## 4 Conclusions  
In this study, an integrated framework was developed and implemented to calculate plant density and evaluate the health of plants within GSIs in the MSU campus using UAV-based imagery. The DJI Phantom 4 Pro drone, equipped with (effectively) a 20-megapixel camera and a Survey3N NIR 12 MP camera were utilized to capture both UAV-based RGB and NIR images. Additionally, WebODM, ArcMap, PyCharm, and Canopeo were integrated into the framework for the evaluation process. The results indicated that the proposed programming codes successfully predicted plant density with relatively low errors and high R\({}^{2}\) values. The plant density of components (e.g., trees, grass, soil, and unhealthy trees) as well as of entire bioretention and green-roof sites were evaluated. The NDVI results revealed that the CTTH had a higher NDVI value than the CBEIS, possibly  
Figure 6: Plant density prediction. (**a**) Library building (72.76%) and (**b**) green roof of business building (63.63%).  
Figure 7: RGB, NIR, and NDVI maps of GSI in MSU Tyler Hall. In each map, 6 sections were divided by sideways. Section 1, section 2, section 3 (top row, left to right) while section 4, section 5, section 6 (bottom row, left to right).  
due to its later construction and proper maintenance practices. Meanwhile, the lower plant density and NDVI values suggested the necessity for implementing a proper maintenance plan for the GSIs. Plant health is critical to mitigate stormwater runoff, control the floor, improve water quality, increase attractiveness of facilities to the public, and appreciate property values. This study demonstrated the potential of using this framework to calculate plant density and monitor the plant health using UAV imagery, providing a rapid and cost-effective tool for assessing the plant performance of the rapidly growing GSIs. In future studies, the effects of UAV-image collection height, building ages, and GSI types will be investigated and analyzed to further enhance the understanding of GSI dynamics.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14104118/s1](https://www.mdpi.com/article/10.3390/app14104118/s1)]([https://www.mdpi.com/article/10.3390/app14104118/s1](https://www.mdpi.com/article/10.3390/app14104118/s1)), Programming code to predict the plant density and Figure S1: Selected subcomponents of GSIs. (a) Trees, (b) grass, (c) soil, and (d) unhealthy trees.  
[PERSON] coordinated projects and received grants from the Office of Technology Transfer (OTT) at Morgan State University (MSU); [PERSON] and [PERSON] reviewed published literature reviews and found the research gaps; [PERSON] collected, processed, and analyzed images; [PERSON] and [PERSON] wrote the draft manuscript; [PERSON] and [PERSON] reviewed this manuscript and provided constructive comments and suggestions to improve the quality of article. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Office of Technology Transfer (OTT) at Morgan State University (MSU), grant number I-GAP Award # OTT-STT-120.  
Not applicable.  
The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to corresponding author.  
The author ([PERSON]) would like to acknowledge the Office of Technology Transfer (OTT) and the School of Graduate Studies (SGS) at Morgan State University (MSU) for providing a partial financial support for this study.  
The authors declare no conflicts of interest.