The protection of medicinal plants has been effective as a key factor in preserving the environment of medicinal plants. As such, this paper aims to map the environment of medicinal plants based on biodiversity and indigenous knowledge, in which its role is constantly seen in environmental studies. The study method was based on field survey in the target areas: Morvarid, Heiderabad, Dehmoord, Fath al-Mubin in Darab city in Fars province. Based on the findings of the study, a total of 89 species belonging to 43 families in the target areas were identified, with the highest frequency belonging to the Mint family. According to the results of studies, Anghozeh, Baneh, Thyme Shirazi, Arjan, Kenar, Jashir (Prangos), Lemon balm, Myrtus, cumins, and Kakuti in need of protective measures. Combining indigenous plant ethnological knowledge with new technologies along with high genetic diversity will be the way to control damage and protect the effective genes of medicinal plants. Ultimately, the elimination of the inheritance of desirable plant genes will lead to the erosive growth and acceleration of the regression of plant cover, which is considered as a rich chain and preserver of soil sanctity and stability of nature in the environment.

The advancement of Geographic Information System (GIS) technology through 3D modeling has significantly improved disaster risk analysis, particularly for landslides. This study utilized Unmanned Aerial Vehicles (UAVs) and Agisoft Metashape software to produce accurate 3D models, which were used to identify the location, volume, displacement, and distribution of landslide impacts in Tawangmangu Sub-district, Karanganyar Regency. This area is characterized by hilly topography with slopes > 45% and frequent land-use changes that exacerbate landslide risks. The 3D modeling process involved several key steps: aerial image acquisition using UAVs at an altitude of 126 meters, photo processing with Agisoft Metashape to generate orthomosaic maps, Digital Elevation Models (DEM), and geospatial analysis. Camera calibration was performed to enhance accuracy, while risk analyze using overlay and scoring methods were applied to hazard, vulnerability, and community results revealed that most of Tawangmangu Sub-district falls into the medium-risk category for landslides, covering an area of 4023.45 hectares, with the highest risk levels identified in Sepanjang and Tawangmangu villages. The 3D models indicated translational landslides, with soil displacement volumes ranging from -5409.3 m(3) to -991, 808 m(3), causing infrastructure damage and road closures. Mitigation efforts integrated UAV technology for realtime monitoring and indigenous knowledge in the form of coping strategies passed down through generations. UAV data was also utilized for disaster simulation, community training, and evidence-based mitigation planning, such as designing retaining walls and evacuation routes. This study highlights the importance of combining UAV technology and indigenous knowledge to enhance community capacity for sustainable and independent disaster risk reduction in landslide-prone areas.