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Forest fires, though part of a natural forest renewal process, when frequent and on a large -scale, have detrimental impacts on biodiversity, agroforestry systems, soil erosion, air, and water quality, infrastructures, and the economy. Portugal endures extreme forest fires, with a record extent of burned areas in 2017. These complexes of extreme wildfire events (CEWEs) concentrated in a few days but with highly burned areas are, among other factors, linked to severe fire weather conditions. In this study, a comparison between several fire danger indices (named ‘multi-indices diagnosis’) is performed for the control period 2001–2021, 2007 and 2017 (May–October) for the Fire Weather Index (FWI), Burning Index (BI), Forest Fire Danger Index (FFDI), Continuous Haines Index (CHI), and the Keetch–Byram Drought Index (KBDI). Daily analysis for the so-called Pedrógão Grande wildfire (17 June) and the October major fires (15 October) included the Spread Component (SC), Ignition Component (IC), Initial Spread Index (ISI), Buildup Index (BUI), and the Energy Release Component (ERC). Results revealed statistically significant high above-average values for most of the indices for 2017 in comparison with 2001–2021, particularly for October. The spatial distribution of BI, IC, ERC, and SC had the best performance in capturing the locations of the two CEWEs that were driven by atmospheric instability along with a dry environment aloft. These results were confirmed by the hotspot analysis that showed statistically significant intense spatial clustering between these indices and the burned areas. The spatial patterns for SC and ISI showed high values associated with high velocities in the spread of these fires. The outcomes allowed us to conclude that since fire danger depends on several factors, a multi-indices diagnosis can be highly relevant. The implementation of a Multi-index Prediction Methodology should be able to further enhance the ability to track and forecast unique CEWEs since the shortcomings of some indices are compensated by the information retrieved by others, as shown in this study. Overall, a new forecast method can help ensure the development of appropriate spatial preparedness plans, proactive responses by civil protection regarding firefighter management, and suppression efforts to minimize the detrimental impacts of wildfires in Portugal.
Forest fires, though part of a natural forest renewal process, when frequent and on a large -scale, have detrimental impacts on biodiversity, agroforestry systems, soil erosion, air, and water quality, infrastructures, and the economy. Portugal endures extreme forest fires, with a record extent of burned areas in 2017. These complexes of extreme wildfire events (CEWEs) concentrated in a few days but with highly burned areas are, among other factors, linked to severe fire weather conditions. In this study, a comparison between several fire danger indices (named ‘multi-indices diagnosis’) is performed for the control period 2001–2021, 2007 and 2017 (May–October) for the Fire Weather Index (FWI), Burning Index (BI), Forest Fire Danger Index (FFDI), Continuous Haines Index (CHI), and the Keetch–Byram Drought Index (KBDI). Daily analysis for the so-called Pedrógão Grande wildfire (17 June) and the October major fires (15 October) included the Spread Component (SC), Ignition Component (IC), Initial Spread Index (ISI), Buildup Index (BUI), and the Energy Release Component (ERC). Results revealed statistically significant high above-average values for most of the indices for 2017 in comparison with 2001–2021, particularly for October. The spatial distribution of BI, IC, ERC, and SC had the best performance in capturing the locations of the two CEWEs that were driven by atmospheric instability along with a dry environment aloft. These results were confirmed by the hotspot analysis that showed statistically significant intense spatial clustering between these indices and the burned areas. The spatial patterns for SC and ISI showed high values associated with high velocities in the spread of these fires. The outcomes allowed us to conclude that since fire danger depends on several factors, a multi-indices diagnosis can be highly relevant. The implementation of a Multi-index Prediction Methodology should be able to further enhance the ability to track and forecast unique CEWEs since the shortcomings of some indices are compensated by the information retrieved by others, as shown in this study. Overall, a new forecast method can help ensure the development of appropriate spatial preparedness plans, proactive responses by civil protection regarding firefighter management, and suppression efforts to minimize the detrimental impacts of wildfires in Portugal.
The timber industry has increased considerably in recent decades to meet human needs for wood. In Portugal, Eucalyptus plantations are the most common use of forested land, presenting the largest coverage of Eucalyptus globulus in Europe. Although it is established that this landscape can affect biodiversity patterns, it is not clear what its role in shaping small mammals’ body condition is. Here, we tested the effect of Eucalyptus plantations on small mammals’ body condition, together with vegetation structure, weather, predators/competitors’ abundance, and parasites’ prevalence, using the Scaled Mass Index (SMI) as a surrogate. Capture of small mammals took place in 11 study areas in central Portugal from 2019 to 2022. The drivers’ influence was tested using structural equation models (SEM). The response of body condition to Eucalyptus is species-specific, with Crocidura russula displaying better individual condition in native habitats (i.e., there was an indirect negative effect of Eucalyptus plantations). The overall model suggested that deer abundance, precipitation, and forest integrity promoted higher body condition levels, while wild boar abundance had an adverse effect. The management of these plantations must ensure the integrity of the remnants of native patches and control of highly abundant competitors (e.g., wild boar) to maintain a healthy and functional small mammal community.
A delimitação de corpos de água com recurso a imagens de satélite desempenha umpapel crucial em diversas aplicações, como monitorização ambiental, planeamento derecursos hídricos, planeamento na defesa contra a incêndios e na análise dasalteraçõesclimáticas. Neste trabalho, pretendemos explorar a aplicação daaprendizagem profunda tendo por base oFramework Detectron2, nageraçãoautomática depolígonos que representamcorpos de águacomopequenasalbufeiras,lagos,charcos e reservatórios.A caracterização eficiente das disponibilidades hídricasdos reservatórios, albufeiras e barragenspermite uma melhor e maiseficientemonitorização dos Planos de Água (PA), bem como a boa gestão desses mesmosrecursos. A área geográfica de estudo e as metodologias desenvolvidas, encontra-seenquadrada nas áreas de jurisdição da Administração da Região Hidrográfica doAlentejo, Departamentos desconcentrados da Agência portuguesa do Ambiente, I.P..Foidesenvolvidoum conjunto de dados abrangente e personalizado composto porimagens de satélite de alta resolução e rótulos anotados manualmente, identificandoas áreas correspondentes aos corpos de água, para treinar o modelo.Foi utilizada aarquiteturaResNet-50 combinada com aMask R-CNN, presentesno Detectron2, pararealizar a tarefa de deteção de objetos em gerale segmentação respetivamente. Emseguida, treinamos o modelo de aprendizagem profunda utilizando o nosso conjuntode dados na plataforma Google Colab, aproveitando o poder computacional dasunidades de processamento gráfico (GPU).A vantagem de usara FrameworkDetectron2 é a sua capacidade rápida e eficiente dedelimitação de corpos de águaem grandes volumes de dados,comparativamente aométodo tradicional, oqual envolve um processo manual de análise e marcaçãodospolígonosnas imagens de satéliteatravés de pessoal especializado,apresentandoelevados custos em termos de recursos humanos, económicose com elevadamorosidade.Na(Figura-1)é possível observar dois corpos de água corretamente segmentadosutilizando o método proposto.Esta abordagem pode impulsionar o desenvolvimento detécnicas mais precisas e eficientes para a deteção e delimitação de característicashidrológicas em imagens de satéliteuma vez que conseguimos segmentar corpos deágua com dimensões de até 121 m2.A abordagem implementada neste trabalho podeser aplicada a outras áreas temáticas como por exemplo a deteção de incêndios,blooms de algas, identificação de estruturas urbanas, delimitação de florestas e cultivos agrícolas.
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