Swiss Federal Institute for Forest, Snow and Landscape Research Preserving Switzerland's natural heritage Achilleas Psomas January 23rd,2006 University of Zurich Remote Sensing for the protection and conservation of Swiss dry meadows and pastures

Swiss Federal Institute for Forest, Snow and Landscape Research Outline Introduction-Problem description Project Objectives Research plan - Scaling Scientific collaboration Remote Sensing – Spectral Reflectance Potential Outcome

Swiss Federal Institute for Forest, Snow and Landscape Research Introduction Dry meadows and pastures in Switzerland are species-rich habitats resulting from a traditional agricultural land use. Compositional and structural characteristics depend on climate, topography, and the cultural history of each area 40% of plant and over 50% of animal species present on dry meadows are classified as endangered 90% of dry grasslands have been transformed to other land cover types

Swiss Federal Institute for Forest, Snow and Landscape Research Introduction Based on the Federal Law on the Protection of Nature and Landscape, the most valuable grasslands areas should be mapped and evaluated TWW Project "Dry Grassland in Switzerland"(Trockenwiesen und –weiden) Initiated in 1995 Creation of a federal inventory so ecologically valuable grasslands could be given an increased protection provided for by law. Limitations of existing methods Time consuming Very expensive Monitoring difficult

Swiss Federal Institute for Forest, Snow and Landscape Research Research Objectives - Research Plan General Objective To develop, apply, and validate different methods based on remote sensing datasets and techniques for identification and monitoring of dry meadows and pastures in Switzerland Main Project blocks: Part A: Field Spectrometry-(Plot to Field) Part B: Imaging Spectrometry-(Field to Region) Part C: Multitemporal Landsat TM approach-(Region to Landscape)

Swiss Federal Institute for Forest, Snow and Landscape Research Scaling-General PartSensor Spatial Resolution Spectral Resolution Spatial CoverageAltitude A) Field Spectrometry ASD Field Spectroradiometer 0.5m2150 bands6-8 fields/day1.5m B) Imaging Spectrometry HyMap5m128 bands12km x 4km3km C) Multitemporal Landsat TM Landsat TM30m7 bands180km x 180km700km

Swiss Federal Institute for Forest, Snow and Landscape Research Scaling-I PartSensor Spatial Resolution Spectral Resolution Spatial CoverageAltitude A) Field Spectrometry ASD Field Spectroradiometer 0.5m2150 bands6-8 fields/day1.5m

Swiss Federal Institute for Forest, Snow and Landscape Research Scaling-II PartSensor Spatial Resolution Spectral Resolution Spatial CoverageAltitude B) Imaging Spectrometry HyMap5m128 bands12km x 4km3km

Swiss Federal Institute for Forest, Snow and Landscape Research Scaling-III PartSensor Spatial Resolution Spectral Resolution Spatial CoverageAltitude C) Multitemporal Landsat TM Landsat TM30m7 bands180km x 180km700km

Swiss Federal Institute for Forest, Snow and Landscape Research Scaling-II

Swiss Federal Institute for Forest, Snow and Landscape Research Remote Sensing – Spectral Reflectance The total amount of radiation that strikes an object is referred to as the incident radiation incident radiation = reflected radiation + absorbed radiation + transmitted radiation

Swiss Federal Institute for Forest, Snow and Landscape Research Remote Sensing – Spectral Reflectance Spectral Response of Vegetation

Swiss Federal Institute for Forest, Snow and Landscape Research Scientific collaboration ● Swiss Federal Research Institute WSL,Switzerland ● Remote Sensing Laboratories,University of Zurich Switzerland. ● Wageningen University, The Netherlands ● Oak Ridge National Laboratory,Utah State University,USA

Swiss Federal Institute for Forest, Snow and Landscape Research Potential Outcome-Benefits ● Valuable scientific outcome since assessing the status and historical range of spectral variability of conserved grasslands has not been performed to date. ● Strong scientific collaboration and networking. ● A well tested set of statistical tools for identification, planning and monitoring of dry meadows in Switzerland. ● Significant reduction of financial costs for monitoring purposes. ● A faster and more efficient response to grassland-type change since field teams can go directly to pre-selected “hot spots”. ● A merge of traditional sampling methodologies with cutting edge technology like advanced remote sensing sensors.

Swiss Federal Institute for Forest, Snow and Landscape Research Thank you for your attention

Swiss Federal Institute for Forest, Snow and Landscape Research Field Spectrometry II

Swiss Federal Institute for Forest, Snow and Landscape Research Research Plan Main Project blocks: Part A: Field Spectrometry-(Plot to Field) Part B: Imaging Spectrometry-(Field to Region) Part C: Multitemporal Landsat TM approach-(Region to Landscape)

Swiss Federal Institute for Forest, Snow and Landscape Research Research Objectives General Objective To develop, apply, and validate different methods based on remote sensing datasets and techniques for identification and monitoring of dry meadows and pastures in Switzerland Specific Objectives Examine the potential of using the seasonal variability in spectral reflectance for discriminating dry meadows and pastures. Identify the best spectral wavelengths to discriminating grasslands of different type. Which are the spectral wavelengths with statistical significant differences? Identify the optimal time or times during the growing season for discriminating different types of grasslands.

Swiss Federal Institute for Forest, Snow and Landscape Research Discussion-Further steps Increased spectral resolution of hyperspectral recordings provide big opportunities for discriminating grassland types. Multitemporal recordings give a better understanding of the differences between grassland types during the growing season. Analysis on continuum removed spectra gave a more stable but smaller number of significant wavelengths, enhancing certain features and smoothing others. Spectral variability within the grasslands is important and needs to be taken into consideration. Processing of the data and statistical analysis is done in R, easily repeated and adjustable.