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Comparison of physical-based models to measure forest resilience to fire as a function of burn severity

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  • معلومة اضافية
    • بيانات النشر:
      MDPI
    • الموضوع:
      2022
    • Collection:
      UVaDOC - Repositorio Documental de la Universidad de Valladolid
    • نبذة مختصرة :
      Producción Científica ; We aimed to compare the potential of physical-based models (radiative transfer and pixel unmixing models) for evaluating the short-term resilience to fire of several shrubland communities as a function of their regenerative strategy and burn severity. The study site was located within the perimeter of a wildfire that occurred in summer 2017 in the northwestern Iberian Peninsula. A pre- and post-fire time series of Sentinel-2 satellite imagery was acquired to estimate fractional vegetation cover (FVC) from the (i) PROSAIL-D radiative transfer model inversion using the random forest algorithm, and (ii) multiple endmember spectral mixture analysis (MESMA). The FVC retrieval was validated throughout the time series by means of field data stratified by plant community type (i.e., regenerative strategy). The inversion of PROSAIL-D featured the highest overall fit for the entire time series (R2 > 0.75), followed by MESMA (R2 > 0.64). We estimated the resilience of shrubland communities in terms of FVC recovery using an impact-normalized resilience index and a linear model. High burn severity negatively influenced the short-term resilience of shrublands dominated by facultative seeder species. In contrast, shrublands dominated by resprouters reached pre-fire FVC values regardless of burn severity. ; Ministerio de Economía y Competitividad y Fondo Europeo de Desarrollo Regional (FEDER) - (project AGL2017-86075-C2-1-R) ; Junta de Castilla y León - (project LE005P20) ; British Ecological Society - (project SR22-100154)
    • File Description:
      application/pdf
    • ISSN:
      2072-4292
    • Relation:
      https://www.mdpi.com/2072-4292/14/20/5138; https://doi.org/10.3390/rs14205138; Remote Sensing, 2022, Vol. 14, Nº. 20, 5138; https://uvadoc.uva.es/handle/10324/61584; 5138; 20; Remote Sensing; 14
    • الرقم المعرف:
      10.3390/rs14205138
    • Rights:
      Atribución 4.0 Internacional ; info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0/ ; © 2022 The Authors
    • الرقم المعرف:
      edsbas.70309071