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    <identifier>10.57760/sciencedb.17901</identifier>
    <datestamp>2024-11-01T15:34:18Z</datestamp>
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  <dc:date>2024-11-01</dc:date>
  <dc:title>Forest aboveground biomass product of the Qinghai-Tibet Plateau in 2013-2023</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.17901</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>The accurate estimation of aboveground biomass (AGB) reserves on the Qinghai-Tibet Plateau serves as a crucial ecological security measure within China. It is of vital importance to monitor and protect the ecosystem, study the carbon cycle, estimate the carbon storage and reveal the interaction between forest and climate on the Plateau. The estimation of forest AGB in the Qinghai-Tibet Plateau is a challenging endeavour, due to the Plateau's high altitude, complex climate and varied topography, which exhibit significant spatial heterogeneity. This study employed the random forest regression tree model to analyse forest AGB samples collected on the ground. Potential driving factors, including surface reflectance, vegetation index, meteorological data and topographic data, were considered. Feature selection was used to enhance model accuracy, with the optimal model achieving an R2 of 0.78 and an RMSE of 33.79 t/ha. The high spatial resolution (30 m) forest AGB product for the Qinghai-Tibet Plateau in 2013-2023 have been generated. The product can reflect the temporal and spatial distribution pattern of forest biomass in the Qinghai-Tibet Plateau, and provide scientific reference for the formulation of forest protection and management policies in the region.</dc:description>
  <dc:subject>Forest; Aboveground biomass; Random forest; Remote sensing; The Qinghai-Tibet Plateau</dc:subject>
  <dc:creator>Yan Shijie</dc:creator>
  <dc:creator>Zhang Xiaomei</dc:creator>
  <dc:creator>He Guojin</dc:creator>
  <dc:creator>Long Tengfei</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.11922/11-6035.csd.2024.0113.zh</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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