Top-Down Urban Forest Assessment Tools
Top-down approaches are used to describe urban tree canopies (UTCs), which are simply areas of trees that — when viewed from above — block the view of the ground. Accuracy of top-down assessments depends on image quality, or spatial resolution. The cost of top-down assessments varies depending on the technology and programs used, as well as the level of analysis and tools in the project scope. Some data sets are free to access online, while others can cost several thousand dollars. Below are the common categories of top-down assessment tools and specific programs and software that can be used to conduct these types of analyses.
Aerial photos are easy to access through services like Google Earth and i-Tree Canopy, which uses Google Maps. By looking at different areas and dates, users are able to get a more complete picture of the urban forest. However, photo interpretation is limited in detail, which can lead to misinterpretation of data. Data also cannot be summarized on multiple scales.
- High-resolution imagery of a UTC uses digital aerial imagery gathered from satellites to create a cover map with detailed — typically less than one meter pixel resolution — and accurate tree canopy data. It can be used with GIS, another top-down approach, and is able to show users potential spaces to plant trees at different scales. This method may be time consuming and costly, ranging anywhere from $5,000 to $40,000, and requires a trained technician and special software to develop cover maps. Discover how Phoenix used high-resolution imagery.
- i-Tree Canopy uses Google Maps aerial imagery to conduct a tree cover assessment. The user is able to assign boundaries on the online map to create a defined project area. i-Tree Canopy then generates random sample points within the project area. The user is able to zoom-in on each point and choose from a list of cover types for that point. The more points that are defined, the more accurate the cover estimate will be. For example, 100 sample points usually have a standard error of about 4.6 percent, while 1,000 points will have a standard error of about 1.4 percent.
i-Tree Vue uses National Land Cover Database (NLCD) satellite imagery and data (updated by Google Maps) to assess an area’s land cover, which includes tree canopy and some ecosystem services. Modeling with i-Tree Vue can be used to plan out future impacts and benefits of planting scenarios. Outputs such as carbon sequestration, pollution removal and carbon storage can be calculated based on modeling scenarios.
- NLCD is a 16-class land cover classification database, available for free online, that includes the entire conterminous United States (the lower 48 states) at a spatial resolution of 30 meters. The 2006 NLCD database is based primarily on classification of Landsat Enhanced Thematic Mapper+ using 2006 satellite data. However, NLCD also tends to underestimate canopy cover by 10 percent.
GIS software (such as Esri ArcGIS and open source packages such as GRASS, Quantum, GDAL and Saga) works with maps and geographic information online and is used for creating and using maps; compiling geographic data; analyzing mapped information; sharing and discovering geographic information; using maps and geographic information in a range of applications; and managing geographic information in a database. GIS software prices can range from free online tools to several thousand dollars a year.
- InVEST (Integrated Valuation of Environmental Services and Tradeoffs) is a host of tools that map and value the ecosystem services in a given area. The user inputs GIS data such as land use, land cover and topography.
Remote Sensing-based Tools
Remote sensing is a type of geospatial technology used to map and monitor urban canopy cover and other urban landscape features. Passive remote sensing works by detecting naturally emitted or reflected radiation, while active remote sensing sends out energy in order to sense and measure the energy reflected back. This tool can track ecosystem features such as carbon storage and heat island effect. The accuracy of remote sensing can depend on factors like technology performance, viewing conditions and quality of data processing. Remote sensing is around 85 percent accurate. Remote sensing costs include data acquisition, image processing and image interpretation, causing a range in price range from a few hundred dollars to several thousand.
- LiDAR (Light Detection and Ranging) relies on reflected light, and LiDAR sensors emit their own energy in the form of a laser. The advantage of LiDAR is that it essentially sees through shadows. Incorporating LiDAR into the tree canopy mapping efforts will improve the ability to detect trees, particularly smaller, recently planted trees, resulting in a more accurate and visually coherent representation of a city’s tree canopy. Assessments that use LiDAR are very accurate and can cost several thousands of dollars.
Hyperspectral imagery is a process that collects information across an electromagnetic spectrum through sensors and provides highly detailed mapping of changes in reflected energy. Hyperspectral imagery is a very high-resolution type of remote sensing that measures 300 spectral bands or energy levels. Sensors that detect energy wavelength create digital maps based on the leaf-pigments (the chlorphyll or color) that are detected. This technology can be expensive, but cheaper and smaller alternatives, like IMEC, are being developed. Milwaukee and Oakville, Ontario, for example, have used this technology to map their ash trees in order to prepare for and manage emerald ash borer outbreaks.