Smart Sensor Networks for Real-Time Forest Health Monitoring Systems

Thesis Abstract

Real-time monitoring of forest health is constrained by delayed detection of biotic and abiotic stressors, limited spatial resolution, and data fragmentation across disparate sensor platforms. This study addresses the urgent need for an integrated, ICT-driven framework that enables continuous, scalable, and actionable insights for forest management through smart sensor networks. The aim is to develop and validate an end-to-end system that combines heterogeneous sensor modalities, edge computing, and data analytics to detect early signs of disease, drought stress, pest outbreaks, and canopy health degradation in temperate mixed forests. Specific objectives are (1) to design a modular sensor network architecture that supports low-power, long-range communication, in-situ data preprocessing, and secure data transmission; (2) to implement an adaptive sampling strategy and a multi-sensor fusion model that integrates microclimate, hyperspectral reflectance, acoustic emissions, and dendrometer signals for robust health indicators; (3) to develop machine learning and statistical models for real-time anomaly detection, stress classification, and trend analysis, with explicit attention to model transferability across sites; (4) to evaluate system performance in a 12-month field deployment across three forest stands totaling 180 hectares, with 200 sensor nodes and 60 autonomous environmental stations; and (5) to assess the socio-ecological usefulness of the system for decision-making under climate variability. Methodologically, the research adopts a pragmatic mixed-methods design underpinned by the Diffusion of Innovations theory and the Activity Theory framework to ground user interactions and system adoption. The population comprises temperate forest stands within the study region, with the sampling frame including three stands representing varying topography and species composition. A stratified random sampling approach yields 200 deployed sensor nodes (including soil moisture probes, leaf-clip spectrometers, acoustic sensors, dendrometers, and microclimate stations) and 60 fixed environmental stations. Data collection instruments integrate low-power microcontrollers, solar-powered gateways, spectral sensors, acoustic microphones, and vibration/strain sensors, all synchronized via a time-stamped, centralized cloud repository with edge-computing capabilities at gateway nodes. Validity and reliability are ensured through calibration campaigns, cross-validation with satellite-derived vegetation indices, and repeat measurements across seasonal cycles. Data analysis follows a multi-tiered plan (i) data quality assessment and preprocessing, (ii) sensor fusion using a Bayesian hierarchical model to estimate health indices, (iii) real-time anomaly detection via unsupervised methods (Isolation Forest) and supervised classifiers (Random Forest, Gradient Boosting) trained on labeled events from prior outbreaks, (iv) time-series analysis (ARIMA/Prophet) for trend detection, and (v) spatial analysis using variograms and kriging to map risk at stand-level scales. Model specification includes a formalized health prediction equation incorporating canopy reflectance metrics, soil moisture, temperature-humidity profiles, stem-radius change, and acoustic event rates. Ethical considerations address data sovereignty, stakeholder consent, and transparent communication of uncertainty. Expected findings indicate that the integrated sensor network can reduce detection lag for forest stress events from weeks to 1–2 days, increase accuracy in stress classification to above 85% in cross-site validation, and produce high-resolution risk maps with 10–20 m spatial granularity. The Bayesian fusion model is anticipated to outperform single-sensor indicators in early drought stress and pest damage detection, while edge computing will demonstrably decrease data latency and bandwidth requirements. The study will also reveal practical insights into user acceptance, workflow integration, and decision-support value among forest managers. The contribution to knowledge includes (i) a validated, scalable architecture for real-time forest health monitoring that harmonizes heterogeneous sensing modalities, (ii) a transferable multi-sensor fusion framework with robust uncertainty quantification for early stress detection, (iii) empirical evidence on the benefits and limitations of edge-enabled, ICT-driven forest surveillance in temperate ecosystems, and (iv) guidance on governance, data standards, and user-centered design for operational deployment. The study concludes that smart sensor networks, when properly calibrated and integrated with decision-support tools, can transform forest health management by enabling proactive interventions, optimizing resource allocation, and enhancing resilience to climate-induced disturbances. Recommendations include expanding cross-ecosystem deployment to diverse forest types, refining automated alert thresholds through continuous learning, and establishing open data platforms to accelerate comparative research and policy uptake.