remote sensing

Using GIS in urban forestry mapping

Accelerated urbanization and rapid growth of cities have dramatically changed the natural landscape. In this challenging scenario, the role of trees in urban environments is of great importance. Studies have been pointed out how the implementation of urban tree planting can be hailed as a nature-based solution to mitigate environmental impacts, being one of the strategies to help to strengthen the global response to climate change [1] [2] [3]. Urban trees provide essential ecosystem services such as air pollution mitigation, surface temperature reduction, and carbon sequestration for the inner-city population. Moreover, they provide aesthetic, social, and economic benefits to cities and their inhabitants.

In this regard, finding accurate methods for mapping the spatial distribution of urban forestry provides valuable insights for green infrastructure managers and is crucial for guiding planners and decision-makers on how to use public resources when designing and managing open spaces. In Rio de Janeiro, I have been participating in a pioneer study - in partnership with the Open Space Systems Lab (SEL-RJ) from the Federal University of Rio de Janeiro – that is using a combination of remote sensing and geoprocessing methods to identify, categorize and quantify urban forestry in the city.

The study uses the Normalised Difference Vegetation Index (NDVI) to extract the vegetation cover from high-resolution aerial imagery. Using GIS tools, these aerial raster images are then transformed in vector data - a set of editable polygons that represents the mapped urban trees. As a final step, urban trees are categorized based on their location – within private or public open spaces - and their areas are calculated according to the sum of the tree crowns mapped for each category.

Figure 2. The sequence of images illustrates the results of the urban forestry mapping stages in public and private areas of the city.

Figure 1. The sequence of images illustrates the results of the urban forestry mapping stages in public and private areas of the city. Image source: Bruno Ragi.

After modeling the urban forestry inventory, the study will input other environmental and social-economic variables (e.g., microclimate conditions and air quality, access to education, healthcare, and public safety) as an effort to deeper understand how cities can harness the benefits of urban trees.

The project is still in progress and its outcomes will be published by the end of the year.

Figure 3. Kernel density analysis showing the density of trees at Rio de Janeiro’s central region. This type of analysis has been used to point out the lack of trees in specific parts of the city and also to help the group better understand what kind of strategies can be developed to increase urban forestry across the city.

Figure 2. Kernel density analysis showing the density of trees at Rio de Janeiro’s central region. This type of analysis has been used to point out the lack of trees in specific parts of the city and also to help the group better understand what kind of strategies can be developed to increase urban forestry across the city. Image source: Bruno Ragi.

REFERENCES

[1] DE CONINCK, Heleen et al. Strengthening and implementing the global response. In: Global warming of 1.5° C: Summary for policymakers. IPCC-The Intergovernmental Panel on Climate Change, 2018. p. 313-443.

[2] LÜTTGE, Ulrich; BUCKERIDGE, Marcos. Trees: structure and function and the challenges of urbanization. 2020.

[3] SEIFERLING, Ian et al. Green streets− Quantifying and mapping urban trees with street-level imagery and computer vision. Landscape and Urban Planning, v. 165, p. 93-101, 2017.