Enhancing environmental enforcement with near real-time monitoring: Likelihood-based detection of structural expansion of intensive livestock farms
Much environmental enforcement in the United States has historically relied on either self-reported data or physical, resource-intensive, infrequent inspections. Advances in remote sensing and computer vision, however, have the potential to augment compliance monitoring by detecting early warning si...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0303243421001707 |
| _version_ | 1817978498215575552 |
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| author | Ben Chugg Brandon Anderson Seiji Eicher Sandy Lee Daniel E. Ho |
| author_facet | Ben Chugg Brandon Anderson Seiji Eicher Sandy Lee Daniel E. Ho |
| author_sort | Ben Chugg |
| collection | DOAJ |
| description | Much environmental enforcement in the United States has historically relied on either self-reported data or physical, resource-intensive, infrequent inspections. Advances in remote sensing and computer vision, however, have the potential to augment compliance monitoring by detecting early warning signs of noncompliance. We demonstrate a process for rapid identification of significant structural expansion using Planet’s 3 m/pixel satellite imagery products and focusing on Concentrated Animal Feeding Operations (CAFOs) in the US as a test case. Unpermitted building expansion has been a particular challenge with CAFOs, which pose significant health and environmental risks. Using new hand-labeled dataset of 145,053 images of 1,513 CAFOs, we combine state-of-the-art building segmentation with a likelihood-based change-point detection model to provide a robust signal of building expansion (AUC = 0.86). A major advantage of this approach is that it can work with higher cadence (daily to weekly), but lower resolution (3 m/pixel), satellite imagery than previously used in similar environmental settings. It is also highly generalizable and thus provides a near real-time monitoring tool to prioritize enforcement resources in other settings where unpermitted construction poses environmental risk, e.g. zoning, habitat modification, or wetland protection. |
| first_indexed | 2024-04-13T22:30:07Z |
| format | Article |
| id | doaj.art-c75405aeb40749c3a5d8d7504a3998e2 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-13T22:30:07Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-c75405aeb40749c3a5d8d7504a3998e22022-12-22T02:26:57ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322021-12-01103102463Enhancing environmental enforcement with near real-time monitoring: Likelihood-based detection of structural expansion of intensive livestock farmsBen Chugg0Brandon Anderson1Seiji Eicher2Sandy Lee3Daniel E. Ho4Stanford University, United StatesStanford University, United StatesStanford University, United StatesStanford University, United StatesCorresponding author.; Stanford University, United StatesMuch environmental enforcement in the United States has historically relied on either self-reported data or physical, resource-intensive, infrequent inspections. Advances in remote sensing and computer vision, however, have the potential to augment compliance monitoring by detecting early warning signs of noncompliance. We demonstrate a process for rapid identification of significant structural expansion using Planet’s 3 m/pixel satellite imagery products and focusing on Concentrated Animal Feeding Operations (CAFOs) in the US as a test case. Unpermitted building expansion has been a particular challenge with CAFOs, which pose significant health and environmental risks. Using new hand-labeled dataset of 145,053 images of 1,513 CAFOs, we combine state-of-the-art building segmentation with a likelihood-based change-point detection model to provide a robust signal of building expansion (AUC = 0.86). A major advantage of this approach is that it can work with higher cadence (daily to weekly), but lower resolution (3 m/pixel), satellite imagery than previously used in similar environmental settings. It is also highly generalizable and thus provides a near real-time monitoring tool to prioritize enforcement resources in other settings where unpermitted construction poses environmental risk, e.g. zoning, habitat modification, or wetland protection.http://www.sciencedirect.com/science/article/pii/S0303243421001707Structural expansionTime seriesMaximum likelihoodAnimal feeding operations |
| spellingShingle | Ben Chugg Brandon Anderson Seiji Eicher Sandy Lee Daniel E. Ho Enhancing environmental enforcement with near real-time monitoring: Likelihood-based detection of structural expansion of intensive livestock farms International Journal of Applied Earth Observations and Geoinformation Structural expansion Time series Maximum likelihood Animal feeding operations |
| title | Enhancing environmental enforcement with near real-time monitoring: Likelihood-based detection of structural expansion of intensive livestock farms |
| title_full | Enhancing environmental enforcement with near real-time monitoring: Likelihood-based detection of structural expansion of intensive livestock farms |
| title_fullStr | Enhancing environmental enforcement with near real-time monitoring: Likelihood-based detection of structural expansion of intensive livestock farms |
| title_full_unstemmed | Enhancing environmental enforcement with near real-time monitoring: Likelihood-based detection of structural expansion of intensive livestock farms |
| title_short | Enhancing environmental enforcement with near real-time monitoring: Likelihood-based detection of structural expansion of intensive livestock farms |
| title_sort | enhancing environmental enforcement with near real time monitoring likelihood based detection of structural expansion of intensive livestock farms |
| topic | Structural expansion Time series Maximum likelihood Animal feeding operations |
| url | http://www.sciencedirect.com/science/article/pii/S0303243421001707 |
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