Google Earth Engine Üzerinde Sentinel-2 Uydu Görüntüleri Kullanılarak Yanan Alanların Farklı Eşik Değerlerinde Belirlenmesi

Sinan Demir

doi:10.48123/rsgis.1264208

EN TR

Determination of Burned Areas at Different Threshold Values Using Sentinel-2 Satellite Images on Google Earth Engine

Abstract

It is important to detect burned areas and plan rehabilitation efforts, due to the threat of wildfires to ecosystems and biological diversity. Remote sensing technologies play a critical role in identifying changes in land cover and detecting burned areas. Therefore, it is important to use satellite imagery and Google Earth Engine (GEE) to detect burned areas and determine changes in land cover. In this study, a suitable code block was developed on the GEE platform to identify burned areas using high-resolution Sentinel-2 satellite imagery. For this purpose, a suitable code block has been developed to determine land cover changes and burnt areas by using Normalized Difference Vegetation Indices (NDVI) calculated from Sentinel-2A satellite images for the study area in September 2020 and September 2021, based on different threshold values generated from difference layers, and masking the global water surface data from these layers. As a result, a threshold value of 0.3 was identified in the high-resolution satellite image that could be used without mixing land cover and burned areas. The areal extent of the obtained threshold value was determined to be 93% (68254 hectares) when compared with Moderate Resolution Imaging Spectroradiometer (MODIS) burned area data. The developed code block using Sentinel-2 satellite images on the GEE platform can help monitor and track changes in burned areas.

Keywords

GEE , Sentinel-2 , Burned area , Threshold , Spatial analysis

Google Earth Engine Üzerinde Sentinel-2 Uydu Görüntüleri Kullanılarak Yanan Alanların Farklı Eşik Değerlerinde Belirlenmesi

Öz

Yangınların ekosistemleri ve biyolojik çeşitliliği tehdit etmesi nedeniyle, yanan alanların tespiti ve rehabilite çalışmalarının planlanması önemlidir. Uzaktan algılama teknolojileri, arazi örtüsü değişimi ve yanan alanların belirlenmesinde kritik bir rol oynamaktadır. Bu nedenle, uydu görüntüleri ve Google Earth Engine (GEE) kullanarak yanan alanlarının tespit edilmesi ve arazi örtüsündeki değişimin belirlenmesi önemlidir. Çalışmada GEE platformunda uygun kod bloğu geliştirilerek yanan alanların yüksek çözünürlüklü Sentinel-2 uydu görüntüsü ile belirlenmesi amaçlanmıştır. Bu amaçla, çalışma alanının 2020 Eylül ve 2021 Eylül aylarına ait Sentinel-2A uydu görüntülerinden hesaplanan Normalleştirilmiş Fark Bitki Örtüsü İndeksleri (NDVI) farklı eşik değerlerine göre (0.2, 0.3, 0.4, 0.5, 0.6) oluşturulan fark katmanlarından, küresel su yüzeyi verisi maskelenerek arazi örtüsü değişimi ve yanan alanların belirlenmesi için uygun kod bloğu geliştirilmiştir. Çalışma sonucunda, farklı eşik değerlerine sahip yüksek çözünürlüklü uydu görüntüsünde, 0.3 eşik değerinde arazi örtüsü ve yanan alan karışması olmadan kullanılabilecek eşik değeri belirlenmiştir. Elde edilen eşik değerinin alansal büyüklüğü, Moderate Resolution Imaging Spectroradiometer (MODIS) yanan alan verileri ile karşılaştırıldığında %93’ü (68254 hektar) olduğu belirlenmiştir. GEE platformunda Sentinel-2 uydu görüntülerinin geliştirilen kod bloğu kullanılarak, yanan alanlardaki değişikliklerin izlenmesine ve takip edilmesine yardımcı olabileceği önerilmektedir.

Anahtar Kelimeler

GEE , Sentinel-2 , Yanan alan , Eşik değeri , Konumsal analiz

Kaynakça

Abdikan, S., Bayik, C., Sekertekin, A., Bektas Balcik, F., Karimzadeh, S., Matsuoka, M., & Balik Sanli, F. (2022). Burned area detection using multi-sensor SAR, optical, and thermal data in Mediterranean pine forest. Forests, 13(2), 347. doi: 10.3390/f13020347.
Akay, A. E., Podolskaia, E., & Uçar, Z. (2021). Effects of Improving Forest Road Standards on Shortening the Arrival Time of Ground-based Firefighting Teams Accessing to the Forest Fires. European Journal of Forest Engineering, 7(1) , 32-38. doi: 10.33904/ejfe.952174.
Alkan Akıncı, H., & Akıncı, H. (2023). Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey. Earth Science Informatics, 16(1), 397-414.
Amos, C., Petropoulos, G. P., & Ferentinos, K. P. (2019). Determining the use of Sentinel-2A MSI for wildfire burning & severity detection. International Journal of Remote Sensing, 40(3), 905-930.
Arıkan, C., Tümer, İ. N., Aksoy, S., & Sertel, E. (2022, June). Determination of burned areas using Sentinel-2A imagery and machine learning classification algorithms. In 4th Intercontinental Geoinformation Days, 2022. Proceedings. (pp. 43-46).
Ayele, G. T., Seka, A. M., Taddese, H., Jemberrie, M. A., Ndehedehe, C. E., Demissie, S. S., & Melesse, A. M. (2022). Relationship of attributes of soil and topography with land cover change in the Rift Valley Basin of Ethiopia. Remote Sensing, 14(14), 3257. doi: 10.3390/rs14143257.
Bahşi, K., Ustaoğlu, B., Aksoy, S., & Sertel, E. (2023). Estimation of emissions from crop residue burning in Türkiye using remotely sensed data and the Google Earth Engine platform. Geocarto International, 38(1), 2157052. doi: 10.1080/10106049.2022.2157052.
Bo, W., Liu, J., Fan, X., Tjahjadi, T., Ye, Q., & Fu, L. (2022). BASNet: Burned Area Segmentation Network for Real-Time Detection of Damage Maps in Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 60, 1627913. doi: 10.1109/TGRS.2022.3197647.
Boschetti, L., Roy, D., Hoffmann, A. A., & Humber, M. (2009, November 10). MODIS Collection 5 Burned Area Product-MCD45. User’s Guide, Ver. 2, 1-2. Retrieved from https://www.fao.org/fileadmin/templates/gfims/docs/ MODIS_Burned_Area_User_Guide_2.0.pdf.
Brovelli, M. A., Sun, Y., & Yordanov, V. (2020). Monitoring forest change in the amazon using multi-temporal remote sensing data and machine learning classification on Google Earth Engine. ISPRS International Journal of Geo-Information, 9(10), 580. doi: 10.3390/ijgi9100580.

Chen, W., Moriya, K., Sakai, T., Koyama, L., & Cao, C. X. (2016). Mapping a burned forest area from Landsat TM data by multiple methods. Geomatics, Natural Hazards and Risk, 7(1), 384-402.
Chuvieco, E., Martin, M. P., & Palacios, A. (2002). Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. International Journal of Remote Sensing, 23(23), 5103-5110.
Chuvieco, E., Mouillot, F., Van der Werf, G. R., San Miguel, J., Tanase, M., Koutsias, N., ... & Giglio, L. (2019). Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment, 225, 45-64. doi: 10.1016/j.rse.2019.02.013.
Das, P., Behera, M. D., Barik, S. K., Mudi, S., Jagadish, B., Sarkar, S., ... & Chauhan, P. S. (2022). Shifting cultivation induced burn area dynamics using ensemble approach in Northeast India. Trees, Forests and People, 7, 100183. doi: 10.1016/j.tfp.2021.100183.
Demir, N. (2020). NDVI analysis of Australian Bushfires with cloud computing. Turkish Journal of Remote Sensing and GIS, 1(2), 78-84.
Dengiz, O., Dedeoğlu, M., & Kaya, N. S. (2022). Determination of the relationship between rice suitability classes and satellite ımages with different time series for Yeşil Küre Farm Lands. Yuzuncu Yıl University Journal of Agricultural Sciences, 32(3) , 507-526.
Dursun, İ., & Yazıcı, N. (2022). Köppen-Trewartha ve Thornthwaite yöntemlerine göre Isparta yöresi iklim tipinin belirlenmesi. Doğal Afetler ve Çevre Dergisi, 8(2), 264-279.
FAO. (2015, Şubat 23). Global Administrative Unit Layers (GAUL) level 1. Food and Agriculture Organization of the United Nations. Retrieved From http://www.fao.org/geonetwork/srv/en/metadata.show?id=12691.
GEE. (2021, Şubat 23). Google earth engine [computer software]. Retrieved From https://earthengine.google.com/.
Hosseini, A., Hashemzadeh, M., & Farajzadeh, N. (2022). UFS-Net: A unified flame and smoke detection method for early detection of fire in video surveillance applications using CNNs. Journal of Computational Science, 61, 101638. doi: /10.1016/j.jocs.2022.101638.
Hu, X., Ban, Y., & Nascetti, A. (2021). Uni-temporal multispectral imagery for burned area mapping with deep learning. Remote Sensing, 13(8), 1509. doi: /10.3390/rs13081509.
JRC. (2016, Şubat 23). Global surface water occurrence version 1.0 (1984-2015) european commission. Joint Research Centre (JRC). Retrieved from https://global-surface-water.appspot.com/download.
Kuzucuoğlu, C., Çiner, A., & Kazancı, N. (2019). The geomorphological regions of Turkey. In Kuzucuoğlu, C., Çiner, A., Kazancı, N. (Eds.), Landscapes and Landforms of Turkey (pp. 41-178), Springer, Cham.
Lacouture, D. L., Broadbent, E. N., & Crandall, R. M. (2020). Detecting vegetation recovery after fire in a fire-frequented habitat using normalized difference vegetation index (NDVI). Forests, 11(7), 749. doi: /10.3390/f11070749.
Lasaponara, R. (2006). Estimating spectral separability of satellite derived parameters for burned areas mapping in the Calabria region by using SPOT-Vegetation data. Ecological Modelling, 196(1-2), 265-270.
Lasaponara, R., Abate, N., Fattore, C., Aromando, A., Cardettini, G., & Di Fonzo, M. (2022). On the use of Sentinel-2 NDVI time series and Google Earth Engine to detect land-use/land-cover changes in fire-affected areas. Remote Sensing, 14(19), 4723. doi: 10.3390/rs14194723.
Liu, L., Zhang, Q., Guo, Y., Chen, E., Li, Z., Li, Y., ... & Ri, A. (2023). Mapping the Distribution and Dynamics of Coniferous Forests in Large Areas from 1985 to 2020 Combining Deep Learning and Google Earth Engine. Remote Sensing, 15(5), 1235. doi: 10.3390/rs15051235.
Long, T., Zhang, Z., He, G., Jiao, W., Tang, C., Wu, B., ... & Yin, R. (2019). 30 m resolution global annual burned area mapping based on LANDSAT Images and Google Earth Engine. Remote Sensing, 11(5), 489. doi: 10.3390/rs11050489.
Mathewos, M., Lencha, S. M., & Tsegaye, M. (2022). Land use and land cover change assessment and future predictions in the Matenchose Watershed, Rift Valley Basin, using CA-Markov simulation. Land, 11(10), 1632. doi: /10.3390/land11101632.
MGM. (2023, Şubat 23). Türkiye İklim İstatistikleri Meteoroloji Genel Müdürlüğü. Retrieved From https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=ISPARTA.
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.
OGM. (2023, Mayıs 2). Resmi İstatistikler, Orman Genel Müdürlüğü. Retrieved From https://www.ogm.gov.tr/tr/e-kutuphane/resmi-istatistikler.
Özçelik, H. (2018). Flora of Dedegül Mountain and its effects to agricultural production of lakes region. Türk Bilimsel Derlemeler Dergisi, 11(2), 38-46.
Özgül, N. (1976). Toroslar'm bazı temel jeoloji özellikleri. Bulletin of the Geological Society of Turkey, 19, 65-78.
Payra, S., Sharma, A., & Verma, S. (2023). Application of remote sensing to study forest fires. In A.K. Singh & S. Tiwari (Eds.), Atmospheric Remote Sensing: Principles and Applications Earth Observation (pp. 239-260). Elsevier Science.
Pulvirenti, L., Squicciarino, G., Fiori, E., Fiorucci, P., Ferraris, L., Negro, D., ... & Puca, S. (2020). An automatic processing chain for near real-time mapping of burned forest areas using sentinel-2 data. Remote Sensing, 12(4), 674. doi: 10.3390/rs12040674.
Ranagalage, M., Morimoto, T., Simwanda, M., & Murayama, Y. (2021). Spatial analysis of urbanization patterns in four rapidly growing south Asian cities using Sentinel-2 Data. Remote Sensing, 13(8), 1531. doi: /10.3390/rs13081531.
Roca, M., Navarro, G., García-Sanabria, J., & Caballero, I. (2022). Monitoring sand spit variability using Sentinel-2 and Google Earth Engine in a Mediterranean Estuary. Remote Sensing, 14(10), 2345. doi: 10.3390/rs14102345.
Roteta, E., Bastarrika, A., Franquesa, M., & Chuvieco, E. (2021a). Landsat and Sentinel-2 Based Burned Area Mapping Tools in Google Earth Engine. Remote Sensing, 13(4), 816. doi: 10.3390/rs13040816.
Roteta, E., Bastarrika, A., Ibisate, A., & Chuvieco, E. (2021b). A preliminary global automatic burned-area algorithm at medium resolution in Google Earth Engine. Remote Sensing, 13(21), 4298. doi: 10.3390/rs13214298.
Sertel, E., Topaloğlu, R. H., Şallı, B., Yay Algan, I., & Aksu, G. A. (2018). Comparison of landscape metrics for three different level land cover/land use maps. ISPRS International Journal of Geo-Information, 7(10), 408. doi: /10.3390/ijgi7100408.
Sevinç, V. (2023). Mapping the forest fire risk zones using artificial intelligence with risk factors data. Environmental Science and Pollution Research, 30(2), 4721-4732.
Seydi, S. T., Akhoondzadeh, M., Amani, M., & Mahdavi, S. (2021). Wildfire damage assessment over Australia using sentinel-2 imagery and MODIS land cover product within the google earth engine cloud platform. Remote Sensing, 13(2), 220. doi: /10.3390/rs13020220.
Şener, E. & Davraz, A. (2021). Yağış tabanlı farklı indisler kullanılarak meteorolojik kuraklık analizi: Isparta örneği. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 12(1), 404-418.
Şenol, H., Alaboz, P., & Dengiz, O. (2020). Evaluation of the physico-chemical and nutrient elements status of soils formed on different parent materials using interpolation method. Anadolu Tarım Bilimleri Dergisi, 35(3), 505-516.
Topaloğlu, R. H., Aksu, G. A., Ghale, Y. A. G., & Sertel, E. (2022). High-resolution land use and land cover change analysis using GEOBIA and landscape metrics: A case of Istanbul, Turkey. Geocarto International, 37(25), 9071-9097.
Weslati, O., Bouaziz, S., & Sarbeji, M. M. (2023). Modelling and assessing the spatiotemporal changes to future land use change scenarios using remote sensing and CA-markov model in the mellegue catchment. Journal of the Indian Society of Remote Sensing, 51(1), 9-29.
Williams, A. P., Abatzoglou, J. T., Gershunov, A., Guzman‐Morales, J., Bishop, D. A., Balch, J. K., & Lettenmaier, D. P. (2019). Observed impacts of anthropogenic climate change on wildfire in California. Earth's Future, 7(8), 892-910.
Xu, W., Wooster, M. J., Polehampton, E., Yemelyanova, R., & Zhang, T. (2021). Sentinel-3 active fire detection and FRP product performance-Impact of scan angle and SLSTR middle infrared channel selection. Remote Sensing of Environment, 261, 112460. doi: /10.1016/j.rse.2021.112460.
Xu, L., Herold, M., Tsendbazar, N. E., Masiliūnas, D., Li, L., Lesiv, M., ... & Verbesselt, J. (2022). Time series analysis for global land cover change monitoring: A comparison across sensors. Remote Sensing of Environment, 271, 112905. doi: /10.1016/j.rse.2022.112905.
Xulu, S., Mbatha, N., & Peerbhay, K. (2021). Burned Area Mapping over the Southern Cape Forestry Region, South Africa Using Sentinel Data within GEE Cloud Platform. ISPRS International Journal of Geo-Information, 10(8), 511. doi: /10.3390/ijgi10080511.
Yılmaz, O. S., Acar, U., Sanli, F. B., Gulgen, F., & Ates, A. M. (2023). Mapping burn severity and monitoring CO content in Türkiye’s 2021 Wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform. Earth Science Informatics, 16(1), 221-240.
Zeybek, M., & Kalyoncu, H. (2016). The determination of water quality of Kargı Stream (Antalya, Turkey) in terms of physicochemical parameters. Ege Journal of Fisheries and Aquatic Sciences, 33(3), 223-231.
Zhang, H. K., Roy, D. P., Yan, L., Li, Z., Huang, H., Vermote, E., ... & Roger, J. C. (2018). Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences. Remote Sensing of Environment, 215, 482-494. doi: /10.1016/j.rse.2018.04.031.
Zhang, Q., Ge, L., Zhang, R., Metternicht, G. I., Liu, C., & Du, Z. (2021). Towards a deep-learning-based framework of Sentinel-2 imagery for automated active fire detection. Remote Sensing, 13(23), 4790. doi: /10.3390/rs13234790.

Ayrıntılar

Birincil Dil

Türkçe

Konular

Fotogrametri ve Uzaktan Algılama

Bölüm

Araştırma Makalesi

Yazarlar

Sinan Demir ^*
0000-0002-1119-1186
Türkiye

Erken Görünüm Tarihi

26 Eylül 2023

Yayımlanma Tarihi

28 Eylül 2023

Gönderilme Tarihi

13 Mart 2023

Kabul Tarihi

7 Haziran 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 4 Sayı: 2

DOI

https://doi.org/10.48123/rsgis.1264208

IZ

https://izlik.org/JA37TX49ZR

APA

Demir, S. (2023). Google Earth Engine Üzerinde Sentinel-2 Uydu Görüntüleri Kullanılarak Yanan Alanların Farklı Eşik Değerlerinde Belirlenmesi. Türk Uzaktan Algılama ve CBS Dergisi, 4(2), 262-275. https://doi.org/10.48123/rsgis.1264208

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

Determination of Burned Areas at Different Threshold Values Using Sentinel-2 Satellite Images on Google Earth Engine

Abstract

Keywords

Google Earth Engine Üzerinde Sentinel-2 Uydu Görüntüleri Kullanılarak Yanan Alanların Farklı Eşik Değerlerinde Belirlenmesi

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Erken Görünüm Tarihi

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

Assessment of pre- and post-fire erosion using the RUSLE equation in a watershed affected by the forest fire on Google Earth Engine: the study of Manavgat River Basin

Digital Mapping Burn Severity in Agricultural and Forestry Land over a Half-Decade Using Sentinel Satellite Images on the Google Earth Engine Platform: A Case Study in Isparta Province

Prediction of Canopy Cover for Agricultural Land Classification in Land Parcel Identification System (LPIS) Data Using Planet-Scope Multispectral Images: A Case Study of Gelendost District

Impact of forest fire severity on soil physical and chemical properties in pine and scrub forests in high Andean zones of Peru

Post-fire erosion dynamics in the Dim River Basin: a remote sensing and Google Earth Engine approach

Integrating remote sensing and machine learning for methane emission prediction in Konya