Characterization of indicator tree species in neotropical environments and implications for geological mapping

• Autor(a) principal: Amaral, Cibele Hummel do
• Data de Publicação: 2018
• Outros Autores: Almeida, Teodoro Isnard Ribeiro de, Souza Filho, Carlos Roberto de, Roberts, Dar A., Fraser, Stephen James, Alves, Marcos Nopper, Botelho, Moreno
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: https://doi.org/10.1016/j.rse.2018.07.009; http://www.locus.ufv.br/handle/123456789/22394
• topic: Geobotany; Cerrado biome; Sedimentary formations; Multivariate analysis; Spectroscopy; Spectral mixture analysis

Geobotanical remote sensing (GbRS) in the strict sense is an indirect approach to obtain geological information in heavily vegetated areas for mineral prospecting and geological mapping. Using ultra- and hyperspectral technologies, the goals of this research comprise the definition and mapping of Neotropical tree species that are associated with geological facies (here called geo-environments) as well as their spectral discrimination at leaf and crown scales. This work also aims to investigate the possible relationship between leaf and crown spectral and chemical properties. The study was developed at the Mogi-Guaçu Ecological Station, in the Cerrado domain, southeastern Brazil. Data from 70 sample units, such as sediment texture and species from inventories, were first analyzed through vectorial quantization using Self-Organizing Maps (SOM). Principal Component Analysis and Spearman's ranked correlation coefficients were used to define geo-environments and target-species, respectively. Biochemical and visible to shortwave infrared (VSWIR) point spectral data (350–2500 nm) were collected from the leaves of the target-species, during both rainy and dry seasons. Spectral data from target-species crowns were obtained from hyperspectral images (530–2.532 nm, ProSpecTIR-VS sensor) with 1 m spatial resolution, and acquired in the beginning of the dry season. These spectra were classified using Multiple Endmember Spectral Mixture Analysis (MESMA) with two endmembers (EMs). Based on the MESMA results with two EMs, the best dataset per target-species was chosen for pixel-based image unmixing with three EMs (target-species, other vegetation types and shade). From 121 species sampled in the field, two proved to be associated with floodplains (Alluvial Deposits sequence), two with hills and plateaus of the Aquidauna Formation (Carboniferous sedimentary rocks, Paraná Basin), and two more with a specific facies of the Aquidauna Formation that has a distinctive presence of coarse and very coarse sand. Five target-species were well discriminated at the leaf scale, reaching 90.0% and 85.0% of global accuracies in the rainy season and in the dry season, respectively. Accurate spectral discrimination appears to be linked to the considerable biochemical variability of their leaves in both seasons. Three species were discriminated at the crown scale, with 70.6% of global accuracy. When eight other landscape scale vegetation classes were included in the analyses, only Qualea grandiflora Mart. produced a satisfactory accuracy (61.1% and 100% of producer and user's accuracies, respectively). The spatial distribution of its fraction in the unmixed image, particularly, matches with the geological facies to which it was associated in the field. Ecological requirements for successfully mapping indicator species include broad and random distribution of the target-species' population, and singular physiological, phenological (and spectral) behavior at the imagery acquisition date. Our study shows that, even in tropical conditions, it is possible to use plant species mapping to support geological delineation, where rock exposures are typically rare.