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

Year 2023, Volume: 4 Issue: 2, 262 - 275, 28.09.2023
https://doi.org/10.48123/rsgis.1264208

Abstract

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.

References

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

Year 2023, Volume: 4 Issue: 2, 262 - 275, 28.09.2023
https://doi.org/10.48123/rsgis.1264208

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.

References

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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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There are 55 citations in total.

Details

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

Sinan Demir 0000-0002-1119-1186

Early Pub Date September 26, 2023
Publication Date September 28, 2023
Submission Date March 13, 2023
Acceptance Date June 7, 2023
Published in Issue Year 2023 Volume: 4 Issue: 2

Cite

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