Networking the forest infrastructure towards near real-time monitoring - A white paper

BioSense: An automated sensing node for organismal and environmental biology

Automated remote sensing has revolutionized the fields of wildlife ecology and environmental science. Yet, a cost-effective and flexible approach for large scale monitoring has not been fully developed, resulting in a limited collection of high-resolution data. Here, we describe BioSense, a low-cost and fully programmable automated sensing platform for applications in bioacoustics and environmental studies. Our design offers customization and flexibility to address a broad array of research goals and field conditions. Each BioSense is programmed through an integrated Raspberry Pi computer board and designed to collect and analyze avian vocalizations while simultaneously collecting temperature, humidity, and soil moisture data. We illustrate the different steps involved in manufacturing this sensor including hardware and software design and present the results of our laboratory and field testing in southwestern United States.

From Industry 5.0 to Forestry 5.0: Bridging the gap with Human-Centered Artificial Intelligence

Purpose of the Review

Recent technological innovations in Artificial Intelligence (AI) have successfully revolutionized many industrial processes, enhancing productivity and sustainability, under the paradigm of Industry 5.0. It offers opportunities for the forestry sector such as predictive analytics, automation, and precision management, which could transform traditional forest operations into smart, effective, and sustainable practices. The paper sets forth to outline the evolution from Industry 5.0 and its promising transition into Forestry 5.0. The purpose is to elucidate the status of these developments, identify enabling technologies, particularly AI, and uncover the challenges hindering the efficient adoption of these techniques in forestry by presenting a framework.

Recent Findings

However, the gap between potential and practical implementation is primarily due to logistical, infrastructural, and environmental challenges unique to the forestry sector. The solution lies in Human-Centered AI, which, unlike the Industry 4.0 paradigm, aims to integrate humans into the loop rather than replace them, thereby fostering safe, secure, and trustworthy Human-AI interactions.

Summary

The paper concludes by highlighting the need for Human-Centered AI development for the successful transition to Forestry 5.0 - where the goal is to support the human workers rather than substituting them. A multidisciplinary approach involving technologists, ecologists, policymakers, and forestry practitioners is essential to navigate these challenges, leading to a sustainable and technologically advanced future for the forestry sector. In this transformation, our focus remains on ensuring a balance between increased productivity, nature conservation and social licence, worker safety and satisfaction.

Stand age diversity (and more than climate change) affects forests' resilience and stability, although unevenly

Stand age significantly influences the functioning of forest ecosystems by shaping structural and physiological plant traits, affecting water and carbon budgets. Forest age distribution is determined by the interplay of tree mortality and regeneration, influenced by both natural and anthropogenic disturbances. Unfortunately, human-driven alteration of tree age distribution presents an underexplored avenue for enhancing forest stability and resilience. In our study, we investigated how age impacts the stability and resilience of the forest carbon budget under both current and future climate conditions. We employed a state-of-the-science biogeochemical, biophysical, validated process-based model on historically managed forest stands, projecting their future as undisturbed systems, i.e., left at their natural evolution with no management interventions (i.e., forests are left to develop undisturbed). Such a model, forced by climate data from five Earth System Models under four representative climate scenarios and one baseline scenario to disentangle the effect of climate change, spanned several age classes as representative of the current European forests' context, for each stand. Our findings indicate that Net Primary Production (NPP) peaks in the young and middle-aged classes (16- to 50-year-old), aligning with longstanding ecological theories, regardless of the climate scenario. Under climate change, the beech forest exhibited an increase in NPP and maintained stability across all age classes, while resilience remained constant with rising atmospheric CO2 and temperatures. However, NPP declined under climate change scenarios for the Norway spruce and Scots pine sites. In these coniferous forests, stability and resilience were more influenced. These results underscore the necessity of accounting for age class diversity -lacking in most, if not all, the current Global Vegetation Models - for reliable and robust assessments of the impacts of climate change on future forests' stability and resilience capacity. We, therefore, advocate for customized management strategies that enhance the adaptability of forests to changing climatic conditions, taking into account the diverse responses of different species and age groups to climate.

Tracking effects of extreme drought on coniferous forests from space using dynamic habitat indices

Terrestrial ecosystems such as coniferous forests in Central Europe are experiencing changes in health status following extreme droughts compounding with severe heat waves. The increasing temporal resolution and spatial coverage of earth observation data offer new opportunities to assess these dynamics. Dense time-series of optical satellite data allow for computing Dynamic Habitat Indices (DHIs), which have been predominantly used in biodiversity studies. However, DHIs cover three aspects of vegetation changes that could be affected by drought: annual productivity, minimum cover, and seasonality. Here, we evaluate the health status of coniferous forests in the federal state of Hesse in Germany over the period 2017-2020 including the severe drought year of 2018 using DHIs based on the Normalized Difference Vegetation Index (NDVI) for drought assessment. To identify the most important variables affecting coniferous forest die-off, a series of environmental variables together with the three DHIs components were used in a logistic regression (LR) model. Each DHI component changed significantly across non-damaged and damaged sites in all years (p-value 0.05). When comparing 2017 to 2019, DHI-based annual productivity decreased and seasonality increased. Most importantly, none of the DHI components had reached pre-drought conditions, which likely indicates a change in ecosystem functioning. We also identified spatially explicit areas highly affected by drought. The LR model revealed that in addition to common environmental parameters related to temperature, precipitation, and elevation, DHI components were the most important factors explaining the health status. Our analysis demonstrates the potential of DHIs to capture the effect of drought events on Central European coniferous forest ecosystems. Since the spaceborne data are available at the global level, this approach can be applied to track the dynamics of ecosystem conditions in other regions, at larger spatial scales, and for other Land Use/Land Cover types.

Drought alters aboveground biomass production efficiency: Insights from two European beech forests

The fraction of photosynthetically assimilated carbon that trees allocate to long-lasting woody biomass pools (biomass production efficiency - BPE), is a key metric of the forest carbon balance. Its apparent simplicity belies the complex interplay between underlying processes of photosynthesis, respiration, litter and fruit production, and tree growth that respond differently to climate variability. Whereas the magnitude of BPE has been routinely quantified in ecological studies, its temporal dynamics and responses to extreme events such as drought remain less well understood. Here, we combine long-term records of aboveground carbon increment (ACI) obtained from tree rings with stand-level gross primary productivity (GPP) from eddy covariance (EC) records to empirically quantify aboveground BPE (= ACI/GPP) and its interannual variability in two European beech forests (Hainich, DE-Hai, Germany; Sorø, DK-Sor, Denmark). We found significant negative correlations between BPE and a daily-resolved drought index at both sites, indicating that woody growth is de-prioritized under water limitation. During identified extreme years, early-season drought reduced same-year BPE by 29 % (Hainich, 2011), 31 % (Sorø, 2006), and 14 % (Sorø, 2013). By contrast, the 2003 late-summer drought resulted in a 17 % reduction of post-drought year BPE at Hainich. Across the entire EC period, the daily-to-seasonal drought response of BPE resembled that of ACI, rather than that of GPP. This indicates that BPE follows sink dynamics more closely than source dynamics, which appear to be decoupled given the distinctive climate response patterns of GPP and ACI. Based on our observations, we caution against estimating the magnitude and variability of the carbon sink in European beech (and likely other temperate forests) based on carbon fluxes alone. We also encourage comparable studies at other long-term EC measurement sites from different ecosystems to further constrain the BPE response to rare climatic events.

Linkage between growth phenology and climate-growth responses along landscape gradients in boreal forests

Boreal forests represent an important carbon sink and, therefore, significantly contribute to climate change mitigation. Tree-ring width series of boreal species reflect climate variation at the moment of tree-ring formation but also lagged climatic effects from dormancy preceding tree-ring formation and antecedent growing seasons. However, little is known about how the growth sensitivity to climate in specific intra-annual periods varies across the landscape. Here, we assessed growth responses to climate variation during the 45 months preceding the tree-ring formation for nine boreal stands of Picea glauca and Picea mariana distributed along the gradients of elevation and slope aspect. We combined process-based modeling of wood formation and remote sensing data to determine growth phenology at each site. Next, we classified intra-annual seasons with significant climate-growth correlations based on the timing of dormancy and growth periods. Both the phenology and the climate-growth relationships systematically shifted with elevation and, to a lower extent, also with slope orientation at the treeline. The mean duration of the growing season varied between 100 days at treelines above 900 m and 160 days at lowlands below 500 m. The growth at treelines was stimulated by temperature in the summer of the tree-ring formation year and two years before tree-ring formation. The period of significant climate-growth correlations during the current summer did not exceed three months in agreement with the local duration of the growing season. The growth of trees in lower elevations was instead stimulated by high temperature during the dormancy periods but restricted by high temperature in antecedent summer seasons. In conclusion, our study highlights the linkage between the timing of climate-growth sensitivity and growth phenology, primarily determined by proximity to the treeline. Consequently, accounting for landscape gradients in growth phenology is crucial for upscaling the climatic limits of boreal stands' growth as climate change progresses.

Vegetation browning: global drivers, impacts, and feedbacks

As global climate conditions continue to change, disturbance regimes and environmental drivers will continue to shift, impacting global vegetation dynamics. Following a period of vegetation greening, there has been a progressive increase in remotely sensed vegetation browning globally. Given the many societal benefits that forests provide, it is critical that we understand vegetation dynamic alterations. Here, we review associative drivers, impacts, and feedbacks, revealing the complexity of browning. Concomitant increases in browning include the weakening of ecosystem services and functions and alterations to vegetation structure and species composition, as well as the development of potential positive climate change feedbacks. Also discussed are the current challenges in browning detection and understanding associated impacts and feedbacks. Finally, we outline recommended strategies.