基于CASA模型的植被NPP样本提取研究
Journal: Ecological Environment and Protection DOI: 10.12238/eep.v8i1.2446
Abstract
本研究基于CASA模型和空间叠加分析方法,提出了一种估算高空间分辨率(30米)NPP训练样本的方法。研究结果表明：(1)提取的NPP的结果具有较高的空间分辨率,相比MODIS 500米NPP数据集具有更高的空间异质性；(2)提取的NPP的结果对地形更为敏感,在复杂地形区域具有较高合理性；(3)本研究提出的方法具有较高的可行性,提取的NPP的结果可以补充没有实地监测NPP的区域作为NPP“真值”使用,进行样点采样后的配合训练特征可作为训练样本,提升机器学习模型在NPP估算中的精度和泛化能力。本研究结果可为生态系统碳汇监测、碳循环研究、气候变化影响评估等提供重要数据支持。
Keywords
植被净初级生产力；CASA模型；训练样本；机器学习；地形校正
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