Avaliação de áreas de pastagens a partir de bibliotecas espectrais, dados lidar e imagens aéreas
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2020-01-07
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Universidade Federal de Goiás
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Grasslands are important environments for global food security as they are responsible for the production of meat and milk from ruminant animals. Unfortunately, the negative consequences of the expansion of pasture areas is the loss of biodiversity, especially in the Brazilian Cerrado where more than 23% of natural vegetation has been converted to pasture. Thus, it is necessary to look for solutions to maintain the growth of food production without deforestation focusing on the recovery of degraded areas and intensification of use in underused places. Through hyperspectral data collected in the field, aerophotogrammetric data obtained by RPAS and laser pulses emitted by airborne LiDAR sensor, this work aims to evaluate the use of these data in pastures under different management and different seasonality conditions. The experiment area is the Rio Vermelho Basin (BHRV). In this region we were collected spectral data over 17 months in five pasture areas of 500 x 500 meters to compose a spectrum-temporal library. RPAS data were also collected in 2019 and LiDAR in 2015 and 2018 along a 50 km by 200 meters transect. The library built from spectral data was able to represent variations related to seasonality and management of pasture areas. The LiDAR point clouds on pastures were able to produce canopy height information faithful to the landscape observed in the field. The results obtained with RPAS proved to be insufficient to reach the objective, requiring more experiments to be usable. The spectro-temporal library formed exclusively by data sampled at pasture and the use of data from LiDAR showed a remarkable ability to describe the landscape and its nuances. However, further studies are still needed to better understand the results and validate the techniques.
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MESQUITA, Vinícius Vieira. Avaliação de áreas de pastagens a partir de bibliotecas espectrais, dados lidar e imagens aéreas. 2020. 65 f. Dissertação (Mestrado em Ciências Ambientais) - Universidade Federal de Goiás, Goiânia, 2020.