Risk assessment for wildfire occurrence in high-voltage power line corridors by using remote-sensing techniques: a case study in Hubei Province, China

Zhang, Xiaozhi; Chen, Zhiguo; Wu, Wenhao; Li, Tao

doi:10.1080/01431161.2016.1220032

Cited by 42 publications

(30 citation statements)

References 45 publications

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“…The large-scale increase of this infrastructure necessitates the assessment of its degree of impact on vegetation. One of the typical environmental management strategies in the State Grid of China is to clear vegetation (e.g., grass and shrubs) along HVPTLs within a certain buffer zone (e.g., 50-100 m) every two to three years [6]. Forest fragmentation caused by the construction of HVPTLs has been reported in a previous study [7].…”

Section: Introductionmentioning

confidence: 91%

Do High-Voltage Power Transmission Lines Affect Forest Landscape and Vegetation Growth: Evidence from a Case for Southeastern of China

Lin

2019

Forests

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The rapid growth of the network of high-voltage power transmission lines (HVPTLs) is inevitably covering more forest domains. However, no direct quantitative measurements have been reported of the effects of HVPTLs on vegetation growth. Thus, the impacts of HVPTLs on vegetation growth are uncertain. Taking one of the areas with the highest forest coverage in China as an example, the upper reaches of the Minjiang River in Fujian Province, we quantitatively analyzed the effect of HVPTLs on forest landscape fragmentation and vegetation growth using Landsat imageries and forest inventory datasets. The results revealed that 0.9% of the forests became edge habitats assuming a 150 m depth-of-edge-influence by HVPTLs, and the forest plantations were the most exposed to HVPTLs among all the forest landscape types. Habitat fragmentation was the main consequence of HVPTL installation, which can be reduced by an increase in the patch density and a decrease in the mean patch area (MA), largest patch index (LPI), and effective mesh size (MESH). In all the landscape types, the forest plantation and the non-forest land were most affected by HVPTLs, with the LPI values decreasing by 44.1 and 20.8%, respectively. The values of MESH decreased by 44.2 and 32.2%, respectively. We found an obvious increasing trend in the values of the normalized difference vegetation index (NDVI) in 2016 and NDVI growth during the period of 2007 to 2016 with an increase in the distance from HVPTL. The turning points of stability were 60 to 90 meters for HVPTL corridors and 90 to 150 meters for HVPTL pylons, which indicates that the pylons have a much greater impact on NDVI and its growth than the lines. Our research provides valuable suggestions for vegetation protection, restoration, and wildfire management after the construction of HVPTLs.

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Section: Introductionmentioning

confidence: 91%

Do High-Voltage Power Transmission Lines Affect Forest Landscape and Vegetation Growth: Evidence from a Case for Southeastern of China

Lin

2019

Forests

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“…Power lines have unique geographical characteristics as follows: distributed regularly between two neighboring electric towers or poles with sag and other physical properties [2,6,13,41]. This knowledge can be used to screen power lines in large LiDAR data scene.…”

Section: Power Line Corridor Direction Constructionmentioning

confidence: 99%

Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

et al. 2017

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Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one of the most important topics for electric power management. However, this is very challenging over complex urban areas, where power lines are in close proximity to buildings and trees. In this paper, we presented a new, semi-automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) local neighborhood selection, (iii) spatial structural feature extraction, and (iv) SVM classification. We introduced the power line corridor direction for candidate point filtering and multi-scale slant cylindrical neighborhood for spatial structural features extraction. In a detailed evaluation involving seven scales and four types for local neighborhood selection, 26 structural features, and two datasets, we demonstrated that the use of multi-scale slant cylindrical neighborhood for individual 3D points significantly improved the power line classification. The experiments indicated that precision, recall and quality rate of power line classification is more than 98%, 98% and 97%, respectively. Additionally, we showed that our approach can reduce the whole processing time while achieving high accuracy.

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“…The power lines are parallel or distributed regularly between two neighboring electric poles or towers with sag [2,15,35,36]. Due to this unique geographical characteristics and large data volume, the candidate filtering aims to collect all of the possible power lines points.…”

Section: Power Line Candidate Filteringmentioning

confidence: 99%

Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data

et al. 2018

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Power lines classification is important for electric power management and geographical objects extraction using LiDAR (light detection and ranging) point cloud data. Many supervised classification approaches have been introduced for the extraction of features such as ground, trees, and buildings, and several studies have been conducted to evaluate the framework and performance of such supervised classification methods in power lines applications. However, these studies did not systematically investigate all of the relevant factors affecting the classification results, including the segmentation scale, feature selection, classifier variety, and scene complexity. In this study, we examined these factors systematically using airborne laser scanning and mobile laser scanning point cloud data. Our results indicated that random forest and neural network were highly suitable for power lines classification in forest, suburban, and urban areas in terms of the precision, recall, and quality rates of the classification results. In contrast to some previous studies, random forest yielded the best results, while Naïve Bayes was the worst classifier in most cases. Random forest was the more robust classifier with or without feature selection for various LiDAR point cloud data. Furthermore, the classification accuracies were directly related to the selection of the local neighborhood, classifier, and feature set. Finally, it was suggested that random forest should be considered in most cases for power line classification.

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Risk assessment for wildfire occurrence in high-voltage power line corridors by using remote-sensing techniques: a case study in Hubei Province, China

Cited by 42 publications

References 45 publications

Do High-Voltage Power Transmission Lines Affect Forest Landscape and Vegetation Growth: Evidence from a Case for Southeastern of China

Do High-Voltage Power Transmission Lines Affect Forest Landscape and Vegetation Growth: Evidence from a Case for Southeastern of China

Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data

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