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Abstract(s)
A produção nacional de castanhas representa um fator crucial para a economia regional, especialmente em Trás-os-Montes, Nordeste de Portugal. As áreas ocupadas com castanheiros têm vindo a aumentar ao longo dos últimos anos. Sendo assim, esta dissertação tem como principal objetivo, analisar, avaliar e delimitar espacialmente os castanheiros, identificando a distribuição da cobertura vegetal ocupada por castanheiros (Castanea sativa Mill.) na região Nordeste (NE) de Portugal, utilizando recursos de imagem do Satélite Sentinel 2, dos últimos cinco anos, 2017, 2018, 2019, 2020 e 2021, com enfoque na utilização de bandas espectrais pelo cálculo do Índice de Vegetação por diferença Normalizada (NDVI), para identificar e analisar o castanheiro ao longo das estações do ano e sua distribuição espacial, diferenciando a cobertura florestal da cobertura de pomares. As imagens de satélite foram obtidas a partir do servidor EO Browser. Realizou-se o tratamento das imagens nos softwares ArcGis e Google Earth, e, para o tratamento dos dados obtidos utilizou-se o software Excel. Neste estudo constatou-se que os castanheiros se adaptam às variações espaciais e, porém, em áreas com maiores altitudes ocorre maior concentração e distribuição de castanheiros (entre 500 e 1000 metros) , estando distribuídos em sua maior parte nos concelhos de Vinhais e Bragança com altitudes médias de 714 e 799 metros, respetivamente. Os valores de NDVI variam ao longo do ano, esse fato está relacionado ao estado fenológico dos castanheiros em conjunto com as distinções climáticas das estações ao longo do ano. No outono e inverno valores de NDVI são menores já no verão e primavera são maiores uma vez que os castanheiros possuem mais visibilidade no NIR. O valor máximo de NDVI para as florestas foi de 0,70 frente ao 0,56 para os pomares. Os valores de NDVI são sensíveis às variações climáticas, eventos como geadas e de neve impactam diretamente na leitura espectral da imagem, sendo assim, o NDVI pode ser um indicador bioclimático.
The national chestnut production represents a crucial factor for the regional economy, especially in Trás-os-Montes, Northeast Portugal. The areas occupied with chestnut trees have been increasing over the last years. Therefore, this dissertation has as main objective, to analyse, evaluate and spatially delimit the chestnut trees, identifying the distribution of the vegetation cover occupied by chestnut trees (Castanea sativa Mill. ) in the Northeast (NE) region of Portugal, using Sentinel 2 Satellite image resources from the last five years, 2017, 2018, 2019, 2020 and 2021, focusing on the use of spectral bands by calculating the Normalized Difference Vegetation Index (NDVI), to identify and analyse the chestnut tree throughout the seasons and its spatial distribution, differentiating the forest cover from the orchard cover. The satellite images were obtained from the EO Browser server. The treatment of the images was carried out in ArcGis and Google Earth software, and, for the treatment of the data obtained, Excel software was used. This study showed that the chestnut-trees adapt to spatial variations and, however, in areas with higher altitudes occurs greater concentration and distribution of chestnut trees (between 500 and 1000 meters), being distributed mostly in the municipalities of Vinhais and Bragança with average altitudes of 714 and 799 meters, respectively. The NDVI values vary throughout the year, this fact is related to the phenological state of the chestnut-trees together with the climatic distinctions of the seasons throughout the year. In autumn and winter NDVI values are lower, whereas in summer and spring they are higher because the chestnut-trees have more visibility in the NIR. The maximum NDVI value for the forests was 0.70 versus 0.56 for the orchards. The NDVI values are sensitive to climatic variations, events such as frost and snow impact directly on the spectral reading of the image, therefore, the NDVI can be a bioclimatic indicator.
The national chestnut production represents a crucial factor for the regional economy, especially in Trás-os-Montes, Northeast Portugal. The areas occupied with chestnut trees have been increasing over the last years. Therefore, this dissertation has as main objective, to analyse, evaluate and spatially delimit the chestnut trees, identifying the distribution of the vegetation cover occupied by chestnut trees (Castanea sativa Mill. ) in the Northeast (NE) region of Portugal, using Sentinel 2 Satellite image resources from the last five years, 2017, 2018, 2019, 2020 and 2021, focusing on the use of spectral bands by calculating the Normalized Difference Vegetation Index (NDVI), to identify and analyse the chestnut tree throughout the seasons and its spatial distribution, differentiating the forest cover from the orchard cover. The satellite images were obtained from the EO Browser server. The treatment of the images was carried out in ArcGis and Google Earth software, and, for the treatment of the data obtained, Excel software was used. This study showed that the chestnut-trees adapt to spatial variations and, however, in areas with higher altitudes occurs greater concentration and distribution of chestnut trees (between 500 and 1000 meters), being distributed mostly in the municipalities of Vinhais and Bragança with average altitudes of 714 and 799 meters, respectively. The NDVI values vary throughout the year, this fact is related to the phenological state of the chestnut-trees together with the climatic distinctions of the seasons throughout the year. In autumn and winter NDVI values are lower, whereas in summer and spring they are higher because the chestnut-trees have more visibility in the NIR. The maximum NDVI value for the forests was 0.70 versus 0.56 for the orchards. The NDVI values are sensitive to climatic variations, events such as frost and snow impact directly on the spectral reading of the image, therefore, the NDVI can be a bioclimatic indicator.
Description
Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do Paraná
Keywords
Castanea Sativa Satélite Sentinel Deteção remota Bandas espectrais Portugal