Development of a unified framework for environmental monitoring based on a global sampling grid system (GSG)

环境监测已成为许多与生态和环境挑战有关的国际和国家进程中的关键因素。防治毁林和森林退化(REDD+)只是一个例子，表明监测森林资源的状况和变化至关重要，作为按业绩付款的基础，各国或项目应因其在避免/减少毁林和森林退化方面所做的努力而得到补偿。许多森林覆盖变化或碳储量的地图产品是通过基于模型的遥感分析生成的，这些分析将作为确定全球毁林水平的基准，特别是热带地区的毁林水平。通过谷歌地球、微软虚拟地球(Bing)或NASA World Wind等虚拟地球，免费获得的高分辨率和地理参考图像的大型档案越来越多，但在全球环境监测背景下，科学应用程序仍然相对未充分利用这些档案。它们可以作为以设计为基础的抽样研究的基础，通过对遥感图像中相对较小的观察单位进行视觉解释。统一的估算和数据存储框架将有助于利用这些全球数据来源，并将不同区域和空间范围的研究成果结合起来。该项目旨在提出一种可扩展和仿射的全球抽样设计，并将为其应用制定一个概念框架，重点是森林资源。特别强调利用免费提供的遥感图像。计划中的项目的一个科学关键要素是为优化遥感图像目视解译的观测单元制定方法学基础。观测设计与分类/解译图像单元的可靠性之间存在相互作用。该项目的这一方面是通过研究基于设计的推理的各个方面来解决的，这些方面容易出现错误。

Environmental monitoring has been emerging as key factor in many international and national processes related to ecological and environmental challenges. The combat against deforestation and forest degradation (REDD+) is but one example showing that monitoring of the state and change of forest resources is essential as a basis for performance based payments when countries or projects shall be compensated for their efforts in avoiding/reducing deforestation and forest degradation. Many wall-to-wall map products of forest cover change or carbon stocks have been generated by model-based remote sensing analysis that shall serve as benchmark to define global deforestation levels, especially for tropical regions. The increasing availability of large archives of freely available high resolution and geo-referenced imagery through virtual globes like Google Earth, Microsoft Virtual Earth (bing) or NASA World Wind and others is still relatively underexploited by scientific applications in this global environmental monitoring context. They could serve as basis for design-based sampling studies by visual interpretation of relatively small observation units in remotely sensed imagery. A unified framework for estimation and data storage would help to utilize these global data sources and to integrate research studies from different regions and spatial extent. The project aims at proposing a scalable and affine global sampling design and will develop a conceptual framework for its application with a focus on forest resources. Special emphasis is laid on utilizing freely available remote sensing imagery. A scientific key element of the planned project is the development of methodological basis for the optimization of observation units for visual interpretation of remote sensing imagery. There is an interplay between the observation design and the reliability of classified/interpreted image units. This dimension of the project is addressed by researching into aspects of design-based inference with error-prone observations.