High-resolution topography for Digital Terrain Model (DTM) in Keller Peninsula, Maritime Antarctica.

High resolution topography (HRT) surveys is an important tool to model landscapes, especially in zones subjected to strong environmental changes, such as Antarctica, where landform is highly influenced by cryoclasty and permafrost melting. The aim of this work was to obtain a high accurate DTM for Keller Peninsula, Maritime Antarctica. The survey study was assessed in the 2014/2015 and 2015/2016 during the austral summer, by using Terrestrial Laser Scanner (TLS). In order to cover 8 km² of the Peninsula, the TLS equipment was installed in 81 different points. Results of the DTM generated by TLS (hereafter, HRT-DTM), and the terrain variables Aspect, Slope and Hillshade obtained were compared with previous models generated by aerophotographic survey (hereafter, APG-DTM). RMSE for the HRT and APG-DTM were 0.726 and 2.397 m, respectively. Spatial resolution of the DTMs was 0.20 m. Morphometric variables obtained from the two methods presented visual differences on the thematic maps, especially related to the Aspect. Generalization was the main process, whereas interpolation occurred for the HRT survey, being the process of choice for the APG method. A large number of points are obtained by the TLS, providing a dense cloud of points, spatially well-distributed, enabling the generalization process to obtain surface models with high performance.

Soil-vegetation relationships and community structure in a "terra-firme"-white-sand vegetation gradient in Viruá National Park, northern Amazon, Brazil

ABSTRACT Viruá National Park encompasses a vast and complex system of hydromorphic sandy soils covered largely by the white sand vegetation ("Campinarana") ecosystem. The purpose of this study was to investigate a vegetation gradient of "terra-firme"-white sand vegetation at the Viruá National Park. Nine plots representing three physiognomic units were installed for floristic and phytosociological surveys as well as to collect composite soil samples. The data were subjected to assessments of floristic diversity and similarity, phytosociological parameters and to statistical analyses, focused on principal components (PC) and canonical correspondence analysis (CCA). The vegetation of the Campinaranas types and Forest differed in biomass and species density. Ten species, endemic to Brazil, were particularly well-represented. PC and CCA indicated a clear distinction between the studied plots, based on measured soil variables, especially base sum and clay, which were the most differentiating properties between Campinarana and Forest; For the separation of the Campinarana types, the main distinguishing variable was organic matter content and cation exchange capacity. Higher similarity of Campinaranas was associated to a monodominant species and the lower similarity of Forest was related to the high occurrence of locally rare species.

Combining climatic and soil properties better predicts covers of Brazilian biomes.

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.