Urban Heat Island Research

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Quantifying and mapping spatial patterns of extreme intra-urban heat variability was my main focus during my time as a Geospatial Research Analyst at the SUPR Lab. As I transitioned into graduate school, I continued researching better ways to identify areas of cities where people are most at risk. A major difference between the method used in my research and common methods were the in-situ (or, "on the ground") measurements taken in the field. These measurements allow for high-resolution and high-accuracy machine-learning models to be created, as opposed to low-resolution and low-accuracy satellite data employed in other studies.

This research eventually moved beyond Portland, and I have worked with local governments, non-profits, and universities to accomplish field data collection in:

• The greater Portland Metropolitan Area
• Eugene, Oregon, USA
• Richmond, Virginia, USA
• Hermosillo, Sonora, Mexico
• Surrey, British Columbia, Canada
• Albuquerque, New Mexico, USA
• Tacoma, Washington, USA
• Washington, DC, USA
• Baltimore, Maryland, USA

Interactive UHI Map.

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Publications:

Portions of this work have been published in the peer-review journal Climate as Towards Systematic Prediction of Urban Heat Islands: Grounding Measurements, Assessing Modeling Techniques

Additional research pertaining to public health and urban heat has also been peer-reviewed and published by the U.S. Center for Disease Control as Developing High-Resolution Descriptions of Urban Heat Islands: A Public Health Imperative

Press Links:

• "Where Are Portland's Hottest, Most Polluted Areas?" Oregon Public Broadcasting.
• "Heat wave brings out scientists and points to need for cooling centers." Eugene Weekly.

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Canopy Analytics

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The Canopy Analytics tool was developed in order to identify large trees (which contribute greatly to reductions in temperatures in air pollution) that are most in need of preservation. The tool allows users to specify heights, building distances, and other factors that the City of Portland deems crucial in determining the risk a tree has of being removed.

I created the tree data from LiDAR height models and a multi-stage local maxima function, which was then coupled with infrared imagery. I custom coded the map and the functionality therein in JavaScript using the LeafletJS API.

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Plantability

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The Plantability tool is part of a substantial effort by Portland Parks and Recreation Urban Forestry and the Sustaining Urban Places Research Lab to understand the social, environmental, and technical aspects involved in increasing the overall canopy cover within the city. The development of this tool included collaboration with multiple community groups and public agencies and bureaus.

I created the right-of-way polygons in front of each parcel through a very complex process involving the 'growing' and 'shrinking' of street centerlines and parcel boundaries. I custom coded the map and the functionality therein in JavaScript using the LeafletJS API.

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Nitrogen Dioxide Mitigation and High-Resolution Population Mapping

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This project was a collaboration between the Sustaining Urban Places Research Lab, Willamette Partnership, and Clean Water Services. The aim of the project was to assess the reduction in vehicular emissions due to a massive tree planting campaign led by Clean Water Services.

Alongside multi-year canopy growth scenarios and pollution mitigation models, I created a high-resolution (1ft) dasymetric population map for the tri-county area which allowed for direct health impacts to be analyzed.

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Publications:

This work has been published in the peer-reviewed International Journal of Environmental Research and Public Health as Integrating High-Resolution Datasets to Target Mitigation Efforts for Improving Air Quality and Public Health in Urban Neighborhoods

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Deciduous / Evergreen Canopy Classification

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This data was created in collaboration with Oregon Metro and published in the Regional Land Information System (RLIS).

The classification has a high overall accuracy (88%; Kappa = 0.75) and a spatial resolution of 1m². In order to attain such a high accuracy, we developed an ensemble machine learning system which incorporated techniques such as Neural Networks, Random Forests, and Support Vector Machines. The ensemble was trained on ~900 variables which included trigonometric crown measures, spectral indices, and localized terrain characteristics. In addition to the classification, this project also identified ~13.4 million trees in the Portland metro area.

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Lidar Processing and Classification

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Above: Classified lidar pointcloud. Burnside St. and the U.S. Bancorp Tower, Downtown Portland, Oregon.

Throughout my time at the Sustaining Urban Places Research Lab and during my Masters of Urban Studies research, lidar has been a constant focus. Using a combination of proprietary and open-source tools, I have processed several terabytes of 3D pointcloud data and converted it into useable vector and raster GIS datasets.

Above: Height above ground raster (black and white) and canopy biomass metric (green) created from a classified lidar Pointcloud in central Portland, Oregon.

Above: From the high-resolution lidar-derived terrain rasters, I have calculated the total solar radiation (in kWh/m²) in an area close to Tryon State Park, Oregon. On the left is the unaltered data; on the right, the trees have been removed from the pointcloud prior to solar radiation processing in order to measure the impact trees have in the area.

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Vulnerability and Urban Heat

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This project seeks to provide information to public health and planning practitioners as to the resiliency of specific sub-neighborhoods in Portland, Oregon in terms of environmental health detriments. The tool allows the user to specify weights of multiple variables that reduce an individual's ability to cope to environmental stressors, which updates the map with locations most pertinent to the user. This socio-environmental analysis was developed through a multi-stage interagency multi-criteria decision making exercise. The following agencies, bureaus, and organizations were involved in the process:

• Institute for Sustainable Solutions, Portland State University
• Sustaining Urban Places Research Lab
• Bureau of Planning and Sustainability, City of Portland, Oregon
• Bureau of Environmental Services, City of Portland, Oregon
• Department of Environmental Quality, State of Oregon
• Multnomah County, Oregon
• Oregon Health Authority, State of Oregon
• Oregon Public Health Institute
• Coalition for a Livable Future

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Urban Forest Descriptions

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Above: Three sequoias that were saved from development-related removal after protests (Eastmoreland, Portland, Oregon).   [map]

Through extensive remote sensing and lidar exercises, I have created unique descriptors of Portland, Oregon's urban forests. Throughout presentations with various government and community groups, I have shared my findings in hopes of raising awareness of the magnificent trees that surround us.

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Above: At 252ft, this is the tallest tree in the City of Portland.   [map]

Above: The 126ft copper beech enveloped by Portland State University's Library. South Park Blocks, Portland, Oregon.   [map]

Above: A 137ft tree is temporarily spared from development - a crane from an abutting construction site can be see in the upper-right. N Williams St., Portland, Oregon.   [map]

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