Remote sensing (RS) facilitates forest inventory across a wide range of variables required by the UNFCCC as well as by other agreements and processes. The Conventional model-based (CMB) estimator supports wall-to-wall RS data, while Hybrid estimators support surveys where RS data are available as a sample. However, the connection between these two types of monitoring procedures has been unclear, hindering the reconciliation of wall-to-wall and non-wall-to-wall use of RS data in practical applications and thus potentially impeding cost-efficient deployment of high-end sensing instruments for large area monitoring. Consequently, our objectives are to (1) shed further light on the connections between different types of Hybrid estimators, and between CMB and Hybrid estimators, through mathematical analyses and Monte Carlo simulations; and (2) compare the effects and explore the tradeoffs related to the RS sampling design, coverage rate, and cluster size on estimation precision. Primary findings are threefold: (1) the CMB estimator represents a special case of Hybrid estimators, signifying that wall-to-wall RS data is a particular instance of sample-based RS data; (2) the precision of estimators in forest inventory can be greater for stratified non-wall-to-wall RS data compared to wall-to-wall RS data; (3) otherwise cost-prohibitive sensing, such as LiDAR and UAV, can support large scale monitoring through collecting RS data as a sample. These conclusions may reconcile different perspectives regarding choice of RS instruments, data acquisition, and cost for continuous observations, particularly in the context of surveys aiming at providing data for mitigating climate change.

中文翻译：

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遥感森林清查中某些基于模型的估计量的联系


遥感 （RS） 有助于根据 UNFCCC 以及其他协议和流程要求的广泛变量进行森林清查。传统基于模型的 （CMB） 估计器支持全面 RS 数据，而混合估计器支持 RS 数据作为样本提供的调查。然而，这两种类型的监测程序之间的联系尚不清楚，阻碍了在实际应用中对 RS 数据的墙到墙和非墙到墙使用的协调，从而可能阻碍用于大面积监测的高端传感仪器的成本效益部署。因此，我们的目标是 （1） 通过数学分析和蒙特卡洛模拟，进一步阐明不同类型的 Hybrid 估计器之间以及 CMB 和 Hybrid 估计器之间的联系;（2） 比较效果并探索与 RS 采样设计、覆盖率和聚类大小相关的权衡对估计精度的影响。主要发现有三个方面：（1） CMB 估计器代表了混合估计器的一种特殊情况，表明墙到墙的 RS 数据是基于样本的 RS 数据的特定实例;（2） 与围墙 RS 数据相比，分层非围墙到墙 RS 数据的森林清查中估计量的精度可能更高;（3） 其他成本高昂的传感，如 LiDAR 和 UAV，可以通过收集 RS 数据作为样本来支持大规模监测。这些结论可能会调和关于 RS 工具选择、数据采集和连续观测成本的不同观点，尤其是在旨在为缓解气候变化提供数据的调查背景下。