A Complete Environmental Intelligence System for LiDAR-Based Vegetation Management in Power-Line Corridors

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2021-12, Vol.13 (24), p.5159
• Main Authors: Mongus, Domen, Brumen, Matej, Žlaus, Danijel, Kohek, Štefan, Tomažič, Roman, Kerin, Uroš, Kolmanič, Simon
• Format: Article
• Language: English
• Subjects: Blackouts; Classification; Corridors; Data integration; digital twin; Ecological niches; Eigenvalues; Electric power; encroaching vegetation detection; Feature extraction; Forest management; Intelligence; LiDAR; Power lines; Regression models; Remote sensing; Risk assessment; Simulation; Thematic mapping; three growth simulation; Vegetation; Vegetation growth; vegetation management
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13245159

This paper presents the first complete approach to achieving environmental intelligence support in the management of vegetation within electrical power transmission corridors. Contrary to the related studies that focused on the mapping of power lines, together with encroaching vegetation risk assessment, we realised predictive analytics with vegetation growth simulation. This was achieved by following the JDL/DFIG data fusion model for complementary feature extraction from Light Detection and Ranging (LiDAR) derived data products and auxiliary thematic maps that feed an ensemble regression model. The results indicate that improved vegetation growth prediction accuracy is obtained by segmenting training samples according to their contextual similarities that relate to their ecological niches. Furthermore, efficient situation assessment was then performed using a rasterised parametrically defined funnel-shaped volumetric filter. In this way, RMSE≈1 m was measured when considering tree growth simulation, while a 0.37 m error was estimated in encroaching vegetation detection, demonstrating significant improvements over the field observations.