A Statistical Framework for Evaluating the Effectiveness of Vegetation Management in Reducing Power Outages Caused during Storms in Distribution Networks

This paper develops a statistical framework to analyze the effectiveness of vegetation management at reducing power outages during storms of varying severity levels. The framework was applied on the Eversource Energy distribution grid in Connecticut, USA based on 173 rain and wind events from 2005–2020, including Hurricane Irene, Hurricane Sandy, and Tropical Storm Isaias. The data were binned by storm severity (high/low) and vegetation management levels, where a maximum applicable length of vegetation management for each circuit was determined, and the data were divided into four bins based on the actual length of vegetation management performed divided by the maximum applicable value (0–25%, 25–50%, 50–75%, and 75–100%). Then, weather and overhead line length normalized outage statistics were taken for each group. The statistics were used to determine the effectiveness of vegetation management and its dependence on storm severity. The results demonstrate a higher reduction in damages for lower-severity storms, with a reduction in normalized outages between 45.8% and 63.8%. For high-severity events, there is a large increase in effectiveness between the highest level of vegetation management and the two lower levels, with 75–100% vegetation management leading to a 37.3% reduction in trouble spots. Yet, when evaluating system reliability, it is important to look at all storms combined, and the results of this study provide useful information on total annual trouble spots and allow for analysis of how various vegetation management scenarios would impact trouble spots in the electric grid. This framework can also be used to better understand how more rigorous vegetation management standards (applying ETT) help reduce outages at an individual event level. In future work, a similar framework may be used to evaluate other resilience improvements.

A Multiview Semantic Vegetation Index for Robust Estimation of Urban Vegetation Cover

Urban vegetation growth is vital for developing sustainable and liveable cities in the contemporary era since it directly helps people’s health and well-being. Estimating vegetation cover and biomass is commonly done by calculating various vegetation indices for automated urban vegetation management and monitoring. However, most of these indices fail to capture robust estimation of vegetation cover due to their inherent focus on colour attributes with limited viewpoint and ignore seasonal changes. To solve this limitation, this article proposed a novel vegetation index called the Multiview Semantic Vegetation Index (MSVI), which is robust to color, viewpoint, and seasonal variations. Moreover, it can be applied directly to RGB images. This Multiview Semantic Vegetation Index (MSVI) is based on deep semantic segmentation and multiview field coverage and can be integrated into any vegetation management platform. This index has been tested on Google Street View (GSV) imagery of Wyndham City Council, Melbourne, Australia. The experiments and training achieved an overall pixel accuracy of 89.4% and 92.4% for FCN and U-Net, respectively. Thus, the MSVI can be a helpful instrument for analysing urban forestry and vegetation biomass since it provides an accurate and reliable objective method for assessing the plant cover at street level.

Significance of Urban Vegetation on Lawns Regarding the Risk of Fire

Urban green infrastructure significantly influences the functioning of a city and the comfort of its residents. Lawns are an essential element of public greenery. They represent a live component, and if they are lacking, of low quality, or neglected, this will cause major problems in the urban environment. The vegetation structure of urban grassy areas changes under the influence of different management methods used for their maintenance. The main goal of this study was to evaluate the species diversity of urban lawns and to determine the influence of this vegetation on factors based on the representation of the species found. Three sites with urban lawns were chosen in a built-up city area where different types of vegetation management were applied: Typical management, in which grassy areas are mowed twice a year; intensive management, in which lawns are mowed several times a year and the biomass is removed; and extensive management, in which lawns are mowed irregularly, once a year at most, and the biomass is left unevenly on the site. Extensive management and unkempt urban grassy areas represent a high risk of fire due to the presence of plant species that produce great amounts of biomass. Combined with dry and warm weather, the dead biomass can lead to outbreaks of fire. Extensive management of urban grassy areas brings some benefits, such as lower maintenance costs and increased biodiversity and bioretention. On the other hand, intensive management reduces the risk of fire and the biodiversity of the plant community. Attention should be paid to the composition of vegetative species and their functions that could threaten the safety of residents, with the risk of fire being one of them. However, the vegetation biomass of grassy areas affected by management practices is only a precondition for the risk of fire because weather and drought occurrence play important roles as well.

Effects of vegetation management on the composition and diversity of the insect community at Tianjin Binhai International Airport, China

The vegetation community affects the composition and diversity of the insect community in grasslands. To explore the effects of vegetation management on insect community abundance and diversity, regular mowing of the vegetation was conducted, and tall fescue (Festuca arundinacea) and ryegrass (Lolium perenne) were exclusively planted at Tianjin Binhai International Airport. A total of 1886 insects were collected, representing 8 orders, 23 families, and 29 species; Acrididae (Orthoptera), Coccinellidae (Coleoptera), and Chironomidae (Diptera) were the dominant taxa. The abundance and biomass of insects in the turf areas were significantly lower than those in the control area and were reduced by 45.8 and 48.5% in the ryegrass area, respectively. In all areas, insect abundance and biomass peaked in summer, and the abundance of individuals and taxa decreased as the temperature decreased. Greater diversity and richness were found in summer compared with the other two seasons, and the turf areas had lower diversity and richness indices than the control areas in spring and summer. Our results suggest that the abundance, biomass and diversity of insects can be effectively decreased by artificial regulation of grassland vegetation at the airport, the planting of a single turfgrass – specifically ryegrass had the greatest effect. The present study provides a theoretical basis for the ecological control of insects at the airport.