Drone Photogrammetry-Based Meticulous Monitoring and Management of Urban Park Green Space

Abstract

Urban park green spaces combine multiple ecological benefits and meet the entertainment and leisure needs of residents, and have attracted the attention of urban managers and various stakeholders. The benefits of urban park green space mainly come from vegetation, facilities, and the spaces they create. To better monitor and manage individual urban parks with data, suitable remote sensing methods and suitable protocols are needed for recording, evaluation, and visualization. Drones have become a potential tool for future monitoring and management of individual urban parks due to their ease of operation, cost-effectiveness, and ability to derive multiple types of data. However, previous relevant studies have not considered the particularity of urban park green space vegetation and the facilities and spaces that provide cultural value. Monitoring of vegetation mainly focused on homogeneous green spaces such as artificial forests, orchards, and street trees. Research on facilities focused on campus, cultural heritage, and road-supporting facilities. Therefore, it is necessary to explore how drones can be used to assess objects of concern (vegetation, facilities, and spaces) in urban park monitoring and management. This study draws inspiration from a variety of drone processing methods to explore a processing workflow and effective data suitable for heterogeneous urban park green spaces, making full use of all data that can be derived from drones. The results show that drones can effectively estimate tree heights through three-dimensional point clouds in heterogeneous green spaces. Missions with high-quality flight parameters have an accuracy comparable to missions targeting homogeneous green areas. At the same time, drones can effectively estimate crown width through orthophotos in heterogeneous green spaces. In addition, drones demonstrate excellent accuracy, efficiency, and good visualization when monitoring facilities and spaces. Therefore, it is feasible and effective to apply various data from drones (i.e., aerial photos, point clouds, orthophotos, and digital surface model (DSM)) in urban park green space monitoring and management. This will contribute significantly to the digital development of urban park green spaces, as well as the advancement of urban landscape ecology, landscape architecture, urban planning, and urban forestry disciplines.