Development of an integrated forest carbon monitoring system with field sampling and remote sensing for tropical forests in Indonesia

森林在减缓气候变化方面发挥着重要作用。然而，一个主要问题是，通过减少砍伐森林和森林退化，以及通过促进可持续森林管理，对森林碳汇方面取得的成就进行科学、透明和可核查的监测。在种类繁多、人迹罕至的湿润热带林区，监测工作尤其困难。拟议的研究将有助于改进潮湿热带森林具有挑战性的条件下的森林碳监测。基于样本的实地观测和基于模型的生物量预测将与全区域卫星遥感图像(RapidEye)和剥离激光雷达图像样本相联系。将从(1)碳估计的精确度和(2)碳区划的精确度方面进一步开发和分析连接这些数据源的技术。拟议的项目意味着对基于样本的森林清查(生物量重采样技术、归因于无反应)和遥感应用于森林监测(区域化、激光雷达数据基于样本的应用)的方法改进进行研究。本研究的核心是分析每个数据源给系统带来的误差方差分量。这种误差分析将有助于确定最优资源分配，以有效改进森林碳监测系统。

Forests play a relevant role in mitigation of climate change. A major issue, however, is the scientifically well founded, transparent and verifyable monitoring of achievements in forest carbon sequestration through reduction of deforestation and forest degradation, and through fostering sustainable forest management. Monitoring is particularly difficult in diverse and inaccessible humid tropical forest areas. The proposed research will contribute to the improvement of forest carbon monitoring under the challenging conditions of humid tropical forests. Sample based field observations and model based biomass predictions will be linked to area-wide satellite remote sensing imagery (RapidEye) and to strip samples of LiDAR imagery. Techniques of linking these data sources will be further developed and analysed with respect to (1) precision of carbon estimation and (2) accuracy of carbon regionalization. The proposed project implies research on methodological improvements of both sample based forest inventories (resampling techniques for biomass, imputation of non-response) and remote sensing application to forest monitoring (regionalization, sample based application of LiDAR data). At the core of this research is the analysis of the error variance components that each data source brings into the system. Such error analysis will allow identifying optimal resource allocation for the efficient improvement of forest carbon monitoring systems.