Identifying Priority Areas for Vegetation Management in the Context of Energy Distribution Networks Using PlanetScope Images
In Brazil, approximately 30% of unscheduled interruptions of energy supply are caused by fires and vegetation interference in the energy distribution networks, resulting in great losses for companies of the electricity sector. To reduce the interruptions caused by these kinds of events, the energy d...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/9/2170 |
| _version_ | 1797502955265458176 |
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| author | Marcelo Pedroso Curtarelli Diego Jacob Kurtz Taisa Pereira Salgueiro |
| author_facet | Marcelo Pedroso Curtarelli Diego Jacob Kurtz Taisa Pereira Salgueiro |
| author_sort | Marcelo Pedroso Curtarelli |
| collection | DOAJ |
| description | In Brazil, approximately 30% of unscheduled interruptions of energy supply are caused by fires and vegetation interference in the energy distribution networks, resulting in great losses for companies of the electricity sector. To reduce the interruptions caused by these kinds of events, the energy distribution companies continually monitor and manage the vegetation in the vicinity of electric cables. However, due to the great extension and capillarity of the networks, it is not always possible to cover the entire network, and it is necessary to define priority segments to be managed. Taking into the account this context, the main objective of this study was to develop multi-criteria indicators to identify segments of the energy distribution networks with higher priority for management, based on vegetation attributes extracted from remote sensing images. For this purpose, we tested two artificial intelligence algorithms, support vector machine (SVM) and artificial neural networks (ANN), to automatically identify different classes of vegetation using PlanetScope images. Our results showed that the ANN algorithm presented better results for the vegetation classification when compared to the results obtained with the SVM algorithm. The application of the developed indicators showed adherent results, even in densely urbanized areas. We hope that the use of the developed indicators can help Brazilian energy distribution companies in optimizing vegetation management and consequently reducing unscheduled interruptions. |
| first_indexed | 2024-03-10T03:43:34Z |
| format | Article |
| id | doaj.art-07b96f9a147a4d86a307548e3fb42eeb |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:43:34Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-07b96f9a147a4d86a307548e3fb42eeb2023-11-23T09:11:40ZengMDPI AGRemote Sensing2072-42922022-04-01149217010.3390/rs14092170Identifying Priority Areas for Vegetation Management in the Context of Energy Distribution Networks Using PlanetScope ImagesMarcelo Pedroso Curtarelli0Diego Jacob Kurtz1Taisa Pereira Salgueiro2Green Economy Center, CERTI Foundation, Florianopolis 88040-970, BrazilGreen Economy Center, CERTI Foundation, Florianopolis 88040-970, BrazilGreen Economy Center, CERTI Foundation, Florianopolis 88040-970, BrazilIn Brazil, approximately 30% of unscheduled interruptions of energy supply are caused by fires and vegetation interference in the energy distribution networks, resulting in great losses for companies of the electricity sector. To reduce the interruptions caused by these kinds of events, the energy distribution companies continually monitor and manage the vegetation in the vicinity of electric cables. However, due to the great extension and capillarity of the networks, it is not always possible to cover the entire network, and it is necessary to define priority segments to be managed. Taking into the account this context, the main objective of this study was to develop multi-criteria indicators to identify segments of the energy distribution networks with higher priority for management, based on vegetation attributes extracted from remote sensing images. For this purpose, we tested two artificial intelligence algorithms, support vector machine (SVM) and artificial neural networks (ANN), to automatically identify different classes of vegetation using PlanetScope images. Our results showed that the ANN algorithm presented better results for the vegetation classification when compared to the results obtained with the SVM algorithm. The application of the developed indicators showed adherent results, even in densely urbanized areas. We hope that the use of the developed indicators can help Brazilian energy distribution companies in optimizing vegetation management and consequently reducing unscheduled interruptions.https://www.mdpi.com/2072-4292/14/9/2170nanosatsvegetation mappingartificial intelligenceenergy distribution networksunscheduled interruptions |
| spellingShingle | Marcelo Pedroso Curtarelli Diego Jacob Kurtz Taisa Pereira Salgueiro Identifying Priority Areas for Vegetation Management in the Context of Energy Distribution Networks Using PlanetScope Images Remote Sensing nanosats vegetation mapping artificial intelligence energy distribution networks unscheduled interruptions |
| title | Identifying Priority Areas for Vegetation Management in the Context of Energy Distribution Networks Using PlanetScope Images |
| title_full | Identifying Priority Areas for Vegetation Management in the Context of Energy Distribution Networks Using PlanetScope Images |
| title_fullStr | Identifying Priority Areas for Vegetation Management in the Context of Energy Distribution Networks Using PlanetScope Images |
| title_full_unstemmed | Identifying Priority Areas for Vegetation Management in the Context of Energy Distribution Networks Using PlanetScope Images |
| title_short | Identifying Priority Areas for Vegetation Management in the Context of Energy Distribution Networks Using PlanetScope Images |
| title_sort | identifying priority areas for vegetation management in the context of energy distribution networks using planetscope images |
| topic | nanosats vegetation mapping artificial intelligence energy distribution networks unscheduled interruptions |
| url | https://www.mdpi.com/2072-4292/14/9/2170 |
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