Scholarlink

<p>This study developed a 30-m resolution annual cropland dataset spanning 1988–2024 to resolve the unstable data quality and high sample acquisition costs in mapping cropland distributions in two agricultural regions of the Qinghai–Tibet Plateau (QTP): the Hehuang Valley (HV) and middle basin of the Yarlung Zangbo River and its two tributaries (the Lhasa and Nianchu rivers; MBYZR and LNR, respectively). This dataset was generated using Landsat imagery and training samples derived from visual interpretation. An initial classification was conducted using a Random Forest classifier. To ensure the stability of training sample quality across time, a sample cleaning approach was applied annually, based on spectral consistency constraints, allowing for the temporal extension of samples. The dataset demonstrated high classification accuracy, whereas the MBYZR and LNR demonstrated better classification performance, reflecting strong stability and robustness. Both regions showed favorable results regarding precision and recall, validating this approach’s effectiveness in multi-temporal remote sensing classification. Therefore, this dataset provides critical support for cropland monitoring, food security assessment, and agricultural adaptation in QTP studies, offering a practical reference for time-series sample construction and transfer in remote sensing classification.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Spatial distribution dataset of 30-m resolution cropland in agricultural regions, Qinghai-Tibet Plateau

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月23日

查看全文
  • Xingsheng Xia,
  • Shenghui Lv,
  • Meijuan Liu,
  • Meng Yan,
  • Qiong Chen,
  • Yaozhong Pan

摘要

This study developed a 30-m resolution annual cropland dataset spanning 1988–2024 to resolve the unstable data quality and high sample acquisition costs in mapping cropland distributions in two agricultural regions of the Qinghai–Tibet Plateau (QTP): the Hehuang Valley (HV) and middle basin of the Yarlung Zangbo River and its two tributaries (the Lhasa and Nianchu rivers; MBYZR and LNR, respectively). This dataset was generated using Landsat imagery and training samples derived from visual interpretation. An initial classification was conducted using a Random Forest classifier. To ensure the stability of training sample quality across time, a sample cleaning approach was applied annually, based on spectral consistency constraints, allowing for the temporal extension of samples. The dataset demonstrated high classification accuracy, whereas the MBYZR and LNR demonstrated better classification performance, reflecting strong stability and robustness. Both regions showed favorable results regarding precision and recall, validating this approach’s effectiveness in multi-temporal remote sensing classification. Therefore, this dataset provides critical support for cropland monitoring, food security assessment, and agricultural adaptation in QTP studies, offering a practical reference for time-series sample construction and transfer in remote sensing classification.