Evaluating Classified MODIS Satellite Imagery as a Stratification Tool

The Forest Inventory and Analysis (FIA) program of the USDA Forest Service collects forest attribute data on permanent plots arranged on a hexagonal network across all 50 states and Puerto Rico. Due to budget constraints, sample sizes sufficient to satisfy national FIA precision standards are seldom achieved for most inventory variables unless the estimation process is enhanced with ancillary data. When used to create strata for stratified estimation, satellite imagery can be effective ancillary data. The National Land Cover Dataset (NLCD), a land cover classification based on satellite imagery, has been used to produce substantial increases in the precision of statewide forest inventory estimates. Because inventories are conducted on an annual basis, it is desirable to create strata using a product that is updated more frequently than the 10-year update cycle of the NLCD. In particular, data from the MODIS sensor are available every 1-2 days, although at a much coarser spatial resolution than the Landsat data used in the creation of the NLCD (250-1000m vs. 30m). In this study, the effectiveness of strata created by classifying MODIS satellite imagery is compared to that of strata created from the NLCD. Results indicate that precision decreases by 0.9 percent per million acres when using a 1-km dataset versus a 30-m dataset.