## 2 Methods
### Plant Density and Health Identification Framework  
Figure 1 depicts a schematic diagram outlining the workflow for estimating plant density and evaluating the plant health. There were three major steps involved in plant density and health evaluation using the instrumentation and software outlined in Section 2.1. The first step entailed installing a MAPIR camera into the drone and planning the flight path. The DJI GS Pro app was employed to generate flight paths for the drone to automatically capture UAV-based imagery. During flight planning, various parameters were configured to optimize image quality. The primary parameters included flight height, front and side overlap ratio, flight path parallel to the boundary of bioretention, estimated flight duration, and total image quantity to assign drones to fly along the designated path, autonomously capturing images in sequence. For instance, in the case of bioretention, imagery was acquired at an altitude of 35.05 m (115 feet) and at a speed of 1.83 m per second (4.1 mile per hour) with front and side overlap of 90% and 95%, respectively, following a lawn mower pattern to ensure comprehensive coverage. This high-overlap setting ensured that the near infrared (NIR) camera mounted on the drone captured imagery with sufficient overlap to facilitate accurate data collection and processing. Upon completion of the flight mission, two sets of images were collected: red, green, blue (RGB) and NIR images from DJI-drone-equipped RGB camera and additional mounted MAPIR camera. There were two file formats of raw data in the SD card: (1) RAW format, preserving pixel information and (2) JPG format, containing metadata. Both files were required to process the TIFF output and create NIR images. The second step involved preprocessing of the captured images into orthophotos by WebOpenDroneMap (WebODM) Software (v1.9.2). After checking, sorting, and integrating the input RGB and NIR images, we separately imported each set to WebODM software (v1.9.2). Default settings were applied for generating high-resolution orthomosaics to  
Figure 1: Schematic diagram of framework for plant density and health identification. obtain two orthophotos for each flight. One was the RGB orthophoto from the Phantom 4 Pro CMOS camera and the other one was the NIR orthophoto from Survey3N NIR camera. The orthomosaic was generated at a resolution of 1 cm/pixel, chosen to minimize distortion in the orthomosaic and ensure adequate detail for further analysis. Then, a JPG image was created for motion structure processing using WebODM software. Figure 2 shows the UAV-based RGB orthophoto (about 30 images were generated) of GSIs within the campus. The UAV-based image notably exhibits superior resolution (as highlighted with the red arrow) and sensitivity compared to surrounding areas captured by Google Map, which relies on satellite and aerial imagery to create detailed maps and images of the GSIs. Similarly, recent research also concluded that the UAV-based images showed more details of all classes of habitat conditions than the satellite-based Sentinel-2 maps [29]. The third step involved predicting plant density and evaluating plant health using the Canopeo application and PyCharm software (v2020.3.3), in conjunction with ArcMap software (v10.8.1). The ArcMap software was utilized to crop RGB and NIR orthophotos to identical regions of interest (ROIs), focusing exclusively on the GSI areas. Subsequently, the cropped RGB orthophoto was imported into Canopeo and PyCharm software to obtain the plant density percentage using the Python programming codes (Supplementary Materials). In the provided programming code, a pixel intensity value threshold was established to convert green pixels into white and the rest of the pixels into black. The number of white pixels over total pixel numbers was counted to obtain the plant density percentage. Additionally, RGB images captured above the plants were imported to analyze plant density via the Canopeo application.