Amazonian floristics and anthropization assessment through multi and hyperspectral remote sensing

• Ano de defesa: 2022
• Autor(a) principal: Silva, João Lucas Della
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Mato Grosso; Brasil; Instituto de Ciências Naturais, Humanas e Sociais (ICNHS) – Sinop; UFMT CUS - Sinop; Programa de Pós-Graduação em Ciências Ambientais
• Palavras-chave em Português: Sensoriamento remoto do ambiente; Sensoriamento remoto hiperespectral; Índices de vegetação; Espécies florestais amazônicas; Dinâmica do carbono; CNPQ::OUTROS::CIENCIAS; Remote sensing of environment; Hyperspectral remote sensing; Vegetation indices; Amazonian forest species; Carbon dynamics
• Link de acesso: http://ri.ufmt.br/handle/1/4494

The management of forest resources with an ecological bias involves phytosociological characterization, and becomes fundamental when observing the plant species of the Amazon. Among the methods of forest inventory, remote sensing applies to research and monitoring of these resources with technologies based on spectrometry. Just as important, the analysis of the dynamics of land use and occupation of native areas are associated with the management of natural resources and the monitoring of anthropized areas. The applicability of this science's technologies is verified in this work, initially in the distinction between forest species by means of spectroradiometry and later in the validation of the hyperspectral model for estimating carbon dioxide absorption capacity in multispectral orbital data. In the first verification, FieldSpec® 4 Hi-Res spectroradiometer-based data for four Amazonian tree species (i. e. Bertholletia excelsa, Cedrela fissilis, Euterpe Oleracea and Schizolobium parahyba) were subjected to Principal Component (PC) analysis and Discriminant Analysis, which permeated the distinction of spectral signatures and models representing these signatures (RID, representative bands and vegetation indices). The success in differentiation by this method is observed with the variability of the data being described by PC1 greater than 99% with the three dimensionality reduction models applied. Observing the PC results applied to the spectral indices, Singh's criterion allowed to verify that the NPCI and CARI2 indices were mostly responsible for the differentiation of the spectral signatures of the evaluated species, which are indices associated with the interaction of chlorophyll with solar radiation in the visible spectrum. The second study deals with the comparison of CO2Flux index results between the hyperspectral sensor AisaFENIX and the multispectral orbital sensors OLI/Landsat-8, MSI/Sentinel-2 and PlanetScope, with this index applied in an adapted way. This study was carried out over a scene with native forest, pasture and bare soil areas in the city of Alta Floresta, in southern Amazonia. Through analysis of variance, it was observed that the CO2Flux index is poorly related to the PRI index, one of the base indices for CO2Flux. Furthermore, the results suggest that MSI/Sentinel-2 are statistically similar to the AisaFENIX sensor in the anthropized areas. Finally, the temporal variability of these results may improve these conclusions, given the relationship between water content in the canopy to the base indices of the CO2Flux.