Publicações

Mapping tree species at landscape scale to provide information for ecologists and forest managers is a new challenge for the remote sensing community. Here, we tested the potential of a recent deep learning algorithm to identify and segment tree species associated with forest disturbance in very high-resolution multispectral images (0.3 m) from WorldView-3 satellite. The study was conducted in a region of the critically endangered Brazilian Atlantic Rainforest, which is a global priority for biodiversity conservation due to its abundance of species of flora and fauna occurring across an extremely fragmented and degraded landscape. The convolutional network generated in this study for identifying trees from different species was trained with about 1500 high-resolution true colour synthetic optical images and their labelled masks for each species. Additionally, we created a new framework for measuring disturbance levels within forest fragments based on the spatial distribution of individual disturbance-related trees. Our deep learning network segmented tree species with overall accuracies of above 95% and Dice coefficients of above 0.85. Then, the segmentation of tree species was produced over a region >1000 km² using WorldView-3 Red, Green and Blue bands pan-sharpened at 0.3 m. We found that the crowns of disturbance-related species covered between 1 and 5 % of the natural forest canopies. Our results based on the trees distribution shown that disturbance tends to increase with fragment size and revealed information that were not accessible from classical landscape fragmentation analysis, which is mainly based on size and connection of the forest fragments. We are still far from recognizing all the species, however, species that are indicator of disturbance and early successional stage of forests can be accurately mapped. Our work shows how deep learning algorithm can support applications such as mapping tree species and forest disturbance at the landscape scale from space.

The lack of Information on the nitrogen cycle in Latin America is a serious impediment to provide a proper evaluation and projection on how human activity is altering nitrogen pools and turnover at regional and global scales. In the scope of the Nitrogen Human Environment Network (Nnet Project), we propose a conceptual framework of nitrogen emissions drivers in Latin America to understand the complexity of nitrogen dynamics in the region, the diverse drivers and impacts on human health. Besides, the conceptual framework emphasizes the crucial role of political decisions and institutional forcefulness in providing and implementing appropriate mechanisms to deal with the benefits versus costs situation of nitrogen use. Some questions are discussed under the current socio-economic status of Latin American countries: (i) What factors are responsible for the increasing of emissions from nitrogen use? (ii) Is population growth the ultimate cause for the continuous expansion of Nr emissions? (iii) Are there other factors influencing this presumable foremost cause? (iv) What is the role of government institutions in provoking this situation and finally in controlling and reversing this trend? Finally, we provided a wide survey to search for nitrogen in current Latin American policies, specifically in the Nnet Project case-study countries. At large, all countries have reported general measures and mechanisms to deal with nitrogen, but only as a greenhouse gas (N2O). Specific and/or unified policies dealing directly with N emissions were not observed; however, some isolated measures with linkages to nitrogen have been considered in the legislation of some countries.