inst/app/www/methods.md

In Metropolitan-Council/planting.shade: Tree planting and preservation prioritization tool

Methods

Growing Shade developed out of a collaboration between the Metropolitan Council, The Nature Conservancy, and Tree Trust. We thank members of the advisory group for initial consultations and thank all individuals who provided feedback during the development phase of this project.

Methods and data sources for the analyses presented within the Growing Shade are detailed below. Please contact us if you have questions or feedback.

Prioritization layer

Priority variables were sourced from several locations including:

• Demographic and socioeconomic information comes from the 2020 decennial census, American Community Survey 5-Year Summary File (2017-2021), and the Equity Considerations data published by the Metropolitan Council. Census block group data is used.
• Health information comes from the PLACES dataset published by the Center of Disease Control and Prevention (2023). PLACES data is reported by census tract.
• Tree canopy and green space information is obtained from the Copernicus Sentinel-2 satellite mission.
• Climate data including temperatures and flood risk was processed using the Climate Vulnerability Assessment published by the Metropolitan Council.

Priority variables were standardized and scaled so that the z-score was normally distributed on a 0-10 scale (by multiplying the normal distribution of the z-score for each variable by 10).

Based on user-defined selection of priority variables, standardized scores are averaged to create a single, integrated priority value.

Tree canopy

Growing Shade uses and shows a tree canopy layer from 2021. A machine learning method was created in Google Earth Engine and used to detect tree cover from other land cover types using Sentinel-2 satellite imagery. Any areas identified as open water or cultivated cropland were removed.

Next, the tree canopy as identified with Sentinel-2 data was calibrated to the tree canopy identified in 2015 using LiDAR data from 2011 (Twin Cities Metropolitan area 1-meter land cover classification). With 1000 equal-area regions across the 7-county area, a scaling factor of 0.885 was used to bring the Sentinel data in line with on-the-ground tree canopy. This scaling factor is appropriate for our methods of using 10 m x 10 m resolution data, which is often larger than tree canopies. This scaling factor makes our data align very closely with other reports (r^2 = 0.96) while still leveraging the scalability and temporal accuracy of our method.

Metropolitan-Council/planting.shade documentation built on Feb. 25, 2024, 3:15 a.m.