Intensity evaluation of fire and restoration process of the forest using remote sensing techniques (Case Study: North Ukraine)

Journal of Nature and Spatial Sciences (JONASS)

دوره 3، شماره 2 - شماره پیاپی 6، دی 2023، صفحه 40-57   

نوع مقاله: Case Study

شناسه دیجیتال (DOI): 10.30495/jonass.2023.1988433.1073

نویسندگان

Moslem Dehnavi Eelagh^{* 1}؛ Ali Taheri²

¹PhD student of Geospatial Information Systems, School of Surveying Engineering and Geospatial Information, College of Engineering, University of Tehran, Tehran, Iran

²Masters student of Geospatial Information Systems, School of Surveying Engineering and Geospatial Information College of Engineering, University of Tehran, Iran

چکیده

Background and objective: In recent years, we have witnessed the growth of forest fires due to severe climate changes and increased human activities. These fires impose many destructive effects on the environment and human health. Therefore, it is necessary to identify and measure the intensity of forest fires and plan for the revitalization of vegetation.
Materials and methods: This study aims to investigate the intensity of the fire in the forest areas of northern Ukraine using Sentinel 2 satellite images and using the indicators of different normalized burn ratios (dNBR), relatively different normalized burn ratios (RdNBR), and relativized burn ratio ( RBR) in the Google Earth Engine (GEE) cloud platform and comparing the results of the extent of the fire area extracted from the indicators with the data available by the European Forest Fire Information System (EFFIS). Also, the Normalized Difference Vegetation Index (NDVI) was used to investigate the process of forest cover restoration.
Results and conclusion: The results showed that the RBR and RdNBR indices in study areas A and B have been able to estimate the fire extent with 1.43% and 5.96% differences compared to EFFIS data. Also, the results of the NDVI index showed that after two years of the fire, in study areas A and B, 76.06% and 58.86% of the damaged forest cover improved, respectively.

کلیدواژه‌ها

dNBR؛ RdNBR؛ RBR؛ NDVI؛ GEE

مراجع

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. https://doi.org/10.3390/f13020347  

Alkhatib, A. A. (2014). A review on forest fire detection techniques. International Journal of Distributed Sensor Networks, 10(3), 597368.  https://doi.org/10.1155/2014/597368

Amroussia, M., Viedma, O., Achour, H., & Abbes, C. (2023). Predicting Spatially Explicit Composite Burn Index (CBI) from Different Spectral Indices Derived from Sentinel 2A: A Case of Study in Tunisia. Remote Sensing, 15(2), 335. https://doi.org/10.3390/rs15020335

Bar, S., Parida, B. R., & Pandey, A. C. (2020). Landsat-8 and Sentinel-2 based Forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya. Remote Sensing Applications: Society and Environment, 18, 100324. https://doi.org/10.1016/j.rsase.2020.100324

Boucher, J., Beaudoin, A., Hébert, C., Guindon, L., & Bauce, É. (2016). Assessing the potential of the differenced Normalized Burn Ratio (dNBR) for estimating burn severity in eastern Canadian boreal forests. International Journal of Wildland Fire, 26(1), 32-45. https://doi.org/10.1071/WF15122

Cai, L., & Wang, M. (2022). Is the RdNBR a better estimator of wildfire burn severity than the dNBR? A discussion and case study in southeast China. Geocarto International, 37(3), 758-772. https://doi.org/10.1080/10106049.2020.1737973

Chambel, M. R., Climent, J., Pichot, C., & Ducci, F. (2013). Mediterranean pines (Pinus halepensis Mill. and brutia Ten.). Forest tree breeding in Europe: current state-of-the-art and perspectives, 229-265. https://doi.org/10.1007/978-94-007-6146-9_5

Chowdhury, E. H., & Hassan, Q. K. (2015). Operational perspective of remote sensing-based forest fire danger forecasting systems. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 224-236. https://doi.org/10.1016/j.isprsjprs.2014.03.011

Delcourt, C. J., Combee, A., Izbicki, B., Mack, M. C., Maximov, T., Petrov, R., . . . van Wees, D. (2021). Evaluating the differenced normalized burn ratio for assessing fire severity using sentinel-2 imagery in northeast siberian larch forests. Remote Sensing, 13(12), 2311. https://doi.org/10.3390/rs13122311

Dindaroglu, T., Babur, E., Yakupoglu, T., Rodrigo-Comino, J., & Cerda, A. (2021). Evaluation of geomorphometric characteristics and soil properties after a wildfire using Sentinel-2 MSI imagery for future fire-safe forest. Fire safety journal, 122, 103318. https://doi.org/10.1016/j.firesaf.2021.103318

Fassnacht, F. E., Schmidt-Riese, E., Kattenborn, T., & Hernández, J. (2021). Explaining Sentinel 2-based dNBR and RdNBR variability with reference data from the bird’s eye (UAS) perspective. International Journal of Applied Earth Observation and Geoinformation, 95, 102262. https://doi.org/10.1016/j.jag.2020.102262 

Ghermandi, L., Lanorte, A., Oddi, F. J., & Lasaponara, R. (2019). Assessing Fire Severity in Semiarid Environments with the DNBR and RDNBR Indices.

Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., & Aryal, J. (2019). Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables. Fire, 2(3), 50. https://doi.org/10.3390/fire2030050

Goodwin, N. R., & Collett, L. J. (2014). Development of an automated method for mapping fire history captured in Landsat TM and ETM+ time series across Queensland, Australia. Remote Sensing of Environment, 148, 206-221. https://doi.org/10.1016/j.rse.2014.03.021

Guk, E., Bar-Massada, A., & Levin, N. (2023). Constructing a Comprehensive National Wildfire Database from Incomplete Sources: Israel as a Case Study. Fire, 6(4), 131. https://doi.org/10.3390/fire6040131

Gülci, S., Yüksel, K., GÜMÜŞ, S., & Michael, W. (2021). Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry. European Journal of Forest Engineering, 7(2), 57-66. https://doi.org/10.33904/ejfe.1031090

Huete, A., & Jackson, R. (1987). Suitability of spectral indices for evaluating vegetation characteristics on arid rangelands. Remote Sensing of Environment, 23(2), 213-IN218. https://doi.org/10.1016/0034-4257(87)90038-1

Jiang, Z., Huete, A. R., Didan, K., & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10), 3833-3845. https://doi.org/10.1016/j.rse.2008.06.006

Key, C. H., & Benson, N. C. (1999). Measuring and remote sensing of burn severity. Paper presented at the Proceedings joint fire science conference and workshop.

Li, Q. (2018). Forest bathing: How trees can help you find health and happiness: Penguin.

Maillard, O. (2023). Post-Fire Natural Regeneration Trends in Bolivia: 2001–2021. Fire, 6(1), 18. https://doi.org/10.3390/fire6010018

Mallinis, G., Mitsopoulos, I., & Chrysafi, I. (2018). Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece. GIScience & Remote Sensing, 55(1), 1-18. https://doi.org/10.1080/15481603.2017.1354803

Miller, J. D., Knapp, E. E., Key, C. H., Skinner, C. N., Isbell, C. J., Creasy, R. M., & Sherlock, J. W. (2009). Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sensing of Environment, 113(3), 645-656. https://doi.org/10.1016/j.rse.2008.11.009

Näsi, R., Honkavaara, E., Lyytikäinen-Saarenmaa, P., Blomqvist, M., Litkey, P., Hakala, T., . . . Holopainen, M. (2015). Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sensing, 7(11), 15467-15493. https://doi.org/10.3390/rs71115467

Pacheco, A. d. P., da Silva Junior, J. A., Ruiz-Armenteros, A. M., Henriques, R. F. F., & de Oliveira Santos, I. (2023). Analysis of Spectral Separability for Detecting Burned Areas Using Landsat-8 OLI/TIRS Images under Different Biomes in Brazil and Portugal. Forests, 14(4), 663. https://doi.org/10.3390/f14040663

Santos, S. M. B. d., Bento-Gonçalves, A., Franca-Rocha, W., & Baptista, G. (2020). Assessment of burned forest area severity and postfire regrowth in chapada diamantina national park (Bahia, Brazil) using dnbr and rdnbr spectral indices. Geosciences, 10(3), 106. https://doi.org/10.3390/geosciences10030106

Soverel, N. O., Perrakis, D. D., & Coops, N. C. (2010). Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada. Remote Sensing of Environment, 114(9), 1896-1909. https://doi.org/10.1016/j.rse.2010.03.013

Stankova, N. (2023). Post-Fire Recovery Monitoring Using Remote Sensing: A Review. Aerospace Research in Bulgaria, 35, 192-200. https://doi.org/10.3897/arb.v35.e19

Teodoro Carlón Allende, T., López Granados, E. M., & Mendoza, M. E. (2021). Identifying future climatic change patterns at basin level in Baja California, México. Journal of Nature and Spatial Sciences (JONASS), 1(2), 56-74.

Veraverbeke, S., Lhermitte, S., Verstraeten, W. W., & Goossens, R. (2010). The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: The case of the large 2007 Peloponnese wildfires in Greece. Remote Sensing of Environment, 114(11), 2548-2563. https://doi.org/10.1016/j.rse.2010.05.029

Widodo, J., Sulaiman, A., Awaluddin, A., Riyadi, A., Nasucha, M., Perissin, D., & Sri Sumantyo, J. T. (2019). Application of SAR interferometry using ALOS-2 PALSAR-2 data as precise method to identify degraded peatland areas related to forest fire. Geosciences, 9(11), 484. https://doi.org/10.3390/geosciences9110484

Williamson, G. J., Ellis, T. M., & Bowman, D. M. (2022). Double-Differenced dNBR: Combining MODIS and Landsat Imagery to Map Fine-Grained Fire MOSAICS in Lowland Eucalyptus Savanna in Kakadu National Park, Northern Australia. Fire, 5(5), 160. https://doi.org/10.3390/fire5050160

Wu, S., Wang, J., Yan, Z., Song, G., Chen, Y., Ma, Q., . . . Guo, Z. (2021). Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations. ISPRS Journal of Photogrammetry and Remote Sensing, 171, 36-48. https://doi.org/10.1016/j.isprsjprs.2020.10.017

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. https://doi.org/10.3390/ijgi10080511

Ye, J., Wang, N., Sun, M., Liu, Q., Ding, N., & Li, M. (2023). A New Method for the Rapid Determination of Fire Disturbance Events Using GEE and the VCT Algorithm—A Case Study in Southwestern and Northeastern China. Remote Sensing, 15(2), 413. https://doi.org/10.3390/rs15020413

Zarei, M., Tazeh, M., Moosavi, V., & Kalantari, S. (2021). Evaluating the changes in Gavkhuni Wetland using MODIS satellite images in 2000-2016. Journal of Nature and Spatial Sciences (JONASS), 1(1), 27-41.