Consequences of Landsat Image Strata Classification Errors on Bias and Variance of Inventory Estimates: A Forest Inventory Case Study

Abstract

Use of remotely sensed (e.g., Landsat) imagery for developing sampling frame strata for large-scale inventories of natural resources has potential for increasing sampling efficiency and lowering cost by reducing required sample sizes. Sampling frame errors are inherent with the use of this technology, either from user misclassification or due to flawed technology. Knowledge of these sampling frame errors is important, as they inflate the variance of inventory estimates, particularly poststratified estimates. Forest inventory estimates from the Mississippi Institute for Forest Inventory (MIFI) were utilized to study the extent to which Geographic Information System classification errors (sampling frame errors) affect forest volume and area mean and variance estimates. MIFI's high sampling intensity provided a unique opportunity to quantify the magnitude that different levels of misclassification ultimately have on mean and variance estimates. A variance calculator was developed to assess the impact of various levels of misclassification on least and most variable summary estimates of cubic meter volume percent and total area. The standard error estimates for mean and total volume decreased when plots were reallocated to their correct strata. The increased efficiency obtained from correcting misclassifications illustrates that the loss in precision due to misclassifying inventory strata is consequential. Knowledge and correction of these errors provides a natural-resource-based professional or investor using land classification/inventory data the best minimum risk information possible. A complete set of variance estimators for poststratified means and total area estimates with sampling frame errors are presented and compared to estimators without sampling frame errors.