Tracking Trends in Fractional Forest Cover Change using Long Term Data from AVHRR and MODIS

Friday, 19 December 2014
Do-Hyung Kim1,2, Charlene DiMiceli2, Robert Allen Sohlberg2, Matthew Hansen2, Mark Carroll3, Maureen Kelly2 and John R Townshend2, (1)University of Maryland College Park, College Park, MD, United States, (2)University of Maryland, College Park, MD, United States, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States
Tree cover affects terrestrial energy and water exchanges, photosynthesis and transpiration, net primary production, and carbon and nutrient fluxes. Accurate and long-term continuous observation of tree cover change is critical for the study of the gradual ecosystem change. Tree cover is most commonly inferred from categorical maps which may inadequately represent within-class heterogeneity for many analyses. Alternatively, Vegetation Continuous Fields data measures fractions or proportions of pixel area. Recent development in remote sensing data processing and cross sensor calibration techniques enabled the continuous, long-term observations such as Land Long-Term Data Records. Such data products and their surface reflectance data have enhanced the possibilities for long term Vegetation Continuous Fields data, thus enabling the estimation of long term trend of fractional forest cover change. In this presentation, we will summarize the progress in algorithm development including automation of training selection for deciduous and evergreen forest, the preliminary results, and its future applications to relate trends in fractional forest cover change and environmental change.