newell, G., Mellor, A. 2018. The utility of Random Forests for wildfire severity mapping. Remote Sensing of Environment, 2018 (216): 374–384.
  • Çinar, T., Taşpinar, F. Aydin, A. 2024. Analysis and estimation of gaseous air pollutant emissions emitted into the atmosphere during Manavgat and Milas wildfire episodes using remote sensing data and ground measurements. Air Quality, Atmosphere & Health, 17(3): 559-579.
  • Dalponte, M., Solano-Correa, Y. T., Frizzera, L., Gianelle, D., 2022. Mapping a European Spruce Bark Beetle Outbreak Using Sentinel-2 Remote Sensing Data. Remote Sensing, 14(13): 3135.
  • Değermenci, A.S. 2023. Determining the effects of changes in land use on carbon storage in above-ground biomass with NDVI. Global Nest Journal, 25(3): 27-36.
  • Dereli, M. A. 2018. Monitoring and prediction of urban expansion using multilayer perceptron neural network by remote sensing and GIS technologies: a case study from Istanbul Metropolitan City. Fresenius Environmental Bulletin, 27(12a): 9336-9344.
  • Dewanto, B.E.B., Jatmiko, R.H. 2021. Estimation of aboveground carbon stock using SAR Sentinel-1 imagery in samarinda city. International Journal of Remote Sensing and Earth Sciences, 18(1): 103-116.
  • Dhanaraj, K., Angadi, D.P. 2022. Land use land cover mapping and monitoring urban growth using remote sensing and GIS techniques in Mangaluru, India. GeoJournal, (872): 1133-1159.
  • Dobbs, C., Nitschke, C. R., Kendal, D. 2014. Global drivers and tradeoffs of three urban vegetation ecosystem services. PLoS One, 9(11): e113000.
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Toplam 92 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Fotogrametri ve Uzaktan Algılama
Bölüm Research Articles
Yazarlar

Zennure Uçar 0000-0003-1413-0036

Erken Görünüm Tarihi 5 Haziran 2024
Yayımlanma Tarihi 27 Haziran 2024
Gönderilme Tarihi 11 Nisan 2024
Kabul Tarihi 9 Mayıs 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Uçar, Z. (2024). Spatio-Temporal Analysis of Carbon Storage in Urban Areas After Wildfires: The Case of Marmaris Fire. European Journal of Forest Engineering, 10(1), 43-53. https://doi.org/10.33904/ejfe.1467509

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