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Google Earth Engine ile Türkiye'de Yanmış Alanların MODIS ve FireCCI51 Küresel Yanmış Alan Uydu Gözlem Verileriyle Karşılaştırmalı Değerlendirilmesi

Yıl 2024, , 69 - 82, 28.03.2024
https://doi.org/10.48123/rsgis.1410382

Öz

Bu çalışmada, Google Earth Engine (GEE) platformunda MODIS MCD64A1 ve European Space Agency (ESA) Fire_cci v. 5.1 (FireCCI51) küresel yanmış alan gözlem verileri kullanılarak Türkiye'de 2015-2020 yılları arasında meydana gelen yanmış alanların konumsal ve zamansal dağılımları belirlenmiştir. Elde edilen sonuçlar, Avrupa Orman Yangınları Bilgi Sistemi (EFFIS) verileriyle karşılaştırılarak yanmış alanların doğruluğu ve kapsamı değerlendirilmiştir. Çalışma kapsamında incelenen dönem boyunca, FireCCI51'in MCD64A1'e göre daha yüksek konumsal çözünürlüğe sahip olması ve küçük yanmış alanları daha hassas bir şekilde belirleme kapasitesine sahip olması daha fazla yanmış alan miktarları hesaplamasını sağlamıştır. Ayrıca, yanmış alanlardaki arazi örtüsü türlerinin belirlenmesinde MODIS MCD12Q1 arazi örtüsü sınıflandırma ürünü kullanılmıştır. 2015-2020 yılları arasında Türkiye'de meydana gelen yangınlar sonucunda yanmış bölgeler içerisinde, her iki veri setine göre de, arazi örtüsü türlerinde ekili alanlar en yüksek yanma oranına sahiptir. MODIS MCD64A1 verileri, ekili alanlardaki yanmış alan oranını % 88,93 ile % 91,80 arasında, FireCCI51 ise % 78,23 ile % 89,53 arasında belirlemiştir. Ülkemizde yanmış alanların konumsal dağılımı, özellikle Akdeniz, Ege ve Güneydoğu Anadolu bölgelerinde yoğunlaşmıştır.

Kaynakça

  • Akyürek, Z. (2023). Türkiye’deki 2000 – 2021 yılları arasındaki bitki örtüsü yangınlarının mekânsal analizi. Turkish Journal of Remote Sensing and GIS, 4(1), 33–46. https://doi.org/10.48123/rsgis.1179051
  • Amos, C., Petropoulos, G. P., & Ferentinos, K. P. (2018). Determining the use of Sentinel-2A MSI for wildfire burning & severity detection. International Journal of Remote Sensing, 40(3), 905–930. https://doi.org/10.1080/01431161.2018.1519284
  • Campagnolo, M., Oom, D., Padilla, M., & Pereira, J. (2019). A patch-based algorithm for global and daily burned area mapping. Remote Sensing of Environment, 232, 111288. https://doi.org/10.1016/j.rse.2019.111288
  • Chuvieco, E., Lizundia-Loiola, J., Pettinari, M. L., Ramo, R., Padilla, M., Tansey, K., … Plummer, S. (2018). Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth System Science Data, 10(4), 2015–2031. https://doi.org/10.5194/essd-2018-46
  • Chuvieco, E., Mouillot, F., van der Werf, G. R., San Miguel, J., Tanase, M., Koutsias, N., … Giglio, L. (2019a). Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment, 225, 45–64. https://doi.org/10.1016/j.rse.2019.02.013
  • Chuvieco, E., Pettinari, M. L., Lizundia Loiola, J., Storm, T., & Padilla Parellada, M. (2019b). ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Grid product, version 5.1 [Data set]. Centre for Environmental Data Analysis. https://dx.doi.org/10.5285/3628cb2fdba443588155e15dee8e5352
  • Demir, S. & Dursun, I. (2023). Determining burned areas using different threshold values of NDVI with sentinel-2 satellite images on gee platform: a case study of Muğla province. International Journal of Sustainable Engineering and Technology, 7(2), 117-130.
  • Demirel, Y., & Türk, T. (2023). Türkiye’de 2015 ile 2022 yılları arasında meydana gelen orman yangınlarının coğrafi bilgi sistemleri ile zamansal ve mekânsal analizi. Journal of Geodesy and Geoinformation, 10(2), 136–150. https://doi.org/10.9733/JGG.2023R0010.T
  • EFFIS Fuel Map. (2023). EFFIS Data and Services. https://effis.jrc.ec.europa.eu/applications/data-and-services EFFIS. (2023). European Forest Fire Information System. http://effis.jrc.ec.europa.eu/
  • Filipponi, F. (2019). Exploitation of Sentinel-2 time series to map burned areas at the national level: A case study on the 2017 Italy wildfires. Remote Sensing, 11(6), 622. https://doi.org/10.3390/rs11060622
  • Fornacca, D., Ren, G., & Xiao, W. (2017). Performance of three MODIS Fire products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a mountainous area of Northwest Yunnan, China, characterized by frequent small fires. Remote Sensing, 9(11), 1131. https://doi.org/10.3390/rs9111131
  • Friedl, M., & Sulla-Menashe, D. (2022). MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MCD12Q1.061
  • Gholamrezaie, H., Hasanlou, M., Amani, M., & Mirmazloumi, S. M. (2022). Automatic Mapping of Burned Areas Using Landsat 8 Time-Series Images in Google Earth Engine: A Case Study from Iran. Remote Sensing, 14(24), 6376. https://doi.org/10.3390/rs14246376
  • Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., & Justice, C. O. (2018). The Collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment, 217, 72–85. https://doi.org/10.1016/j.rse.2018.08.005
  • Giglio, L., Descloitres, J., Justice, C. O., & Kaufman, Y. J. (2003). An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment, 87(2–3), 273–282. https://doi.org/10.1016/s0034-4257(03)00184-6
  • Hall, J. V., Argueta, F., & Giglio, L. (2021). Validation of MCD64A1 and FireCCI51 cropland burned area mapping in Ukraine. International Journal of Applied Earth Observation and Geoinformation, 102, 102443. https://doi.org/10.1016/j.jag.2021.102443
  • Humber, M. L., Boschetti, L., Giglio, L., & Justice, C. O. (2019). Spatial and temporal intercomparison of four global burned area products. International Journal of Digital Earth, 12(4), 460–484. https://doi.org/10.1080/17538947.2018.1433727
  • Kalivas, D., Petropoulos, G. P., Athanasiou, I., & Kollias, V. J. (2013). An intercomparison of burnt area estimates derived from key operational products: the Greek wildland fires of 2005–2007. Nonlinear Processes in Geophysics, 20(3), 397-409. https://doi.org/10.5194/npg-20-397-2013
  • Katagis, T., & Gitas, I. Z. (2022). Assessing the Accuracy of MODIS MCD64A1 C6 and FireCCI51 Burned Area Products in Mediterranean Ecosystems. Remote Sensing, 14(3), 602. https://doi.org/10.3390/rs14030602
  • Kaufman, Y. J., Tanré, D., & Boucher, O. (2002). A satellite view of aerosols in the climate system. Nature, 419(6903), 215–223. https://doi.org/10.1038/nature01091
  • Kavzoğlu, T., Çölkesen, İ., Tonbul H., & Öztürk M. Y. (2021). Uzaktan Algılama Teknolojileri ile Orman Yangınlarının Zamansal Analizi: 2021 Yılı Akdeniz ve Ege Yangınları. In T. Kavzoğlu (Ed.) Orman yangınları sebepleri, etkileri, izlenmesi, alınması gereken önlemler ve rehabilitasyon faaliyetleri (pp. 219–251). Türkiye Bilimler Akademisi.
  • Liu, Y., Stanturf, J., & Goodrick, S. (2010). Trends in global wildfire potential in a changing climate. Forest Ecology and Management, 259(4), 685–697. https://doi.org/10.1016/j.foreco.2009.09.002
  • Lizundia-Loiola, J., Otón, G., Ramo, R., & Chuvieco, E. (2020). A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data. Remote Sensing of Environment, 236, 111493. https://doi.org/10.1016/j.rse.2019.111493
  • LP DAAC, (2023). MODIS MCD64A1: MODIS/Terra+Aqua Burned Area Monthly L3 Global 500 m SIN Grid. USGS. https://lpdaac.usgs.gov/products/mcd64a1v006/
  • Moreno, M. V., Conedera, M., Chuvieco, E., & Pezzatti, G. B. (2014). Fire regime changes and major driving forces in Spain from 1968 to 2010. Environmental Science & Policy, 37, 11–22. https://doi.org/10.1016/j.envsci.2013.08.005
  • Moreno, M. V., Laurent, P., & Mouillot, F. (2021). Global intercomparison of functional pyrodiversity from two satellite sensors. International Journal of Remote Sensing, 42(24), 9523–9541. https://doi.org/10.1080/01431161.2021.1999529
  • Mouillot, F., Schultz, M. G., Yue, C., Cadule, P., Tansey, K., Ciais, P., & Chuvieco, E. (2014). Ten years of global burned area products from spaceborne remote sensing—A review: Analysis of user needs and recommendations for future developments. International Journal of Applied Earth Observation and Geoinformation, 26, 64–79. https://doi.org/10.1016/j.jag.2013.05.014
  • Orman Genel Müdürlüğü. (2020). Türkiye Orman Varlığı 2020. https://www.ogm.gov.tr/tr/ormanlarimiz-sitesi/TurkiyeOrmanVarligi/Yayinlar/2020%20T%C3%BCrkiye%20Orman%20Varl%C4%B1%C4%9F%C4%B1.pdf
  • Petropoulos, G. P., Knorr, W., Scholze, M., Boschetti, L., & Karantounias, G. (2010). Combining ASTER multispectral imagery analysis and support vector machines for rapid and cost-effective post-fire assessment: A case study from the greek wildland fires of 2007. Natural Hazards and Earth System Sciences, 10(2), 305-317. https://doi.org/10.5194/nhess-10-305-2010
  • Rasul, A., Ibrahim, G. R. F., Hameed, H. M., & Tansey, K. (2020). A trend of increasing burned areas in Iraq from 2001 to 2019. Environment, Development and Sustainability, 23(4), 5739–5755. https://doi.org/10.1007/s10668-020-00842-7
  • Roy, D. P. (1999). Multi-temporal active-fire based burn scar detection algorithm. International Journal of Remote Sensing, 20(5), 1031–1038. https://doi.org/10.1080/014311699213073
  • San-Miguel-Ayanz, J., Schulte, E., Schmuck, G., Camia, A., Strobl, P., Liberta, G., … Amatulli, G. (2012). Comprehensive monitoring of wildfires in Europe: The European forest fire information system (EFFIS). In J. Tiefenbacher (Ed.), Approaches to managing disaster-Assessing hazards, emergencies and disaster impacts (pp. 87-108). IntechOpen.
  • Stephens, S. L., Agee, J. K., Fulé, P. Z., North, M. P., Romme, W. H., Swetnam, T. W., & Turner, M. G. (2013). Managing Forests and Fire in Changing Climates. Science, 342(6154), 41–42. https://doi.org/10.1126/science.1240294
  • Sulla-Menashe, D., & Friedl, M. A., (2018, May 14). User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product. USGS. https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf
  • Tonbul, H., Colkesen, I., & Kavzoglu, T. (2019, 7–9 Kasım). Forest fire and burn severity analysis in Cefalu region of Italy using Sentinel-2 imagery [Conference presentation]. International Symposium on Applied Geoinformatics (ISAG-2019), Istanbul, Turkey.
  • Tonbul, H., Colkesen, I., & Kavzoglu, T. (2022). Pixel- and Object-Based ensemble learning for forest burn severity using USGS FIREMON and Mediterranean condition dNBRs in Aegean ecosystem (Turkey). Advances in Space Research, 69(10), 3609–3632. https://doi.org/10.1016/j.asr.2022.02.051
  • Turco, M., Herrera, S., Tourigny, E., Chuvieco, E., & Provenzale, A. (2019). A comparison of remotely-sensed and inventory datasets for burned area in Mediterranean Europe. International Journal of Applied Earth Observation and Geoinformation, 82, 101887. https://doi.org/10.1016/j.jag.2019.05.020
  • Vetrita, Y., Cochrane, M. A., Suwarsono, Priyatna, M., Sukowati, K. A. D., & Khomarudin, M. R. (2021). Evaluating accuracy of four MODIS-derived burned area products for tropical peatland and non-peatland fires. Environmental Research Letters, 16(3), 035015. https://doi.org/10.1088/1748-9326/abd3d1
  • Yıldız, C., Cömert, R., Tanyaş, H., Yılmaz, A., Akbaş, A., Akay, S. S., … Görüm, T. (2023). The effect of post-wildfire management practices on vegetation recovery: Insights from the Sapadere fire, Antalya, Türkiye. Frontiers in Earth Science, 11, 1174155. https://doi.org/10.3389/feart.2023.1174155
  • Zhang, S., Zhao, H., Wu, Z., & Tan, L. (2022). Comparing the ability of burned area products to detect crop residue burning in China. Remote Sensing, 14(3), 693. https://doi.org/10.3390/rs14030693
  • Zhou, L., Wang, Y., Chi, Y., Wang, S., & Wang, Q. (2019). Contrasting Post-Fire Dynamics between Africa and South America based on MODIS Observations. Remote Sensing, 11(9), 1074. https://doi.org/10.3390/rs11091074

Comparative Assessment of Burned Areas in Turkey with MODIS and FireCCI51 Global Burned Area Satellite Observation Data using Google Earth Engine

Yıl 2024, , 69 - 82, 28.03.2024
https://doi.org/10.48123/rsgis.1410382

Öz

In this study, the spatial and temporal distributions of burned areas in Turkey between 2015 and 2020 were identified using MODIS MCD64A1 and European Space Agency (ESA) FireCCI51 global burned area observation data on the Google Earth Engine platform. The results obtained were compared with EFFIS data to evaluate the accuracy and comprehensiveness of burned areas. During the period examined in the study, FireCCI51's higher spatial resolution compared to MCD64A1 and its ability to determine small burned areas more precisely enabled it to calculate higher burned area amounts. Additionally, MODIS MCD12Q1 land cover classification product was used to determine land cover types in burned areas. Among the areas burned by fires in Turkey between 2015 and 2020, cultivated areas have the highest burn rate in land cover types according to both datasets. MODIS MCD64A1 data determined the burned area rate in cropland between 88.93% and 91.80%, and FireCCI51 determined the burned area rate between 78.23% and 89.53%. The spatial distribution of burnt areas in our country is concentrated especially in the Mediterranean, Aegean and Southeastern Anatolia regions.

Kaynakça

  • Akyürek, Z. (2023). Türkiye’deki 2000 – 2021 yılları arasındaki bitki örtüsü yangınlarının mekânsal analizi. Turkish Journal of Remote Sensing and GIS, 4(1), 33–46. https://doi.org/10.48123/rsgis.1179051
  • Amos, C., Petropoulos, G. P., & Ferentinos, K. P. (2018). Determining the use of Sentinel-2A MSI for wildfire burning & severity detection. International Journal of Remote Sensing, 40(3), 905–930. https://doi.org/10.1080/01431161.2018.1519284
  • Campagnolo, M., Oom, D., Padilla, M., & Pereira, J. (2019). A patch-based algorithm for global and daily burned area mapping. Remote Sensing of Environment, 232, 111288. https://doi.org/10.1016/j.rse.2019.111288
  • Chuvieco, E., Lizundia-Loiola, J., Pettinari, M. L., Ramo, R., Padilla, M., Tansey, K., … Plummer, S. (2018). Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth System Science Data, 10(4), 2015–2031. https://doi.org/10.5194/essd-2018-46
  • Chuvieco, E., Mouillot, F., van der Werf, G. R., San Miguel, J., Tanase, M., Koutsias, N., … Giglio, L. (2019a). Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment, 225, 45–64. https://doi.org/10.1016/j.rse.2019.02.013
  • Chuvieco, E., Pettinari, M. L., Lizundia Loiola, J., Storm, T., & Padilla Parellada, M. (2019b). ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Grid product, version 5.1 [Data set]. Centre for Environmental Data Analysis. https://dx.doi.org/10.5285/3628cb2fdba443588155e15dee8e5352
  • Demir, S. & Dursun, I. (2023). Determining burned areas using different threshold values of NDVI with sentinel-2 satellite images on gee platform: a case study of Muğla province. International Journal of Sustainable Engineering and Technology, 7(2), 117-130.
  • Demirel, Y., & Türk, T. (2023). Türkiye’de 2015 ile 2022 yılları arasında meydana gelen orman yangınlarının coğrafi bilgi sistemleri ile zamansal ve mekânsal analizi. Journal of Geodesy and Geoinformation, 10(2), 136–150. https://doi.org/10.9733/JGG.2023R0010.T
  • EFFIS Fuel Map. (2023). EFFIS Data and Services. https://effis.jrc.ec.europa.eu/applications/data-and-services EFFIS. (2023). European Forest Fire Information System. http://effis.jrc.ec.europa.eu/
  • Filipponi, F. (2019). Exploitation of Sentinel-2 time series to map burned areas at the national level: A case study on the 2017 Italy wildfires. Remote Sensing, 11(6), 622. https://doi.org/10.3390/rs11060622
  • Fornacca, D., Ren, G., & Xiao, W. (2017). Performance of three MODIS Fire products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a mountainous area of Northwest Yunnan, China, characterized by frequent small fires. Remote Sensing, 9(11), 1131. https://doi.org/10.3390/rs9111131
  • Friedl, M., & Sulla-Menashe, D. (2022). MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MCD12Q1.061
  • Gholamrezaie, H., Hasanlou, M., Amani, M., & Mirmazloumi, S. M. (2022). Automatic Mapping of Burned Areas Using Landsat 8 Time-Series Images in Google Earth Engine: A Case Study from Iran. Remote Sensing, 14(24), 6376. https://doi.org/10.3390/rs14246376
  • Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., & Justice, C. O. (2018). The Collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment, 217, 72–85. https://doi.org/10.1016/j.rse.2018.08.005
  • Giglio, L., Descloitres, J., Justice, C. O., & Kaufman, Y. J. (2003). An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment, 87(2–3), 273–282. https://doi.org/10.1016/s0034-4257(03)00184-6
  • Hall, J. V., Argueta, F., & Giglio, L. (2021). Validation of MCD64A1 and FireCCI51 cropland burned area mapping in Ukraine. International Journal of Applied Earth Observation and Geoinformation, 102, 102443. https://doi.org/10.1016/j.jag.2021.102443
  • Humber, M. L., Boschetti, L., Giglio, L., & Justice, C. O. (2019). Spatial and temporal intercomparison of four global burned area products. International Journal of Digital Earth, 12(4), 460–484. https://doi.org/10.1080/17538947.2018.1433727
  • Kalivas, D., Petropoulos, G. P., Athanasiou, I., & Kollias, V. J. (2013). An intercomparison of burnt area estimates derived from key operational products: the Greek wildland fires of 2005–2007. Nonlinear Processes in Geophysics, 20(3), 397-409. https://doi.org/10.5194/npg-20-397-2013
  • Katagis, T., & Gitas, I. Z. (2022). Assessing the Accuracy of MODIS MCD64A1 C6 and FireCCI51 Burned Area Products in Mediterranean Ecosystems. Remote Sensing, 14(3), 602. https://doi.org/10.3390/rs14030602
  • Kaufman, Y. J., Tanré, D., & Boucher, O. (2002). A satellite view of aerosols in the climate system. Nature, 419(6903), 215–223. https://doi.org/10.1038/nature01091
  • Kavzoğlu, T., Çölkesen, İ., Tonbul H., & Öztürk M. Y. (2021). Uzaktan Algılama Teknolojileri ile Orman Yangınlarının Zamansal Analizi: 2021 Yılı Akdeniz ve Ege Yangınları. In T. Kavzoğlu (Ed.) Orman yangınları sebepleri, etkileri, izlenmesi, alınması gereken önlemler ve rehabilitasyon faaliyetleri (pp. 219–251). Türkiye Bilimler Akademisi.
  • Liu, Y., Stanturf, J., & Goodrick, S. (2010). Trends in global wildfire potential in a changing climate. Forest Ecology and Management, 259(4), 685–697. https://doi.org/10.1016/j.foreco.2009.09.002
  • Lizundia-Loiola, J., Otón, G., Ramo, R., & Chuvieco, E. (2020). A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data. Remote Sensing of Environment, 236, 111493. https://doi.org/10.1016/j.rse.2019.111493
  • LP DAAC, (2023). MODIS MCD64A1: MODIS/Terra+Aqua Burned Area Monthly L3 Global 500 m SIN Grid. USGS. https://lpdaac.usgs.gov/products/mcd64a1v006/
  • Moreno, M. V., Conedera, M., Chuvieco, E., & Pezzatti, G. B. (2014). Fire regime changes and major driving forces in Spain from 1968 to 2010. Environmental Science & Policy, 37, 11–22. https://doi.org/10.1016/j.envsci.2013.08.005
  • Moreno, M. V., Laurent, P., & Mouillot, F. (2021). Global intercomparison of functional pyrodiversity from two satellite sensors. International Journal of Remote Sensing, 42(24), 9523–9541. https://doi.org/10.1080/01431161.2021.1999529
  • Mouillot, F., Schultz, M. G., Yue, C., Cadule, P., Tansey, K., Ciais, P., & Chuvieco, E. (2014). Ten years of global burned area products from spaceborne remote sensing—A review: Analysis of user needs and recommendations for future developments. International Journal of Applied Earth Observation and Geoinformation, 26, 64–79. https://doi.org/10.1016/j.jag.2013.05.014
  • Orman Genel Müdürlüğü. (2020). Türkiye Orman Varlığı 2020. https://www.ogm.gov.tr/tr/ormanlarimiz-sitesi/TurkiyeOrmanVarligi/Yayinlar/2020%20T%C3%BCrkiye%20Orman%20Varl%C4%B1%C4%9F%C4%B1.pdf
  • Petropoulos, G. P., Knorr, W., Scholze, M., Boschetti, L., & Karantounias, G. (2010). Combining ASTER multispectral imagery analysis and support vector machines for rapid and cost-effective post-fire assessment: A case study from the greek wildland fires of 2007. Natural Hazards and Earth System Sciences, 10(2), 305-317. https://doi.org/10.5194/nhess-10-305-2010
  • Rasul, A., Ibrahim, G. R. F., Hameed, H. M., & Tansey, K. (2020). A trend of increasing burned areas in Iraq from 2001 to 2019. Environment, Development and Sustainability, 23(4), 5739–5755. https://doi.org/10.1007/s10668-020-00842-7
  • Roy, D. P. (1999). Multi-temporal active-fire based burn scar detection algorithm. International Journal of Remote Sensing, 20(5), 1031–1038. https://doi.org/10.1080/014311699213073
  • San-Miguel-Ayanz, J., Schulte, E., Schmuck, G., Camia, A., Strobl, P., Liberta, G., … Amatulli, G. (2012). Comprehensive monitoring of wildfires in Europe: The European forest fire information system (EFFIS). In J. Tiefenbacher (Ed.), Approaches to managing disaster-Assessing hazards, emergencies and disaster impacts (pp. 87-108). IntechOpen.
  • Stephens, S. L., Agee, J. K., Fulé, P. Z., North, M. P., Romme, W. H., Swetnam, T. W., & Turner, M. G. (2013). Managing Forests and Fire in Changing Climates. Science, 342(6154), 41–42. https://doi.org/10.1126/science.1240294
  • Sulla-Menashe, D., & Friedl, M. A., (2018, May 14). User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product. USGS. https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf
  • Tonbul, H., Colkesen, I., & Kavzoglu, T. (2019, 7–9 Kasım). Forest fire and burn severity analysis in Cefalu region of Italy using Sentinel-2 imagery [Conference presentation]. International Symposium on Applied Geoinformatics (ISAG-2019), Istanbul, Turkey.
  • Tonbul, H., Colkesen, I., & Kavzoglu, T. (2022). Pixel- and Object-Based ensemble learning for forest burn severity using USGS FIREMON and Mediterranean condition dNBRs in Aegean ecosystem (Turkey). Advances in Space Research, 69(10), 3609–3632. https://doi.org/10.1016/j.asr.2022.02.051
  • Turco, M., Herrera, S., Tourigny, E., Chuvieco, E., & Provenzale, A. (2019). A comparison of remotely-sensed and inventory datasets for burned area in Mediterranean Europe. International Journal of Applied Earth Observation and Geoinformation, 82, 101887. https://doi.org/10.1016/j.jag.2019.05.020
  • Vetrita, Y., Cochrane, M. A., Suwarsono, Priyatna, M., Sukowati, K. A. D., & Khomarudin, M. R. (2021). Evaluating accuracy of four MODIS-derived burned area products for tropical peatland and non-peatland fires. Environmental Research Letters, 16(3), 035015. https://doi.org/10.1088/1748-9326/abd3d1
  • Yıldız, C., Cömert, R., Tanyaş, H., Yılmaz, A., Akbaş, A., Akay, S. S., … Görüm, T. (2023). The effect of post-wildfire management practices on vegetation recovery: Insights from the Sapadere fire, Antalya, Türkiye. Frontiers in Earth Science, 11, 1174155. https://doi.org/10.3389/feart.2023.1174155
  • Zhang, S., Zhao, H., Wu, Z., & Tan, L. (2022). Comparing the ability of burned area products to detect crop residue burning in China. Remote Sensing, 14(3), 693. https://doi.org/10.3390/rs14030693
  • Zhou, L., Wang, Y., Chi, Y., Wang, S., & Wang, Q. (2019). Contrasting Post-Fire Dynamics between Africa and South America based on MODIS Observations. Remote Sensing, 11(9), 1074. https://doi.org/10.3390/rs11091074
Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Fotogrametri ve Uzaktan Algılama
Bölüm Araştırma Makaleleri
Yazarlar

Hasan Tonbul 0000-0003-4817-6542

Erken Görünüm Tarihi 24 Mart 2024
Yayımlanma Tarihi 28 Mart 2024
Gönderilme Tarihi 26 Aralık 2023
Kabul Tarihi 9 Mart 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Tonbul, H. (2024). Google Earth Engine ile Türkiye’de Yanmış Alanların MODIS ve FireCCI51 Küresel Yanmış Alan Uydu Gözlem Verileriyle Karşılaştırmalı Değerlendirilmesi. Türk Uzaktan Algılama Ve CBS Dergisi, 5(1), 69-82. https://doi.org/10.48123/rsgis.1410382

Creative Commons License
Turkish Journal of Remote Sensing and GIS (Türk Uzaktan Algılama ve CBS Dergisi), Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanlanmıştır.