Vegetational succession assessment in a fragment of the Brazilian Atlantic Forest.

Vegetational succession assessment is an important step for better management practices, providing relevant quantitative and qualitative information. With the advancements of remote sensing algorithms and access to data, land use and land cover (LULC) monitoring has become increasingly feasible and important for the evaluation of changes in the landscape at different spatial and temporal scales. This study aims to analyze the vegetation succession achieved by a project funded by the Brazilian Environmental Ministry (Ministério do Meio Ambiente, in Portuguese) intended to recover degraded areas. A 2014 and a 2019 LULC map was generated using high-resolution (10 cm) images. Given the great challenge of classifying high-resolution images, three classification algorithms were compared. The techniques to regenerate degraded areas were efficient to increase arboreal vegetation area by more than 30% between 2014 and 2019. Land cover and land use change monitoring is of paramount importance to strengthen sustainable practices, especially in the highly threatened Atlantic Forest biome. This study also shows that funding opportunities are essential for projects that make such actions possible, including the present research and the analysis of environmental regeneration.

Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN.

Olive tree growing is an important economic activity in many countries, mostly in the Mediterranean Basin, Argentina, Chile, Australia, and California. Although recent intensification techniques organize olive groves in hedgerows, most olive groves are rainfed and the trees are scattered (as in Spain and Italy, which account for 50% of the world's olive oil production). Accurate measurement of trees biovolume is a first step to monitor their performance in olive production and health. In this work, we use one of the most accurate deep learning instance segmentation methods (Mask R-CNN) and unmanned aerial vehicles (UAV) images for olive tree crown and shadow segmentation (OTCS) to further estimate the biovolume of individual trees. We evaluated our approach on images with different spectral bands (red, green, blue, and near infrared) and vegetation indices (normalized difference vegetation index-NDVI-and green normalized difference vegetation index-GNDVI). The performance of red-green-blue (RGB) images were assessed at two spatial resolutions 3 cm/pixel and 13 cm/pixel, while NDVI and GNDV images were only at 13 cm/pixel. All trained Mask R-CNN-based models showed high performance in the tree crown segmentation, particularly when using the fusion of all dataset in GNDVI and NDVI (F1-measure from 95% to 98%). The comparison in a subset of trees of our estimated biovolume with ground truth measurements showed an average accuracy of 82%. Our results support the use of NDVI and GNDVI spectral indices for the accurate estimation of the biovolume of scattered trees, such as olive trees, in UAV images.

Green Spaces, Land Cover, Street Trees and Hypertension in the Megacity of São Paulo.

Proximity to green spaces has been shown to be beneficial to several cardiovascular outcomes in urban spaces. Few studies, however, have analyzed the relationship between these outcomes and green space or land cover uses in low-medium income megacities, where the consequences of rapid and inordinate urbanization impose several health hazards. This study used a subgroup of the dataset from The Brazilian Longitudinal Study of Adult Health ELSA-BRASIL (n= 3418) to identify the correlation between the medical diagnosis of hypertension and green spaces in the megacity of São Paulo. Land cover classification was performed based on the random forest algorithm using geometrically corrected aerial photography (orthophoto). Three different indicators of exposure to green spaces were used: number of street trees, land cover and number of parks within 1 km. We used logistic regression models to obtain the association of the metrics exposure and health outcomes. The number of street trees in the regional governments (OR = 0.937 and number of parks within 1 km (OR = 0.876) were inversely associated with a diagnosis of hypertension. Sixty-three percent of the population had no parks within 1 km of their residence. Our data indicate the need to encourage large-scale street tree planting and increase the number of qualified parks in megacities.