Monitoring The Regeneration Process of Areas Destroyed by Forest Fires Aided by Google Earth Engine
Öz
Abstract
Aim of study: Our world is exposed to forest fires and threatens both natural and human environments. Remote sensing is one of the effective techniques to monitor forest fires. However, accessing and processing data on the field is challenging for researchers as it is costly and time-consuming.
Area of study: In this study, the Mersin-Gülnar fire that happened in 2008 in the Mersin region was investigated.
Materials and methods: Starting from 2000, data from the MODIS satellite images were used to monitor the forest's regeneration process along the forest fire's complete timeline. For this, analyzes were made over 471 Normalized Difference Vegetation Index (NDVI) MODIS satellite data from 2000 until 2020. The analyses were made in Google Earth Engine.
Main results: According to the data processed on the Google Earth Engine platform, the vegetation cover was damaged after the fire. As a result of the examined 471 MODIS images, it was observed that the recovery process of the study area after a forest fire takes an average of 10 years.
Highlights: Remote sensing methodologies and satellite datasets provide powerful functionality for assessing the damage caused by forest fires. This study is an example that the recovery period of forest fires is long, and it brings many difficulties together with other natural events.
Anahtar Kelimeler
Google Earth Engine, Remote Sensing, Forest Fire, MODIS, NDVI
Kaynakça
- Avcı, M. & Boz, K. (2017). Mersin-Gülnar ormanlarında yangın sorunu, yangınların dağılımı ve büyük yangınların değerlendirilmesi. Turkish Journal Of Forestry, 18(2), 160-170.
- Canty, M. J., Nielsen, A. A., Conradsen, K. & Skriver, H. (2020). Statistical Analysis of Changes in Sentinel-1 Time Series on the Google Earth Engine. Remote Sensing, 12(1), 46.
- Clemente, R. H., Cerrillo, R. M. N. & Gitas, I. Z. (2009). Monitoring post-fire regeneration in Mediterranean ecosystems by employing multitemporal satellite imagery. International Journal of Wildland Fire, 18(6), 648-658.
- Demir, N. (2020). NDVI Analysis of Australian Bushfires with Cloud Computing. Türk Uzaktan Algılama ve CBS Dergisi, 1(2), 78-84.
- Escuin, S., Navarro-Cerrillo, R. M. & Fernandez, P. (2006). Assessment of post-fire vegetation cover using spectral mixture analysis. Application and comparison of different endmember characterization methods. Investigación Agraria: Sistemas y Recursos Forestales, 15, 107-119.
- Henry, M. & Hope, A. (1998). Monitoring post-burn recovery of chaparral vegetation in southern California using multi-temporal satellite data. International journal of remote sensing, 19(16), 3097-3107.
- Huang, H., Chen, Y., Clinton, N., Wang, J., Wang, X., Liu, C., . . .& Zheng, Y. (2017). Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sensing of Environment, 202, 166-176.
- Ireland, G. & Petropoulos, G. P. (2015). Exploring the relationships between post-fire vegetation regeneration dynamics, topography and burn severity: A case study from the Montane Cordillera Ecozones of Western Canada. Applied Geography, 56, 232-248.
- Kaplan, G. (2020). Broad-Leaved and Coniferous Forest Classification in Google Earth Engine Using Sentinel Imagery. Paper presented at the Presented at the 1st International Electronic Conference on Forests.
- Li, Z., Fraser, R., Jin, J., Abuelgasim, A., Csiszar, I., Gong, P., . . .& Hao, W. (2003). Evaluation of algorithms for fire detection and mapping across North America from satellite. Journal of Geophysical Research: Atmospheres, 108(D2).