Satellite image-based maps: Scientific inference or pretty pictures?

The scientific method has been characterized as having two distinct components, Discovery and Justification. Discovery emphasizes ideas and creativity, focuses on conceiving hypotheses and constructing models, and is generally regarded as lacking a formal logic. Justification begins with the hypotheses and models and ends with a valid scientific inference. Unlike Discovery, Justification has a formal logic whose rules must be rigorously followed to produce valid scientific inferences. In particular, when inferences are based on sample data, the rules of the logic of Justification require assessments of bias and precision. Thus, satellite image-based maps that lack such assessments for parameters of populations depicted by the maps may be of little utility for scientific inference; essentially, they may be just pretty pictures. Probability- and model-based approaches are explained, illustrated, and compared for producing inferences for population parameters using a map depicting three land cover classes: non-forest, coniferous forest, and deciduous forest. The maps were constructed using forest inventory data and Landsat imagery. Although a multinomial logistic regression model was used to classify the imagery, the methods for assessing bias and precision can be used with any classification method. For probability-based approaches, the difference estimator was used, and for model-based inference, a bootstrap approach was used.

Research Highlights
► Inferences are constructed for parameters of populations depicted by maps.
► Map classes include non-forest, coniferous forest, and deciduous forest.
► Approaches use Landsat and forest inventory data with a logistic regression model.
► Probability-based inference uses simple random sampling and model-assisted estimators.
► Model-based inference uses bootstrap to estimate variances.
► Inferential techniques can be used with any classification technique.