Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2022, Cilt: 3 Sayı: 2, 58 - 63, 30.12.2022

Ezgi Tükel Kaan Kalkan

https://doi.org/10.48053/turkgeo.1140975
Cited By: 1

Öz

Kaynakça

  • Acharya, T.D., & Yang, I. (2015). Exploring Landsat 8. International Journal of IT, Engineering and Applied Sciences Research (IJIEASR), 4(4), 4-10.
  • Akay, A.E., & Şahin, H. (2019). Forest fire risk mapping using GIS techniques and AHP method: a case study in Bodrum (Turkey). European Journal of Forest Engineering, 5(1), 25-35.
  • Boer, M.M., Macfarlane, C., Norris, J., Sadler, R.J., Wallace, J., & Grierson, P.F. (2008). Mapping burned areas and burn severity patterns in SW Australian eucalypt forest using remotely-sensed changes in leaf area index. Remote Sensing of Environment, 112(12), 4358-4369.
  • Castro-Basurto, K., Jijon-Veliz, F., Medina, W., & Velasquez, W. (2021). Outside dynamic evacuation routes to escape a wildfire: A prototype app for forest firefighters. Sustainability, 13(13), 7295.
  • Daşdemir, İ., Aydın, F., & Ertuğrul, M. (2021). Factors affecting the behavior of large forest fires in Turkey. Environmental Management, 67(1), 162-175.
  • Değerliyurt M., & Çabuk S. (2015). Defining geography with geographical information systems Eastern. Geogr J 20:37–48.
  • Dimitrakopoulos, A.P., Mitrakos, D., & Christoforou, V. (2002). Concepts of wildland fire protection of cultural monuments and national parks in Greece. Case study: Digital telemetry networks at the forest of Ancient Olympia. Fire Technology, 38(4), 363-372.
  • EFFIS (European Forest Fire Information System), 2021. EFFIS Annual Country Statistics for TR – Turkey, retrieved from https://effis.jrc.ec.europa.eu/apps/effis.statistics/effisestimates, access date: 12/01/2022.
  • Eugenio, F.C., Dos Santos, A.R., Fiedler, N.C., Ribeiro, G.A., Da Silva, A.G., Dos Santos, Á.B., Paneto G.G., & Schettino, V.R. (2016). Applying GIS to develop a model for forest fire risk: A case study in Espírito Santo, Brazil. Journal of environmental management, 173, 65-71.
  • Flasse, S.P., & Ceccato, P. (1996). A contextual algorithm for AVHRR fire detection. International Journal of Remote Sensing, 17(2), 419-424.
  • Geneletti, D., & Van Duren, I. (2008). Protected area zoning for conservation and use: A combination of spatial multicriteria and multiobjective evaluation. Landscape and urban planning, 85(2), 97-110.
  • Giddey, B.L., Baard, J.A., & Kraaij, T. (2022). Verification of the differenced Normalised Burn Ratio (dNBR) as an index of fire severity in Afrotemperate Forest. South African Journal of Botany, 146, 348-353.
  • Gigović, L., Pourghasemi, H. R., Drobnjak, S., & Bai, S. (2019). Testing a new ensemble model based on SVM and random forest in forest fire susceptibility assessment and its mapping in Serbia’s Tara National Park. Forests, 10(5), 408.
  • Gürbüzer, M. (2021). Amos’ ta Yeni Araştırmalar ve Bulgular. Cedrus, 9, 219-249.
  • Hall, R. J., Freeburn, J.T., De Groot, W.J., Pritchard, J.M., Lynham, T.J., & Landry, R. (2008). Remote sensing of burn severity: experience from western Canada boreal fires. International Journal of Wildland Fire, 17(4), 476-489.
  • Hantson, S., Pueyo, S., & Chuvieco, E. (2016). Global fire size distribution: from power law to log normal. International journal of wildland fire, 25(4), 403-412.
  • Hjortsø, C. N., Stræde, S., & Helles, F. (2006). Applying multicriteria decision-making to protected areas and buffer zone management: A case study in the Royal Chitwan National Park, Nepal. Journal of forest economics, 12(2), 91-108.
  • Humphrey, G. J., Gillson, L., & Ziervogel, G. (2021). How changing fire management policies affect fire seasonality and livelihoods. Ambio, 50(2), 475-491.
  • Javad, M., Baharin, A., Barat, M., & Farshid, S. (2014). Using frequency ratio method for spatial landslide prediction. Research Journal of Applied Sciences, Engineering and Technology, 7(15), 3174–3180.
  • Konkathi, P., & Shetty, A. (2019). Assessment of burn severity using different fire indices: A case study of Bandipur National Park. In 2019 IEEE Recent Advances in Geoscience and Remote Sensing: Technologies, Standards and Applications (TENGARSS) (pp. 151-154) IEEE.
  • Marino, E., Guillén-Climent, M., Ranz Vega, P., & Tomé, J. (2016). Fire severity mapping in Garajonay National Park: Comparison between spectral indices. Flamma: Madrid, Spain, 7, 22-28.
  • Nikhil, S., Danumah, J. H., Saha, S., Prasad, M. K., Rajaneesh, A., Mammen, P. C., ... & Kuriakose, S. L. (2021). Application of GIS and AHP method in forest fire risk zone mapping: a study of the Parambikulam tiger reserve, Kerala, India. Journal of Geovisualization and Spatial Analysis, 5(1), 1-14.
  • Nuthammachot, N., & Stratoulias, D. (2021). Multicriteria decision analysis for forest fire risk assessment by coupling AHP and GIS: method and case study. Environment, Development, and Sustainability, 23(12), 17443-17458.
  • Roos, C. I., Swetnam, T.W., Ferguson, T.J., Liebmann, M. J., Loehman, R.A., Welch, J.R., ... & Kiahtipes, C.A. (2021). Native American fire management at an ancient wildland–urban interface in the Southwest United States. Proceedings of the National Academy of Sciences, 118(4), e2018733118.
  • Rozario, P.F., Madurapperuma, B.D., & Wang, Y. (2018). Remote sensing approach to detect burn severity risk zones in Palo Verde National Park, Costa Rica. Remote Sensing, 10(9), 1427.
  • Sandamali, K.U., & Chathuranga, K.A.M. (2021). Quantification of Burned Severity of the Forest Fire using Sentinel-2 Remote Sensing Images: A Case Study in the Ella Sri Lanka. Research and Reviews: Journal of Environmental Sciences, 3(3), 1-12.
  • Smith, A. M., Eitel, J.U., & Hudak, A.T. (2010). Spectral analysis of charcoal on soils: Implicationsfor wildland fire severity mapping methods. International Journal of Wildland Fire, 19(7), 976-983.
  • Stankova, N., & Nedkov, R. (2015). Monitoring forest regrowth with different burn severity using aerial and Landsat data. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) IEEE.
  • Stroppiana, D., Bordogna, G., Sali, M., Boschetti, M., Sona, G., & Brivio, P.A. (2021). A Fully Automatic, Interpretable and Adaptive Machine Learning Approach to Map Burned Area from Remote Sensing. ISPRS International Journal of Geo-Information, 10(8), 546.
  • Teodoro, A., Duarte, L., Sillero, N., Gonçalves, J. A., Fonte, J., Gonçalves-Seco, L., Pinheiro da Luz, L.M., & Dos Santos Beja, N.M.R. (2015). An integrated and open source GIS environmental management system for a protected area in the south of Portugal. In Earth Resources and Environmental Remote Sensing/GIS Applications VI (Vol. 9644, pp. 143-154). SPIE.
  • Tien Bui, D., Bui, Q.T., Nguyen, Q.P., Pradhan, B., Nampak, H., & Trinh, P.T. (2017). A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agricultural and Forest Meteorology, 233, 32-44.
  • Vlassova, L., Pérez-Cabello, F., Mimbrero, M.R., Llovería, R.M., & García-Martín, A. (2014). Analysis of the relationship between land surface temperature and wildfire severity in a series of landsat images. Remote Sensing, 6(7), 6136-6162.
  • White, J.D., Ryan, K.C., Key, C.C., & Running, S.W. (1996). Remote sensing of forest fire severity and vegetation recovery. International Journal of Wildland Fire, 6(3), 125-136.
  • Yaman, A. (2022). Amos arkeolojik yüzey araştırmalarında ele geçen seramikler. Olba, 30, 113-127.
  • Url-1: https://www.haberturk.com/mugla-haberleri/89560498-tarihi-amos-antik-kenti-cevresindeki-yangina-toma-mudahelesi (last accessed 05.08.2022)
  • Url-2: https://www.trthaber.com/haber/turkiye/marmaristeki-yangin-amos-antik-kentine-ilerliyor-599654.html (last accessed 12 June 2022)
  • Url-3: https://www.usgs.gov/landsat-missions/landsat-normalized-burn-ratio (last accessed 12 June 2022)

Analysis of the Threat of Forest Fires to Ancient Cities by GIS and Remote Sensing Methods

Yıl 2022, Cilt: 3 Sayı: 2, 58 - 63, 30.12.2022

Ezgi Tükel Kaan Kalkan

https://doi.org/10.48053/turkgeo.1140975
Cited By: 1

Öz

Forest fires have been more common in recent years and caused extensive damage. Not only settlements and natural life but also historical places and ancient cities are at risk of forest fires. This study discussed forest fires in Turkey in the summer of 2021. Forest fire risk classifications were determined using Landsat-8 images. The Normalized Burn Ratio (NBR) and Differenced Normalized Burn Ratio (dNBR) indices were used to assess the area impacted by fire and to create fire risk classes. Furthermore, the burned and unburned areas in different zones from the Amos ancient city in the Marmaris were calculated using remote sensing methods. Thus, areas that should be protected from the fire were determined in future studies for fire risk areas.

Anahtar Kelimeler

Forest Fires Archaeological Stei Fire Risk Burn Severity Landsat-8

Kaynakça

  • Acharya, T.D., & Yang, I. (2015). Exploring Landsat 8. International Journal of IT, Engineering and Applied Sciences Research (IJIEASR), 4(4), 4-10.
  • Akay, A.E., & Şahin, H. (2019). Forest fire risk mapping using GIS techniques and AHP method: a case study in Bodrum (Turkey). European Journal of Forest Engineering, 5(1), 25-35.
  • Boer, M.M., Macfarlane, C., Norris, J., Sadler, R.J., Wallace, J., & Grierson, P.F. (2008). Mapping burned areas and burn severity patterns in SW Australian eucalypt forest using remotely-sensed changes in leaf area index. Remote Sensing of Environment, 112(12), 4358-4369.
  • Castro-Basurto, K., Jijon-Veliz, F., Medina, W., & Velasquez, W. (2021). Outside dynamic evacuation routes to escape a wildfire: A prototype app for forest firefighters. Sustainability, 13(13), 7295.
  • Daşdemir, İ., Aydın, F., & Ertuğrul, M. (2021). Factors affecting the behavior of large forest fires in Turkey. Environmental Management, 67(1), 162-175.
  • Değerliyurt M., & Çabuk S. (2015). Defining geography with geographical information systems Eastern. Geogr J 20:37–48.
  • Dimitrakopoulos, A.P., Mitrakos, D., & Christoforou, V. (2002). Concepts of wildland fire protection of cultural monuments and national parks in Greece. Case study: Digital telemetry networks at the forest of Ancient Olympia. Fire Technology, 38(4), 363-372.
  • EFFIS (European Forest Fire Information System), 2021. EFFIS Annual Country Statistics for TR – Turkey, retrieved from https://effis.jrc.ec.europa.eu/apps/effis.statistics/effisestimates, access date: 12/01/2022.
  • Eugenio, F.C., Dos Santos, A.R., Fiedler, N.C., Ribeiro, G.A., Da Silva, A.G., Dos Santos, Á.B., Paneto G.G., & Schettino, V.R. (2016). Applying GIS to develop a model for forest fire risk: A case study in Espírito Santo, Brazil. Journal of environmental management, 173, 65-71.
  • Flasse, S.P., & Ceccato, P. (1996). A contextual algorithm for AVHRR fire detection. International Journal of Remote Sensing, 17(2), 419-424.
  • Geneletti, D., & Van Duren, I. (2008). Protected area zoning for conservation and use: A combination of spatial multicriteria and multiobjective evaluation. Landscape and urban planning, 85(2), 97-110.
  • Giddey, B.L., Baard, J.A., & Kraaij, T. (2022). Verification of the differenced Normalised Burn Ratio (dNBR) as an index of fire severity in Afrotemperate Forest. South African Journal of Botany, 146, 348-353.
  • Gigović, L., Pourghasemi, H. R., Drobnjak, S., & Bai, S. (2019). Testing a new ensemble model based on SVM and random forest in forest fire susceptibility assessment and its mapping in Serbia’s Tara National Park. Forests, 10(5), 408.
  • Gürbüzer, M. (2021). Amos’ ta Yeni Araştırmalar ve Bulgular. Cedrus, 9, 219-249.
  • Hall, R. J., Freeburn, J.T., De Groot, W.J., Pritchard, J.M., Lynham, T.J., & Landry, R. (2008). Remote sensing of burn severity: experience from western Canada boreal fires. International Journal of Wildland Fire, 17(4), 476-489.
  • Hantson, S., Pueyo, S., & Chuvieco, E. (2016). Global fire size distribution: from power law to log normal. International journal of wildland fire, 25(4), 403-412.
  • Hjortsø, C. N., Stræde, S., & Helles, F. (2006). Applying multicriteria decision-making to protected areas and buffer zone management: A case study in the Royal Chitwan National Park, Nepal. Journal of forest economics, 12(2), 91-108.
  • Humphrey, G. J., Gillson, L., & Ziervogel, G. (2021). How changing fire management policies affect fire seasonality and livelihoods. Ambio, 50(2), 475-491.
  • Javad, M., Baharin, A., Barat, M., & Farshid, S. (2014). Using frequency ratio method for spatial landslide prediction. Research Journal of Applied Sciences, Engineering and Technology, 7(15), 3174–3180.
  • Konkathi, P., & Shetty, A. (2019). Assessment of burn severity using different fire indices: A case study of Bandipur National Park. In 2019 IEEE Recent Advances in Geoscience and Remote Sensing: Technologies, Standards and Applications (TENGARSS) (pp. 151-154) IEEE.
  • Marino, E., Guillén-Climent, M., Ranz Vega, P., & Tomé, J. (2016). Fire severity mapping in Garajonay National Park: Comparison between spectral indices. Flamma: Madrid, Spain, 7, 22-28.
  • Nikhil, S., Danumah, J. H., Saha, S., Prasad, M. K., Rajaneesh, A., Mammen, P. C., ... & Kuriakose, S. L. (2021). Application of GIS and AHP method in forest fire risk zone mapping: a study of the Parambikulam tiger reserve, Kerala, India. Journal of Geovisualization and Spatial Analysis, 5(1), 1-14.
  • Nuthammachot, N., & Stratoulias, D. (2021). Multicriteria decision analysis for forest fire risk assessment by coupling AHP and GIS: method and case study. Environment, Development, and Sustainability, 23(12), 17443-17458.
  • Roos, C. I., Swetnam, T.W., Ferguson, T.J., Liebmann, M. J., Loehman, R.A., Welch, J.R., ... & Kiahtipes, C.A. (2021). Native American fire management at an ancient wildland–urban interface in the Southwest United States. Proceedings of the National Academy of Sciences, 118(4), e2018733118.
  • Rozario, P.F., Madurapperuma, B.D., & Wang, Y. (2018). Remote sensing approach to detect burn severity risk zones in Palo Verde National Park, Costa Rica. Remote Sensing, 10(9), 1427.
  • Sandamali, K.U., & Chathuranga, K.A.M. (2021). Quantification of Burned Severity of the Forest Fire using Sentinel-2 Remote Sensing Images: A Case Study in the Ella Sri Lanka. Research and Reviews: Journal of Environmental Sciences, 3(3), 1-12.
  • Smith, A. M., Eitel, J.U., & Hudak, A.T. (2010). Spectral analysis of charcoal on soils: Implicationsfor wildland fire severity mapping methods. International Journal of Wildland Fire, 19(7), 976-983.
  • Stankova, N., & Nedkov, R. (2015). Monitoring forest regrowth with different burn severity using aerial and Landsat data. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) IEEE.
  • Stroppiana, D., Bordogna, G., Sali, M., Boschetti, M., Sona, G., & Brivio, P.A. (2021). A Fully Automatic, Interpretable and Adaptive Machine Learning Approach to Map Burned Area from Remote Sensing. ISPRS International Journal of Geo-Information, 10(8), 546.
  • Teodoro, A., Duarte, L., Sillero, N., Gonçalves, J. A., Fonte, J., Gonçalves-Seco, L., Pinheiro da Luz, L.M., & Dos Santos Beja, N.M.R. (2015). An integrated and open source GIS environmental management system for a protected area in the south of Portugal. In Earth Resources and Environmental Remote Sensing/GIS Applications VI (Vol. 9644, pp. 143-154). SPIE.
  • Tien Bui, D., Bui, Q.T., Nguyen, Q.P., Pradhan, B., Nampak, H., & Trinh, P.T. (2017). A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agricultural and Forest Meteorology, 233, 32-44.
  • Vlassova, L., Pérez-Cabello, F., Mimbrero, M.R., Llovería, R.M., & García-Martín, A. (2014). Analysis of the relationship between land surface temperature and wildfire severity in a series of landsat images. Remote Sensing, 6(7), 6136-6162.
  • White, J.D., Ryan, K.C., Key, C.C., & Running, S.W. (1996). Remote sensing of forest fire severity and vegetation recovery. International Journal of Wildland Fire, 6(3), 125-136.
  • Yaman, A. (2022). Amos arkeolojik yüzey araştırmalarında ele geçen seramikler. Olba, 30, 113-127.
  • Url-1: https://www.haberturk.com/mugla-haberleri/89560498-tarihi-amos-antik-kenti-cevresindeki-yangina-toma-mudahelesi (last accessed 05.08.2022)
  • Url-2: https://www.trthaber.com/haber/turkiye/marmaristeki-yangin-amos-antik-kentine-ilerliyor-599654.html (last accessed 12 June 2022)
  • Url-3: https://www.usgs.gov/landsat-missions/landsat-normalized-burn-ratio (last accessed 12 June 2022)
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yer Bilimleri ve Jeoloji Mühendisliği (Diğer)
Bölüm Research Articles
Yazarlar

Ezgi Tükel 0000-0002-8675-2128

Kaan Kalkan 0000-0002-2732-5425

Erken Görünüm Tarihi 28 Aralık 2022
Yayımlanma Tarihi 30 Aralık 2022
Gönderilme Tarihi 5 Temmuz 2022
Kabul Tarihi 31 Ağustos 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 3 Sayı: 2

Kaynak Göster

APA Tükel, E., & Kalkan, K. (2022). Analysis of the Threat of Forest Fires to Ancient Cities by GIS and Remote Sensing Methods. Turkish Journal of Geosciences, 3(2), 58-63. https://doi.org/10.48053/turkgeo.1140975

Cited By

Advancements in Wildfire Detection and Prediction: An In-Depth Review
International Journal of Innovative Technology and Exploring Engineering
https://doi.org/10.35940/ijitee.B9774.13020124
 Kapak Resmi İndir