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

为印度尼西亚热带森林开发具有实地采样和遥感功能的综合森林碳监测系统

基本信息

批准号：
204152256
负责人：
Professor Dr. Christoph Kleinn
金额：
--
依托单位：
Abteilung Waldinventur und Fernerkundung
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2012
资助国家：
德国
起止时间：
2011-12-31 至 2015-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/204152256?language=en
关键词：
Development integrated forest carbon monitoring

项目摘要

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.

森林在减缓气候变化方面发挥着相关作用。然而，一个主要问题是通过减少毁林和森林退化，以及通过促进可持续森林管理，对森林固碳方面的成就进行科学上有根据、透明和可核查的监测。在多样化和难以进入的潮湿热带森林地区，监测尤其困难。本研究将有助于改善潮湿热带森林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）联系起来，并与激光雷达图像的剥离样本联系起来。将根据(1)碳估计的精度和(2)碳区划的准确性进一步发展和分析连接这些数据源的技术。拟议的项目意味着研究基于样本的森林清查（生物量的重新采样技术、无响应的归算）和遥感应用于森林监测（区划、基于样本的激光雷达数据应用）的方法改进。本研究的核心是分析各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佳资源分配，以便有效改进森林碳监测系统。