Time Series Prediction for Monitoring Peatland and Wetland Conditions Using Remoteensing Data Article Swipe
Song Guo
,
Yuxin Hu
,
Jing Xu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7128351/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7128351/v1
Monitoring peatlands and wetlands is essential for assessing environmental health and managing ecosystems, yet traditional methods of monitoring these areas often face challenges due to the complexity and scale of the task. Remote sensing offers a powerful tool for observing large and difficult-to-reach areas but integrating remote sensing data with time series prediction models for effective monitoring remains underdeveloped. Current approaches, which often rely on traditional statistical methods or shallow machine learning techniques, struggle with issues such as handling large volumes of data, complex temporal patterns, and the non-linear relationships inherent in environmental datasets. This paper proposes a novel method leveraging deep learning-based time series prediction for monitoring peatland and wetland conditions, utilizing remote sensing data, which captures both the spatial and temporal dynamics of these ecosystems. The approach utilizes advanced convolutional neural networks (CNNs) to learn complex spatial and temporal patterns from multispectral and panchromatic satellite imagery. The proposed method improves prediction accuracy by effectively handling large datasets, minimizing the loss of spectral and spatial resolution through sophisticated fusion techniques. Experimental results demonstrate that our method outperforms traditional in terms of accuracy, reliability, and computational efficiency, offering a robust framework for real-time monitoring of peatlands and wetlands. This work showcases the potential of integrating deep learning into remote sensing for environmental management, particularly in large-scale ecological monitoring.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-7128351/v1
- https://www.researchsquare.com/article/rs-7128351/latest.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413734983
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413734983Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-7128351/v1Digital Object Identifier
- Title
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Time Series Prediction for Monitoring Peatland and Wetland Conditions Using Remoteensing DataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-27Full publication date if available
- Authors
-
Song Guo, Yuxin Hu, Jing XuList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-7128351/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-7128351/latest.pdfDirect link to full text PDF
- 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://www.researchsquare.com/article/rs-7128351/latest.pdfDirect OA link when available
- Concepts
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Peat, Wetland, Series (stratigraphy), Environmental science, Time series, Hydrology (agriculture), Geography, Statistics, Geology, Mathematics, Ecology, Archaeology, Geotechnical engineering, Paleontology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.integrating | 46, 205 |
| abstract_inverted_index.large-scale | 216 |
| abstract_inverted_index.management, | 213 |
| abstract_inverted_index.monitoring. | 218 |
| abstract_inverted_index.outperforms | 178 |
| abstract_inverted_index.statistical | 67 |
| abstract_inverted_index.techniques, | 73 |
| abstract_inverted_index.techniques. | 171 |
| abstract_inverted_index.traditional | 15, 66, 179 |
| abstract_inverted_index.Experimental | 172 |
| abstract_inverted_index.panchromatic | 146 |
| abstract_inverted_index.particularly | 214 |
| abstract_inverted_index.reliability, | 184 |
| abstract_inverted_index.computational | 186 |
| abstract_inverted_index.convolutional | 132 |
| abstract_inverted_index.environmental | 9, 93, 212 |
| abstract_inverted_index.multispectral | 144 |
| abstract_inverted_index.relationships | 90 |
| abstract_inverted_index.sophisticated | 169 |
| abstract_inverted_index.learning-based | 103 |
| abstract_inverted_index.underdeveloped. | 59 |
| abstract_inverted_index.difficult-to-reach | 43 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.39338629 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |