###### Abstract
Furthermore, plants in GSIs offer additional benefits such as urban greening (enhancing aesthetics), creating habitats for insects and diverse species, and further enhancing air quality [12; 13; 14]. The effectiveness of GSIs is contingent upon several factors, including plant traits (e.g., plant density), plant health, plant species, soil properties, media types, and retention time [12]. Integrated studies of the impact of trees on air pollution suggest that management of plant canopy cover (also referred to as plant density) could serve as a viable strategy for enhancing air quality and achieving clean air standards [10]. Moreover, the morphological traits of plants (e.g., root structure and plant canopy architecture) play a crucial role in managing the hydrological cycle [12]. Increased vegetation cover is correlated to increased environmental and ecological co-benefits such as habitat enhancement and bird species richness [2; 15]. Conversely, low plant density increases the risk of weed invasion and subsequently escalates maintenance costs [16]. Plant density is a critical factor in crop growth and yield, exerting influence over both inter- and intraspecific competition for vital resources (e.g., water, nutrients, and radiation) [17]. Traditionally, monitoring plant density relied on ground-level counts conducted within quadrats or segmented areas [18; 19]. Manual plant counting in the field is labor-intensive and disruptive, rendering it impractical for large-scale assessments. In response, an algorithm was developed to count corn plants (or plant density) and estimate plant location and intra-row spacing in segmented images [20]. Machine-vision-based methods have emerged to automate the measurement of wheat plant density during early stages using high-resolution imagery [17; 21]. Maize plant density from UAV red, green, and blue (RGB) images was also estimated to investigate the impact of image ground sampling distance (GSD) on the performance of maize plant detection at the three-to-five-leaves stage using a faster region-based convolutional neural network (RCNN) object-detection algorithm [22]. More recently, advancements in UAV imagery coupled with deep-learning techniques have enabled the estimation of plant density, including tillers, in wheat plants [19]. However, there remains a scarcity of research focused on plant density estimation specifically within GSIs. Healthy plants not only optimize the functionality of GSIs, but also contribute to the attractiveness of facilities to the public and the appreciation of property values. Vegetation indices, such as the normalized difference vegetation index (NDVI) and the normalized difference red edge (NDRE) index, offer crucial insights into vegetation characteristics, including growth, vigor, and plant dynamics [23; 24; 25]. NDVI and NDRE indices serve as reliable indicators for plant health and field assessment [25; 26]. The NDVI is derived from multispectral information as a normalized ratio between the red and near infrared bands [26]. The NDVI is a widely used vegetation index and a way to measure aggregate urban greenness and ecosystem service benefits by evaluating both the quality and quantity of vegetation through the spectral band math of remotely sensed images [2; 27; 28]. In numerous studies, the NDVI has been applied to investigate specific aspects of land degradation or habitat condition within conservation areas, including examining vegetation cover change, assessing forage quantity and quality, monitoring soil erosion and salinization, and studying the impacts of wildfire and pollutants on vegetation cover [29]. More recently, the NDVI has been utilized as a metric to discern trends in vegetation and to spatially track the installation of GSIs [2]. Therefore, in this study, the NDVI was selected as the primary metric for evaluating plant health within the GSIs of the university campus.