Predizendo riqueza de espécies de plantas com imagens de satélite, no maior núcleo de florestas secas da América do Sul Areia 2016

Abstract

The assessment of biodiversity has been recognized as an indicator of ecosystem health for several initiatives worldwide. Satellite remote sensing (SRS) have introduced tools that facilitate the search for information related to plant diversity. With this perspective, this study aimed to verify if spectral variables, from Landsat satellite, can be used as plant species diversity indicators in the Caatinga, the largest dry forests core of South America. To obtain the plant diversity data (richness and Shannon index), an exhaustive exploration of phytosociological researches was carried out in the study area. To evaluate the possible association between spectral variables and plant diversity, Pearson correlation analyzes were performed. Regressions were performed to verify the best predictive models for species richness. The results indicate that there is a high positive association between richness and the infrared band near (R = 0,744; P <0.001). This spectral variable can be used as an indicator of species richness using the potential and quadratic regression functions (R² = 0,61 and R ² = 0,56 p <0.001, respectively). Thus, it is concluded that Landsat satellite potential to estimate species richness in dry forest from South America, lies mainly in the near infrared band, allowing to assess biodiversity at multiple scales and providing a continuous source of information for monitoring purposes.