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Robustness of model-based high-resolution prediction of forest biomass against different field plot designs

机译:基于模型的高分辨率森林生物量对不同田间区划设计的鲁棒性

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摘要

BackgroundParticipatory forest monitoring has been promoted as a means to engage local forest-dependent communities in concrete climate mitigation activities as it brings a sense of ownership to the communities and hence increases the likelihood of success of forest preservation measures. However, sceptics of this approach argue that local community forest members will not easily attain the level of technical proficiency that accurate monitoring needs. Thus it is interesting to establish if local communities can attain such a level of technical proficiency. This paper addresses this issue by assessing the robustness of biomass estimation models based on air-borne laser data using models calibrated with two different field sample designs namely, field data gathered by professional forester teams and field data collected by local communities trained by professional foresters in two study sites in Nepal. The aim is to find if the two field sample data sets can give similar results (LiDAR models) and whether the data can be combined and used together in estimating biomass.
机译:背景技术促进参与式森林监测是使当地依赖森林的社区参与具体的气候减缓活动的一种手段,因为它给社区带来了主人翁感,因此增加了森林保护措施成功的可能性。但是,对此方法的怀疑者认为,当地社区的森林成员不会轻易达到准确监测所需的技术水平。因此,很有必要确定当地社区是否可以达到这样的技术水平。本文通过使用两种不同的野外样品设计(分别由专业林务员团队收集的野外数据和由专业林务员培训的当地社区收集的野外数据)校准的基于机载激光数据的生物量评估模型的鲁棒性来评估此问题。尼泊尔的两个学习场所。目的是确定两个现场样本数据集是否可以给出相似的结果(LiDAR模型),以及这些数据是否可以组合在一起并一起用于估算生物量。

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