Regressões Robusta E Linear Para Estimativa De Biomassa via Imagem Sentinel Em Uma Floresta Tropical

Abstract

RESUMOA preocupação com as mudanças climáticas globais tem motivado diversos pesquisadores a encontrar métodos eficazes para a quantificação de biomassa florestal e carbono estocado em florestas tropicais, uma vez que, estas atuam de forma mitigatória e compensatória desses efeitos. O sensoriamento remoto tem sido utilizado de forma eficaz e com grande potencial para a estimativas em larga escala, com destaque para dados de Radar de Abertura Sintética (SAR) e imagens multiespectrais. Os estudos desenvolvidos com essa finalidade utilizaram diversas técnicas para associar a biomassa acima do solo (AGB) com os dados obtidos por sensoriamento remoto, entretanto, a aplicação da regressão robusta ainda não está sendo utilizada para tal finalidade. Sendo assim, o objetivo do presente estudo é avaliar o desempenho da regressão robusta comparando com a regressão linear, que é tradicionalmente utilizada, além de avaliar o potencial da utilização dos dados oriundos do satélite Sentinel 1 e 2 para a estimativa de AGB. Neste âmbito, foram utilizadas imagens multiespectrais (Sentinel 2), imagem SAR (Sentinel 1) e como variável resposta a AGB obtida a partir de dados Light Detection and Ranging (LiDAR). A AGB foi estimada por dois métodos de regressão: robusta e linear. Os modelos de regressão robusta e linear apresentaram desempenho semelhante, com R²aj. variando entre 0,33 a 0,34, erro padrão da estimativa de 48 Mg.ha -1 e raiz do erro médio quadrático de 16%. Conclui-se que não houve diferença significativa entre a regressão linear e a regressão robusta para esse conjunto de dados, indicando que a regressão robusta não se sobressaiu com a presença de outliers e que existe potencial na utilização de dados oriundos do satélite Sentinel. ABSTRACTConcern about global climate change has motivated several researchers to find effective methods for the quantification of forest biomass and carbon stored in tropical forests, since they act in a mitigating and compensatory way. Remote sensing has been used effectively and with great potential for large scale estimation, with emphasis on Synthetic Aperture Radar (SAR) data and multispectral images. The studies already developed for this purpose have used several techniques to associate above-ground biomass (AGB) with the data obtained by remote sensing, however, the application of robust regression is not yet being used for this purpose. Thus, the objective of the present study is to evaluate the robust regression performance in comparison with the linear regression, that is traditionally used, besides evaluating the potential of the data from Sentinel 1 and 2 satellite. In this context, multispectral images (Sentinel 2), SAR image (Sentinel 1) and as variable response to AGB obtained from data Light Detection And Ranging (LiDAR). AGB was estimated by two regression methods: robust and linear. The robust and linear regression models presented similar performance, with R²aj. ranging from 0.33 to 0.34, standard error of the estimate of 48 Mg.ha -1 and root mean square error of 16%. It is ...

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