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Title: Fuel load and fuel moisture characterization using remote sensing data over Portugal
Authors: Santos, Filippe
Couto, Flavio
Dias, Susana
Ribeiro, Nuno
Salgado, Rui
Keywords: biomass
moisture content
remote sensing
Issue Date: 10-Oct-2022
Citation: Santos FLM, Couto FT, Dias SS, Ribeiro NA, Salgado R (2022) Fuel load and fuel moisture characterization using remote sensing data over Portugal. In.: 9º Congresso Florestal Nacional, 10-14 October 2022, Funchal, Madeira, Portugal. ID: 335, p. 248.
Abstract: Fire is a worldwide and complex phenomenon with a critical role in water and carbon cycles. Furthermore, Portugal will be warmer and drier than nowadays under future scenario projections linked to climate changes. In such a context, to cover large areas with temporal regularity, remote sensing can be helpful for a better understanding of surface vegetation representation. Therefore, it is essential to understand the vegetation dynamics and fire susceptibility. In the project, one goal is to improve the fuel load and moisture content representation through satellite data. For this work, we used two above-ground biomass (AGB) datasets: first, samples collected by “Instituto da Conservação da Natureza e das Florestas” (ICNF) in 2015 for the Portuguese National Forest Inventory; and second, AGB derived from ~3.000 trees in-situ dendrometric variables measurements (total height, tree diameter at 1.30m above the ground) on site in the Herdade da Mitra at the University of Evora in 2020 and 2021. Otherwise, we are collecting biweekly sample data over two field sites (Herdade da Mitra and Serra de Ossa) to evaluate fuel moisture time series and compare them with remote sensing data from satellites such as Sentinel-2 and Landsat-8. In addition, we used drone imagery from DJI Phantom 4 Multispectral, which has six spectral bands, including an infrared channel.
Type: lecture
Appears in Collections:ICT - Comunicações - Em Congressos Científicos Nacionais

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