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

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

Abstract

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.

References

  • Adab, H., Kanniah, K.D., Solaimani, K. 2013. Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65:1723-1743.
  • 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.
  • Dobbs, C., Hernández-Moreno, Á., Reyes-Paecke, S., Miranda, M.D. 2018. Exploring temporal dynamics of urban ecosystem services in Latin America: The case of Bogota (Colombia) and Santiago (Chile). Ecological Indicators, (85): 1068-1080.
  • Escuin, S., Navarro, R., Fernández, 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.
  • European Forest Fire Information System. 2023. Area burned by wildfires and Number of fires in Turkey from 2006 to 2023. https://effis.jrc.ec.europa.eu/ apps/effis.statistics/estimates/TUR
  • Filizzola, C., Corrado, R., Marchese, F., Mazzeo, G., Paciello, R., Pergola, N., Tramutoli, V. 2017. RST-FIRES, an exportable algorithm for early-fire detection and monitoring: Description, implementation, and field validation in the case of the MSG-SEVIRI sensor. Remote Sensing of Environment, 19: e2–e25.
  • Food and Agricultural Organization of the United Nations (FAO). 2007. Fire management global assessment 2006. A thematic study prepared in the framework of the Global Forest Resources Assessment 2005. FAO Forestry Paper, 151.
  • Fu, Z., Li, D., Hararuk, O., Schwalm, C., Luo, Y., Yan, L., Niu, S. 2017. Recovery time and state change of terrestrial carbon cycle after disturbance. Environmental Research Letters, 12(10): 104004.
  • Ganteaume, A., Camia, A., Jappiot, M., San-Miguel-Ayanz, J., Long-Fournel, M., Lampin, C. 2013. A review of the main driving factors of forest fire ignition over Europe. Environmental management, (51): 651-662.
  • Goodwin, N.R., Coops, N.C., Wulder, M. A., Gillanders, S., Schroeder, T. A., Nelson, T. 2008. Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sensing of Environment, 112(9): 3680-3689.
  • Green, K., Kempka, D., Lackey, L. (1994). Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric engineering and remote sensing, 60(3): 331-337.
  • Harrison, S.P., Marlon, J.R., Bartlein, P.J. 2010. Fire in the Earth system (pp. 21-48). Springer Netherlands.
  • Hashim, M., Kanniah, K.D., Ahmad, A.R., Rasib, A.W., Ibrahim, A.L. 2004. The use of AVHRR data to determine the concentration of visible and invisible tropospheric pollutants originating from a 1997 forest fire in Southeast Asia. International Journal of Remote Sensing, 25(21): 4781-4794.
  • Hastuti, A. W., Suniada, K. I., Islamy, F. 2018. Carbon stock estimation of mangrove vegetation using remote sensing in Perancak Estuary, Jembrana District, Bali. International Journal of Remote Sensing and Earth Sciences, 14(2): 137-150.
  • Huesca, M., Riaño, D., Ustin, S.L. 2019. Spectral mapping methods applied to LiDAR data: Application to fuel type mapping. International Journal of Applied Earth Observation and Geoinformation, (74): 159–168.
  • Hutyra, L. R., Yoon, B., Alberti, M. 2011a. Terrestrial carbon stocks across a gradient of urbanization: a study of the Seattle, WA region. Global Change Biology, 17(2): 783-797.
  • Hutyra, L.R., Yoon, B., Hepinstall-Cymerman, J., Alberti, M. 2011b. Carbon consequences of land cover change and expansion of urban lands: A case study in the Seattle metropolitan region. Landscape and urban planning, 103(1): 83-93.
  • Kantarcioglu, O., Kocaman, S., Schindler, K. 2023. Artificial neural networks for assessing forest fire susceptibility in Türkiye. Ecological Informatics, 75, 102034.
  • 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.
  • Keleş, S., Günlü, A., Ercanli, İ. 2021. Estimating aboveground stand carbon by combining Sentinel-1 and Sentinel-2 satellite data: a case study from Turkey. In Forest Resources Resilience and Conflicts, 117-126.
  • Key, C.H., N.C. Benson. 1999. Measuring and remote sensing of burn severity: the CBI and NBR. Pages 15–17 in L.F. Neuenschwander and K.C. Ryan (eds.). Proceedings Joint Fire Science Conference and Workshop. Volume II. University of Idaho and Association of Wildland Fire, Boise.
  • Key, C. H., Benson, N., Ohlen, D., Howard, S., McKinley, R., Zhu, Z. 2002. The normalized burn ratio and relationships to burn severity: Ecology, remote sensing and implementation. In Proceedings of the Ninth Forest Service Remote Sensing Applications Conference, April, San Diego, CA, USA (pp. 8-12).
  • Kirschbaum, M.U. 2003. To sink or burn? A discussion of the potential contributions of forests to greenhouse gas balances through storing carbon or providing biofuels. Biomass and Bioenergy, 24(4-5): 297-310.
  • Konijnendijk, C.C., Randrup, T.B. 2004. Urban forestry. Encyclopedia of Forest Sciences (Eds. Burley,J., Evans, J. and Younquist, JA). Elsevier Science, London. page 471-478. Konijnendijk, C.C., Ricard, R.M., Kenney, A., Randrup, T.B. 2006. Defining urban forestry–A comparative perspective ofNorth America and Europe. Urban Forestry & Urban Greening, 4(3-4): 93-103.
  • Krebs, P., Pezzatti, G.B., Mazzoleni, S., Talbot, L.M. and Conedera, M. 2010. Fire regime: history and definition of a key concept in disturbance ecology. Theory in Biosciences, 129:53-69.
  • Kumar, N., Yamaç, S.S., Velmurugan, A. 2015. Applications of remote sensing and GIS in natural resource management. Journal of the Andaman Science Association, 20(1): 1-6.
  • Liu, G., Li, J., Nie, P. 2022. Tracking the history of urban expansion in Guangzhou (China) during 1665–2017: Evidence from historical maps and remote sensing images. Land Use Policy. 112: 105773.
  • McBride, J. 2011. Mapping Chicago area urban tree canopy using color infrared imagery. LUMA-GIS Thesis. McPherson, E. G., Simpson, J. R., Xiao, Q., Wu, C. 2011. Million trees Los Angeles canopy cover and benefit assessment. Landscape and Urban Planning, 99(1): 40-50.
  • Mirzaei, M., Bertazzon, S., Couloigner, I. 2018. Modeling wildfire smoke pollution by integrating land use regression and remote sensing data: Regional multi-temporal estimates for public health and exposure models. Atmosphere, 9(9): 335.
  • Muğla İl Kültür ve Turizm Bakanlığı (Muğla Provincial Ministry of Culture and Tourism). 2022. Webpage. https://mugla.ktb.gov.tr/
  • Myeong, S., Nowak, D.J., Duggin, M.J. 2006. A temporal analysis of urban forest carbon storage using remote sensing. Remote Sensing of Environment. 101: 277–282. http://dx.doi.org/10.1016/ j.rse.2005.12.001
  • Navalgund, R.R., Jayaraman, V., Roy, P.S. 2007. Remote sensing applications: An overview. Current science, 1747-1766.
  • Nowak, D.J., Rowntree, R.A., McPherson, E.G., Sisinni, S.M., Kerkmann, E.R., Stevens, J.C. 1996. Measuring and analyzing urban tree cover. Landscape and Urban Planning, 36(1): 49-57.
  • Nowak, D.J., Noble, M.H., Sisinni, S.M. and Dwyer, J.F. 2001. People and trees: assessing the US urban forest resource. Journal of Forestry, 99(3):37-42.
  • Nowak, D.J., Greenfield, E.J. 2010. Evaluating the National Land Cover Database tree canopy and impervious cover estimates across the conterminous United States: a comparison with photo-interpreted estimates. Environmental management, 46(3): 378-390.
  • Oumar, Z., Mutanga, O. 2011. The potential of remote sensing technology for the detection and mapping of Thaumastocoris peregrinus in plantation forests. Southern Forests: A Journal of Forest Science, 73(1): 23-31.
  • Ozenen Kavlak, M., Cabuk, S.N., Cetin, M. 2021. Development of forest fire risk map using geographical information systems and remote sensing capabilities: Ören case. Environmental Science and Pollution Research, 28(25): 33265-33291.
  • Pasher, J., McGovern, M., Khoury, M., Duffe, J. 2014. Assessing carbon storage and sequestration by Canada's urban forests using high resolution earth observation data. UrbanForestry & Urban Greening, 13(3): 484-494. Picotte, J.J., Robertson, K.M. 2010. Accuracy of remote sensing wildland fire-burned area in southeastern US Coastal plain habitats. Proceedings of the 24th Tall Timbers Fire Ecology Conference: The Future of Prescribed Fire: Public Awareness, Health, and Safety. Tall Timbers Research Station, Tallahassee, Florida, USA. pp: 91-98
  • Pyne, S.J., Andrews, P.L., Laven, R.D. 1996. Introduction to wildland fire. Wiley, New York.
  • Rahman, S., Chang, H.C., Hehir, W., Magilli, C., Tomkins, K. 2018. Inter-comparison of fire severity indices from moderate (MODIS) and moderate-to-high spatial resolution (LANDSAT 8 & SENTINEL-2A) satellite sensors. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, July. (pp. 2873-2876). IEEE.
  • Reichstein, M., Bahn, M., Ciais, P., Frank, D., Mahecha, M.D., Seneviratne, S.I., Zscheischler, J., Beer, C., Buchmann, N., Frank, D.C. and Papale, D. 2013. Climate extremes and the carbon cycle. Nature. 500(7462): 287-295.
  • Richardson, J.J., Moskal, L.M. 2014. Uncertainty in urbanforest canopy assessment: Lessons from Seattle, WA, USA. Urban Forestry & Urban Greening 13(1): 152-157.
  • Ricotta, C., Bajocco, S., Guglietta, D., Conedera, M. 2018. Assessing the influence of roads on fire ignition: does land cover matter? Fire.1(2): 24.
  • Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publications, 351(1):309.
  • Rwanga, S.S., Ndambuki, J.M. 2017. Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS. International Journal of Geosciences, (8): 611-622. https://doi.org/ 10.4236/ijg.2017.84033
  • Saatchi, S., Halligan, K., Despain, D.G., Crabtree, R.L. 2007. Estimation of Forest Fuel Load From Radar Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing, (45): 1726–1740.
  • Safford, H., Larry, E., McPherson, E.G., Nowak, D.J., Westphal, L.M. 2013. Urban Forests and Climate Change. USDepartment of Agriculture, Forest Service, Climate ChangeResource Center r. www.fs. usda.gov/ccrc/topics/urban-forests.
  • Sağlam, S., Elvan, O.D., 2017. Kent ormanlarının Türkiye’deki gelişimi ve hukuki durumu (Progress and legal status of urban forests in Turkey). Kastamonu Universitesi Orman Fakültesi Dergisi, 17(4): 669-681.
  • Samiappan, S., Hathcock, L., Turnage, G., McCraine, C., Pitchford, J., Moorhead, R. 2019. Remote Sensing of Wildfire Using a Small Unmanned Aerial System: Post-Fire Mapping, Vegetation Recovery and Damage Analysis in Grand Bay, Mississippi/Alabama, USA. Drones, 3: 43.
  • Sanga-Ngoie, K., Iizuka, K. and Kobayashi, S. 2012. Estimating CO2 sequestration by forests in Oita Prefecture, Japan, by combining Landsat ETM+ and ALOS satellite remote sensing data. Remote Sensing, 4(11):3544-3570. https://doi.org/10.3390/rs4113544
  • Sannigrahi, S., Pilla, F., Basu, B., Basu, A.S., Sarkar, K., Chakraborti, S., Joshi, P.K., Zhang, Q., Wang, Y., Bhatt, S. Bhatt, A. 2020. Examining the effects of forest fire on terrestrial carbon emission and ecosystem production in India using remote sensing approaches. Science of the Total Environment, 725: 138331.
  • Satir, O., Berberoglu, S., Donmez, C. 2016. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomatics, Natural Hazards and Risk, 7: 1645–1658.
  • Sever, L., Leach, J., Bren, L. 2012. Remote sensing of post-fire vegetation recovery; a study using Landsat 5 TM imagery and NDVI in North-East Victoria. Journal of Spatial Science, (57): 175–191.
  • Shalaby, A., Tateishi, R. 2007. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied geography, 27(1): 28-41.
  • Shanafelt, D.W., Serra-Diaz, J.M., Bocquého, G. 2023. Measuring uncertainty in ecosystem service correlations as a function of sample size. Ecosystem Services, 63: 101546.
  • Singh, S., Singh, H., Sharma, V., Shrivastava, V., Kumar, P., Kanga, S., Sahu, N., Meraj, G., Farooq, M., Singh, S.K. 2021. Impact of forest fires on air quality in Wolgan valley, New South Wales, Australia—A mapping and monitoring study using Google Earth Engine. Forests, 13(1): 4.
  • Szpakowski, D.M., Jensen, J.L. 2019. A review of the applications of remote sensing in fire ecology. Remote sensing, 11(22): 2638.
  • Tonyaloğlu, E.E. 2020. Spatiotemporal dynamics of urban ecosystem services in Turkey: The case of Bornova, Izmir. Urban Forestry & Urban Greening, 49: 126631.
  • Tucker, C. J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment. 8(2): 127-150.
  • Turkish Statistical Institute (TUIK). 2022. Population and Demography, Address Based Population Registration System Bulletin. Retrieved from https://data.tuik.gov.tr/Bulten/Index?p=The-Results-of-Address-Based-Population-Registration-System-2022-49685&dil=2
  • Turner, D. P., Guzy, M., Lefsky, M. A., Ritts, W. D., Van Tuyl, S., Law, B. E., 2004. Monitoring forest carbon sequestration with remote sensing and carbon cycle modeling. Environmental management. (33): 457-466.
  • Ucar, Z., Bettinger, P., Merry, K., Akbulut, R., Siry, J., 2018. Estimation of urban woody vegetation cover using multispectral imagery and LiDAR. Urban Forestry & Urban Greening. (29): 248-260.
  • United Nation, The World Urbanization Prospects (2018).Retrieved from https://www.un.org/en/events/citiesday/assets/pdf/the_worlds_cities_in_2018_data_booklet.pdf.
  • Vatandaşlar, C., Abdikan, S., 2022. Carbon stock estimation by dual-polarized synthetic aperture radar (SAR) and forest inventory data in a Mediterranean forest landscape. Journal of Forestry Research. 33(3): 827-838.
  • Vicharnakorn P, Shrestha RP, Nagai M, Salam AP, Kiratiprayoon S., 2014. Carbon Stock Assessment Using Remote Sensing and Forest Inventory Data in Savannakhet, Lao PDR. Remote Sensing. 6(6):5452-5479. https://doi.org/10.3390/rs6065452
  • Wallemacq, P., Below, R., McClean, D., 2018. Economic losses, poverty & disasters: 1998-2017. United Nations Office for Disaster Risk Reduction.
  • Wicaksono, P., Danoedoro, P., Hartono, H., Nehren, U., Ribbe, L. 2011. Preliminary work of mangrove ecosystem carbon stock mapping in small island using remote sensing: above and below ground carbon stock mapping on medium resolution satellite image. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 8174: 408-417. SPIE. (7 October 2011). https://doi.org/ 10.1117/ 12.897926
  • Wold Bank. 2017. Dünya Bankası Göstergeleri. https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS?contextual=default
  • Xu, G., Zhong, X. 2017. Real-time wildfire detection and tracking in Australia using geostationary satellite: Himawari-8. Remote Sensing Letters, 8(11): 1052-1061.
  • Yin, S., Wang, X., Guo, M., Santoso, H., Guan, H. 2020. The abnormal change of air quality and air pollutants induced by the forest fire in Sumatra and Borneo in 2015. Atmospheric research, 243, 105027.
Year 2024, , 43 - 53, 27.06.2024
https://doi.org/10.33904/ejfe.1467509

Abstract

References

  • Adab, H., Kanniah, K.D., Solaimani, K. 2013. Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65:1723-1743.
  • 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.
  • Dobbs, C., Hernández-Moreno, Á., Reyes-Paecke, S., Miranda, M.D. 2018. Exploring temporal dynamics of urban ecosystem services in Latin America: The case of Bogota (Colombia) and Santiago (Chile). Ecological Indicators, (85): 1068-1080.
  • Escuin, S., Navarro, R., Fernández, 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.
  • European Forest Fire Information System. 2023. Area burned by wildfires and Number of fires in Turkey from 2006 to 2023. https://effis.jrc.ec.europa.eu/ apps/effis.statistics/estimates/TUR
  • Filizzola, C., Corrado, R., Marchese, F., Mazzeo, G., Paciello, R., Pergola, N., Tramutoli, V. 2017. RST-FIRES, an exportable algorithm for early-fire detection and monitoring: Description, implementation, and field validation in the case of the MSG-SEVIRI sensor. Remote Sensing of Environment, 19: e2–e25.
  • Food and Agricultural Organization of the United Nations (FAO). 2007. Fire management global assessment 2006. A thematic study prepared in the framework of the Global Forest Resources Assessment 2005. FAO Forestry Paper, 151.
  • Fu, Z., Li, D., Hararuk, O., Schwalm, C., Luo, Y., Yan, L., Niu, S. 2017. Recovery time and state change of terrestrial carbon cycle after disturbance. Environmental Research Letters, 12(10): 104004.
  • Ganteaume, A., Camia, A., Jappiot, M., San-Miguel-Ayanz, J., Long-Fournel, M., Lampin, C. 2013. A review of the main driving factors of forest fire ignition over Europe. Environmental management, (51): 651-662.
  • Goodwin, N.R., Coops, N.C., Wulder, M. A., Gillanders, S., Schroeder, T. A., Nelson, T. 2008. Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sensing of Environment, 112(9): 3680-3689.
  • Green, K., Kempka, D., Lackey, L. (1994). Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric engineering and remote sensing, 60(3): 331-337.
  • Harrison, S.P., Marlon, J.R., Bartlein, P.J. 2010. Fire in the Earth system (pp. 21-48). Springer Netherlands.
  • Hashim, M., Kanniah, K.D., Ahmad, A.R., Rasib, A.W., Ibrahim, A.L. 2004. The use of AVHRR data to determine the concentration of visible and invisible tropospheric pollutants originating from a 1997 forest fire in Southeast Asia. International Journal of Remote Sensing, 25(21): 4781-4794.
  • Hastuti, A. W., Suniada, K. I., Islamy, F. 2018. Carbon stock estimation of mangrove vegetation using remote sensing in Perancak Estuary, Jembrana District, Bali. International Journal of Remote Sensing and Earth Sciences, 14(2): 137-150.
  • Huesca, M., Riaño, D., Ustin, S.L. 2019. Spectral mapping methods applied to LiDAR data: Application to fuel type mapping. International Journal of Applied Earth Observation and Geoinformation, (74): 159–168.
  • Hutyra, L. R., Yoon, B., Alberti, M. 2011a. Terrestrial carbon stocks across a gradient of urbanization: a study of the Seattle, WA region. Global Change Biology, 17(2): 783-797.
  • Hutyra, L.R., Yoon, B., Hepinstall-Cymerman, J., Alberti, M. 2011b. Carbon consequences of land cover change and expansion of urban lands: A case study in the Seattle metropolitan region. Landscape and urban planning, 103(1): 83-93.
  • Kantarcioglu, O., Kocaman, S., Schindler, K. 2023. Artificial neural networks for assessing forest fire susceptibility in Türkiye. Ecological Informatics, 75, 102034.
  • 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.
  • Keleş, S., Günlü, A., Ercanli, İ. 2021. Estimating aboveground stand carbon by combining Sentinel-1 and Sentinel-2 satellite data: a case study from Turkey. In Forest Resources Resilience and Conflicts, 117-126.
  • Key, C.H., N.C. Benson. 1999. Measuring and remote sensing of burn severity: the CBI and NBR. Pages 15–17 in L.F. Neuenschwander and K.C. Ryan (eds.). Proceedings Joint Fire Science Conference and Workshop. Volume II. University of Idaho and Association of Wildland Fire, Boise.
  • Key, C. H., Benson, N., Ohlen, D., Howard, S., McKinley, R., Zhu, Z. 2002. The normalized burn ratio and relationships to burn severity: Ecology, remote sensing and implementation. In Proceedings of the Ninth Forest Service Remote Sensing Applications Conference, April, San Diego, CA, USA (pp. 8-12).
  • Kirschbaum, M.U. 2003. To sink or burn? A discussion of the potential contributions of forests to greenhouse gas balances through storing carbon or providing biofuels. Biomass and Bioenergy, 24(4-5): 297-310.
  • Konijnendijk, C.C., Randrup, T.B. 2004. Urban forestry. Encyclopedia of Forest Sciences (Eds. Burley,J., Evans, J. and Younquist, JA). Elsevier Science, London. page 471-478. Konijnendijk, C.C., Ricard, R.M., Kenney, A., Randrup, T.B. 2006. Defining urban forestry–A comparative perspective ofNorth America and Europe. Urban Forestry & Urban Greening, 4(3-4): 93-103.
  • Krebs, P., Pezzatti, G.B., Mazzoleni, S., Talbot, L.M. and Conedera, M. 2010. Fire regime: history and definition of a key concept in disturbance ecology. Theory in Biosciences, 129:53-69.
  • Kumar, N., Yamaç, S.S., Velmurugan, A. 2015. Applications of remote sensing and GIS in natural resource management. Journal of the Andaman Science Association, 20(1): 1-6.
  • Liu, G., Li, J., Nie, P. 2022. Tracking the history of urban expansion in Guangzhou (China) during 1665–2017: Evidence from historical maps and remote sensing images. Land Use Policy. 112: 105773.
  • McBride, J. 2011. Mapping Chicago area urban tree canopy using color infrared imagery. LUMA-GIS Thesis. McPherson, E. G., Simpson, J. R., Xiao, Q., Wu, C. 2011. Million trees Los Angeles canopy cover and benefit assessment. Landscape and Urban Planning, 99(1): 40-50.
  • Mirzaei, M., Bertazzon, S., Couloigner, I. 2018. Modeling wildfire smoke pollution by integrating land use regression and remote sensing data: Regional multi-temporal estimates for public health and exposure models. Atmosphere, 9(9): 335.
  • Muğla İl Kültür ve Turizm Bakanlığı (Muğla Provincial Ministry of Culture and Tourism). 2022. Webpage. https://mugla.ktb.gov.tr/
  • Myeong, S., Nowak, D.J., Duggin, M.J. 2006. A temporal analysis of urban forest carbon storage using remote sensing. Remote Sensing of Environment. 101: 277–282. http://dx.doi.org/10.1016/ j.rse.2005.12.001
  • Navalgund, R.R., Jayaraman, V., Roy, P.S. 2007. Remote sensing applications: An overview. Current science, 1747-1766.
  • Nowak, D.J., Rowntree, R.A., McPherson, E.G., Sisinni, S.M., Kerkmann, E.R., Stevens, J.C. 1996. Measuring and analyzing urban tree cover. Landscape and Urban Planning, 36(1): 49-57.
  • Nowak, D.J., Noble, M.H., Sisinni, S.M. and Dwyer, J.F. 2001. People and trees: assessing the US urban forest resource. Journal of Forestry, 99(3):37-42.
  • Nowak, D.J., Greenfield, E.J. 2010. Evaluating the National Land Cover Database tree canopy and impervious cover estimates across the conterminous United States: a comparison with photo-interpreted estimates. Environmental management, 46(3): 378-390.
  • Oumar, Z., Mutanga, O. 2011. The potential of remote sensing technology for the detection and mapping of Thaumastocoris peregrinus in plantation forests. Southern Forests: A Journal of Forest Science, 73(1): 23-31.
  • Ozenen Kavlak, M., Cabuk, S.N., Cetin, M. 2021. Development of forest fire risk map using geographical information systems and remote sensing capabilities: Ören case. Environmental Science and Pollution Research, 28(25): 33265-33291.
  • Pasher, J., McGovern, M., Khoury, M., Duffe, J. 2014. Assessing carbon storage and sequestration by Canada's urban forests using high resolution earth observation data. UrbanForestry & Urban Greening, 13(3): 484-494. Picotte, J.J., Robertson, K.M. 2010. Accuracy of remote sensing wildland fire-burned area in southeastern US Coastal plain habitats. Proceedings of the 24th Tall Timbers Fire Ecology Conference: The Future of Prescribed Fire: Public Awareness, Health, and Safety. Tall Timbers Research Station, Tallahassee, Florida, USA. pp: 91-98
  • Pyne, S.J., Andrews, P.L., Laven, R.D. 1996. Introduction to wildland fire. Wiley, New York.
  • Rahman, S., Chang, H.C., Hehir, W., Magilli, C., Tomkins, K. 2018. Inter-comparison of fire severity indices from moderate (MODIS) and moderate-to-high spatial resolution (LANDSAT 8 & SENTINEL-2A) satellite sensors. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, July. (pp. 2873-2876). IEEE.
  • Reichstein, M., Bahn, M., Ciais, P., Frank, D., Mahecha, M.D., Seneviratne, S.I., Zscheischler, J., Beer, C., Buchmann, N., Frank, D.C. and Papale, D. 2013. Climate extremes and the carbon cycle. Nature. 500(7462): 287-295.
  • Richardson, J.J., Moskal, L.M. 2014. Uncertainty in urbanforest canopy assessment: Lessons from Seattle, WA, USA. Urban Forestry & Urban Greening 13(1): 152-157.
  • Ricotta, C., Bajocco, S., Guglietta, D., Conedera, M. 2018. Assessing the influence of roads on fire ignition: does land cover matter? Fire.1(2): 24.
  • Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publications, 351(1):309.
  • Rwanga, S.S., Ndambuki, J.M. 2017. Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS. International Journal of Geosciences, (8): 611-622. https://doi.org/ 10.4236/ijg.2017.84033
  • Saatchi, S., Halligan, K., Despain, D.G., Crabtree, R.L. 2007. Estimation of Forest Fuel Load From Radar Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing, (45): 1726–1740.
  • Safford, H., Larry, E., McPherson, E.G., Nowak, D.J., Westphal, L.M. 2013. Urban Forests and Climate Change. USDepartment of Agriculture, Forest Service, Climate ChangeResource Center r. www.fs. usda.gov/ccrc/topics/urban-forests.
  • Sağlam, S., Elvan, O.D., 2017. Kent ormanlarının Türkiye’deki gelişimi ve hukuki durumu (Progress and legal status of urban forests in Turkey). Kastamonu Universitesi Orman Fakültesi Dergisi, 17(4): 669-681.
  • Samiappan, S., Hathcock, L., Turnage, G., McCraine, C., Pitchford, J., Moorhead, R. 2019. Remote Sensing of Wildfire Using a Small Unmanned Aerial System: Post-Fire Mapping, Vegetation Recovery and Damage Analysis in Grand Bay, Mississippi/Alabama, USA. Drones, 3: 43.
  • Sanga-Ngoie, K., Iizuka, K. and Kobayashi, S. 2012. Estimating CO2 sequestration by forests in Oita Prefecture, Japan, by combining Landsat ETM+ and ALOS satellite remote sensing data. Remote Sensing, 4(11):3544-3570. https://doi.org/10.3390/rs4113544
  • Sannigrahi, S., Pilla, F., Basu, B., Basu, A.S., Sarkar, K., Chakraborti, S., Joshi, P.K., Zhang, Q., Wang, Y., Bhatt, S. Bhatt, A. 2020. Examining the effects of forest fire on terrestrial carbon emission and ecosystem production in India using remote sensing approaches. Science of the Total Environment, 725: 138331.
  • Satir, O., Berberoglu, S., Donmez, C. 2016. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomatics, Natural Hazards and Risk, 7: 1645–1658.
  • Sever, L., Leach, J., Bren, L. 2012. Remote sensing of post-fire vegetation recovery; a study using Landsat 5 TM imagery and NDVI in North-East Victoria. Journal of Spatial Science, (57): 175–191.
  • Shalaby, A., Tateishi, R. 2007. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied geography, 27(1): 28-41.
  • Shanafelt, D.W., Serra-Diaz, J.M., Bocquého, G. 2023. Measuring uncertainty in ecosystem service correlations as a function of sample size. Ecosystem Services, 63: 101546.
  • Singh, S., Singh, H., Sharma, V., Shrivastava, V., Kumar, P., Kanga, S., Sahu, N., Meraj, G., Farooq, M., Singh, S.K. 2021. Impact of forest fires on air quality in Wolgan valley, New South Wales, Australia—A mapping and monitoring study using Google Earth Engine. Forests, 13(1): 4.
  • Szpakowski, D.M., Jensen, J.L. 2019. A review of the applications of remote sensing in fire ecology. Remote sensing, 11(22): 2638.
  • Tonyaloğlu, E.E. 2020. Spatiotemporal dynamics of urban ecosystem services in Turkey: The case of Bornova, Izmir. Urban Forestry & Urban Greening, 49: 126631.
  • Tucker, C. J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment. 8(2): 127-150.
  • Turkish Statistical Institute (TUIK). 2022. Population and Demography, Address Based Population Registration System Bulletin. Retrieved from https://data.tuik.gov.tr/Bulten/Index?p=The-Results-of-Address-Based-Population-Registration-System-2022-49685&dil=2
  • Turner, D. P., Guzy, M., Lefsky, M. A., Ritts, W. D., Van Tuyl, S., Law, B. E., 2004. Monitoring forest carbon sequestration with remote sensing and carbon cycle modeling. Environmental management. (33): 457-466.
  • Ucar, Z., Bettinger, P., Merry, K., Akbulut, R., Siry, J., 2018. Estimation of urban woody vegetation cover using multispectral imagery and LiDAR. Urban Forestry & Urban Greening. (29): 248-260.
  • United Nation, The World Urbanization Prospects (2018).Retrieved from https://www.un.org/en/events/citiesday/assets/pdf/the_worlds_cities_in_2018_data_booklet.pdf.
  • Vatandaşlar, C., Abdikan, S., 2022. Carbon stock estimation by dual-polarized synthetic aperture radar (SAR) and forest inventory data in a Mediterranean forest landscape. Journal of Forestry Research. 33(3): 827-838.
  • Vicharnakorn P, Shrestha RP, Nagai M, Salam AP, Kiratiprayoon S., 2014. Carbon Stock Assessment Using Remote Sensing and Forest Inventory Data in Savannakhet, Lao PDR. Remote Sensing. 6(6):5452-5479. https://doi.org/10.3390/rs6065452
  • Wallemacq, P., Below, R., McClean, D., 2018. Economic losses, poverty & disasters: 1998-2017. United Nations Office for Disaster Risk Reduction.
  • Wicaksono, P., Danoedoro, P., Hartono, H., Nehren, U., Ribbe, L. 2011. Preliminary work of mangrove ecosystem carbon stock mapping in small island using remote sensing: above and below ground carbon stock mapping on medium resolution satellite image. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 8174: 408-417. SPIE. (7 October 2011). https://doi.org/ 10.1117/ 12.897926
  • Wold Bank. 2017. Dünya Bankası Göstergeleri. https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS?contextual=default
  • Xu, G., Zhong, X. 2017. Real-time wildfire detection and tracking in Australia using geostationary satellite: Himawari-8. Remote Sensing Letters, 8(11): 1052-1061.
  • Yin, S., Wang, X., Guo, M., Santoso, H., Guan, H. 2020. The abnormal change of air quality and air pollutants induced by the forest fire in Sumatra and Borneo in 2015. Atmospheric research, 243, 105027.
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Details

Primary Language English
Subjects Photogrammetry and Remote Sensing
Journal Section Research Articles
Authors

Zennure Uçar 0000-0003-1413-0036

Early Pub Date June 5, 2024
Publication Date June 27, 2024
Submission Date April 11, 2024
Acceptance Date May 9, 2024
Published in Issue Year 2024

Cite

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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