Abstract
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.
Highlights
As electrification is becoming a pillar of social [1,2], economic [3] and environmental sustainability [2,4,5], power transmission lines are under increasing burden
We propose a new approach for achieving Environmental Intelligence in vegetation management using structured data fusion of Light Detection and Ranging (LiDAR)-generated data products with complementary thematic maps and administrative data sources, i.e., development of a digital twin [37]
As confirmed by the results, the proposed approach brings about an efficient environmental intelligence for improved vegetation management in power line corridors
Summary
As electrification is becoming a pillar of social [1,2], economic [3] and environmental sustainability [2,4,5], power transmission lines are under increasing burden. Ortega et al [23] performed a reconstruction of wires based on the catenary equation using particle swarm optimisation after an initial classification of pylons and wires, and their segmentation into individual conductors While these traditional methods achieved mapping of pylons, followed by recognition of wires, more recent approaches focused on improving their performances [24]. A complete LiDAR data processing pipeline for fusion of the derived data products (like digital terrain models, canopy height models and 3D data about power lines), with cadastral data and other important thematic maps for vegetation management, such as, for example, distribution of tree spices and soil pH maps, An efficient approach for encroaching vegetation detection that enables accurate assessment of corridor clearance and provides future threat assessment, and.
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