Evidences in favour of spatially-balanced sampling for assessing forest change

What is it about?

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.

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Why is it important?

Albeit large literature from remote sensing community proposes to unbalance the sampling effort in those portions of the survey area where changes are likely to occur, from our study it is clearly apparent, on the contrary, that spatial balance provides the best performance while stratification with unbalanced assignment is generally worse, and achieves performance comparable to that provided by spatial balance only for nearly balanced assignments.

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