Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation

The area of land use or land cover change obtained directly from a map may differ greatly from the true area of change because of map classification error. An error-adjusted estimator of area can be easily produced once an accuracy assessment has been performed and an error matrix constructed. The estimator presented is a stratified estimator which is applicable to data acquired using popular sampling designs such as stratified random, simple random and systematic (the stratified estimator is often labeled a poststratified estimator for the latter two designs). A confidence interval for the area of land change should also be provided to quantify the uncertainty of the change area estimate. The uncertainty of the change area estimate, as expressed via the confidence interval, can then subsequently be incorporated into an uncertainty analysis for applications using land change area as an input (e.g., a carbon flux model). Accuracy assessments published for land change studies should report the information required to produce the stratified estimator of change area and to construct confidence intervals. However, an evaluation of land change articles published between 2005 and 2010 in two remote sensing journals revealed that accuracy assessments often fail to include this key information. We recommend that land change maps should be accompanied by an accuracy assessment that includes a clear description of the sampling design (including sample size and, if relevant, details of stratification), an error matrix, the area or proportion of area of each category according to the map, and descriptive accuracy measures such as user's, producer's and overall accuracy. Furthermore, mapped areas should be adjusted to eliminate bias attributable to map classification error and these error-adjusted area estimates should be accompanied by confidence intervals to quantify the sampling variability of the estimated area. Using data from the published literature, we illustrate how to produce error-adjusted point estimates and confidence intervals of land change areas. A simple analysis of uncertainty based on the confidence bounds for land change area is applied to a carbon flux model to illustrate numerically that variability in the land change area estimate can have a dramatic effect on model outputs.