Assessing the macro-scale patterns of urban tree canopy cover in Brazil using high-resolution remote sensing images

Abstract

This study creates a 0.5 m resolution urban tree canopy (UTC) cover dataset using high-resolution remote sensing images based on the deep learning method to clarify urban tree-cover characteristics in Brazilian cities. The results revealed that the UTC cover of Brazilian cities is spatially heterogeneous, ranging from 5% to 34%. There was a difference in UTC coverage between the old and new urban areas, with the average largest difference near 5%. More than 76% urban population exposure to UTC coverage of 0∼0.2. Most cities have a relatively high inequality in human exposure to urban tree-covered spaces, especially in northeastern and southeastern Brazil. Results from the geographical detector models show climatic factors play a major role in determining the UTC cover patterns in Brazilian cities, followed by socioeconomic, geographical, soil, and urbanization factors. This study suggests the Brazilian government pay more attention to greening renovation projects in old urban areas and formulate effective urban tree irrigation policies for cities with limited autumn and winter rainfall. The study also suggests follow-up research on UTC cover patterns that consider the effects of race, urban history, city structure, land use, and local government policy factors to further support the goals of sustainable development in Brazilian cities.