This paper explores the application of unmanned vehicles for reconnaissance purposes, specifically to verify potential threat in inaccessible and swampy areas. The study introduces a novel integration of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs) with AI-driven fire detection algorithms, enabling automated wildfire monitoring in sensitive ecosystems. The proposed framework enhances terrain adaptability by optimizing nominal ground pressure for UGV mobility and limple-mentation UAV-based computer vision for real-time risk assessment. Nowadays, the supervision of ecosystems has acquired significance, with a special focus on the man-agement of national parks. One the largest areas is Biebrza National Park, sprawls across 59,223 hectares, preserving pristine wetland ecosystems and housing remarka-ble biodiversity. Regular patrols are essential to enforce regulations and thwart illegal activities. It should be highlighted that destructive fire in the year 2020 ravaged a por-tion of the park, emphasizing the need for comprehensive monitoring and protection. This work aligns with conservation policies by enabling data-driven interventions for wildfire risk mitigation. Integrating Unmanned Ground Vehicles (UGV), Unmanned Aerial Vehicles (UAV), and remote sensing with artificial intelligence (AI) algorithms allows national park safety improvement. This study analyzes the movement requirements of ground platforms in swampy and marshland areas and explores the reliability requirements for UAVs. Additionally, metrics for fire threat detection are discussed.