Study Area:

The project was conducted in Karnali Province, a region characterized by its rugged terrain, extensive forest cover, and significant biodiversity. This province is pivotal for forest conservation efforts and has been facing challenges related to illegal logging, poaching, and other forest crimes. The diverse landscape, which includes dense forests, steep slopes, and remote areas, presents both opportunities and challenges for implementing advanced monitoring technologies.

Karnali Province’s vast and often inaccessible forests make traditional monitoring methods difficult and resource-intensive. The feasibility study aimed to explore the potential of drone technology to enhance forest management practices, improve surveillance capabilities, and address illegal activities effectively. By leveraging drones, the project sought to provide innovative solutions for real-time monitoring, data collection, and crime detection in these remote and challenging environments.

 Project Objectives:

– Feasibility Study: Assess the practicalities and benefits of using drone technology for forest monitoring and crime control in Karnali Province. This involved evaluating the effectiveness of drones in various forest conditions and understanding their potential impact on improving forest management practices.

– Prototype Development: Develop and test prototypes of drones equipped with specialized sensors and imaging technology for forest monitoring and crime control. The prototypes aimed to address specific challenges such as capturing high-resolution images in dense foliage, navigating rugged terrain, and providing actionable data for forest management.

 Deliverables:

– Ortho Photos: High-resolution ortho photos were produced to provide accurate visual representation of the forest areas. These images were essential for detailed analysis and monitoring of forest conditions and changes over time.

– Contour Maps: The contour maps offered detailed elevation data of the forest terrain, helping in understanding the topographical features and planning drone flight paths for efficient coverage.

– Digital Surface Models (DSM): DSMs provided a 3D representation of the forest, including vegetation and other surface features. These models were useful for analyzing canopy structure and detecting changes in the forest environment.

– Digital Terrain Models (DTM): DTMs represented the bare-earth surface by removing vegetation and man-made structures. This model was crucial for understanding the underlying terrain and improving the accuracy of forest monitoring and management.

– Topographical Maps: Comprehensive topographical maps combined all the data sets, offering a detailed overview of the forest landscape. These maps were vital for strategic planning, including the placement of monitoring equipment and design of patrol routes for forest crime control.

The project aimed to provide the Forest Research and Training Centre with advanced tools and methodologies to enhance forest monitoring and combat illegal activities effectively, leveraging drone technology’s capabilities in a challenging and ecologically significant region.