UCLA

This dissertation, largely funded by the County of Los Angeles to optimize its urban forest planning and management, conducts a critical examination of the implications, applications, and limitations of remote sensing technology in applied science. Leveraging technologies such as Light Detection and Ranging (LiDAR) and high-resolution optical data, remote sensing has offered unparalleled insights into the Earth's surface and atmosphere. Specific applications, such as tree crown segmentation and creation of precise 3D environmental models, have proven invaluable in fields including ecology and urban planning. However, this technology is not without its challenges. An over-reliance on remote sensing data, absent corroborative ground-truthing, can lead to flawed conclusions. Furthermore, using low-resolution imagery for policy decisions concerning fine-scale, on-the-ground issues could create discrepancies between the data and actual conditions, undermining the effectiveness of interventions. In my first and second chapter of this dissertation, I demonstrate that remote sensing technology has become indispensable, particularly for monitoring individual species over time for urban forest management. By identifying and delineating individual tree crowns, this technology enables accurate estimation of species composition and condition. This detailed information facilitates species health monitoring and early disease detection, thereby aiding effective management interventions. However, the effectiveness of remote sensing in urban forestry necessitates continuous data update and integration of ground-based observations for validation. In my third chapter, I transition from the practical applications of remote sensing in urban forestry, and lean on previous research experience, including my collaboration with Los Angeles County, to delve into the social dimensions of this technology. The concept of "socializing the pixel" provides a critical framework for understanding how remote sensing operates within and is influenced by sociopolitical contexts. This perspective recognizes that remote sensing is not a purely objective tool but is embedded within social structures and power dynamics. Stakeholder engagement is thus crucial to consider diverse perspectives in data interpretation and application, enhancing the technology's effectiveness and local relevance. Conversely, power imbalances between remote sensing experts and non-experts, and denial of local knowledge can lead to biased results or misinterpretation of data. Hence, returning to the concept of "socializing the pixel" is critical to ensure effective and equitable use of remote sensing technology.