SNF - Projects
Contact Persons
Keywords
Vegetation & Ecosystems, Goniometry, Water Resources
Vegetation & Ecosystems
Within the vegetation research statistical and special hyperspectral analysis procedures are used to develop new methods to predict canopy biochemistry, such as nitrogen and carbon concentration or water content. Biochemical processes are all related to the foliar chemistry of vegetation and thus to the carbon and nitrogen cycles. Hence, biochemical information products contribute to many environmental applications. For instance ecosystem models can be parameterized with the generated products that can help to better understand CO2 fluxes and net primary production (NPP) in the framework of the Kyoto Protocol. Traditional measurement of forest canopy level biochemistry is time-consuming, expensive and spatially constrained. Remote sensing allows for repeatable and continuous prediction of biochemical information over a wide spatial scale and thus facilitates the understanding of ecosystem functions. For the retrieval of biochemistry products to be used for environmental applications, the transfer of the developed methods from airborne hyperspectral to spaceborne data is fundamental. This transfer involves spectral and spatial up-scaling. Additionally, spaceborne reflectance data contain angular effects due to the sensor field of view and observation geometry, which can finally influence biochemistry estimates. However, multi-angular reflectance data contain added information about vegetation structure. Since correct biochemistry mapping is linked to accurate vegetation structure, forest biochemistry products may be improved with multi-angular data. Our goals in the field of biochemistry prediction are to transfer the developed airborne-based methods to spaceborne data and to evaluate different methods for up-scaling. With the help of multi-angular data we want to investigate the quantitative and qualitative influences of angular effects on different spectral transformation methods and biochemistry prediction.
Goniometry
Field and laboratory goniometer measurements are used to assess the spectrodirectional reflectance properties of ground surface targets. Many applications, such as BRDF (bidirectional reflectance distribution function) correction of remote sensing data and quantitative retrieval of vegetation, snow or soil parameters require accurate knowledge of spectrodirectional surface reflectance properties. Most accurate BRDF retrieval is based on the availability of simultaneously measured incoming radiation data as well as reflected radiation data at high spectral and angular resolution. RSL’s recently built dual-view goniometer system fully addresses these requirements and can serve as a reference instrument for collaboration in ground based spectrodirectional data acquisition and validation. Our goals in the field of goniometry are to implement algorithms for accurate BRDF retrievals and to quantitatively investigate angular effects in derived biophysical and biochemical surface parameters. This also includes the study of the relationship between field and laboratory measurements and the potential establishment of field-lab transfer functions.
Water resources (Daniel Odermatt)
The SNF project targets at the key aim of the joint EU, ESA GMES initiative to establish operational services for the assessment of water resources in terms of quality, quantity and usage. It has been defined as a major challenge in the scope of GMES activities and it is of crucial importance in most developing countries and at a global level (EC, 2005). RSL is developing new methodology (semi-empirical and analytical methods) for the retrieval of water constituents in order to establish scientific algorithm development activities with special emphasis on APEX retrieval algorithms for water constituent s retrieval and the discrimination of macro phytes and algae types. Thanks to the unique performance, the APEX instrument will facilitate the observation of regional scale features (e.g., Harmful Algae Blooms) and enable the study of complex waters with unprecedented accuracy.The development of remote sensing algorithms to retrieve phytoplankton species and physiology is a challenging endeavor of high importance to assess biological activities in the water and therefore water quality by better means