Ideal remote sensor land cover extraction for national to regional carbon flux monitoring constitutes highly accurate change detection on an annual basis, a challenge magnified in the cloud-occluded tropics. Focusing on seven Central American countries from Belize to Panama, this study tested the feasibility of yearly land cover extraction from MODIS surface reflectance composites, ancillary land cover maps, and country-produced reference polygons. GIS models were created to automate the country-specific process of generating annual input tables for a greenhouse gas inventory tool. MODIS-favorable results in a six-category schema suggest that improvements may depend on international reference data warehousing and interoperability.

Ground vegetation influences habitat selection and provides critical resources for survival and reproduction of animals. Researchers often employ visual methods to estimate ground cover, but these approaches may be prone to observer bias. We therefore evaluated a method using digital photographs of vegetation to objectively quantify percent ground cover of grasses, forbs, shrubs, litter, and bare ground within 90 plots of 2m(2). We carried out object-based image analysis, using a software program called eCognition, to divide photographs into different vegetation classes (based on similarities among neighboring pixels) to estimate percent ground cover for each category. We used the Kappa index of agreement (KIA) to quantify correctly classified, randomly selected segments of all images. Our KIA values indicated strong agreement (> 80%) of all vegetation categories, with an average of 90-96% (SE = 5%) of shrub, litter, forb, and grass segments classified correctly. We also created artificial plots with known percentages of each vegetation category to evaluate the accuracy of software predictions. Observed differences between true cover and eCognition estimates for each category ranged from 1 to 4%. This technique provides a repeatable and reliable way to estimate percent ground cover that allows quantification of classification accuracy.