A machine learning and remote sensing approach for accurate forest sub-compartment level vegetation cover change monitoring

The proposed approach offers a robust and accurate solution for fine-scale forest change monitoring, it enhances the scientific basis for sustainable forest resource management.

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Publication Details

Journal
Frontiers in Plant Science
Published
2026-01-26
DOI
https://doi.org/10.3389/fpls.2026.1741992
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00
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article

A machine learning and remote sensing approach for accurate forest sub-compartment level vegetation cover change monitoring

Danzi Wu, Yingze Tian, Wenjie Zhang, Xiaohui Su
Frontiers in Plant Science
Remote Sensing in Agriculture
article

A machine learning and remote sensing approach for accurate forest sub-compartment level vegetation cover change monitoring

Danzi Wu, Yingze Tian, Wenjie Zhang, Xiaohui Su
article en

Abstract

The proposed approach offers a robust and accurate solution for fine-scale forest change monitoring, it enhances the scientific basis for sustainable forest resource management.

Frontiers in Plant ScienceVol. 17
Beijing Forestry University (CN), Jiangxi Province Forestry Survey Planning Institute (CN)
Life in Land
Openalex Percentile: Top 81%
Remote Sensing in Agriculture
DOIOpen Access PDFOpenAlex
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A machine learning and remote sensing approach for accurate forest sub-compartment level vegetation cover change monitoring — Danzi Wu, Yingze Tian, et al. · Frontiers in Plant Science (2026) | TGRS Research Map | TGRS