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Satellite Mapping of 30 m Time-Series Forest Distribution in Hunan, China, Based on a 25-Year Multispectral Imagery and Environmental Features.

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Title: Satellite Mapping of 30 m Time-Series Forest Distribution in Hunan, China, Based on a 25-Year Multispectral Imagery and Environmental Features.
Authors: Liu, Rong1,2 (AUTHOR), Zhang, Gui1,2 (AUTHOR) zgui@csuft.edu.cn, Chen, Aibin1,2 (AUTHOR), Yi, Jizheng2 (AUTHOR)
Source: Remote Sensing. Feb2026, Vol. 18 Issue 3, p426. 32p.
Subjects: Forest mapping, Multispectral imaging, Forest monitoring, Deep learning, Time series analysis, Landsat satellites, Habitats, Satellite-based remote sensing
Geographic Terms: China, Hunan Sheng (China)
Abstract: Highlights: What are the main findings? A 25-year (1999–2023) forest mapping at 30 m resolution using multi-source Landsat series, DEM, and climate data. A deep learning framework integrates multi-temporal imagery and environmental factors for forest cover dynamics. What is the implication of the main findings? Validation with 9000 manual samples and official statistics confirms high accuracy (OA > 92%) and reliability. Superior to existing products in capturing fine-scale spatial patterns and complex forest boundaries. Forests play a critical role in Earth's ecosystem, yet monitoring their long-term, large-scale spatiotemporal dynamics remains a significant challenge. This study addresses this gap by developing an integrated framework to map annual forest distribution in Hunan, China, from 1999 to 2023 at a high resolution of 30 m. Our methodology combines multi-temporal satellite imagery (Landsat 5/7/8/9) with key environmental variables, including digital elevation models, temperature, and precipitation data. To efficiently reconstruct historical maps, training samples were automatically derived from a reliable 2023 forest product using a transferable logic, drastically reducing manual annotation effort. Comprehensive evaluations demonstrate the robustness of our approach: (1) Qualitative analyses reveal superior spatial detail and temporal consistency compared to existing global forest maps. (2) Rigorous quantitative validation based on ∼9000 reference samples confirms high and stable accuracy (∼92.4%) and recall (∼91.9%) over the 24-year period. (3) Furthermore, comparisons with government forestry statistics show strong agreement, validating the practical utility of the data. This work provides a valuable, accurate long-term dataset that forms a scientific basis for critical downstream applications such as ecological conservation planning, carbon stock assessment, and climate change research, thereby highlighting the transformative potential of multi-source data fusion and automated methods in advancing geospatial monitoring. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
  Group: Ti
  Data: Satellite Mapping of 30 m Time-Series Forest Distribution in Hunan, China, Based on a 25-Year Multispectral Imagery and Environmental Features.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Rong%22">Liu, Rong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Gui%22">Zhang, Gui</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zgui@csuft.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Aibin%22">Chen, Aibin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yi%2C+Jizheng%22">Yi, Jizheng</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Feb2026, Vol. 18 Issue 3, p426. 32p.
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink><br /><searchLink fieldCode="DE" term="%22Hunan+Sheng+%28China%29%22">Hunan Sheng (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? A 25-year (1999–2023) forest mapping at 30 m resolution using multi-source Landsat series, DEM, and climate data. A deep learning framework integrates multi-temporal imagery and environmental factors for forest cover dynamics. What is the implication of the main findings? Validation with 9000 manual samples and official statistics confirms high accuracy (OA > 92%) and reliability. Superior to existing products in capturing fine-scale spatial patterns and complex forest boundaries. Forests play a critical role in Earth's ecosystem, yet monitoring their long-term, large-scale spatiotemporal dynamics remains a significant challenge. This study addresses this gap by developing an integrated framework to map annual forest distribution in Hunan, China, from 1999 to 2023 at a high resolution of 30 m. Our methodology combines multi-temporal satellite imagery (Landsat 5/7/8/9) with key environmental variables, including digital elevation models, temperature, and precipitation data. To efficiently reconstruct historical maps, training samples were automatically derived from a reliable 2023 forest product using a transferable logic, drastically reducing manual annotation effort. Comprehensive evaluations demonstrate the robustness of our approach: (1) Qualitative analyses reveal superior spatial detail and temporal consistency compared to existing global forest maps. (2) Rigorous quantitative validation based on ∼9000 reference samples confirms high and stable accuracy (∼92.4%) and recall (∼91.9%) over the 24-year period. (3) Furthermore, comparisons with government forestry statistics show strong agreement, validating the practical utility of the data. This work provides a valuable, accurate long-term dataset that forms a scientific basis for critical downstream applications such as ecological conservation planning, carbon stock assessment, and climate change research, thereby highlighting the transformative potential of multi-source data fusion and automated methods in advancing geospatial monitoring. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.3390/rs18030426
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 32
        StartPage: 426
    Subjects:
      – SubjectFull: Forest mapping
        Type: general
      – SubjectFull: Multispectral imaging
        Type: general
      – SubjectFull: Forest monitoring
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Time series analysis
        Type: general
      – SubjectFull: Landsat satellites
        Type: general
      – SubjectFull: Habitats
        Type: general
      – SubjectFull: Satellite-based remote sensing
        Type: general
      – SubjectFull: China
        Type: general
      – SubjectFull: Hunan Sheng (China)
        Type: general
    Titles:
      – TitleFull: Satellite Mapping of 30 m Time-Series Forest Distribution in Hunan, China, Based on a 25-Year Multispectral Imagery and Environmental Features.
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            NameFull: Liu, Rong
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            NameFull: Zhang, Gui
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            NameFull: Chen, Aibin
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            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
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