Design-based mapping of land use/land cover classes with bootstrap estimation of precision by nearest-neighbour interpolation

Abstract

Land use/land cover mapping is usually performed by classifying satellite imagery (e.g., Landsat, Sentinel) for the whole survey region using classification algorithms implemented with training data. Subsequently, probabilistic samples are usually implemented with the main purpose of assessing the accuracy of these maps by comparing the map class and the ground condition determined for the sampled units. The main proposal of this paper is to directly exploit these probabilistic samples to estimate the land use/land cover class at any location of the survey region in a design-based framework by the well-known nearest-neighbour interpolator. For the first time, the design-based consistency of nearest-neighbour maps (i.e., categorical variables) is theoretically proven and a pseudo-population bootstrap estimator of their precision is proposed and discussed. These nearest-neighbour maps provide the ability to place mapping within a rigorous design-based inference framework, in contrast to most traditional mapping approaches which often are implemented with no inferential basis or by necessity (due to lack of a probabilistic sample) model-based inference. A simulation study is performed on an estimated land use map in Southern Tuscany (Italy)—taken as the true map—to check the finite-sample performance of the proposal as well as the matching of the area coverage estimates arising from the map with those achieved by traditional estimators. The Italian land use map arising from the IUTI surveys and the U.S. land cover map arising from the LCMAP program are considered as case studies.