annual maps of gloabl forest management types from 2001-2020

<p>This dataset provided the annual global fine composition of forests from a management perspective. Based on multi-data sources such as MOD13Q1, human footprint, and DEM, we extracted variables such as vegetation growth characteristics, local texture information, human activities, and terrain property. Using machine learning and change detection methods, &nbsp;annual global maps of forest management types with a spatial resolution of 250 m were generated for the detailed composition of forests from 2001 to 2020 from a forest management perspective. Forest management types were defined into six categories: natural regeneration forests (both unmanaged and managed), plantation forests (rotation &gt;15 years and &le;15 years), oil palm plantations, and agroforestry. Point-scale validation results indicated an overall accuracy ranging from 75.55% to 96.26%. The annual forest management type data holds significant importance in understanding the fine composition of forests.</p> <div>1:unmanaged&nbsp;naturally regenerated forest</div> <div>2:managed&nbsp;naturally regenerated forest</div> <div>3:planted forest with rotation &gt;15 years&nbsp;</div> <div>4:planted forest with rotation&le;15 years</div> <div>5:oil palm plantation</div> <div>6:agroforestry</div> <div>7:Others</div> <p>A research article about this dataset, please cite:&nbsp;<span>Hongtao Xu,</span><span>Bin He, </span><span>Lanlan Guo, </span><span>Xing Yan, </span><span>Jinwei Dong, </span><span>Wenping Yuan, </span><span>Xingming Hao, </span><span>Aifeng Lv, </span><span>Xiangqi He, </span><span>Tiewei Li. </span>Changes in the Fine Composition of Global Forests from 2001 to 2020.<span><em> J Remote Sens. </em></span>2024;4:0119.<span>DOI:<a href="https://doi.org/10.34133/remotesensing.0119">10.34133/remotesensing.0119</a></span></p>