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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-km 2 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. [ABSTRACT FROM AUTHOR]

Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009–2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k -nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10–20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher. [ABSTRACT FROM AUTHOR]