What does this research mean for the field?

In the Yellow River Basin, land-use changes are driven by distinct interacting factors—such as vegetation index and population for grassland, and precipitation and population for farmland—and incorporating vegetation index and dry-wet conditions significantly improves land-use simulation accuracy. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To identify the most accurate land-use dataset and analyze the environmental and anthropogenic mechanisms driving land-use transitions across the Yellow River Basin from 2000 to 2020.

August 27, 2026European Journal of Remote SensingOpen Access

Optimal dataset selection and driving mechanisms of land-use change in the Yellow River basin: integrating PLUS model, geodetector and random forest approaches

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Authors

季季雄娟Shanghai Liangyou (China)JWJianshun WangChina Meteorological Administration CLChenli LiuYunnan Normal University

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Implication

Modeling study reveals climate and demographic drivers of land-use transitions in river basins, indicating that environmental indices improve spatial simulation accuracy.

Key Points

To identify the most accurate land-use dataset and analyze the environmental and anthropogenic mechanisms driving land-use transitions across the Yellow River Basin from 2000 to 2020.
Evaluated simulation accuracy across four land-use datasets (CLCD, ESA CCI LC, GlobeLand30, and MCD12Q1) using the PLUS model.
Coupled the PLUS expansion outputs with Geodetector and a random forest classification framework to identify dominant drivers of bidirectional land transitions (transfer-in and transfer-out).
ESA CCI LC achieved the highest simulation accuracy among evaluated datasets, with both Kappa and Overall Accuracy exceeding 0.91.
Constructed land expanded by more than 22% between 2000 and 2020 through conversions of farmland and grassland, while grassland exhibited a decadal growth rate exceeding 0.3%.
Farmland transitions were driven primarily by precipitation and population, forest transitions by vegetation index and moisture conditions, and grassland transitions by population pressure.

Cite This Study

季雄娟 et al. (2026) studied this question.

synapsesocial.com/papers/6a8fe96710c91c1e92620ee2 https://doi.org/10.1080/22797254.2026.2710735

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Implication

Modeling study reveals climate and demographic drivers of land-use transitions in river basins, indicating that environmental indices improve spatial simulation accuracy.

Optimal dataset selection and driving mechanisms of land-use change in the Yellow River basin: integrating PLUS model, geodetector and random forest approaches

Discussion

Member takes

Implication

Key Points

Cite This Study

Optimal dataset selection and driving mechanisms of land-use change in the Yellow River basin: integrating PLUS model, geodetector and random forest approaches

Discussion

Member takes

Implication

Key Points

Cite This Study

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