Identification of Abandoned Jujube Fields Using Multi-Temporal High-Resolution Imagery and Machine Learning Article Swipe
Xingrong Li
,
Chenghai Yang
,
Hongri Zhang
,
Panpan Wang
,
Jia Tang
,
Yanqin Tian
,
Qing Zhang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13040801
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13040801
The jujube industry plays a very important role in the agricultural industrial structure of Xinjiang, China. In recent years, the abandonment of jujube fields has gradually emerged. It is critical to inventory the abandoned land soon after it is generated to adjust agricultural production better and prevent the negative impacts from the abandonment (such as outbreaks of diseases, insect pests, and fires). High-resolution multi-temporal satellite remote sensing images can be used to identify subtle differences among crops and provide a good tool for solving this problem. In this research, both field-based and pixel-based classification approaches using field boundaries were used to estimate the percentage of abandoned jujube fields with multi-temporal high spatial resolution satellite images (Gaofen-1 and Gaofen-6) and the Random Forest algorithm. The results showed that both approaches produced good classification results and similar distributions of abandoned fields. The overall accuracy was 91.1% for the field-based classification and 90.0% for the pixel-based classification, and the Kappa was 0.866 and 0.848 for the respective classifications. The areas of abandoned land detected in the field-based and pixel-based classification maps were 806.09 ha and 828.21 ha, respectively, accounting for 8.97% and 9.11% of the study area. In addition, feature importance evaluations of the two approaches showed that the overall importance of texture features was higher than that of vegetation indices and that 31 October and 10 November were important dates for abandoned land detection. The methodology proposed in this study will be useful for identifying abandoned jujube fields and have the potential for large-scale application.
Related Topics
Concepts
Remote sensing
Pixel
Vegetation (pathology)
Satellite
Agriculture
Satellite imagery
Field (mathematics)
Agricultural land
Cartography
Environmental science
Geography
Physical geography
Forestry
Computer science
Artificial intelligence
Mathematics
Pure mathematics
Archaeology
Engineering
Medicine
Pathology
Aerospace engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs13040801
- OA Status
- gold
- Cited By
- 12
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3133333589
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3133333589Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs13040801Digital Object Identifier
- Title
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Identification of Abandoned Jujube Fields Using Multi-Temporal High-Resolution Imagery and Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-02-22Full publication date if available
- Authors
-
Xingrong Li, Chenghai Yang, Hongri Zhang, Panpan Wang, Jia Tang, Yanqin Tian, Qing ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/rs13040801Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/rs13040801Direct OA link when available
- Concepts
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Remote sensing, Pixel, Vegetation (pathology), Satellite, Agriculture, Satellite imagery, Field (mathematics), Agricultural land, Cartography, Environmental science, Geography, Physical geography, Forestry, Computer science, Artificial intelligence, Mathematics, Pure mathematics, Archaeology, Engineering, Medicine, Pathology, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
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12Total citation count in OpenAlex
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2025: 3, 2024: 1, 2023: 1, 2022: 7Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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