Mapping and monitoring of vegetation regeneration and fuel under major transmission power lines through image and photogrammetric analysis of drone-derived data

The use of drones and remote sensing in combination with geospatial analysis is a cost-efficient way to monitor energy distribution networks, especially those in fire-prone areas. This study investigated the use of image and photogrammetric analysis together with segmentation algorithms to assess vegetation height and volume in power line corridors in Southeast Queensland, Australia. Various fuel reduction techniques, including mega-mulching, spot sprays and cool mosaic burns, were implemented, and drone-generated models were employed to evaluate their effectiveness. The fuel hazard reduction and regrowth in terms of vegetation height and volume were recorded and analysed. Importantly, the study demonstrates a robust correlation (R-2 = 0.9073; df = 1,16; F = 156; p < .001) between field observations and drone-derived models, affirming the efficacy of this method in assessing fuel heights. This validation suggests that the approach could represent a viable, cost-efficient option for future monitoring and management of energy distribution networks in fire-prone areas.

Automated summary

The use of drones and remote sensing combined with geospatial analysis is a cost-efficient method for monitoring energy distribution networks in fire-prone areas. This study evaluated vegetation height and volume in power line corridors in Southeast Queensland, Australia using image and photogrammetric analysis with segmentation algorithms. Drone-generated models were used to assess the effectiveness of various fuel reduction techniques. Strong correlation was observed between field observations and drone-derived models, indicating the efficacy of this approach in assessing fuel heights.