## 3 Results and Discussion
### Unveiling Dynamic Species Diversity: Exploring Variations across Locations, Years, and Seasons

Table 1: Changes in plant species inside and outside the confined field, 2020 to 2022. Discerning Variations in Plant Importance across Seasons, Distances, and Locations: Implications for LMO Confined Field Management  
Notable alterations in the importance values of plants were noted across seasons and distances, with variations depending on specific locations. The confined field exhibited distinct attributes compared to other areas, and distinctions between forested and non-forest zones were also discernible (Figure 4). Forest regions were categorized based on their proximity to the LMO confined field, revealing variations in vegetation relative to distance. Conversely, non-forest regions exhibited pronounced seasonal shifts, particularly in spring 2020, autumn 2021, and spring 2022. These findings suggest consistent segregation in the importance values of vegetation-associated spatial distribution encompassing LMO confined fields over time, heightening the ability to detect unintentional LMO release promptly. In tandem, [PERSON] et al. [34] conducted a study investigating the spatial extent and impact severity of successful invasive species by assessing vegetation criticality. Furthermore, \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**DF**} & \multicolumn{2}{c}{**Richness Index**} & \multicolumn{2}{c}{**Evenness Index**} & \multicolumn{2}{c}{**[PERSON]**} & \multicolumn{2}{c}{**[PERSON]**} \\  & & **F** & **_p_ & **F** & **_p_ & **F** & **_p_ & **F** & **_p_ \\ \hline Year & 2 & 0.95 & 0.393 & 11.71 & \textless{}0.001 & 0.77 & 0.466 & 0.20 & 0.818 \\ Location & 2 & 160.89 & \textless{}0.001 & 330.15 & \textless{}0.001 & 254.93 & \textless{}0.001 & 415.33 & \textless{}0.001 \\ Season & 2 & 1.59 & 0.213 & 5.72 & 0.006 & 0.66 & 0.523 & 1.17 & 0.318 \\ Year \(\times\) Location & 4 & 1.31 & 0.279 & 4.33 & 0.004 & 2.12 & 0.091 & 2.69 & 0.041 \\ Year \(\times\) Season & 4 & 0.93 & 0.456 & 6.01 & \textless{}0.001 & 1.06 & 0.385 & 2.92 & 0.029 \\ Location \(\times\) Season & 4 & 2.07 & 0.097 & 2.87 & 0.032 & 2.97 & 0.027 & 5.26 & 0.001 \\ Year \(\times\) Location \(\times\) Season & 8 & 0.73 & 0.666 & 5.86 & \textless{}0.001 & 1.43 & 0.207 & 4.35 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the general linear model (GLM) applied to the three years of diversity index values from the interior and exterior of the living modified organisms (LMO) confined field. Figure 3: Analyses of the plant community surveyed within the living modified organisms (LMO) confined field and outside of the confined field, 2020 to 2022.  
this study illuminates that seasonal variability in non-forest regions (waterways, playgrounds, and buildings) is more pronounced than in forested regions, attributed mainly to human interventions and artificial management practices. This underscores the significance of distinguishing between human disturbances and natural habitats in LMO confined field monitoring. Aerial Reconnaissance for Precise Data Acquisition: Capturing Imagery at 150 m Altitude over 31.65 Hectares Using Drones  
During drone flights, images were captured from an altitude of 150 m, spanning a total area of 31.65 hectares, with horizontal and vertical overlaps configured at 40%, as shown in Figure 5a. Within a 24 min flight time, 42 aerial photographs were procured. Subsequently, these images underwent approximately one hour of editing. Although most images displayed overlap with at least three aerial counterparts, a few edges exhibited less coverage. Analysis of the aerial images yielded a pixel error of 0.344 cm, facilitating the generation of digital elevation models (DEMs) and ortho-images (Figure 5b). The root means square error (RMSE) for the drone image was calculated to be 0.36, 0.67, and 2.03 m in the X, Y, and Z directions, respectively. This error introduced minor positional discrepancies in the edited images, primarily noticeable in buildings and identifiable terrain.