An Enhanced Algorithm for Active Fire Detection in Croplands Using Landsat-8 OLI Data Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.3390/land12061246
Burning biomass exacerbates or directly causes severe air pollution. The traditional active fire detection (AFD) methods are limited by the thresholds of the algorithms and the spatial resolution of remote sensing images, which misclassify some small-scale fires. AFD for burning straw is interfered with by highly reflective buildings around urban and rural areas, resulting in high commission error (CE). To solve these problems, we developed a multicriteria threshold AFD for burning straw (SAFD) based on Landsat-8 imagery in the context of croplands. In solving the problem of the high CE of highly reflective buildings around urban and rural areas, the SAFD algorithm, which was based on the LightGBM machine learning method (SAFD-LightGBM), was proposed to differentiate active fires from highly reflective buildings with a sample dataset of buildings and active fires and an optimal feature combining spectral features and texture features using the ReliefF feature selection method. The results revealed that the SAFD-LightGBM method performed better than the traditional threshold method, with CE and omission error (OE) of 13.2% and 11.5%, respectively. The proposed method could effectively reduce the interference of highly reflective buildings for active fire detection, and it has general applicability and stability for detecting discrete, small-scale fires in urban and rural areas.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/land12061246
- https://www.mdpi.com/2073-445X/12/6/1246/pdf?version=1687067986
- OA Status
- gold
- Cited By
- 5
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381165946
Raw OpenAlex JSON
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https://openalex.org/W4381165946Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/land12061246Digital Object Identifier
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An Enhanced Algorithm for Active Fire Detection in Croplands Using Landsat-8 OLI DataWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-18Full publication date if available
- Authors
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Yizhu Jiang, Jinling Kong, Yanling Zhong, Qiutong Zhang, Jingya ZhangList of authors in order
- Landing page
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https://doi.org/10.3390/land12061246Publisher landing page
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https://www.mdpi.com/2073-445X/12/6/1246/pdf?version=1687067986Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2073-445X/12/6/1246/pdf?version=1687067986Direct OA link when available
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Remote sensing, Context (archaeology), Environmental science, Computer science, Scale (ratio), Pixel, Feature (linguistics), Random forest, Algorithm, Artificial intelligence, Geography, Cartography, Linguistics, Archaeology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 4, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| primary_location.is_published | True |
| primary_location.raw_source_name | Land |
| primary_location.landing_page_url | https://doi.org/10.3390/land12061246 |
| publication_date | 2023-06-18 |
| publication_year | 2023 |
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