Estimativa do índice de área foliar e umidade do solo por imagens Landsat 8 em diferentes coberturas do solo na Baixada Cuiabana MT

• Ano de defesa: 2018
• Autor(a) principal: Fausto, Marcos Alves
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Mato Grosso; Brasil; Instituto de Física (IF); UFMT CUC - Cuiabá; Programa de Pós-Graduação em Física Ambiental
• Palavras-chave em Português: Índice de vegetação; SEBAL; Água; Solo; CNPQ::CIENCIAS EXATAS E DA TERRA::FISICA; Vegetation index; Water; Soil
• Link de acesso: http://ri.ufmt.br/handle/1/2063

The Baixada Cuiabana Matogrossense is located in the Center-West region of Brazil, in a transition area between the Cerrado and the northern Pantanal of Mato Grosso. This area harbors a wide diversity of natural ecosystems and modified areas. Leaf area index (LAI) is an efficient measure of soil surface cover, as it controls the exchange of mass and energy on a vegetated surface. Thus, the objective of this work was to estimate leaf area index and soil moisture by Landsat 8 images in different soil coverages in the Cuiabana basin, by means of an empirical model developed for the study area. Data collection carried between February 2015 and February 2017 in 11 vegetation coverages, located in the Experimental Farm of the Federal University of Mato Grosso (FEX), in the municipality of Santo Antônio de Leverger - MT. The IAF measurement was performed by a Ceptômetro (Model LP-80) and Soil Moisture by Soil Moisture Sensor (model MP 406) in addition to gravimetric and soil density data. The models developed to estimate the LAI and CAS presented a confidence index above 0.80 compared to the data measured in the field. Correlations (r) and determination coefficients (r2 ) were higher than 0.70. The root mean square error (RMSE) among the LAI values obtained by other models of LAI estimates was higher in comparison to the values estimated by the model proposed in this study. The statistical parameters adopted in the research presented excellent results. The estimates of the proposed models were similar to the data measured in the field on a temporal and spatial scale, corroborating the applicability of the models.