Modeling Pine Plantation NEP Using Landsat

The CASA (Carnegie Ames Stanford Approach) ecosystem process model predicts terrestrial ecosystem fluxes using satellite-based inputs at a maximum geographic resolution of 30 meters to infer variability in forest carbon fluxes. We are using CASA to model pine plantation net ecosystem production (NEP) under a range of standard silvicultural prescriptions, primarily thinning by fertilization interactions. Landsat scenes from WRS path/row 14/35, 21/37, and 16/34 are being used. Within each frame, all available cloud-free scenes within a two- to three-year period have been obtained from the USGS EROS Data Center processed to L1T, and subsequently converted to top-of-atmosphere reflectance using standard methods and the latest calibration parameter files. Atmospheric amelioration started with dark object subtraction (band minimum) and only proceeded to more complex techniques as necessary. Subsequent to preprocessing, the reduced simple ratio (RSR; using global min/max) was calculated for all images for each WRS path/row. Pure pine pixels in each frame were identified using unsupervised classification of the most recent leaf-off scene. We developed four age classes using two decades of Landsat data over each WRS path/row. CASA runs, which require soil parameters, and gridded climate/solar radiation in addition to satellite-derived vegetation indices, are now complete. Soil respiration and productivity estimates are being evaluated using a regionwide network of validation sites spanning the range of loblolly pine (Texas to Virginia). Preliminary results indicate that Landsat-based process modeling (1) is necessary for the scale at which land is actually managed and (2) produces estimates with an accuracy and precision affording improved understanding and management of forest ecosystems.