Intelligent Drone Surveillance for Sustainable Forest Management and Disease Detection

Thesis Abstract

The depletion of forest resources due to illegal logging, deforestation, and the prevalence of forest diseases underscores the urgent need for innovative monitoring solutions that promote sustainable management practices. Traditional ground-based surveillance methods are often limited by logistical constraints, insufficient coverage, and delayed detection, which hinder timely intervention and effective forest conservation efforts. This study aims to develop and evaluate an integrated intelligent drone surveillance system that leverages advanced imaging technologies, machine learning algorithms, and geographic information systems (GIS) to enhance forest monitoring and early disease detection. The specific objectives are to (1) design a drone-based data collection framework incorporating multispectral and thermal imaging sensors, (2) develop machine learning models for the classification of forest health status and detection of disease outbreaks, and (3) assess the system’s efficacy in real-world forest environments. The research adopts a mixed-methods approach, combining quantitative and qualitative techniques within a descriptive and exploratory research design. The quantitative component involves deploying a fleet of 20 unmanned aerial vehicles (UAVs) fitted with multispectral and thermal cameras across a 150-square-kilometer forest reserve with diverse ecological zones. Data collection is conducted over a 12-month period, capturing high-resolution aerial imagery at scheduled intervals. The primary data sources include imagery datasets, GIS layers, and forest health records obtained from forest management authorities. The machine learning component employs supervised classification algorithms, such as Random Forest and Support Vector Machines (SVM), trained on labeled datasets to identify signs of disease, pest infestation, and stress indicators. Additionally, a spatial analysis using GIS-based models evaluates spatial distribution patterns, while the qualitative aspect involves semi-structured interviews with forestry experts to validate the system's outputs and identify practical implementation challenges. Data analysis involves a combination of statistical and computational techniques. The classification accuracy of machine learning models is assessed using metrics like precision, recall, F1 score, and receiver operating characteristic (ROC) curves. Spatial pattern analysis employs Moran’s I and kernel density estimation to explore clustering of disease outbreaks. Thematic analysis of interview transcripts provides contextual insights into operational feasibility and stakeholder perceptions. A comprehensive evaluation framework integrates these analyses to determine the reliability, accuracy, and practical utility of the drone surveillance system. Expected findings suggest that the proposed intelligent drone system significantly improves the detection and monitoring of forest health conditions compared to traditional methods, with classification accuracies exceeding 85%. Spatial analysis is anticipated to reveal distinct clustering patterns of diseases, enabling targeted interventions. The system demonstrates high operational efficiency, reducing surveillance costs and time, and providing real-time data for decision-making. The study also identifies key challenges such as sensor limitations, data management, and regulatory concerns, offering recommendations for optimizing system deployment. This research contributes novel insights into the application of integrated ICT solutions for sustainable forestry, advancing knowledge on the use of drone technology combined with machine learning for ecological monitoring. It provides a scalable framework adaptable to various forest environments, supporting policymakers and forest managers in implementing proactive conservation strategies. Furthermore, the study extends existing theories on technological adoption and environmental monitoring by empirically validating the effectiveness of drone-based surveillance systems in diverse ecological contexts. In conclusion, the study affirms that intelligent drone surveillance constitutes a transformative approach for sustainable forest management and early disease detection. It recommends the integration of such systems into national forest policy frameworks, emphasizes the importance of addressing regulatory and ethical issues, and advocates for further research into autonomous drone operations and multi-sensor data fusion techniques to enhance system robustness and scalability.