Citation:

Iiames, J. LEAF AREA INDEX (LAI) CHANGES DETECTION OF UNDERSTORY VEGETATION IN THE ALBEMARLE-PAMLICO BASIN IKONOS AND LANDSAT ETM+ SATELLITE DATA. Presented at EPA Science Forum 2004, Washington, DC, June 1-3, 2004.

Impact/Purpose:

Our research objectives are to: (a) develop new methods using satellite remote sensor data for the rapid characterization of LC condition and change at regional to national scales; (b) evaluate the utility of the new NASA-EOS MODIS (Moderate Resolution Imaging Spectrometer) leaf area index (LAI) measurements for regional scale application with landscape process models (e.g., biogenic emissions and atmospheric deposition); (c) provide remote sensor derived measurement data to advance the development of the next generation of distributed landscape process-based models to provide a predictive modeling capability for important ecosystem processes (e.g., nutrients, sedimentation, pathogens, etc.); and (d) integrate in situ monitoring measurement networks with UAV and satellite based remote sensor data to provide a continuous environmental monitoring capability.

Description:

The advent of remotely sensed data from satellite platforms has enabled the research community to examine vegetative spatial distributions over regional and global scales. This assessment of ecosystem condition through the synoptic monitoring of terrestrial vegetation extent, biomass, and seasonal dynamics has begun to answer questions related to carbon sequestration and the expansion of greenhouse gases, biogenic emissions and the inputs into air quality, and other significant environmental issues. The validation of these satellite-derived vegetative parameters includes the examination of accumulated variances stemming from data acquisition, to data processing, and to final accuracy assessment. The importance of understanding variation through the entire process involves the significance of these inputs into process-based models. One input of interest, leaf area index (LAI) defined here as one-half the total green leaf area per unit ground surface area has been used for the quantification of surface photosynthesis, evapotranspiration, and annual net primary production used in the calculation of terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation.

The significance of LAI as source data for process-based ecological models has been well documented. Running and Coughlan (1988) ranked LAI as the most important attribute of vegetation structure for characterizing forest canopies over large areas at broad spatial scales using satellite remote sensing data. Most ecosystem process models that simulate carbon and hydrogen cycles require LAI as an input variable. By controlling terrestrial mass and energy fluxes, vegetation plays a vital role in global climate change. Interest in tracking LAI change includes the role forests play in the sequestration of carbon from carbon emissions (Johnsen et al., 2001), and the formation of tropospheric ozone from biogenic emissions of volatile organic compounds (BVOC) naturally released into the atmosphere (Geron et al., 1994).

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