Forest decline is either caused by damage or else by vulnerability due to unfavourable growth conditions or due to unnatural silvicultural systems. Here, we assess forest decline in the Czech Republic (Central Europe) using fuzzy functions, fuzzy sets and fuzzy rating of ecosystem properties over a 1 x 1 km grid. The model was divided into fuzzy functions of the abiotic predictors of growth conditions (F_pred including temperature, precipitation, acid deposition, soil data and relative site insolation) and forest biomass receptors (F_rec including remote sensing data, density and volume of aboveground biomass, and surface humus chemical data). Fuzzy functions were designed at the limits of unfavourable, undetermined or favourable effects on the forest ecosystem health status. Fuzzy sets were distinguished through similarity in a particular membership of the properties at the limits of the forest status margins. Fuzzy rating was obtained from the least difference of F_pred - F_rec. Unfavourable F_pred within unfavourable F_rec indicated chronic damage, favourable F_pred within unfavourable F_rec indicated acute damage, and unfavourable F_pred within favourable F_rec indicated vulnerability. The model in the 1 x 1 km grid was validated through spatial intersection with a point field of uniform forest stands. Favourable status was characterised by soil base saturation (BS) > 50%, BCC/Al > 1, C_org > 1%, MgO > 6 g/kg, and nitrogen deposition < 1200 mol (H^+)/ha.year. Vulnerable forests had BS_humus 46-60%, BCC/AI 9-20 and NDVI approximate to 0.42. Chronic forest damage occurs in areas with low temperatures, high nitrogen deposition, and low soil BS and C_org levels. In the Czech Republic, 10% of forests were considered non-damaged and 77% vulnerable, with damage considered acute in 7% of forests and chronic in 5%. The fuzzy model used suggests that improvement in forest health will depend on decreasing environmental load and restoration concordance between growth conditions and tree species composition.