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Application of Remote Sensing and GIS techniques for detecting burned areas and severity. A case study of the National Park "Dajti Mountain", Albania.

Yıl 2022, Cilt: 3 Sayı: 2, 64 - 74, 30.12.2022
https://doi.org/10.48053/turkgeo.1150655

Öz

Assessment of forest areas affected by wildfire is crucial for designing appropriate management strategies to support post-wildfire restoration. This study integrates Remote Sensing and GIS data to map burned areas and severity, and regeneration of vegetation in a Mediterranean forest type ecosystem (National Park "Dajti Mountain", NPDM), in Albania. Landsat 8 satellite imagery was employed to calculate various spectral indices such as the Normal Burn Ratio Index (NBR), NBR2, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). Burn severity levels were defined by using the dNBR thresholds developed by Key and Benson (2006). The accuracy of burn severity map produced was evaluated by relating field-based Composite Burn Index (CBI) and satellite-derived metrics (dNBR) from Landsat-8. By means of dNBR and dNBR2 we detected and mapped several burned forest areas within the NPDM, at the sites of Shkallë, Qafëmolle, Ibë, Tujan, Derje, Selbë, Surrel and Dajt, which were affected by wildfire during the year 2017. The dNBR produced the best results for burned areas mapping and burn severity assessment (91.7%) over the dNBR2 (89.8%). The dNBR and dNBR2 index maps showed that a total of 103.59 and 105.72 hectares of forests was affected by wildfire. Areas with different levels of burn severity were detected: 17.29 and 23.80% unburned, 43.36 and 45% low, 15.11 and 12.13% moderate, 24.93 and 21.2% high. Overall, the dNBR2 index produced lower percentages of wildfire-affected areas at high and moderate rates compared to the dNBR index while for unburned areas the dNBR2 index resulted in higher percentages. Vegetation recovery during the subsequent growing season was generally good as revealed by the high dNDVI and dEVI values, indicating the reactivation of photosynthetic activity. This information is useful for forest managers/specialists to design relevant strategies for the proper rehabilitation/management of burned forest areas in the future.

Destekleyen Kurum

Faculty of Forestry Sciences, Tirana, Albania

Kaynakça

  • Atak, B.K., & Tonyaloğlu, E.E. (2020). Evaluating spectral indices for estimating burned areas in the case of Izmir/Turkey. Eurasian Journal of Forest Science, 8(1), 49-59.
  • Brown, A.R., Petropoulos, G.P., & Ferentinos, K.P. (2018). Appraisal of the Sentinel-1 & 2 use in a large-scale wildfire assessment: A case study from Portugal's fires of 2017. Applied geography, 100, 78-89.
  • Bruci, E. (2007). Climate change projection for South Eastern Europe. Tirana: HMI, Tirana Polytechnic University.
  • 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.
  • Firewise, (1998). Wildfire News and Notes. Wildland Fire Management Terminology. 12(1), 10.
  • Forestry Sector Study Report, (2021). Sustainable Development of rural areas in Albania – Sector Analyses – 2017.2192.7- 001.00.
  • Hu, X., Ban, Y., & Nascetti, A. (2021). Uni-temporal multispectral imagery for burned area mapping with deep learning. Remote Sensing, 13(8), 1509.
  • Hudak, A.T., Morgan, P., Bobbitt, M.J., Smith, A., Lewis, S. A., Lentile, L.B., Robichaud, P.R., Clark, J.T., & McKinley, R.A. (2007). The relationship of multispectral satellite imagery to immediate fire effects. Fire Ecology, 3(1), 64-90.
  • Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L.G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment, 83(1-2), 195-213.
  • Key, C.H., & Benson, N.C. (2006). Landscape assessment: Sampling and analysis methods. In: FIREMON: Fire Effects Monitoring and Inventory System; Lutes, D.C., Keane, R.E., Caratti, J.F., Key, C.H., Benson, N.C., Sutherland, S., Gangi, L.J., Eds.; Gen. Tech. Rep. RMRS-GTR-164; LA1–LA55 ISBN USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD; U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station: Fort Collins, CO, USA, 2006.
  • Lentile, L.B., Holden, Z.A., Smith, A.M., Falkowski, M. J., Hudak, A.T., Morgan, P., ... & Benson, N. C. (2006). Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire, 15(3), 319-345.
  • 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.
  • Morgan, P., Keane, R., Dillon, G., Jain, T., Hudak, A., Karau, E., Sikkink, P., Holden, Z., & Strand, E. (2014). Challenges of assessing fire and burn severity using field measures. Remote Sensing and Modelling. International Journal of Wildland Fire, 23(8), 1045–1060.
  • Nasery, S., & Kalkan, K. (2020). Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey. Turkish Journal of Geosciences, 1(2), 72-77.
  • Pasho, E., & Alla, A.Q. (2015). Climate impacts on radial growth and vegetation activity of two co-existing Mediterranean pine species. Canadian Journal of Forest Research, 45, 1748–1756.
  • Sacramento, I.F., Machado Michel, R.F., & Siqueira, R.G. (2020). Bitemporal analysis of burned areas in the Atlantic Forest. Sociedade & Natureza, 32, 540-552.
  • San-Miguel-Ayanz, J., Durrant, T., Boca, R., Libertà, G., Branco, A., de Rigo, D., Ferrari, D., Maianti, P., Vivancos, T. A., … & Costa, H. (2018). Forest fires in Europe, Middle East and North Africa 2017; EUR 29318 EN; JRC European Union: Luxembourg, 2018.
  • Saulino, L., Rita, A., Migliozzi, A., Maffei, C., Allevato, E., Garonna, A.P., & Saracino, A. (2020). Detecting burn severity across mediterranean forest types by coupling medium-spatial resolution satellite imagery and field data. Remote Sensing, 12(4), 741.
  • Sirin, A., & Medvedeva, M. (2022). Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires. Remote Sensing, 14(1), 194.
  • Tucker, C. J., Pinzon, J.E., Brown, M.E., Slayback, D. A., Pak, E.W., Mahoney, R. Vermote, E.F., & El Saleous, N. (2005). An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International journal of remote sensing, 26(20), 4485-4498.
  • USGS. (2019). Landsat Surface Reflectance-Derived Spectral Indices. Retrieved 10 June 2022 from https://www.usgs.gov/ land resources/nli/landsat/landsat normalized burn-ratio.
  • Url-1: https://earthexplorer.usgs.gov/ (last accessed 21 June 2022)
  • Warner, T.A., Skowronski, N.S., & Gallagher, R.M. (2017). High spatial resolution burn severity mapping of the New Jersey Pine Barrens with WorldView-3 near-infrared and shortwave infrared imagery. International Journal of Remote Sensing, 38(2), 598-616
Yıl 2022, Cilt: 3 Sayı: 2, 64 - 74, 30.12.2022
https://doi.org/10.48053/turkgeo.1150655

Öz

Kaynakça

  • Atak, B.K., & Tonyaloğlu, E.E. (2020). Evaluating spectral indices for estimating burned areas in the case of Izmir/Turkey. Eurasian Journal of Forest Science, 8(1), 49-59.
  • Brown, A.R., Petropoulos, G.P., & Ferentinos, K.P. (2018). Appraisal of the Sentinel-1 & 2 use in a large-scale wildfire assessment: A case study from Portugal's fires of 2017. Applied geography, 100, 78-89.
  • Bruci, E. (2007). Climate change projection for South Eastern Europe. Tirana: HMI, Tirana Polytechnic University.
  • 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.
  • Firewise, (1998). Wildfire News and Notes. Wildland Fire Management Terminology. 12(1), 10.
  • Forestry Sector Study Report, (2021). Sustainable Development of rural areas in Albania – Sector Analyses – 2017.2192.7- 001.00.
  • Hu, X., Ban, Y., & Nascetti, A. (2021). Uni-temporal multispectral imagery for burned area mapping with deep learning. Remote Sensing, 13(8), 1509.
  • Hudak, A.T., Morgan, P., Bobbitt, M.J., Smith, A., Lewis, S. A., Lentile, L.B., Robichaud, P.R., Clark, J.T., & McKinley, R.A. (2007). The relationship of multispectral satellite imagery to immediate fire effects. Fire Ecology, 3(1), 64-90.
  • Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L.G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment, 83(1-2), 195-213.
  • Key, C.H., & Benson, N.C. (2006). Landscape assessment: Sampling and analysis methods. In: FIREMON: Fire Effects Monitoring and Inventory System; Lutes, D.C., Keane, R.E., Caratti, J.F., Key, C.H., Benson, N.C., Sutherland, S., Gangi, L.J., Eds.; Gen. Tech. Rep. RMRS-GTR-164; LA1–LA55 ISBN USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD; U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station: Fort Collins, CO, USA, 2006.
  • Lentile, L.B., Holden, Z.A., Smith, A.M., Falkowski, M. J., Hudak, A.T., Morgan, P., ... & Benson, N. C. (2006). Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire, 15(3), 319-345.
  • 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.
  • Morgan, P., Keane, R., Dillon, G., Jain, T., Hudak, A., Karau, E., Sikkink, P., Holden, Z., & Strand, E. (2014). Challenges of assessing fire and burn severity using field measures. Remote Sensing and Modelling. International Journal of Wildland Fire, 23(8), 1045–1060.
  • Nasery, S., & Kalkan, K. (2020). Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey. Turkish Journal of Geosciences, 1(2), 72-77.
  • Pasho, E., & Alla, A.Q. (2015). Climate impacts on radial growth and vegetation activity of two co-existing Mediterranean pine species. Canadian Journal of Forest Research, 45, 1748–1756.
  • Sacramento, I.F., Machado Michel, R.F., & Siqueira, R.G. (2020). Bitemporal analysis of burned areas in the Atlantic Forest. Sociedade & Natureza, 32, 540-552.
  • San-Miguel-Ayanz, J., Durrant, T., Boca, R., Libertà, G., Branco, A., de Rigo, D., Ferrari, D., Maianti, P., Vivancos, T. A., … & Costa, H. (2018). Forest fires in Europe, Middle East and North Africa 2017; EUR 29318 EN; JRC European Union: Luxembourg, 2018.
  • Saulino, L., Rita, A., Migliozzi, A., Maffei, C., Allevato, E., Garonna, A.P., & Saracino, A. (2020). Detecting burn severity across mediterranean forest types by coupling medium-spatial resolution satellite imagery and field data. Remote Sensing, 12(4), 741.
  • Sirin, A., & Medvedeva, M. (2022). Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires. Remote Sensing, 14(1), 194.
  • Tucker, C. J., Pinzon, J.E., Brown, M.E., Slayback, D. A., Pak, E.W., Mahoney, R. Vermote, E.F., & El Saleous, N. (2005). An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International journal of remote sensing, 26(20), 4485-4498.
  • USGS. (2019). Landsat Surface Reflectance-Derived Spectral Indices. Retrieved 10 June 2022 from https://www.usgs.gov/ land resources/nli/landsat/landsat normalized burn-ratio.
  • Url-1: https://earthexplorer.usgs.gov/ (last accessed 21 June 2022)
  • Warner, T.A., Skowronski, N.S., & Gallagher, R.M. (2017). High spatial resolution burn severity mapping of the New Jersey Pine Barrens with WorldView-3 near-infrared and shortwave infrared imagery. International Journal of Remote Sensing, 38(2), 598-616
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yer Bilimleri ve Jeoloji Mühendisliği (Diğer)
Bölüm Research Articles
Yazarlar

Edmond Pasho 0000-0001-5699-4858

Arben Q. Alla 0000-0002-6937-2940

Ernest Ramaj 0000-0002-9740-898X

Erken Görünüm Tarihi 28 Aralık 2022
Yayımlanma Tarihi 30 Aralık 2022
Gönderilme Tarihi 31 Temmuz 2022
Kabul Tarihi 21 Ekim 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 3 Sayı: 2

Kaynak Göster

APA Pasho, E., Q. Alla, A., & Ramaj, E. (2022). Application of Remote Sensing and GIS techniques for detecting burned areas and severity. A case study of the National Park "Dajti Mountain", Albania. Turkish Journal of Geosciences, 3(2), 64-74. https://doi.org/10.48053/turkgeo.1150655