从现场观测,机器学习和在克什米尔喜马拉雅地区的地形上增强的全米模型中获得的地面森林生物质的综合数据集
Syed Danish Rafiq Kashani1, Faisal Zahoor Jan1, Imtiyaz Ahmad Bhat1
1Department of Geoinformatics, University of Kashmir, Hazratbal Srinagar 190006, Jammu and Kashmir, India.
Data in brief
|January 30, 2025
概括
这项研究提出了使用现场数据和遥感的克什米尔喜马拉雅森林生物质的新数据集. 它为估计地表生物质 (AGB) 提供了改进的模型,以帮助保护和气候变化研究.
科学领域:
- 森林生态 森林生态
- 遥感 遥感 遥感 遥感
- 生物质估计生物质估计
背景情况:
- 准确的森林动态和地表生物质 (AGB) 数据对于喜马拉雅地区至关重要,特别是对于气候变化下的碳储存评估和保护计划.
- 喜马拉雅山脉的地形具有挑战性,这给传统的森林库存方法带来了重大困难.
研究的目的:
- 为克什米尔喜马拉雅地区创建一个森林库存和AGB综合数据集.
- 开发和验证先进的,特定物种的全度模型,以改进AGB估计.
- 为森林管理,碳监测和山区生态建模提供资源.
主要方法:
- 利用遥感技术与对275个地块的6220棵树的广泛现场测量相结合.
- 收集了树木直径与乳房高度,物种组成和树木密度的数据.
- 开发了新的特定物种的全度模型,集成规范差异植被指数 (NDVI) 和地形数据.
主要成果:
- 为克什米尔喜马拉雅地区生成了手工划分的多时期森林库存和全面的AGB记录.
- 森林类型和物种特定的AGB的捕获变化受高度,斜率和角度的影响.
- 通过将NDVI和地形增强集成到全米模型中,证明了AGB估计准确度的提高.
结论:
- 该数据集为了解森林动态和在具有挑战性的山区环境中储存碳提供了至关重要的资源.
- 开发的全度模型提高了AGB估计的准确性,支持有效的森林管理和保护.
- 这项工作对区域保护战略,生物多样性规划和山区生态系统的气候政策有广泛的应用.
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