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• 1. [Article] Street Trees in the Urban Forest Canopy: Portland, Oregon

  Attempts to identify the contribution of street trees to the overall urban forest of a city have been rare and lack consensus on how to measure that contribution – percentage of trees, percentage of canopy ...

  Citation × Citation Citation Abstract Copy Title: Street Trees in the Urban Forest Canopy: Portland, Oregon Author: Poracsky, Joseph, Banis, David Year: 2005 Attempts to identify the contribution of street trees to the overall urban forest of a city have been rare and lack consensus on how to measure that contribution – percentage of trees, percentage of canopy cover, or percentage of leaf area. The actual numeric values presented in the literature also vary over a broad range and often are based on estimates, extrapolations from aggregated data, or simply stated with no empirical data referenced. This study was undertaken to evaluate the contribution of street trees to canopy in Portland, Oregon. The study involved both visual and digital analysis of multi-band aerial images, field observation, and GIS analysis involving several ancillary data themes. The strategy was to calculate the street tree component (STC) of the urban forest canopy within a sample of areas in the city and generalize that pattern to the whole city. The analysis indicates that street trees constitute 3.4% of the whole study area. In commercial / industrial areas the value drops to only 1.1% and in residential areas rises to 4.8%. For the overall study area the STC of the canopy is 17.2%, a value in the middle of the range of values found in the published literature. In commercial / industrial areas we found a value of 23.7% and a value of 16.6% for residential areas. Residential areas are overall better treed than commercial / industrial areas, but do not rely on street trees to contribute as large a percentage to overall canopy as do commercial / industrial areas. Available planting space in the right-of-way (ROW) is much more nearly constant (or independent of land cover) than the amount of planting space available inside a block. Therefore, the STC tends to vary inversely with the total amount of vegetated area. As the vegetated (read, plantable) area decreases, the ROW becomes a more significant space for planting and the STC increases. Conversely, as the vegetated area increases, the ROW becomes a less significant space for planting and the STC decreases. For both residential and commercial / industrial areas, the percentage of street tree area within the total canopy varies from locale to locale. The relatively small sampling used in this study suggests that neighborhood age and physical characteristics influence the STC in a given neighborhood, particularly for residential areas. Analysis allowed division of the study area into five residential neighborhood groups that attempt to reflect local differences. A larger / more detailed sampling would allow for improved delineation, perhaps below the level of neighborhood. Three generalized spatial patterns emerge for Portland. Street tree canopy as a percent of area seems to follow an east-west gradient, with lower values to the east and higher values to the west. Street trees as a component of the total canopy takes on an inverted-U shape, with higher values in the Inner Eastside and lower values to the east and to the west. The overall tree canopy category takes on just the opposite pattern – a standard-U – with lower values in the Inner Eastside and higher values to the east and west. This study represents a useful starting point for understanding the dynamics of an important and highly visible component of the urban forest. Title: Street Trees in the Urban Forest Canopy: Portland, Oregon Author: Poracsky, Joseph, Banis, David Year: 2005 Attempts to identify the contribution of street trees to the overall urban forest of a city have been rare and lack consensus on how to measure that contribution – percentage of trees, percentage of canopy cover, or percentage of leaf area. The actual numeric values presented in the literature also vary over a broad range and often are based on estimates, extrapolations from aggregated data, or simply stated with no empirical data referenced. This study was undertaken to evaluate the contribution of street trees to canopy in Portland, Oregon. The study involved both visual and digital analysis of multi-band aerial images, field observation, and GIS analysis involving several ancillary data themes. The strategy was to calculate the street tree component (STC) of the urban forest canopy within a sample of areas in the city and generalize that pattern to the whole city. The analysis indicates that street trees constitute 3.4% of the whole study area. In commercial / industrial areas the value drops to only 1.1% and in residential areas rises to 4.8%. For the overall study area the STC of the canopy is 17.2%, a value in the middle of the range of values found in the published literature. In commercial / industrial areas we found a value of 23.7% and a value of 16.6% for residential areas. Residential areas are overall better treed than commercial / industrial areas, but do not rely on street trees to contribute as large a percentage to overall canopy as do commercial / industrial areas. Available planting space in the right-of-way (ROW) is much more nearly constant (or independent of land cover) than the amount of planting space available inside a block. Therefore, the STC tends to vary inversely with the total amount of vegetated area. As the vegetated (read, plantable) area decreases, the ROW becomes a more significant space for planting and the STC increases. Conversely, as the vegetated area increases, the ROW becomes a less significant space for planting and the STC decreases. For both residential and commercial / industrial areas, the percentage of street tree area within the total canopy varies from locale to locale. The relatively small sampling used in this study suggests that neighborhood age and physical characteristics influence the STC in a given neighborhood, particularly for residential areas. Analysis allowed division of the study area into five residential neighborhood groups that attempt to reflect local differences. A larger / more detailed sampling would allow for improved delineation, perhaps below the level of neighborhood. Three generalized spatial patterns emerge for Portland. Street tree canopy as a percent of area seems to follow an east-west gradient, with lower values to the east and higher values to the west. Street trees as a component of the total canopy takes on an inverted-U shape, with higher values in the Inner Eastside and lower values to the east and to the west. The overall tree canopy category takes on just the opposite pattern – a standard-U – with lower values in the Inner Eastside and higher values to the east and west. This study represents a useful starting point for understanding the dynamics of an important and highly visible component of the urban forest. Close

• 2. [Article] Integrating High-Resolution Datasets to Target Mitigation Efforts for Improving Air Quality and Public Health in Urban Neighborhoods

  Reducing exposure to degraded air quality is essential for building healthy cities. Although air quality and population vary at fine spatial scales, current regulatory and public health frameworks assess ...

  Citation × Citation Citation Abstract Copy Title: Integrating High-Resolution Datasets to Target Mitigation Efforts for Improving Air Quality and Public Health in Urban Neighborhoods Author: Shandas, Vivek, Voelkel, Jackson, Rao, Meenakshi, George, Linda A. Year: 2016 Reducing exposure to degraded air quality is essential for building healthy cities. Although air quality and population vary at fine spatial scales, current regulatory and public health frameworks assess human exposures using county- or city-scales. We build on a spatial analysis technique, dasymetric mapping, for allocating urban populations that, together with emerging fine-scale measurements of air pollution, addresses three objectives: (1) evaluate the role of spatial scale in estimating exposure; (2) identify urban communities that are disproportionately burdened by poor air quality; and (3) estimate reduction in mobile sources of pollutants due to local tree-planting efforts using nitrogen dioxide. Our results show a maximum value of 197% difference between cadastrally-informed dasymetric system (CIDS) and standard estimations of population exposure to degraded air quality for small spatial extent analyses, and a lack of substantial difference for large spatial extent analyses. These results provide the foundation for improving policies for managing air quality, and targeting mitigation efforts to address challenges of environmental justice. Title: Integrating High-Resolution Datasets to Target Mitigation Efforts for Improving Air Quality and Public Health in Urban Neighborhoods Author: Shandas, Vivek, Voelkel, Jackson, Rao, Meenakshi, George, Linda A. Year: 2016 Reducing exposure to degraded air quality is essential for building healthy cities. Although air quality and population vary at fine spatial scales, current regulatory and public health frameworks assess human exposures using county- or city-scales. We build on a spatial analysis technique, dasymetric mapping, for allocating urban populations that, together with emerging fine-scale measurements of air pollution, addresses three objectives: (1) evaluate the role of spatial scale in estimating exposure; (2) identify urban communities that are disproportionately burdened by poor air quality; and (3) estimate reduction in mobile sources of pollutants due to local tree-planting efforts using nitrogen dioxide. Our results show a maximum value of 197% difference between cadastrally-informed dasymetric system (CIDS) and standard estimations of population exposure to degraded air quality for small spatial extent analyses, and a lack of substantial difference for large spatial extent analyses. These results provide the foundation for improving policies for managing air quality, and targeting mitigation efforts to address challenges of environmental justice. Close

• 3. [Article] Tree Cover Mapping for Assessing Greater Sage-Grouse Habitat in Eastern Oregon

  We used a predictive model to map canopy cover of vegetation over seven feet in height ("tall woody vegetation") at 30-meter resolution over nearly 29 million acres within and adjacent to the range of ...

  Citation × Citation Citation Abstract Copy Title: Tree Cover Mapping for Assessing Greater Sage-Grouse Habitat in Eastern Oregon Author: Nielsen, Eric M., Noone, Matthew D. Year: 2014 We used a predictive model to map canopy cover of vegetation over seven feet in height ("tall woody vegetation") at 30-meter resolution over nearly 29 million acres within and adjacent to the range of the greater sage-grouse in Oregon (Figure 1). Texture measures computed at various resolutions from color-infrared aerial photography provided the main source of predictor data used to produce the map. Canopy cover was treated as a categorical variable using six cover classes: absent (cover class C0), present at less than 4% (C1), 4 – 10% (C2), 10 – 20% (C3), 20 – 50% (C4), and 50% and over (C5). The map is referenced to conditions in the years 2011 and 2012. Although the specific target of the mapping was western juniper (Juniperus occidentalis), our reference data did not permit separating juniper from other tall woody vegetation during the predictive modeling process. The majority of the tall woody vegetation within the project area is western juniper. However, in high elevation regions, riparian, wetland, and residential areas, other vegetation is occasionally represented. The methodology discussed here produces raw modeled data. It is recommended that prior to use in most applications this raw tree cover product be additionally filtered or masked to eliminate false detections which often occur adjacent to agricultural areas and roads. For species-specific applications, an additional modeling phase is necessary to either eliminate tree cover detections likely to be species other than the target, or to model species importance values associated with each tree occurrence. Title: Tree Cover Mapping for Assessing Greater Sage-Grouse Habitat in Eastern Oregon Author: Nielsen, Eric M., Noone, Matthew D. Year: 2014 We used a predictive model to map canopy cover of vegetation over seven feet in height ("tall woody vegetation") at 30-meter resolution over nearly 29 million acres within and adjacent to the range of the greater sage-grouse in Oregon (Figure 1). Texture measures computed at various resolutions from color-infrared aerial photography provided the main source of predictor data used to produce the map. Canopy cover was treated as a categorical variable using six cover classes: absent (cover class C0), present at less than 4% (C1), 4 – 10% (C2), 10 – 20% (C3), 20 – 50% (C4), and 50% and over (C5). The map is referenced to conditions in the years 2011 and 2012. Although the specific target of the mapping was western juniper (Juniperus occidentalis), our reference data did not permit separating juniper from other tall woody vegetation during the predictive modeling process. The majority of the tall woody vegetation within the project area is western juniper. However, in high elevation regions, riparian, wetland, and residential areas, other vegetation is occasionally represented. The methodology discussed here produces raw modeled data. It is recommended that prior to use in most applications this raw tree cover product be additionally filtered or masked to eliminate false detections which often occur adjacent to agricultural areas and roads. For species-specific applications, an additional modeling phase is necessary to either eliminate tree cover detections likely to be species other than the target, or to model species importance values associated with each tree occurrence. Close