Mapping the Green Infrastructure

Urban areas face a multitude of environmental challenges, from deadly summer high temperatures, to poor air quality, to impaired aquatic ecosystems from stormwater runoff. The traditional gray infrastructure solutions to these challenges are at best costly, and at worst, not feasible. To address these challenges many cities have turned their focus to green solutions. One such solution that has been adopted my numerous cities in the US and Canada is to increase the amount of urban tree canopy. When cities move in this direction the first question they typically ask is How much tree canopy do we currently have?

One would think that with advances in satellite and aerial imaging systems, digital image processing, and automated feature extraction over the past five decades that accurate and detailed land cover information is readily available. Unfortunately it is not. Although the USGS produces tree canopy estimates as part of the National Land Cover Database (NLCD), the relatively coarse resolution of the imagery used (30 meter Landsat data) makes these estimates inaccurate in urbanized landscapes where much of the tree canopy comes from small patches and individual street trees.

High-resolution imagery from multispectral aerial cameras or satellite sensors are far better at detecting small features, but shadowing from buildings results in highly variable spectral values for tree canopy pixels, greatly complicating the ability to consistently map tree canopy over an entire city. Because LiDAR is an active sensing technology it literally has the ability to see through shadows, producing data, which unlike imagery, is generally consistent regardless of the lighting conditions.

When we did our first urban tree canopy assessment for Baltimore in 2004, airborne LiDAR was unavailable. In reviewing the resulting tree canopy map we realized that we failed to detect many of the trees, particularly those that fell within building shadows. Since that first assessment LiDAR has become more commonplace and many cities, including Baltimore, have invested in the technology.

Typically, the driving focus for cities to invest in LiDAR has been the need to obtain accurate bare earth elevation data to support gray infrastructure projects. Fortunately for our tree canopy work, the contractors for these projects also delivered the point cloud. It didnt take us long to realize the benefit of integrating LiDAR into our tree canopy mapping workflow.

When we reassessed the tree canopy in Baltimore using a combination of LiDAR and imagery we found that Baltimores tree canopy was not 20%, as originally thought, but 27%. The jump of 7 percentage points was almost entirely due to our ability to detect small trees in shadowed areas, all thanks to LiDAR. In downtown sections of the city the estimates of tree canopy more than doubled.

Since integrating LiDAR into our automated feature extraction workflows we found that it simultaneously increases the accuracy of the resulting land cover map by an average of 10 percentage points while reducing project costs by up to 75%. What is perhaps even more exciting is that this work has helped to raise the profile of LiDAR among environmental planners and urban foresters, creating advocates for the technology in the process.

Those of us in the geospatial industry will always argue for more and better data, but when the argument comes from managers and decision makers I think it holds more weight. In the coming years LiDAR will play an ever-increasingly important role in urban green infrastructure assessment as we seek to track the progress of urban tree canopy initiatives, and the industry as a whole will see a long-term benefit from projects that take LiDAR data and turn it into actionable information that can be used by decision makers.

About the Author

Jarlath ONeil-Dunne

Jarlath O'Neil-Dunne ... Jarlath O'Neil-Dunne is a researcher with the University of Vermont's (UVM) Spatial Analysis Laboratory (SAL) and also holds a joint appointment with the USDA Forest Service's Northern Research Station. He has over 15 years experience with GIS and remote sensing and is recognized as a leading expert on the design and application of Object-Based Image Analysis Systems (OBIA) for automated land cover mapping. His team at the SAL has generated billions of pixels worth of high-resolution land cover data from a variety of aerial, satellite, and LiDAR sensors in support of urban forestry planning, ecosystem service estimation, and water quality modeling. In addition to his research duties he teaches introductory and advanced courses in GIS and remote sensing using ArcGIS, ERDAS IMAGINE, eCognition, and QT Modeler. He earned a Bachelor of Science in Forestry from the University of New Hampshire, a Masters of Science in Water Resources from the University of Vermont, and certificates in hyperspectral image exploitation and joint GIS operations from the National Geospatial Intelligence College. He is a former officer in the United States Marine Corps where commanded infantry, counter-terrorism, and geospatial intelligence units.
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