The effects of imperfect reference data on remote sensing-assisted estimators of land cover class proportions

Abstract

The gain-loss approach for greenhouse gas inventories requires estimates of areas of human activity and estimates of emissions per unit area for each activity. Stratified sampling and estimation have emerged as a popular and useful statistical approach for estimation of activity areas. With this approach, a map depicting classes of activity is used to stratify the area of interest. For each map class used as a stratum, map units are randomly selected and assessed with respect to an attribute such as forest/non-forest or forest land cover change. Ground observations are generally accepted as the most accurate source of information for these assessments but may be cost-prohibitive to acquire for remote and inaccessible forest regions. In lieu of ground observations, visual interpretations of remotely sensed data such as aerial imagery or satellite imagery are often used with the caveat that the interpretations must be of greater quality than the map data. An unresolved issue pertains to the effects of interpreter error on the bias and precision of the stratified estimators of activity areas.For a 7500-km2 study area in north central Minnesota in the United States of America, combinations of forest inventory plot observations, visual interpretations of aerial imagery, and two forest/non-forest maps were used to assess the effects of interpreter error on stratified estimators of proportion forest and corresponding standard errors. The primary objectives related to estimating the bias and precision of the stratified estimators in the presence of interpreter errors, identifying factors and the levels of those factors that affect bias and precision, and facilitating planning to circumvent and/or mitigate the effects of bias. The primary results were that interpreter error induces bias into the stratified estimators of both land cover class proportion and its standard error. Bias increased with greater inequality in stratum weights, smaller map and interpreter accuracies, fewer interpreters and greater correlations among interpreters. Failure to account for interpreter error produced stratified standard errors that under-estimated actual standard errors by factors as great as 2.3. Greater number of interpreters mitigated the effects of interpreter error on proportion forest estimates, and a hybrid variance estimator accounted for the effects on standard errors.