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• W3025638046 endingPage "555" @default.
• W3025638046 startingPage "555" @default.
• W3025638046 abstract "Shrubs growing in former burnt areas play two diametrically opposed roles. On the one hand, they protect the soil against erosion, promote rainwater infiltration, carbon sequestration and support animal life. On the other hand, after the shrubs’ density reaches a particular size for the canopy to touch and the shrubs’ biomass accumulates more than 10 Mg ha−1, they create the necessary conditions for severe wild fires to occur and spread. The creation of a methodology suitable to identify former burnt areas and to track shrubs’ regrowth within these areas in a regular and a multi temporal basis would be beneficial. The combined use of geographical information systems (GIS) and remote sensing (RS) supported by dedicated land survey and field work for data collection has been identified as a suitable method to manage these tasks. The free access to Sentinel images constitutes a valuable tool for updating the GIS project and for the monitoring of regular shrubs’ accumulated biomass. Sentinel 2 VIS-NIR images are suitable to classify rural areas (overall accuracy = 79.6% and Cohen’s K = 0.754) and to create normalized difference vegetation index (NDVI) images to be used in association to allometric equations for the shrubs’ biomass estimation (R2 = 0.8984, p-value < 0.05 and RMSE = 4.46 Mg ha−1). Five to six years after a forest fire occurrence, almost all the former burnt area is covered by shrubs. Up to 10 years after a fire, the accumulated shrubs’ biomass surpasses 14 Mg ha−1. The results described in this paper demonstrate that Northwest Portugal presents larger shrubland areas and greater shrub biomass accumulation (average 18.3 Mg ha−1) than the Northeast (average 7.7 Mg ha−1) of the country." @default.
• W3025638046 created "2020-05-21" @default.
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• W3025638046 date "2020-05-14" @default.
• W3025638046 modified "2023-10-16" @default.
• W3025638046 title "Shrub Biomass Estimates in Former Burnt Areas Using Sentinel 2 Images Processing and Classification" @default.
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• W3025638046 doi "https://doi.org/10.3390/f11050555" @default.
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