A society grows great when its leaders plant trees whose shade they know they shall never sit. (Greek Proverb)
LiDAR-based mapping of the urban tree canopy provides the accuracy and level of detail needed for canopy change assessment, tree failure risk management, and planning that supports the creation of more equitable and resilient urban environments. Measuring the urban tree canopy with LiDAR data combined with imagery supports a wide range of environmental objectives such as managing storm water runoff, air pollution, and heat island mitigation, among others. The broad utility of these analyses create opportunities for jointly funding data acquisition by municipal departments.
Highlights and “Soundbites” from the Webinar
AppGeo led a team that mapped the urban tree canopy for the City of Cincinnati, Ohio. The following “highlights and soundbites” from the AppGeo “Canopy for the Community” webinar bring together the observations and experiences of the project participants: City of Cincinnati’s Matt Dibona (GIS Systems Analyst) and Crystal Courtney (Division Manager of Natural Resources), joined by Jarlath O’Neil-Dunne (Director, University of Vermont’s Spatial Analysis Laboratory), Brian Coolidge (Project Manager, AppGeo), and Mike Galvin (Consulting Arborist, SavATree), and hosted by Aaron Doucett (Webinar Coordinator, AppGeo).
Using LiDAR for More Accurate Tree Canopy Measurement
Accuracy is critical for tree canopy mapping because the changes in tree canopy from year to year may only be on the order of 3 to 5 percent. A measurement system that is only 95% accurate may be wildly misleading as an indication of whether canopy is increasing or experiencing losses.
Traditional means of measuring canopy from aerial imagery need to be supplemented with additional data such as from LiDAR in order to achieve the necessary accuracy to measure change in the canopy. For example, the changes in imagery collection platforms, in the resolution of the collection, time of day, shadow angles, and so forth make it hard to compare and measure canopy change from one time period to the next.
Slide from presentation showing how LiDAR data collection captures location, mass, height, and elevation, providing many useful variables with which to advance the management of urban tree canopies and landscapes.
[Lidar] enables us to capture the height (and mass) of trees (and other objects) which is really important when we want to look at losses. We can tell communities not only perhaps you’re losing or gaining tree canopy but particularly with the losses, where those losses are happening across your height classes.
(Jarlath O’Neill-Dunn, Director, University of Vermont Spatial Analysis Lab)
Knowing Location and Tree Height Improves Risk Management
The tree points and heights and location relative to infrastructure also can be used to categorize the degree of risk of a tree failing because of its proximity to private property, creating the opportunity for managing urban trees to mitigate risk.
…using this data to set prioritization for risk management, we manage 5,000 acres of park land. We have a risk management plan that spells out exactly what our frequency of inspection will be and for our park boundaries it’s at least once every six years. By having the the tree points in relation to the polygonized risk management zones, we are able to accurately identify exactly how and where we should start.
(City of Cincinnati, Crystal Courtney, Division Manager of Natural Resources)
The data also gave us the ability to say roughly how many trees do we have in parks that we need to be assessing each year, and we’re able to use that information in order to justify our budget and staffing needs in order to do this work to stay within the acceptable zone of liability that we take on as as urban forestry managers.
(City of Cincinnati, Crystal Courtney, Division Manager of Natural Resources)
Achieving Equity with Better Urban Tree Canopy Data
We had a study done in Cincinnati in 2015 that looked at the equitable distribution of canopy based on the 2010 data and what they found is that our canopy is equitably distributed. We’re lucky in that regard, but looking a little deeper what we see is that the majority of that canopy is on undevelopable hillsides, so it’s not necessarily in your front yard, it’s not necessarily on the streets that you live on, so are you actually getting those benefits that we know that trees provide to our community members? Being able to take this data set a step further and really dig into it so you know what it looks like where you live and then communicating that directly to people so that it actually has a tie back to their home.
(City of Cincinnati, Crystal Courtney, Division Manager of Natural Resources)
Heat Islands, Storm Water Runoff, and Air Pollution
The LiDAR data also can be used to measure impervious surfaces and landcover, which have broad utility for the municipality, and can motivate joint funding for data collection programs across departments, such as water and sewer, parks and recreation, community development, public health, etc.
When it comes to tree canopy I think we want to focus on, are the trees in the right place to achieve our objectives, such as storm water runoff, air pollution, and environmental equity? We see in some cities that they may have a lot of tree canopy but then we find pockets through our detailed analysis that they’re actually really lacking in tree canopy. Also through this change analysis what’s really nice is we’re able to provide Matt and Courtney with the detailed information on where the gains and losses are happening, so that they can better understand the drivers of these changes because they’re dealing with a lot of things: urbanization, private landowners decisions, and invasive species. Also tracking where the tree canopy may age out over time versus new plantings, and all of those things give a much more nuanced story that allows them to make very informed decisions
(Jarlath O’Neill-Dunn, Director, University of Vermont Spatial Analysis Lab)
…if you map the impervious surface, and if you have a tree on top of that impervious surface that does make an impact on that impervious surface, so here we’re combining all of those data sets into one. So it’s not just taking a picture of your impervious surface, it’s really taking a picture of the whole land classification set…I think we’re saving a lot of money.
(City of Cincinnati, Matt Dibona, GIS Systems Analyst)
