Intensity evaluation of fire and restoration process of the forest using remote sensing techniques (Case Study: North Ukraine)
Subject Areas : Environment
Moslem Dehnavi Eelagh
1
,
Ali Taheri
2
1 - PhD student of Geospatial Information Systems, School of Surveying Engineering and Geospatial Information, College of Engineering, University of Tehran, Tehran, Iran
2 - Masters student of Geospatial Information Systems, School of Surveying Engineering and Geospatial Information College of Engineering, University of Tehran, Iran
Keywords:
Abstract :
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.
