Estimating tree-related power outages for regional utility network using airborne LiDAR data and spatial statistics
Trees play an integral role in the “green” framework of an urban ecosystem. However, just as they are beneficial to the environment, they can pose a significant risk to utility infrastructure networks, particularly in severe weather events. The objectives of this research were to explore the effect...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0303243421000374 |
| _version_ | 1828296375734894592 |
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| author | Sean Hartling Vasit Sagan Maitiniyazi Maimaitijiang William Dannevik Robert Pasken |
| author_facet | Sean Hartling Vasit Sagan Maitiniyazi Maimaitijiang William Dannevik Robert Pasken |
| author_sort | Sean Hartling |
| collection | DOAJ |
| description | Trees play an integral role in the “green” framework of an urban ecosystem. However, just as they are beneficial to the environment, they can pose a significant risk to utility infrastructure networks, particularly in severe weather events. The objectives of this research were to explore the effect of scale and spatial variation on the relationships between trees and utility assets for vegetation-related power outages through the incorporation of remote sensing and geographic information system (GIS) analysis. Tree location and structural metrics derived from airborne Light Detection and Ranging (LiDAR) data were combined with regional utility network GIS data to test the prediction analysis capabilities of global and local statistics at multiple scales. Pearson’s correlation was carried out to examine the relationships between tree structure and utility asset variables to vegetation-related power outages, including the effect of the resolution, or grid-cell size, on those relationships. To test the performance of global and local regression modeling on outage prediction, ordinary least square (OLS) and geographically weighted regression (GWR) models were evaluated using four explanatory variables (utility wire length, utility pole count, tree canopy area, maximum tree height) at four different grid cell scales (50 m, 500 m, 1 km, 2 km). In general, Pearson’s correlation demonstrated the strongest positive relationship between explanatory variables and power outages when only aggregating 50-m grid cells exhibiting co-location of trees and utility assets to 2-km grid cells. Local regression models performed better than global models at all scales, with GWR producing the highest adjusted R2 and lowest Akaike information criterion (AIC) values of 0.955 and 3213, respectively. Additionally, the performance of OLS and GWR models increased with scale as both models produced the highest adjusted R2 at 2-km grid-cell scale. GWR model outputs demonstrated unique spatial patterning across the study area. This research demonstrated the effect of scale and spatial variation on regression analysis for the estimation of tree-related power outages. |
| first_indexed | 2024-04-13T12:11:09Z |
| format | Article |
| id | doaj.art-ef38ab22c7e940b5a05b24ec2717914d |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-13T12:11:09Z |
| publishDate | 2021-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-ef38ab22c7e940b5a05b24ec2717914d2022-12-22T02:47:29ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322021-08-01100102330Estimating tree-related power outages for regional utility network using airborne LiDAR data and spatial statisticsSean Hartling0Vasit Sagan1Maitiniyazi Maimaitijiang2William Dannevik3Robert Pasken4Geospatial Institute, Saint Louis University, Saint Louis, MO 63108, USA; Department of Earth and Atmospheric Sciences, Saint Louis University, Saint Louis, MO 63108, USAGeospatial Institute, Saint Louis University, Saint Louis, MO 63108, USA; Department of Earth and Atmospheric Sciences, Saint Louis University, Saint Louis, MO 63108, USA; Corresponding author at: Geospatial Institute, Saint Louis University, Saint Louis, MO 63108, USA.Geospatial Institute, Saint Louis University, Saint Louis, MO 63108, USA; Department of Earth and Atmospheric Sciences, Saint Louis University, Saint Louis, MO 63108, USADepartment of Earth and Atmospheric Sciences, Saint Louis University, Saint Louis, MO 63108, USADepartment of Earth and Atmospheric Sciences, Saint Louis University, Saint Louis, MO 63108, USATrees play an integral role in the “green” framework of an urban ecosystem. However, just as they are beneficial to the environment, they can pose a significant risk to utility infrastructure networks, particularly in severe weather events. The objectives of this research were to explore the effect of scale and spatial variation on the relationships between trees and utility assets for vegetation-related power outages through the incorporation of remote sensing and geographic information system (GIS) analysis. Tree location and structural metrics derived from airborne Light Detection and Ranging (LiDAR) data were combined with regional utility network GIS data to test the prediction analysis capabilities of global and local statistics at multiple scales. Pearson’s correlation was carried out to examine the relationships between tree structure and utility asset variables to vegetation-related power outages, including the effect of the resolution, or grid-cell size, on those relationships. To test the performance of global and local regression modeling on outage prediction, ordinary least square (OLS) and geographically weighted regression (GWR) models were evaluated using four explanatory variables (utility wire length, utility pole count, tree canopy area, maximum tree height) at four different grid cell scales (50 m, 500 m, 1 km, 2 km). In general, Pearson’s correlation demonstrated the strongest positive relationship between explanatory variables and power outages when only aggregating 50-m grid cells exhibiting co-location of trees and utility assets to 2-km grid cells. Local regression models performed better than global models at all scales, with GWR producing the highest adjusted R2 and lowest Akaike information criterion (AIC) values of 0.955 and 3213, respectively. Additionally, the performance of OLS and GWR models increased with scale as both models produced the highest adjusted R2 at 2-km grid-cell scale. GWR model outputs demonstrated unique spatial patterning across the study area. This research demonstrated the effect of scale and spatial variation on regression analysis for the estimation of tree-related power outages.http://www.sciencedirect.com/science/article/pii/S0303243421000374Power outage predictionVegetation management for utilityGeographically weighted regressionGrid-cell |
| spellingShingle | Sean Hartling Vasit Sagan Maitiniyazi Maimaitijiang William Dannevik Robert Pasken Estimating tree-related power outages for regional utility network using airborne LiDAR data and spatial statistics International Journal of Applied Earth Observations and Geoinformation Power outage prediction Vegetation management for utility Geographically weighted regression Grid-cell |
| title | Estimating tree-related power outages for regional utility network using airborne LiDAR data and spatial statistics |
| title_full | Estimating tree-related power outages for regional utility network using airborne LiDAR data and spatial statistics |
| title_fullStr | Estimating tree-related power outages for regional utility network using airborne LiDAR data and spatial statistics |
| title_full_unstemmed | Estimating tree-related power outages for regional utility network using airborne LiDAR data and spatial statistics |
| title_short | Estimating tree-related power outages for regional utility network using airborne LiDAR data and spatial statistics |
| title_sort | estimating tree related power outages for regional utility network using airborne lidar data and spatial statistics |
| topic | Power outage prediction Vegetation management for utility Geographically weighted regression Grid-cell |
| url | http://www.sciencedirect.com/science/article/pii/S0303243421000374 |
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