### Mapping Urban Tree Canopy  
To understand the correlations between urban tree canopy (UTC) and other variables, the spatial distribution of trees within a given geography must first be understood. The first step in performing a UTC assessment for a community is to define goals that align with their objectives and values. These goals should be set with the collaboration of community stakeholders and community needs and values related to issues such as storm water management, shading, equity, and public health. Having clear goals at the start of an analysis helps to determine the data needs and analysis methods; a common deliverable from such assessments is a suitability map identifying areas in need of tree planting projects or forest preservation [8; 20; 21]. There are numerous methods to measure UTC using geographic information. At smaller scales, detailed on-the-ground inventories can be conducted where field workers manually count, identify, and map trees. These types of manual approaches are not feasible for large extents, such as a city-wide analysis, given the mass number of trees to be counted, private landowner access issues, and limitations of time and other resources. Techniques  
Figure 1: Locator map of study area: Chattanooga, Tennessee. using GIS and remote sensing are the most common and convenient tools in conducting UTC assessments [22]. One traditional approach to assessing UTC is using the \"dot grid method\". A set of random points is cast upon a grid on either a printed or digital aerial image and the points are interpreted to determine tree cover by the center of each point. The presence or absence is then tallied for each point [23]. This approach is simple, and free digital GIS tools for this method, such as i-Tree Canopy, are available, but this technique is both time-consuming and often lacks a high spatial resolution [8]. Another common and economical method is to use GIS and open-source data provided by federal agencies to measure UTC for a given area. One common dataset, the National Land Cover Database, hosts land cover type, tree canopy cover, impervious surface, and land cover change maps at a 30 m resolution for all the United States. These data are provided by the Multi-Resolution Land Characteristics Consortium (MRLC), a conglomerate of federal agencies, every 5 years [24]. The advantages of the NLCD products are that they are free to the public and date back to 2001, offering the ability to easily perform spatial-temporal analyses of UTC. However, at a resolution of 30 m, the data are too coarse for neighborhood- and city-block-level analyses and tend to under-represent trees, and the temporal representation of the data might not align with a project's needs and goals. Spatially, existing NLCD datasets would not be useful for analyses outside of the United States, although other options exist; for example, Esri's newly released 10-m-resolution Land Cover Map of the World is derived from ESA Sentinel-2 imagery, with plans to update the map annually [25]. However, even a 10-m-resolution dataset still generalizes land cover at the neighbor and block scale. Light Image Detection and Ranging (LiDAR) is another common method of mapping ground objects. LiDAR uses an active sensor attached to a plane, helicopter, or drone to scan an area with lasers to produce a dense point cloud representing the height attributes of objects such as trees, buildings, roads, parking lots, and bare earth across surfaces. LiDAR scans are highly accurate, often providing high resolutions of 2 m or better, and can penetrate cloud cover and shadows, which is problematic in other approaches to aerial image analysis. A drawback is the extreme cost of LiDAR and the infrequency of this type of data collection, which may mean that certain study areas have less coverage [8]. Multispectral imagery analysis is a common method of analyzing Earth objects to overcome the obstacles described above. In brief, light is reflected on the ground by objects, where certain wavelengths are absorbed and others are reflected.