Abstract
The accurate estimation of fire severity, in terms of physical effects that occurred on the tree’s canopies, as well as the accurate mapping of its spatial distribution, is necessary information to optimally quantify and qualify the damage caused by the fire to ecosystems and address the most correct remedial procedures. The development of even more accurate learning algorithms and higher resolution satellite multispectral data have become essential resources in this framework. This study proposes a deep learning approach, exploiting remotely sensed satellite data, to produce an accurate severity map of the effects caused by the devasting fires affecting the Aspromonte National Park forests during the 2021 fire season. Two multispectral Sentinel-2 data, acquired before and after the fires, were classified using an artificial neural network-based model. All the multispectral fire-sensitive bands (visible, near-infra-red, short-infrared) and the respective temporal difference (post-fire—pre-fire) were involved, while the selection of the training pixels was based on field-based observations. Despite the preliminary nature of this study, the map accuracy reached high values (>95%) of F-scoreM (representing the overall accuracy) already since the first test of this configuration, confirming the validity of this approach. The quanti-qualification of the fire effects reported that 35.26 km2 of forest cover was affected, of which: 41.03% and the 26.04% of tree’s canopies were low and moderately affected respectively; the canopies killed but structurally preserved were the 12.88%; the destroyed trees (very-high severity), instead, were the 20.05%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Moreira, F., Ascoli, D., Safford, H., Adams, M.A., Moreno, J.M., Pereira, J.M.C., Catry, F.X., Armesto, J., Bond, W., González, M.E., Curt, T., Koutsias, N., McCaw, L., Price, O., Pausas, J.G., Rigolot, E., Stephens, S., Tavsanoglu, C., Vallejo, V.R., Van Wilgen, B.W., Xanthopoulos, G., Fernandes, P.M.: Wildfire management in Mediterranean-type regions: paradigm change needed. Environ. Res. Lett. 15, 11001 (2020). https://doi.org/10.1088/1748-9326/ab541e
Morresi, D., Vitali, A., Urbinati, C., Garbarino, M.: Forest spectral recovery and regeneration dynamics in stand-replacing wildfires of central Apennines derived from Landsat time series. Remote Sens. 11, (2019). https://doi.org/10.3390/rs11030308
Semeraro, T., Vacchiano, G., Aretano, R., Ascoli, D.: Application of vegetation index time series to value fire effect on primary production in a Southern European rare wetland. Ecol. Eng. 134, 9–17 (2019). https://doi.org/10.1016/j.ecoleng.2019.04.004
Rosa, I.M.D., Pereira, J.M.C., Tarantola, S.: Atmospheric emissions from vegetation fires in Portugal (1990–2008): estimates, uncertainty analysis, and sensitivity analysis. Atmos. Chem. Phys. 11, 2625–2640 (2011). https://doi.org/10.5194/acp-11-2625-2011
De Luis, M., Raventós, J., González-Hidalgo, J.C.: Post-fire vegetation succession in Mediterranean gorse shrublands. Acta Oecol. 30, 54–61 (2006). https://doi.org/10.1016/j.actao.2006.01.005
Mitchell, R.J., Simonson, W., Flegg, L.A., Santos, P., Hall, J.: A comparison of the resilience of four habitats to fire, and the implications of changes in community composition for conservation: a case study from the Serra de Monchique, Portugal. Plant Ecol. Divers. 2, 45–56 (2009). https://doi.org/10.1080/17550870902752421
Montès, N., Ballini, C., Bonin, G., Faures, J.: A comparative study of aboveground biomass of three Mediterranean species in a post-fire succession. Acta Oecol. 25, 1–6 (2004). https://doi.org/10.1016/j.actao.2003.10.002
Riaño, D., Chuvieco, E., Ustin, S., Zomer, R., Dennison, P., Roberts, D., Salas, J.: Assessment of vegetation regeneration after fire through multitemporal analysis of AVIRIS images in the Santa Monica Mountains. Remote Sens. Environ. 79, 60–71 (2002). https://doi.org/10.1016/S0034-4257(01)00239-5
Meng, R., Dennison, P.E., Huang, C., Moritz, M.A., D’Antonio, C.: Effects of fire severity and post-fire climate on short-term vegetation recovery of mixed-conifer and red fir forests in the Sierra Nevada Mountains of California. Remote Sens. Environ. 171, 311–325 (2015). https://doi.org/10.1016/j.rse.2015.10.024
Morresi, D., Marzano, R., Lingua, E., Motta, R., Garbarino, M.: Mapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery. Remote Sens. Environ. 269, 112800 (2022). https://doi.org/10.1016/j.rse.2021.112800
Han, A., Qing, S., Bao, Y., Na, L., Bao, Y., Liu, X., Zhang, J., Wang, C.: Short-term effects of fire severity on vegetation based on Sentinel-2 satellite data. Sustainability 13, 1–22 (2021). https://doi.org/10.3390/su13010432
Saulino, L., Rita, A., Migliozzi, A., Maffei, C., Allevato, E., Garonna, A. Pietro, Saracino, A.: Detecting burn severity across Mediterranean forest types by coupling medium-spatial resolution satellite imagery and field data. Remote Sens. 12, 1–21 (2020). https://doi.org/10.3390/rs12040741
ESA: ESA Sentinel-2 Homepage. https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2
Pereira, J.M.C., Sá, A.C.L., Sousa, A.M.O., Silva, J.M.N., Santos, T.N., Carreiras, J.M.B.: Spectral characterisation and discrimination of burnt areas. In: Remote Sensing of Large Wildfires (1999). https://doi.org/10.1007/978-3-642-60164-4_7
Modica, G., De Luca, G., Messina, G., Praticò, S.: Comparison and assessment of different object-based classifications using machine learning algorithms and UAVs multispectral imagery in the framework of precision agriculture. Eur. J. Remote Sens. 54, 431–460 (2021). https://doi.org/inpress
Bot, K., Borges, J.G.: A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support (2022). https://doi.org/10.3390/inventions7010015
De Luca, G., Silva, J.M.N., Oom, D., Modica, G.: Combined use of Sentinel-1 and Sentinel-2 for burn severity mapping in a mediterranean region. In: Computational Science and Its Applications–ICCSA 2021. Lecture Notes in Computer Science, pp. 139–154 (2021). https://doi.org/10.1007/978-3-030-87007-2_11
Sali, M., Piaser, E., Boschetti, M., Brivio, P.A., Sona, G., Bordogna, G., Stroppiana, D.: A burned area mapping algorithm for sentinel-2 data based on approximate reasoning and region growing. Remote Sens. 13, (2021). https://doi.org/10.3390/rs13112214
Amos, C., Petropoulos, G.P., Ferentinos, K.P.: Determining the use of Sentinel-2A MSI for wildfire burning & severity detection. Int. J. Remote Sens. 40, 905–930 (2019). https://doi.org/10.1080/01431161.2018.1519284
Key, C.H., Benson, N.C.: Landscape assessment (LA) sampling and analysis methods. In: FIREMON: Fire Effects Monitoring and Inventory System (2006)
Knopp, L., Wieland, M., Rättich, M., Martinis, S.: A deep learning approach for burned area segmentation with Sentinel-2 data. Remote Sens. 12, (2020). https://doi.org/10.3390/RS12152422
Hu, X., Ban, Y., Nascetti, A.: Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning (2021). https://doi.org/10.3390/rs13081509
Monaco, S., Pasini, A., Apiletti, D., Colomba, L., Garza, P., Baralis, E.: Improving wildfire severity classification of deep learning U-nets from satellite images. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 5786–5788 (2020). https://doi.org/10.1109/BigData50022.2020.9377867
Farasin, A., Colomba, L., Garza, P.: Double-Step U-Net: A Deep Learning-Based Approach for the Estimation of Wildfire Damage Severity through Sentinel-2 Satellite Data (2020). https://doi.org/10.3390/app10124332
Keras Homepage: Keras Homepage. https://keras.io/
Spampinato, G., Cameriere, P., Caridi, D., Crisafulli, A.: Carta della vegetazione reale del Parco Nazionale dell’Aspromonte (2002)
Aspromonte Park: Aspromonte Park. http://www.parconazionaleaspromonte.it/pagina.php?id=41
Spampinato, G.: Guida alla flora dell’Aspromonte. Laruffa Editore (2014)
Copernicus Access Hub: Copernicus Access Hub. https://scihub.copernicus.eu/
Google Earth: Google Earth. https://earth.google.com/web/
SHAP: SHAP KernelExplainer Doc. https://shap-lrjball.readthedocs.io/en/latest/generated/shap.KernelExplainer.html
Moreno, J.M., Morales-Molino, C., Torres, I., Arianoutsou, M.: Fire in Mediterranean Pine Forests: Past, Present and Future BT-Pines and Their Mixed Forest Ecosystems in the Mediterranean Basin (2021). https://doi.org/10.1007/978-3-030-63625-8_21
Fernández-Manso, A., Quintano, C., Suarez-Seoane, S., Marcos, E., Calvo, L.: Remote Sensing Techniques For Monitoring Fire Damage And Recovery of Mediterranean Pine Forests: Pinus Pinaster and Pinus Halepensis as Case Studies BT-Pines and Their Mixed Forest Ecosystems in the Mediterranean Basin (2021). https://doi.org/10.1007/978-3-030-63625-8_27
Pereira-Pires, J.E., Aubard, V., Ribeiro, R.A., Fonseca, J.M., Silva, J.M.N., Mora, A.: Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network (2020). https://doi.org/10.3390/rs12060909
Quintano, C., Fernández-Manso, A., Roberts, D.A.: Multiple endmember spectral mixture analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries. Remote Sens. Environ. 136, 76–88 (2013). https://doi.org/10.1016/j.rse.2013.04.017
Smith, A.M.S., Lentile, L.B., Hudak, A.T., Morgan, P.: Evaluation of linear spectral unmixing and ∆NBR for predicting post-fire recovery in a North American ponderosa pine forest. Int. J. Remote Sens. 28, 5159–5166 (2007). https://doi.org/10.1080/01431160701395161
Fernandez-Manso, A., Quintano, C., Roberts, D.A.: Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems. Remote Sens. Environ. 184, 112–123 (2016). https://doi.org/10.1016/j.rse.2016.06.015
Rogan, J., Franklin, J.: Mapping wildfire burn severity in southern california forests and shrublands using enhanced thematic mapper imagery. Geocarto Int. 16, 91–106 (2001). https://doi.org/10.1080/10106040108542218
De Santis, A., Chuvieco, E.: Burn severity estimation from remotely sensed data: performance of simulation versus empirical models. Remote Sens. Environ. 108, 422–435 (2007). https://doi.org/10.1016/j.rse.2006.11.022
García-Llamas, P., Suárez-Seoane, S., Fernández-Guisuraga, J.M., Fernández-García, V., Fernández-Manso, A., Quintano, C., Taboada, A., Marcos, E., Calvo, L.: Evaluation and comparison of Landsat 8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems. Int. J. Appl. Earth Obs. Geoinf. 80, 137–144 (2019). https://doi.org/10.1016/j.jag.2019.04.006
Fernández-Manso, A., Fernández-Manso, O., Quintano, C.: SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity. Int. J. Appl. Earth Obs. Geoinf. (2016). https://doi.org/10.1016/j.jag.2016.03.005
Filipponi, F.: BAIS2: burned area index for Sentinel-2. In: Proceedings, p. 5177 (2018). https://doi.org/10.3390/ecrs-2-05177
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
De Luca, G., Modica, G. (2023). Canopy Fire Effects Estimation Using Sentinel-2 Imagery and Deep Learning Approach. A Case Study on the Aspromonte National Park. In: Ieracitano, C., Mammone, N., Di Clemente, M., Mahmud, M., Furfaro, R., Morabito, F.C. (eds) The Use of Artificial Intelligence for Space Applications. AII 2022. Studies in Computational Intelligence, vol 1088. Springer, Cham. https://doi.org/10.1007/978-3-031-25755-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-25755-1_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25754-4
Online ISBN: 978-3-031-25755-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)