Is signal attenuation in forests related to the tree-proximal microclimate? An interpretable KNN–SHAP modelling study

Abstract

Electromagnetic wave attenuation in forests is fundamentally governed by the dielectric properties of the propagation medium. In practice, however, the spatial structure between two forest nodes is extremely difficult to quantify, and forest environments exhibit strong heterogeneity. Forest propagation is therefore often treated as a “black box”, where attenuation is measured empirically between transmitting and receiving nodes and fitted with site-specific models that typically show weak generalization ability. Because microclimate strongly regulates the dielectric constant of air, vegetation, and soil, it provides a physically grounded pathway to explain signal variability beyond purely empirical descriptions. To address the question “Is signal attenuation in forests related to the tree-proximal microclimate?”, we deployed a multi-node LoRa monitoring network in a natural mixed forest and collected more than 52,000 synchronous records of RSSI and tree-, soil-, and air-related microclimate variables across four observation campaigns. An interpretable KNN–SHAP framework was applied to quantify nonlinear effects and threshold behaviors. The results show that RSSI increases with temperature and decreases with humidity and soil moisture, forming a clear “heat-enhanced, moisture-suppressed” propagation pattern. A stable microclimatic window with minimal attenuation emerges under moderate dryness (VPD 1.5–1.9 kPa) and gentle wind (1–4 m s⁻¹), whereas signal degradation intensifies near VPD ≈ 1.0–1.3 kPa and at soil moisture levels of 20–35%. By linking signal attenuation to microclimate-regulated dielectric dynamics, this study moves beyond black-box empirical models and provides a transferable, mechanism-informed basis for forest Internet of Things (IoT) communication.

Keywords

Microclimate / Signal attenuation / KNN–SHAP / Forest IoT / Tree

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