The effect of using complete and partial forested FIA plot data on biomass and forested area classifications from MODIS satellite data
Authors’ objective was to determine at what level biomass and forest area obtained from partial and complete forested plot inventory data compares with forested area and biomass estimates from the national inventory data. A subset of 3819 inventory plots (100% forested, 100% non-forested, mixed-forest/non-forest) was used to classify the land cover and model the biomass in South Carolina. Forest inventory data have been further subdivided into three datasets containing 1) mixed plots at least 50% forest or a 50% non-forest, 2) mixed plots that are at least 75% forested or 75% non-forested, and 3) 100% forested/non-forested plots. Separately, each of these three datasets was used in a decision tree classification process applied to MODIS satellite (250-meter resolution) and ancillary data to classify the land cover and model the forest biomass. The satellite, ancillary, and plot data have been subdivided into three mapping zones (54, 58, and 59) for processing in See5 and Cubist software. Classification results for trials with 100% forested/non-forested and mixed (multi-condition) plots show that overall classification accuracy for the percent of pixels correctly classified (%PCC) increased from 75.4% to 79.2%. Comparison between classified forest area with mixed (75% forest, 75% non-forest) plots and the inventory forest area shows a 10% increase. The forest/non-forest single layer classification from each trial was used to mask out non-forested areas for the forest biomass classification. Accuracy of modeled forest biomass was compared with plot data estimates of forest biomass. Biomass obtained from Cubist models with 100% forested and mixed forest inventory plots when compared to the biomass from the published plot data estimates show a difference of less than 2%.