Prioritizing Street Tree Planting Locations to Increase Benefits for All Citizens: Experience From Joliette, Canada

Abstract

As the climate continues to warm and the world becomes more urbanized, our reliance on trees and the benefits they provide is rapidly increasing. Many cities worldwide are planting trees to offset rising temperatures, trap pollutants, and enhance environmental and human health and well-being. To maximize the benefits of planting trees and avoid further increasing social inequities, a city needs to prioritize where to establish trees by first identifying those areas of greatest need. This work aims to demonstrate a spatially explicit approach for cities to determine these priority locations to achieve the greatest returns on specific benefits. Criteria for prioritization were developed in tandem with the City of Joliette, Canada, and based on nine indicators: surface temperature, tree density, vegetation cover, resilience, tree size and age, presence of species at risk, land use type, socioeconomic deprivation, and potential for active transportation. The City’s preferences were taken into account when assigning different weights to each indicator. The resulting tree planting priority maps can be used to target street tree plantings to locations where trees are needed most. This approach can be readily applied to other cities as these criteria can be adjusted to accommodate specific tree canopy goals and planning constraints. As cities are looking to expand tree canopy, we hope this work will assist in sustaining and growing their urban forest, enabling it to be more resilient and to keep providing multiple and sustained benefits where they are needed the most.

Tree Planting Prioritization in National Capital Region, Philippines Using Remote Sensing, Analytic Hierarchy Process and Gis

“…Indicators may be selected and weights may be assigned through participatory methods or through statistical methods (Morani et al, 2011). The choices may also be based on published literature, existing studies, and practices (Sousa-Silva et al, 2021). In choosing specific indicator combinations and associated weights, it is also important to consider the study objectives, site characteristics, and data availability.…”

“…In this study, the factors that affect tree-planting prioritization were chosen based on published literature and research (Sousa-Silva et al, 2021;Morani et al, 2011;Locke et al, 2010), and in consultation with experts from the DENR-NCR and Department of Science and Technology -National Capital Region (DOST-NCR), and representatives of relevant departments of various LGUs in NCR.They are: (1) population, (2) LST, (3) air quality, (4) tree cover, and (5) land cover. These five were determined to be the factors that best meet the study area's highest priorities, which in this case is to combat the effects of climate change (i.e., global warming) and air pollution and bring trees closer to people.…”

“…where PPI is the resulting planting priority index value, I is the normalized score of a specific indicator, W is the associated weight of a specific factor, and 1, 2, … n are the PPI factors (Sousa-Silva et al, 2021;Lin, 2020;Morani et al, 2011). This was done using the Raster Calculator function of QGIS.…”

“…The resulting index was mapped into a proposed tree planting prioritization scheme in NCR. In this analysis, we used the Jenks natural breaks algorithm to classify and, consequently, visualize the PPI values (Sousa-Silva et al, 2021). This classification method considers the inherent characteristics of the data by minimizing the variation within classes and maximizing the variation among classes resulting in the most similar data being grouped whose class boundaries are set where there are big jumps in the data values.…”

An Urban Forest Diversification Software to Improve Resilience to Global Change

“…SylvCiT provides information on forest structure by showing the distribution of DBH classes and the number of old trees by functional group. Tree size may be used as a proxy to identify old trees (Sousa-Silva et al 2021). We used a combination of 5 Quebec municipal tree inventories to develop a database in which trees of each species (minimum of 20 trees per species) were classified into DBH percentiles.…”