Multi-objective optimization of truck-drone collaborative forestry monitoring

Abstract

Accuracy and timeliness of forestry monitoring are critical for reliable resource assessment and early warning of pest and disease outbreaks. While drone technologies are increasingly used for forestry monitoring, current applications often lack systematic planning. Monitoring operations rely heavily on manual experience, resulting in low efficiency, redundant coverage, and coverage gaps. To address the trade-off between coverage completeness and operational timeliness in large and topographically complex forests, we investigate a truck-drone collaborative forestry monitoring framework that jointly optimizes vehicle routes, candidate docking points, and unmanned aerial vehicle (UAV or drone) coverage paths. The dual objectives of our model are to maximize coverage and minimize total operational time subject to road-network and engineering constraints. To this end, the monitoring region is gridded in a projected coordinate system. Non-monitoring units (e.g., villages, farmlands) identified through remote sensing and field surveys are excluded to reduce redundant searches and monitoring. Road network data serves as constraints for vehicle access and docking point selection. We propose a Fix-and-Optimize (FO) iterative decomposition to decouple and alternately solve two coupled subproblems: docking-point combination selection and sub-region partitioning. Under fixed task region division, clustering-based sampling and variable neighborhood search optimization are performed for docking point combinations. Under fixed docking points, isolated block detection and local redistribution are conducted for sub-regions, and a genetic algorithm is used to optimize the heading angles of boustrophedon flight paths for efficient coverage routes. To improve the solution efficiency for large-scale cases, we devise a mechanism for maintaining and parallel evaluating the Pareto front. In the case study of Qingyuan, China, the proposed method reduces total operational time by 30.06 % at the same coverage level compared with the current manual planning scheme. The Pareto front also reveals a clear trade-off: coverage gains diminish sharply beyond approximately 55 UAV flights, and the 55-flight scheme achieves 96.6 % coverage while reducing the number of sorties by 16.7 % with only a 3.4 % coverage loss relative to the 66-flight full-coverage scheme. Our framework provides a quantitative tool for decision-makers to balance coverage goals with resource constraints, enabling more efficient and effective large-scale forestry monitoring.