NDVI Analysis of Australian Bushfires with Cloud Computing
Öz
Our planet is constantly exposed to massive forest fires, which threaten the natural ecosystem. Determining damages of forest fires has been a subject of extensive research for many years. Remote sensing is one of the effective technologies used for monitoring forest fires. However, accessing and processing data are both costly and time consuming. Therefore, the use of cloud technologies for this purpose is beneficial for rapid response. Australia experienced a series of wildfires from June 2019 to February 2020. These fires are considered as one of the biggest disasters of our age. In our study, Landsat data was used to track the trend of fires across the entire timeline during forest fire events. The Google cloud platform Google Earth Engine was used to obtain the results. Landsat 8 images were processed for each month from June 2019 to March 2020. Landsat 5 images were used to eliminate the clouds. Thus, from June-2019 to March-2020, all images were processed and the damaged areas were determined by NDVI and vegetation analysis. The forest cover reference data of previous years were used for the NDVI threshold value in the study.
Anahtar Kelimeler
Kaynakça
- Akther, M. S., & Hassan, Q. K. (2011). Remote sensing-based assessment of fire danger conditions over boreal forest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(4), 992-999.
- Allison, R. S., Johnston, J. M., Craig, G., & Jennings, S. (2016). Airborne optical and thermal remote sensing for wildfire detection and monitoring. Sensors, 18(8), 1310, doi: 10.3390/s16081310.
- Amalo, L. F., Hidayat, R., & Sulma, S. (2018). Analysis of agricultural drought in east java using vegetation health index. Agrivita, 40(1), 63-73.
- Ambika, A. K., & Mishra, V. (2019). Observational Evidence of Irrigation Influence on Vegetation Health and Land Surface Temperature in India. Geophysical Research Letters, 46, 13441-13451.
- BBC. (2020, January 21). Australia fires: A visual guide to the bushfire crisis. Retrieved from https://www.bbc.com/news/world-australia-50951043.
- Caccamo, G., Bradstock, R., Collins, L., Penman, T., & Watson, P. (2015). Using MODIS data to analyse post-fire vegetation recovery in Australian eucalypt forests. Journal of Spatial Science, 60(2), 341–352.
- Chen, X., Vogelmann, J. E., Rollins, M., Ohlen, D., Key, C. H., Yang, L., … Shi, H. (2011). Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest. International Journal of Remote Sensing, 32(23), 7905–7927.
- Chuvieco, E., & Congalton, R. G. (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote sensing of Environment, 29(2), 147-159.
- Daldegan, G. A., Roberts, D. A., & de Figueiredo Ribeiro, F. (2019). Spectral mixture analysis in Google Earth Engine to model and delineate fire scars over a large extent and a long time-series in a rainforest-savanna transition zone. Remote Sensing of Environment, 232, 111340, doi: 10.1016/j.rse.2019.111340.
- Escuin, S., Navarro, R., & Fernández, P. (2008). Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images. International Journal of Remote Sensing, 29(4), 1053–1073.
