Dr. Petra DOdorico

• We investigate the impact of a 20-yr irrigation on root water uptake (RWU) and drought stress release in a naturally dry Scots pine forest.
• We use a combination of electrical resistivity tomography to image RWU, drone flights to image the crown stress and sensors to monitor soil water content.
• Our findings suggest that increased water availability enhances root growth and resource use efficiency, potentially increasing trees' resistance to future drought conditions by enabling water uptake from deeper soil layers.
• This research highlights the significant role of ecological memory and legacy effects in determining tree responses to environmental changes.

Forests are under pressure and going through rapid changes. However, current inventorying and monitoring (IM) programs are often either disjointed, too narrow in their scope and/or do not operate at fine enough temporal resolutions, which may hinder scientific understanding, the timely supply of information, fast decision making, and may result in the sub-optimal use of resources. For these reasons, there is an urgent need for Advanced Forest Inventorying and Monitoring (AIM) programs to (i) achieve expanded relevance (by augmenting data/information across ecosystem properties and trophic levels), (ii) have increased temporal resolution (by tailored data collection frequency), and (iii) make use of technological advances (by incorporating novel tools and technologies). The Advanced Inventorying and Monitoring for Swiss Forests (SwissAIM) initiative was launched in 2020 to address these needs. SwissAIM builds upon the foundation offered by the existing programs (e.g., national forest inventory, long-term forest ecosystem research, biodiversity monitoring). It aims to offer a collaborative and adaptive framework to enable integrated data collection, evaluation, interpretation, analysis, and modeling. Ideally, it will result in a more responsive system with respect to current and predicted biotic/abiotic stressors that will challenge Swiss forests. Developing such a system implies identifying the information needs of different stakeholders (e.g., science, policy, practice), related technical requirements, and governance frameworks. Here, we present (i) the main features of the SwissAIM initiative (vision, scientific questions and variables, governance and engagement), (ii) the main outcomes of the participatory design process (measurements, sampling, and plot design), (iii) the potential transferability of AIM initiatives outside Switzerland (timing, relevance, practicability), and (iv) the key messages that emerged (i.e., need for advancement, integration and transdisciplinarity, statistical underpinning). Since similar needs related to forest inventorying and monitoring are emerging throughout Europe and elsewhere, the objective of this opinion paper is to share our experience and promote a dialog with those interested in developing AIM initiatives in other countries and regions.

Scots pine (Pinus sylvestris L.) is a common European tree species, and understanding its acclimation to the rapidly changing climate through physiological, biochemical or structural adjustments is vital for predicting future growth. We investigated a long-term irrigation experiment at a naturally dry forest in Switzerland, comparing Scots pine trees that have been continuously irrigated for 17 years (irrigated) with those for which irrigation was interrupted after 10 years (stop) and non-irrigated trees (control), using tree growth, xylogenesis, wood anatomy, and carbon, oxygen and hydrogen stable isotope measurements in the water, sugars and cellulose of plant tissues. The dendrochronological analyses highlighted three distinct acclimation phases to the treatments: irrigated trees experienced (i) a significant growth increase in the first 4 years of treatment, (ii) high growth rates but with a declining trend in the following 8 years and finally (iii) a regression to pre-irrigation growth rates, suggesting the development of a new growth limitation (i.e. acclimation). The introduction of the stop treatment resulted in further growth reductions to below-control levels during the third phase. Irrigated trees showed longer growth periods and lower tree-ring δ¹³C values, reflecting lower stomatal restrictions than control trees. Their strong tree-ring δ¹⁸O and δ²H (O–H) relationship reflected the hydrological signature similarly to the control. On the contrary, the stop trees had lower growth rates, conservative wood anatomical traits, and a weak O–H relationship, indicating a physiological imbalance. Tree vitality (identified by crown transparency) significantly modulated growth, wood anatomical traits and tree-ring δ¹³C, with low-vitality trees of all treatments performing similarly regardless of water availability. We thus provide quantitative indicators for assessing physiological imbalance and tree acclimation after environmental stresses. We also show that tree vitality is crucial in shaping such responses. These findings are fundamental for the early assessment of ecosystem imbalances and decline under climate change.

European beech (Fagus sylvatica L.) forests have recently experienced severe diebacks that are expected to increase in future. Oriental beech (Fagus sylvatica spp. orientalis (Lipsky) Greut. & Burd) is a potential candidate for assisted migration (AM) in European forests due to its greater genetic diversity and potentially higher drought resistance. Yet AM entails not only benefits, but also risks, and it is therefore important to monitor the progression of introduced (sub)species. Here, we demonstrate the potential of leaf spectroscopy to replace resource-intensive genetic analysis and field phenotyping for the discrimination and characterization of these two beech subspecies.
We studied two European beech forests, one in France and one in Switzerland, where Oriental beech from the Greater Caucasus was introduced over 100 years ago. During two summers (2021, 2022), we measured leaf spectral reflectance, leaf morphological and biochemical traits from genotyped adult trees. Subspecies prediction models were developed separately for top-of-canopy leaves (amenable to remote sensing) and bottom-of-canopy leaves (easier to harvest) using partial least squares discriminant analysis (PLS-DA) and different sets of spectral predictors.
Morphological, biochemical and spectra-derived leaf traits indicated that Oriental beech trees at the sites studied were characterized by higher lignin and nitrogen per unit leaf area than European beech, suggesting more protein-rich leaves on a per-area basis. The model based on top-of-canopy leaf reflectance spectra in the short-wave-infrared region (SWIR I: 1450-1750 nm) most accurately distinguished Oriental from European beech (BA = 0.86 ± 0.08, k = 0.72 ± 0.15), closely followed by models based on SWIR II, and on spectra-derived traits (BA ≥ 0.84, k ≥ 0.67).
This study provides a proof-of-principle for the development of spectroscopy-based approaches when monitoring introduced species, subspecies or provenances. Our findings hold promise for upscaling to large forest areas using airborne remote sensing.

Die Waldbewirtschaftung kann bei der Entwicklung wirtschaftlich tragfähiger Strategien zur Milderung der Auswirkungen durch den Klimawandel eine wichtige Rolle spielen. Dazu müssen unter anderem Waldmonitoring-Kapazitäten ausgebaut und neue Technologien eingesetzt werden, die es ermöglichen, effizient, flächendeckend und frühzeitig Anzeichen von Stress zu erfassen. In diesem Beitrag zeigen wir das Potenzial von hochauflösenden multispektralen Drohnendaten für die Früherkennung und Kartierung von Trockenstressreaktionen im Rahmen des WSL-Langzeit-Bewässerungsexperiments Pfynwald auf. Dieser Ansatz könnte es in Zukunft Förstern ermöglichen, gefährdete Waldgebiete schon vor dem Auftreten von sichtbaren Symptomen wie Kronenverlichtung und -verfärbung zu ermitteln und die Anpassungsfähigkeit von lokalen und neuen Herkünften und Arten zu prüfen.

Forest management can play an important role in the development of economically feasible strategies for the mitigation of climate change and the conservation of our forests. However, forest monitoring capacities need to be expanded and new technologies fully embraced, allowing the efficient, area-wide and early detection of vulnerability signs in our forests. In this contribution we demonstrate the potential of drone-based high-resolution multispectral data for the early detection and mapping of tree drought stress responses at the WSL long-term irrigation experiment Pfynwald. This approach could in future allow foresters to identify endangered areas in their forests before the appearance of visible symptoms, such as crown defoliation and discoloration. This approach could further be used to test the adaptation potential of local and new provenances and species.

Radial stem growth is a key ecosystem process resulting in long-term carbon sequestration. Despite recognition of its importance to global carbon cycling, high uncertainties remain regarding how radial growth phenology (e.g., the onset, mid, and cessation of radial growth) will be affected by climate change. In this study, we evaluated to what extent high spatially (3 × 3 m) and temporally (up to daily) resolved satellite imagery from PlanetScope can be used to monitor stem growth phenology. For this, we made use of detailed stem growth phenological observations of six common European tree species measured by automated point dendrometers at 14 distinct sites across Switzerland between 2017 and 2021. These growth phenological observations were then linked through multiple regression modeling with metrics extracted from spectral index time series. Our results show that the remote sensing-based models enable monitoring the onset (root mean squared deviation (RMSD) ranges from 5.96 to 27.04 days) and mid-stages of stem growth (RMSD ranges from 10.20 to 36.34 days) with reasonable accuracy as opposed to the cessation of stem growth that showed low accuracy (RMSD ranges from 16.02 to 153.63 days). The accuracy of the remote sensing-based prediction models and their optimal suite of predictors varied across species. The latter has important implications for the remote sensing of stem growth phenology in mixed forests, suggesting that it is important for satellite sensors to resolve individual tree crowns. Overall, our results suggest the need for novel spectral indices that capture the spectral components of mechanistic linkages between stem growth and canopy properties that go beyond the mere detection of leaf phenology. When employing such spectral indices, remote sensing could make it possible to detect not only shifts in leaf phenology caused by climate change but also those in stem growth on a broad spatial scale.

Global warming and droughts push forests closer to their thermal limits, altering tree carbonuptake and growth. To prevent critical overheating, trees can adjust their thermotolerance(T_crit), temperature and photosynthetic optima (T_opt and A_opt), and canopy temperature (T_can) to stay below damaging thresholds. However, we lack an understanding of how soil droughts affect photosynthetic thermal plasticity and T_can regulation.
In this study, we measured the effect of soil moisture on the seasonal and diurnal dynamics of net photosynthesis (A), stomatal conductance (g_s), and T_can, as well as the thermal plasticityof photosynthesis (T_crit, T_opt, and A_opt), over the course of 1 yr using a long-term irrigationexperiment in a drought-prone Pinus sylvestris forest in Switzerland.
Irrigation resulted in higher needle-level A, g_s, T_opt, and A_opt compared with naturally drought-exposed trees. No daily or seasonal differences in T_can were observed between treatments. Trees operated below their thermal thresholds (T_crit), independently of soil moisture content.
Despite strong T_can and T_air coupling, we provide evidence that drought reduces trees’ temperature optimum due to a substantial reduction of g_s during warm and dry periods of the year. These findings provide important insights regarding the effects of soil drought on thethermal tolerance of P. sylvestris.

• Land carbon dynamics in temperate and boreal ecosystems are sensitive to environmental change. Accurately simulating gross primary productivity (GPP) and its seasonality is key for reliable carbon cycle projections. However, significant biases have been found in early spring GPP simulations of northern forests, where observations often suggest a later resumption of photosynthetic activity than predicted by models.
• Here, we used eddy covariance-based GPP estimates from 39 forest sites that differ by their climate and dominant plant functional types. We used a mechanistic and an empirical light use efficiency (LUE) model to investigate the magnitude and environmental controls of delayed springtime photosynthesis resumption (DSPR) across sites.
• We found DSPR reduced ecosystem LUE by 30-70% at many, but not all site-years during spring. A significant depression of LUE was found not only in coniferous but also at deciduous forests and was related to combined high radiation and low minimum temperatures.
• By embedding cold-acclimation effects on LUE that considers the delayed effects of minimum temperatures, initial model bias in simulated springtime GPP was effectively resolved. This provides an approach to improve GPP estimates by considering physiological acclimation and enables more reliable simulations of photosynthesis in northern forests and projections in a warming climate.

Forests account for nearly 90 % of the world's terrestrial biomass in the form of carbon and they support 80 % of the global biodiversity. To understand the underlying forest dynamics, we need a long-term but also relatively high-frequency, networked monitoring system, as traditionally used in meteorology or hydrology. While there are numerous existing forest monitoring sites, particularly in temperate regions, the resulting data streams are rarely connected and do not provide information promptly, which hampers real-time assessments of forest responses to extreme climate events.
The technology to build a better global forest monitoring network now exists. This white paper addresses the key structural components needed to achieve a novel meta-network.
We propose to complement - rather than replace or unify - the existing heterogeneous infrastructure with standardized, quality-assured linking methods and interacting data processing centers to create an integrated forest monitoring network.
These automated (research topic-dependent) linking methods in atmosphere, biosphere, and pedosphere play a key role in scaling site-specific results and processing them in a timely manner. To ensure broad participation from existing monitoring sites and to establish new sites, these linking methods must be as informative, reliable, affordable, and maintainable as possible, and should be supplemented by near real-time remote sensing data.
The proposed novel meta-network will enable the detection of emergent patterns that would not be visible from isolated analyses of individual sites. In addition, the near real-time availability of data will facilitate predictions of current forest conditions (nowcasts), which are urgently needed for research and decision making in the face of rapid climate change. We call for international and interdisciplinary efforts in this direction.

Vertical gradients in the canopy represent a major challenge for scaling from foliar photosynthesis to ecosystem-level CO₂ fluxes. We tested whether accounting for independent gradients of carboxylation capacity (V_cmax) and photosynthetic electron transport (J_max) improves estimates of forest net ecosystem CO₂ exchange (NEE). We modified the process-based Soil-Plant-Atmosphere (SPA) model to represent different gradients of foliar N allocation to V_cmax and J_max in the canopy, and inversely calibrated the model via Bayesian inference using eddy covariance measurements of NEE and evapotranspiration from a mixed-deciduous forest. Inversely calibrated N allocation resulted in highest V_cmax at the top and highest J_max at the bottom of the canopy, which is similar to N allocation gradients from measured foliar traits. These vertical gradients resulted in the best fit of simulated CO₂ fluxes to measured NEE compared to alternative N allocation schemes, due to a higher, more realistic foliar CO₂ uptake in the lower canopy. Canopy gradients of V_cmax and J_max are thus important drivers of NEE and need to be considered to improve simulations of ecosystem-level CO₂ fluxes of forests.

Precise delineation of individual tree crowns is critical for accurate forest biophysical parameter estimation, species classification, and ecosystem modelling. Multispectral optical remote sensors mounted on low-flying unmanned aerial vehicles (UAVs) can rapidly collect very-high-resolution (VHR) photogrammetric optical data that contain the spectral, spatial, and structural information of trees. State-of-the-art tree crown delineation approaches rely mostly on spectral information and un-derexploit the spatial-contextual and structural information in VHR photogrammetric multispectral data, resulting in crown delineation errors. Here, we propose the spectral, spatial-contextual, and structural information-based individual tree crown delineation (S3-ITD) method, which accurately delineates individual spruce crowns by minimizing the undesirable effects due to intracrown spectral variance and nonuniform illumination/shadowing in VHR multispectral data. We evaluate the performance of the S3-ITD crown delineation method over a white spruce forest in Quebec, Canada. The highest mean intersection over union (IoU) index of 0.83, and the lowest mean crown-area difference (CAD) of 0.14 m², proves the superior crown delineation performance of the S3-ITD method over state-of-the-art methods. The reduction in error by 2.4 cm and 1.0 cm for the allometrically derived diameter at breast height (DBH) estimates compared with those from the WS-ITD and the BF-ITD approaches, respectively, demonstrates the effectiveness of the S3-ITD method to accurately estimate biophysical parameters.

Future climate will be characterized by an increase in frequency and duration of drought and warming that exacerbates atmospheric evaporative demand. How trees acclimate to long-term soil moisture changes and whether these long-term changes alter trees' sensitivity to short-term (day to months) variations of vapor pressure deficit (VPD) and soil moisture is largely unknown. Leaf gas exchange measurements were performed within a long-term (17 years) irrigation experiment in a drought-prone Scots pine-dominated forest in one of Switzerland's driest areas on trees in naturally dry (control), irrigated, and 'irrigation-stop' (after 11 years of irrigation) conditions. Seventeen years of irrigation increased photosynthesis (A) and stomatal conductance (g_s) and reduced g_s sensitivity to increasing VPD and soil drying. Following irrigation-stop, gas exchange decreased only after 3 years. After 5 years, maximum carboxylation (V_cmax) and electron transport (J_max) rates in irrigation-stop recovered to similar levels as to before the irrigation-stop. These results suggest that long-term release from soil drought reduces the sensitivity to VPD and that atmospheric constraints may play an increasingly important role in combination with soil drought. Moreover, our study indicates that structural adjustments lead to an attenuation of initially strong leaf-level acclimation to strong multiple-year drought.

Monitoring early tree physiological responses to drought is key to understanding progressive impacts of drought on forests and identifying resilient species. We combined drone-based multispectral remote sensing with measurements of tree physiology and environmental parameters over two growing seasons in a 100-y-old Pinus sylvestris forest subject to 17-y of precipitation manipulation. Our goal was to determine if drone-based photochemical reflectance index (PRI) captures tree drought stress responses and whether responses are affected by long-term acclimation. PRI detects changes in xanthophyll cycle pigment dynamics, which reflect increases in photoprotective non-photochemical quenching activity resulting from drought-induced photosynthesis downregulation. Here, PRI of never-irrigated trees was up to 10 times lower (higher stress) than PRI of irrigated trees. Long-term acclimation to experimental treatment, however, influenced the seasonal relationship between PRI and soil water availability. PRI also captured diurnal decreases in photochemical efficiency, driven by vapor pressure deficit. Interestingly, five years after irrigation was stopped for a subset of the irrigated trees, a positive legacy effect persisted, with lower stress responses (higher PRI) compared with never-irrigated trees. This study demonstrates the ability of remotely sensed PRI to scale tree physiological responses to an entire forest and the importance of long-term acclimation in determining current drought stress responses.

Diffuse radiation generally increases photosynthetic rates if total radiation is kept constant. Different hypotheses have been proposed to explain this enhancement of photosynthesis, but conclusive results over a wide range of diffuse conditions or about the effect of canopy architecture are lacking. Here, we show the response of canopy photosynthesis to different fractions of diffuse light conditions for five major arable crops (pea, potato, wheat, barley, rapeseed) and cover crops characterized by different canopy architecture. We used 13 years of flux and microclimate measurements over a field with a typical 4 year crop rotation scheme in Switzerland. We investigated the effect of diffuse light on photosynthesis over a gradient of diffuse light fractions ranging from 100% diffuse (overcast sky) to 11% diffuse light (clear-sky conditions). Gross primary productivity (GPP) increased with diffuse fraction and thus was greater under diffuse than direct light conditions if the absolute photon flux density per unit surface area was kept constant. Mean leaf tilt angle (MTA) and canopy height were found to be the best predictors of the diffuse versus direct radiation effect on photosynthesis. Climatic factors, such as the drought index and growing degree days (GDD), had a significant influence on initial quantum yield under direct but not diffuse light conditions, which depended primarily on MTA. The maximum photosynthetic rate at 2,000 µmol m^-2 s^-1 photosynthetically active radiation under direct conditions strongly depended on GDD, MTA, leaf area index (LAI) and the interaction between MTA and LAI, while under diffuse conditions, this parameter depended mostly on MTA and only to a minor extent on canopy height and their interaction. The strongest photosynthesis enhancement under diffuse light was found for wheat, barley and rapeseed, whereas the lowest was for pea. Thus, we suggest that measuring canopy architecture and diffuse radiation will greatly improve GPP estimates of global cropping systems.

Multispectral remote sensing from Unmanned Aerial Vehicle (UAV) platforms is fast becoming an economical approach to gather Very High Resolution (VHR) spatial data at the individual tree level. However, detecting and delineating tree crowns are challenging due to the intrinsic intra-class spectral variability, shadow and noise in VHR images. In addition to the unrealistic assumption that tree crowns do not overlap in forests, most state-of-the-art algorithms using photogrammetric multispectral data rely maximally on the spectral information to delineate tree crowns, resulting in crown delineation errors. The joint use of both the spectral and the spatial contextual information in multispectral data in classification mitigates the effects of intra-class spectral variance and noise, and hence has the potential to accurately delineate crowns. Thus, we proposed an approach to detect and delineate individual tree crowns by performing a Markov Random Field (MRF) based spatial-contextual modelling on a fuzzy classification framework, to achieve accurate crown delineation. The results obtained prove the proposed method to be effective.