### Mapping Urban Tree Canopy
This gives different land cover types each a unique spectral signature. In a spectral image classification process, training samples are collected by an analyst and a machine learning algorithm is applied to identify other pixels in the image with similar spatial and spectral properties. These pixels are classified into land cover types by their pixel values [8]. Earth imaging satellites and other airborne sensors are usually sensitive to specific wavelengths of light, including near-infrared, invisible to the human eye. Multispectral sensors generate data ranging from the visible spectrum (red, blue, green) to near-infrared, while hyperspectral imagery collects hundreds of spectral bands. Perhaps the most common and suitable method of assessing UTC is utilizing high-resolution spectral aerial imagery and land classification techniques [26]. Land cover features in imagery are identified and extracted using automated techniques that produce spatially accurate and high-resolution custom land cover maps. A high resolution is often defined as 1 m or smaller, making this resolution more appropriate for neighborhood- and even parcel-level analysis [8]. When using high-resolution multispectral imagery, object-based image analysis (OBIA) classification offers accuracy advantages over traditional pixel-based classification techniques [27; 28]. While traditional pixel-based classification considers only the spectral characteristics of the pixel clusters in multispectral imagery, object-based classification considers the spectral, geographic, geometric, and topological characteristics of the pixel clusters in multispectral imagery [29]. As a result, object-based classification approaches can yield more accurate, smoother, more interpretable land cover datasets compared to those of pixel-based approaches [30]. This segmentation and classification process can be time-consuming and requires large computational resources, especially when performed at the scale of larger municipality geographies [8]. Emerging techniques utilizing neural networks and deep learning are also proving to be an effective means to classify RGB imagery quickly and efficiently and identify objects such as trees and distinguish land cover types. Little research attention has been given to these methods in ecosystems science until recently, as these methods require a vast number of samples to feed into a deep learning algorithm and the results are often not conducive to field verification. Techniques using small subsets of LiDAR to perform unsupervised image classification have been developed to automatically collect samples. The results of the unsupervised classification are then improved with hand annotation and re-fed into the neural network to increase the accuracy [31]. Users and GIS data and software providers are sharing \"deep learning packages\" with pretrained files to apply to imagery. These packages usually require additional hand-annotated samples to derive meaningful results, as they are biased toward the geography and source imagery, yet they remain a viable option for the classification of trees by saving valuable time for spatial data scientists. These techniques show promise in utilizing the wealth of high-resolution imagery readily available from small satellites and drones, applicable to land management and environmental conservation. For this study, an object-based image analysis and supervised classification method was used with high-resolution multispectral imagery, as described below in Section 2. Given the research needs and resources, it was determined that this was the most appropriate and accessible method for the classification of land cover and mapping of urban tree canopy for our study area.