Abstract
Forest decline, in course of climate change, has become a frequently observed phenomenon. Much of the observed decline has been associated with an increasing frequency of climate change induced hotter droughts while decline induced by flooding, late-frost, and storms also play an important role. As a consequence, tree mortality rates have increased across the globe. Despite numerous studies that have assessed forest decline and predisposing factors for tree mortality, we still lack an in-depth understanding of (I) underlying eco-physiological mechanisms, (II) the influence of varying environmental conditions related to soil, competition, and micro-climate, and (III) species-specific strategies to cope with prolonged environmental stress. To deepen our knowledge within this context, studying tree performance within larger networks seems a promising research avenue. Ideally such networks are already established during the actual period of environmental stress. One approach for identifying stressed forests suitable for such monitoring networks is to assess measures related to tree vitality in near real-time across large regions by means of satellite-borne remote sensing. Within this context, we introduce the European Forest Condition monitor (EFCM)—a remote-sensing based, freely available, interactive web information tool. The EFCM depicts forest greenness (as approximated using NDVI from MODIS at a spatial resolution of roughly 5.3 hectares) for the pixel-specific growing season across Europe and consequently allows for guiding research within the context of concurrent forest performance. To allow for inter-temporal comparability and account for pixel-specific features, all observations are set in relation to normalized difference vegetation index (NDVI) records over the monitoring period beginning in 2001. The EFCM provides both a quantile-based and a proportion-based product, thereby allowing for both relative and absolute comparison of forest greenness over the observational record. Based on six specific examples related to spring phenology, drought, late-frost, tree die-back on water-logged soils, an ice storm, and windthrow we exemplify how the EFCM may help identifying hotspots of extraordinary forest greenness. We discuss advantages and limitations when monitoring forest condition at large scales on the basis of moderate resolution remote sensing products to guide users toward an appropriate interpretation.
Highlights
The higher frequency of more extreme climate conditions in course of climate change poses certain threats on forests worldwide (Allen et al, 2010, 2015; Anderegg et al, 2015; Vitasse et al, 2019; Buras et al, 2020; Kannenberg et al, 2020)
We describe the methodological approach of the European Forest Condition monitor (EFCM) and exemplify its application using specific case studies related to phenology, drought, late-frost, flooding, ice storms, and windthrow
Based on an assessment of the overall development of peak season normalized difference vegetation index (NDVI) quantiles and proportional deviations from the median and six local case-studies, we have shown that the framework behind the European Forest Condition Monitor provides meaningful information on forest condition at a continental and local scale
Summary
The higher frequency of more extreme climate conditions in course of climate change poses certain threats on forests worldwide (Allen et al, 2010, 2015; Anderegg et al, 2015; Vitasse et al, 2019; Buras et al, 2020; Kannenberg et al, 2020). Even though first assessments have identified soil properties, stand structure, and micro-climate as influencing factors of forest decline and tree mortality (Lévesque et al, 2013; Rehschuh et al, 2017; Buras et al, 2018), the eco-physiological processes that govern the fate of single trees still need further investigation (Bascietto et al, 2018; D’Andrea et al, 2019; Dox et al, 2020, 2021; Schuldt et al, 2020) One reason for this important research gap is related to the fact that key eco-physiological processes such as sap flow, regulation of leaf water potential, and changes in xylem conductivity cannot be studied retrospectively for dead trees. It is difficult to forecast where, when, and which trees are going to die in order to install monitoring equipment prior to death
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