GEOSPATIAL MODELING OF ROADSIDE VEGETATION RISK ON DISTRIBUTION POWER LINES IN CONNECTICUT

Abstract

Roadside trees cause almost 90% of the power outages in the forested Northeastern US. Management of roadside vegetation risk on electrical infrastructure demands timely and accurate information on forest conditions. Tasking conventional ground-based scouting methods along thousands of kilometers of powerlines in a repeated fashion are labor-/cost-/time-intense. Geospatial and earth observation (EO) technologies serve as cost-effective tools in monitoring, inspecting, and managing utility corridors. EO technologies, from drones, aircraft, to satellites can efficiently acquire information over large areas at regular intervals while probing forest physical structure and health conditions. LiDAR is a useful data stream for modeling terrain conditions and estimation of multiple forest inventory variables that explain the physical structure of the forest. Various EO imagery provides information on bio-physical characteristics of trees that affect forest health at finer granularity. The goal of this study is to combine multiple environmental variables to develop a spatially-explicit vegetation risk model using machine learning algorithms. Some of the key inputs used in our analysis include LiDAR-derived tree-related variables (e.g., tree height, proximity pixels, canopy cover), LiDAR-derived terrain data (slope, aspect, topographic index), soil characteristics, vegetation management data (tree trimming methods), infrastructure data (wire type), and power outages reported from 2005 to 2017 in Connecticut. Findings of this research will be vital in informing vegetation management decision-making processes, which eventually reduce power outages and the cost of utility corridor maintenance.