Environ Ecol Stat DOI 10.1007/s10651-017-0378-y Spatially-balanced sampling versus unbalanced stratified sampling for assessing forest change: evidences in favour of spatial balance Maria Chiara Pagliarella 1 · Piermaria Corona 2 · Lorenzo Fattorini 1 Received: 13 January 2017 / Revised: 20 March 2017 © Springer Science+Business Media, LLC 2017 Abstract Large-scale remote sensing-based inventories of forest cover are usually carried out by combining unsupervised classifications of satellite pixels into forest/non forest classes (map data) with subsequent time-consuming visual on-screen imagery classification of a probabilistic sample of pixels taken as the ground truth (reference data). In this paper the estimation of forest change from a sample of reference data is approached by: (i) exploiting map data to construct strata in which changes are occurred, and then adopting the stratified sampling joined with the HT estimator with most sampling effort devoted to strata where changes are occurred irrespective of their size, as suggested in most remote sensing literature regarding land change assessments; (ii) adopting a spatial scheme ensuring spatially balanced samples, as suggested in most recent statistical literature regarding spatial surveys, and exploiting the map data in the difference estimator. The results of a comparison performed on an artificial population of reference data generated from a real population of map data recorded in Sardinia (Italy) discourage the use of unbalanced stratified samples Handling Editor: Pierre Dutilleul. B Maria Chiara Pagliarella mariachiara.pagliarella@unisi.it Piermaria Corona piermaria.corona@crea.gov.it Lorenzo Fattorini lorenzo.fattorini@unisi.it 1 Department of Economic and Statistics, University of Siena, Piazza San Francesco, 7, 53110 Siena, Italy 2 Research Centre for Forestry and Wood, Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria, Viale Santa Margherita, 80, 52100 Arezzo, Italy 123