Unseen rare tree species in southeast Brazilian forests:a species abundance distribution approach

Rarity is an important aspect of biodiversity often neglected in ecological studies. Species abundance distributions (SADs) are useful tools to describe patterns of commonness–rarity in ecological communities. Most studies assume field observations of species relative abundances are approximately equal to their true relative abundances, thus dismissing the potential for, and importance of unseen rare species. Here, we adopted the approach proposed by Chao et al. (Ecol, 96:1189–1201, 2015) to estimate the number and abundance of unseen species, and thus the true SADs, for tree species in 48 forest sites in Minas Gerais state, Brazil (4 rainforests, 35 semideciduous forests, and 9 deciduous forests). Also, we assessed the correlations between both unseen and rare species and sampling protocol and environment characteristics (climate, terrain, terrain heterogeneity). We found estimated true SADs invariably had higher species richness values than observed in the surveys, due to the increase in rare species. We estimate that up to 55.6% of tree species per site were unseen (8.5–55.6%), with an average of 26.6%. The estimated percentage of rare species per site was between 31.9% and 72.8%, with an average of 57.78%. We found rarity to be most strongly correlated with the percentage of unidentified trees, local terrain conditions and heterogeneity at site-level. Semideciduous forest and rainforest had similar higher percentages of unseen species (c. 27.2%) when compared to deciduous forests, probably due to the relatively higher local heterogeneity of these forests, which may provide more niches for rare species. Future studies should consider estimating true species abundances to better assess biodiversity. © 2020, Akadémiai Kiadó Zrt

Object-based change detection in the cerrado biome using landsat time series

Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to improve the results of change detection. However, few studies have explored Landsat’s shortwave infrared channel (SWIR 2) to discriminate between seasonal changes and land use/land cover changes (LULCC). Here, we explored the effectiveness of Operational Land Imager (OLI) spectral bands and vegetation indices for detecting deforestation in highly seasonal areas of Brazilian savannas. We adopted object-based image analysis (OBIA), applying a multidate segmentation to an OLI time series to generate input data for discrimination of deforestation from seasonal changes using the Random Forest (RF) algorithm. We found adequate separability between deforested objects and seasonal changes using SWIR 2. Using spectral indices computed from SWIR 2, the RF algorithm generated a change map with an overall accuracy of 88.3%. For deforestation, the producer’s accuracy was 88.0% and the user’s accuracy was 84.6%. The SWIR 2 channel as well as the mid-infrared burn index presented the highest importance among spectral variables computed by the RF average impurity decrease measure. Our results give support to further change detection studies regarding to suitable spectral channels and provided a useful foundation for savanna change detection using an object-based method applied to Landsat time series