EVEOSD - Evaluation and Validation of EO-1 for Sustainable Development
Abstract for EVEOSD
Forests contribute to the environment, economic activity, and atmosphere of nations. At the 1992 United Nations Conference on Environment and Development, the United States, Canada, and most other countries, endorsed the concept of sustainable development as the guiding principle for human activities in the future. Canada has a national need to assess the state of the nation's forests. Canada contains 10% of the world's forests. Multitemporal Landsat data will be a primary source for measuring indicators of sustainable development, such as forest area, forest type, biomass, disturbance, above-ground carbon, reforestation, afforestation, and deforestation, etc. In this project we will use EO-1 data to validate that products obtainable from earlier Landsats can be obtained from ALI and Hyperion. New products not possible with earlier Landsats will also be developed for ALI, Hyperion, and LAC. Comparisons will be made of reflectances obtained from EO-1 and the Landsat series. Atmospheric correction models will be used to obtain the highest accuracies with EO-1. Landsat bands will be synthesized from Hyperion and the continuity with earlier Landsats will be validated. The preferred bands and algorithms for forestry from Hyperion will be identified. We have extensive experience with hyperspectral data for forestry. Classifications of EO-1 data will be compared with earlier Landsats. The efficacy of LAC and Hyperion for atmospheric correction will be assessed. Six test sites across Canada and one US site will be used. The EO-1 data will be evaluated also for the measurement of forest structure, canopy chemistry, LAI, proportional estimation, and land cover change. Comparisons will be made between AVIRIS (Airborne Visible/Infrared Imaging Spectrometer), casi (Compact Airborne Spectrographic Imager), Hyperion, and ground spectral measurements. The combination of Hyperion and ALI will be evaluated in terms of detection of atmospheric conditions, such as opacity and irregular water vapor distributions. Evaluations will be made of methods for autonomous data acquisition for forest products. Experiments will be conducted fusing ALI, Hyperion and LAC with GIS (topography, etc.) data and other satellites, such as Radarsat and Ikonos 2. For this project additional airborne sensing will be conducted to extend ground measurements and to provided additional comparisons with the satellite observations. Sensor performance will be assessed in terms of radiometric and geometric correction, temporal stability, and accuracy of calibrations. The project will be managed out of the Pacific Forestry Centre of Natural Resources Canada.
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