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

• Applicant: Dr. Lutz Fehrmann
• Subject Area: Forestry
• Term: from 2015 to 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 273259202

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.

• DFG Programme: Research Grants
• Co-Investigator: Professor Dr. Christoph Kleinn