Enhancing Landscape Performance Measurement Using Smart Devices, Data Visualization, and Longitudinal Tracking

This dissertation explores the use of smart devices to measure the environmental landscape performance (LP) of landscape projects. It proposes and evaluates an alternative landscape performance measurement framework (ALPMF) with smart device assistance. By providing new measurement methods and tools, it aims to fill some existing and potential gaps in LP and promote its development. LP has been proposed in landscape architecture to measure landscape projects' sustainable benefits. Due to LP research's short development history, some gaps exist, including one-time measurements, a lack of standard evaluation methods, and insufficient measurement tools. Given the advantages of smart devices in data collection and the successful application of smart devices in other design-related fields, this dissertation explores their feasibility as assessment tools in environmental LP studies. It begins by analyzing each LP research case's report listed on the Landscape Performance Series (LPS) website to explore the limitations of traditional measurement methods and tools. Following a survey of professionals' perspectives on LP metrics. Based on the survey results, the researcher selects certain air quality and water quality LP metrics as variables (air temperature, humidity, carbon dioxide, particulate matter, total dissolved solids, and electronic conductivity) for subsequent experiments. Two experiments explore smart devices' strengths and limitations in collecting LP data and measuring landscape projects' LP in terms of accuracy, real-time, spatial resolution, and longitudinal analysis. The researcher proposes the ALPMF and conducts a comparative study with the traditional landscape performance measurement framework (TLPMF) to measure a project's LP. By comparing methods, tools, and results, the study examines the advantages and effectiveness of the ALPMF to a certain extent and explores its limitations. The research results show that smart devices and the ALPMF can provide more accurate, real-time, spatial resolution, and longitudinal LP data. The results also demonstrate the effectiveness of the ALPMP. Furthermore, this dissertation offers several insights and suggestions for further developing smart devices and the ALPMF in LP and landscape architecture. This dissertation fills some research gaps and provides new tools and methods for future LP measurement. It contributes to improving landscape projects' sustainable values and refining the landscape architectural design guidelines. As an interdisciplinary study, it also provides an example of the intersection of landscape architecture with other disciplines, such as mechanical engineering and computer science. It helps to broaden the knowledge boundary of landscape architecture.