This week my colleague Dexter Locke and I had the opportunity to present at the DE / MD APA 2014 Regional Planning Conference. We presented on the suite of urban tree canopy tools available to planners.
While many tree canopy goal efforts have focused disproportionately on tree planting, strategies for all variables in the equation below are needed to realize a tree canopy goal. There are tools to support each component.
Conceptual model of tree canopy goal framework
Imagery + LiDAR data fusion and resulting hi-res 7-class landcover
Fusing these two datasets provides the highest level of accuracy, more than either does on its own.
By mashing up the landcover data with GIS layers of various geographies, we can quantify existing and potential tree canopy cover at a variety of scales from the parcel to the neighborhood to the city to the region. This is an Urban Tree Canopy (UTC) assessment.
UTC Assessment showing existing and potential UTC at the parcel and landscape (3-county) scales
With UTC Prioritization, we determine desired outcomes (why do you want more tree canopy – heat island reduction? environmental equity? stormwater management?), identify GIS data to serve as proxies for those outcomes, and provide a weighted, spatially explicit map to help you target efforts and use limited resources most efficiently.
UTC Prioritization process of converting units of various layers to a common scale and weighting and ranking to maximize efficient use of resources to realize desired outcomes
UTC Market Segments uses market segmentation data to identify what markets are participating in UTC goal efforts and to assess market penetration and access. Results are displayed as an odds ratio where 1:1 is proportional participation/access based on that market segment, a greater odds ratio is better than proportional participation/access, and a lower odds ration is lower than proportional participation/access.
UTC Market Segments assessment of market participation in tree and rain barrel programs in context of existing and potential UTC
UTC Change Detection is based on the methods we use for UTC assessment. However, in this instance we combine four datasets (imagery + LiDAR from one point in time and imagery+ LiDAR from another point in time) and map the specific locations of gain and loss over the time period between the two images (this produces more accurate results than creating a land cover image for one point in time, creating a land cover image for another point in time, and calling the difference between the two results the change over time).
UTC Change Detection mapping canopy gains and losses over a given period
The UTC tool suite is developed and deployed by Dr. Morgan Grove, US Forest Service Northern Research Station; Jarlath O’Neil-Dunne, University of Vermont Spatial Analysis Lab; Dexter Locke, Clark University; and me, Director of the Consulting Group at SavATree.
For more information, please see our Prezi on the UTC tool suite.