Abstract

Prolonged drought and susceptibility to biotic stressors induced an extensive calamity in Norway spruce (Picea abies (L.) Karst.) and widespread crown defoliation in European beech (Fagus sylvatica L.) in Central Europe. For future management decisions, it is crucial to link changes in canopy cover to site conditions. However, current knowledge on the role of soil properties for drought-induced forest disturbance is limited due to the scarcity and low spatial resolution of soil information. We present a fine-scale assessment on the role of soil properties for forest disturbance in Norway spruce and European beech derived from optical remote sensing. A forest disturbance modeling framework based on Sentinel-2 time series was applied on 340 km2 in low mountain ranges of Central Germany. Spatio-temporal information on forest disturbance was calculated at 10 m spatial resolution in the period 2019–2021 and intersected with high-resolution soil information (1:10,000) based on roughly 2850 soil profiles. We found distinct differences in disturbed area, depending on soil type, texture, stoniness, effective rooting depth and available water capacity (AWC). For spruce, we found a polynomial relationship between AWC (R2 = 0.7) and disturbance, with highest disturbed area (65 %) for AWC between 90 and 160 mm. Interestingly, we found no evidence for generally higher disturbance on shallow soils, although stands on the deepest soils were significantly less affected. Noteworthy, sites affected first did not necessarily exhibit highest proportions of disturbed area post-drought, indicating recovery or adaptation. We conclude that site- and species-specific understanding of drought impacts benefits from a combination of remote sensing and fine-scale soil information. Since our approach revealed which sites were affected first and most, it qualifies for prioritizing in situ monitoring activities to most vulnerable stands in acute drought conditions as well as for developing long-term strategies for reforestation and site-specific risk assessment for precision forestry.

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