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ALCC: Temporally consistent annual land cover maps over China from 1985 to 2022 based on an ensemble change detection method

<p><span><span>We develope <span><span>&nbsp;</span>a consistent annual land cover&nbsp;product for China (ALCC)&nbsp; from 1985 to 2022. First, change areas for each year was detected baesd on an ensemble change detection method combing CCDC, BFASTm, and Chow Test. Then, stable training samples were derived from both CLCD and CLUDs. The Random Forest classifier was locally trained and used to classify change areas year by year. Finally, ALCC was generated by updating land cover classification results for change areas and remaining class label the same as the base map forthe unchanged areas. ALCC achieved a mean overall accuracy of 81.11&plusmn;0.67% nationwide and exceeded 72.00% across seven geographical regions.</span></span></span></p>

ShareScore

32/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
16
Reuse readiness
8
Engagement
0